INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(69379, 2019, 'bib', 'Accounting for differential variability in detecting differentially methylated regions', 'Wang, Ya|Teschendorff, Andrew E|Widschwendter, Martin|Wang, Shuang', 'Department of Biostatistics, Mailman School of Public Health, Columbia University, New York, NY, USA.|Department of Womens Cancer, University College London, London, UK.|Department of Womens Cancer, University College London, London, UK.|Department of Biostatistics, Mailman School of Public Health, Columbia University, New York, NY, USA.', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom', 4, 'Algorithms|Analysis of Variance|Breast Neoplasms|Carcinoma, Renal Cell|Case-Control Studies|Computational Biology|Computer Simulation|CpG Islands|DNA Methylation|DNA, Neoplasm|Databases, Genetic|Epigenesis, Genetic|Female|Genetic Variation|Humans|Kidney Neoplasms|Sequence Analysis, DNA', 'Journal Article', '10.1093/bib/bbx097', '29912290', 'DNA methylation plays an essential role in cancer. Differential variability (DV) in cancer was recently observed that contributes to cancer heterogeneity and has been shown to be crucial in detecting epigenetic field defects, DNA methylation alterations happening early in carcinogenesis. As neighboring CpG sites are highly correlated, here, we present a new method to detect differentially methylated regions (DMRs) that uses combined signals from differential methylation and DV between sample groups. We demonstrated in simulation studies the superior performance of the new method than existing methods that use only one type of signals when true DMRs have both. Applications to DNA methylation data of breast invasive carcinoma (BRCA) and kidney renal clear cell carcinoma (KIRC) from The Cancer Genome Atlas (TCGA) and BRCA from Gene Expression Omnibus (GEO) suggest that the new method identified additional cancer-related DMRs that were missed by methods using one type of signals. Replication analyses using two independent BRCA data sets suggest that DMRs detected based on DV are reproducible. Only the new method identified epigenetic field defects when comparing normal tissues adjacent to tumors and normal tissues from age-matched cancer-free women from the GEO BRCA data and confirmed their enrichment in the progression to breast cancer.'),
(69380, 2018, 'bib', 'Exploring the functional impact of alternative splicing on human protein isoforms using available annotation sources', 'Sulakhe, Dinanath|DSouza, Mark|Wang, Sheng|Balasubramanian, Sandhya|Athri, Prashanth|Xie, Bingqing|Canzar, Stefan|Agam, Gady|Gilliam, T Conrad|Maltsev, Natalia', 'Department of Human Genetics, University of Chicago, 920 E. 58th Street, Chicago, IL, USA.|Department of Human Genetics, University of Chicago, 920 E. 58th Street, Chicago, IL, USA.|Department of Human Genetics, University of Chicago, 920 E. 58th Street, Chicago, IL, USA.|Department of Human Genetics, University of Chicago, 920 E. 58th Street, Chicago, IL, USA.|Department of Computer Science and Engineering, Amrita School of Engineering, Bengaluru, Amrita Vishwa Vidyapeetham, Kasavanahalli, Carmelaram P.O., Bengaluru, Karnataka, India.|Department of Human Genetics, University of Chicago, 920 E. 58th Street, Chicago, IL, USA.|Toyota Technological Institute at Chicago, 6045 S. Kenwood Avenue, Chicago, IL, USA.|Department of Computer Science, Illinois Institute of Technology, Chicago, IL, USA.|Department of Human Genetics, University of Chicago, 920 E. 58th Street, Chicago, IL, USA.|Department of Human Genetics, University of Chicago, 920 E. 58th Street, Chicago, IL, USA.', 'United States|United States|United States|United States|India|United States|United States|United States|United States|United States', 1, '-', 'Journal Article', '10.1093/bib/bby047', '29931155', 'In recent years, the emphasis of scientific inquiry has shifted from whole-genome analyses to an understanding of cellular responses specific to tissue, developmental stage or environmental conditions. One of the central mechanisms underlying the diversity and adaptability of the contextual responses is alternative splicing (AS). It enables a single gene to encode multiple isoforms with distinct biological functions. However, to date, the functions of the vast majority of differentially spliced protein isoforms are not known. Integration of genomic, proteomic, functional, phenotypic and contextual information is essential for supporting isoform-based modeling and analysis. Such integrative proteogenomics approaches promise to provide insights into the functions of the alternatively spliced protein isoforms and provide high-confidence hypotheses to be validated experimentally. This manuscript provides a survey of the public databases supporting isoform-based biology. It also presents an overview of the potential global impact of AS on the human canonical gene functions, molecular interactions and cellular pathways.'),
(69381, 2018, 'bib', 'Disease classification: from phenotypic similarity to integrative genomics and beyond', 'Dozmorov, Mikhail G', 'Department of Biostatistics, Virginia Commonwealth University, 830 East Main Street, Richmond, VA, USA.', 'United States', 4, '-', 'Journal Article', '10.1093/bib/bby049', '29939197', 'A fundamental challenge of modern biomedical research is understanding how diseases that are similar on the phenotypic level are similar on the molecular level. Integration of various genomic data sets with the traditionally used phenotypic disease similarity revealed novel genetic and molecular mechanisms and blurred the distinction between monogenic (Mendelian) and complex diseases. Network-based medicine has emerged as a complementary approach for identifying disease-causing genes, genetic mediators, disruptions in the underlying cellular functions and for drug repositioning. The recent development of machine and deep learning methods allow for leveraging real-life information about diseases to refine genetic and phenotypic disease relationships. This review describes the historical development and recent methodological advancements for studying disease classification (nosology).'),
(69382, 2018, 'bib', 'Landscape of the long non-coding RNA transcriptome in human heart', 'Jiang, Chunjie|Ding, Na|Li, Junyi|Jin, Xiyun|Li, Lili|Pan, Tao|Huo, Caiqin|Li, Yongsheng|Xu, Juan|Li, Xia', 'College of Bioinformatics Science and Technology, Harbin Medical University, 194 Xuefu Road, Harbin, Heilongjiang, China.|College of Bioinformatics Science and Technology, Harbin Medical University, 194 Xuefu Road, Harbin, Heilongjiang, China.|College of Bioinformatics Science and Technology, Harbin Medical University, 194 Xuefu Road, Harbin, Heilongjiang, China.|College of Bioinformatics Science and Technology, Harbin Medical University, 194 Xuefu Road, Harbin, Heilongjiang, China.|College of Bioinformatics Science and Technology, Harbin Medical University, 194 Xuefu Road, Harbin, Heilongjiang, China.|College of Bioinformatics Science and Technology, Harbin Medical University, 194 Xuefu Road, Harbin, Heilongjiang, China.|College of Bioinformatics Science and Technology, Harbin Medical University, 194 Xuefu Road, Harbin, Heilongjiang, China.|College of Bioinformatics Science and Technology, Harbin Medical University, 194 Xuefu Road, Harbin, Heilongjiang, China.|College of Bioinformatics Science and Technology, Harbin Medical University, 194 Xuefu Road, Harbin, Heilongjiang, China.|College of Bioinformatics Science and Technology, Harbin Medical University, 194 Xuefu Road, Harbin, Heilongjiang, China.', 'China|China|China|China|China|China|China|China|China|China', 4, '-', 'Journal Article', '10.1093/bib/bby052', '29939204', 'Long non-coding RNAs (lncRNAs) have been revealed to play essential roles in the human cardiovascular system. However, information about their mechanisms is limited, and a comprehensive view of cardiac lncRNAs is lacking from a multiple tissues perspective to date. Here, the landscape of the lncRNA transcriptome in human heart was summarized. We summarized all lncRNA transcripts from publicly available human transcriptome resources (156 heart samples and 210 samples from 29 other tissues) and systematically analysed all annotated and novel lncRNAs expressed in heart. A total of 7485 lncRNAs whose expression was elevated in heart (HE lncRNAs) and 453 lncRNAs expressed in all 30 analysed tissues (EIA lncRNAs) were extracted. Using various bioinformatics resources, methods and tools, the features of these lncRNAs were discussed from various perspectives, including genomic structure, conservation, dynamic variation during heart development, cis-regulation, differential expression in cardiovascular diseases and cancers as well as regulation at transcriptional and post-transcriptional levels. Afterwards, all the features discussed above were integrated into a user-friendly resource named CARDIO-LNCRNAS (http://bio-bigdata.hrbmu.edu.cn/CARDIO-LNCRNAS/ or http://www.bio-bigdata.net/CARDIO-LNCRNAS/). This study represents the first global view of lncRNAs in the human cardiovascular system based on multiple tissues and sheds light on the role of lncRNAs in developments and heart disorders.'),
(69383, 2018, 'bib', 'Versatile interactions and bioinformatics analysis of noncoding RNAs', 'Chen, Qi|Meng, Xianwen|Liao, Qi|Chen, Ming', 'Department of Bioinformatics, The State Key Laboratory of Plant Physiology and Biochemistry, College of Life Sciences, Zhejiang University, Hangzhou, Zhejiang, P. R. China.|Department of Bioinformatics, The State Key Laboratory of Plant Physiology and Biochemistry, College of Life Sciences, Zhejiang University, Hangzhou, Zhejiang, P. R. China.|Department of Bioinformatics, The State Key Laboratory of Plant Physiology and Biochemistry, College of Life Sciences, Zhejiang University, Hangzhou, Zhejiang, P. R. China.|Department of Preventative Medicine, Zhejiang Provincial Key Laboratory of Pathological and Physiological Technology, Medical School of Ningbo University, Ningbo, Zhejiang, P. R. China.', 'China|China|China|China', 0, '-', 'Journal Article', '10.1093/bib/bby050', '29939215', 'Advances in RNA sequencing technologies and computational methodologies have provided a huge impetus to noncoding RNA (ncRNA) study. Once regarded as inconsequential results of transcriptional promiscuity, ncRNAs were later found to exert great roles in various aspects of biological functions. They are emerging as key players in gene regulatory networks by interacting with other biomolecules (DNA, RNA or protein). Here, we provide an overview of ncRNA repertoire and highlight recent discoveries of their versatile interactions. To better investigate the ncRNA-mediated regulation, it is necessary to make full use of innovative sequencing techniques and computational tools. We further describe a comprehensive workflow for in silico ncRNA analysis, providing up-to-date platforms, databases and tools dedicated to ncRNA identification and functional annotation.'),
(69384, 2018, 'bib', 'Function determinants of TET proteins: the arrangements of sequence motifs with specific codes', 'Liu, Dongyang|Li, Guangpeng|Zuo, Yongchun', 'The State key Laboratory of Reproductive Regulation and Breeding of Grassland Livestock, College of Life Sciences, Inner Mongolia University, Hohhot, China.|The State key Laboratory of Reproductive Regulation and Breeding of Grassland Livestock, College of Life Sciences, Inner Mongolia University, Hohhot, China.|The State key Laboratory of Reproductive Regulation and Breeding of Grassland Livestock, College of Life Sciences, Inner Mongolia University, Hohhot, China.', 'China|China|China', 38, '-', 'Journal Article', '10.1093/bib/bby053', '29947743', 'The ten-eleven translocation (TET) proteins play a crucial role in promoting locus-specific reversal of DNA methylation, a type of chromatin modification. Considerable evidences have demonstrated that the sequence motifs with specific codes are important to determine the functions of domains and active sites. Here, we surveyed major studies and reviews regarding the multiple functions of the TET proteins and established the patterns of the motif arrangements that determine the functions of TET proteins. First, we summarized the functional sequence basis of TET proteins and identified the new functional motifs based on the phylogenetic relationship. Next, we described the sequence characteristics of the functional motifs in detail and provided an overview of the relationship between the sequence motifs and the functions of TET proteins, including known functions and potential functions. Finally, we highlighted that sequence motifs with diverse post-translational modifications perform unique functions, and different selection pressures lead to different arrangements of sequence motifs, resulting in different paralogs and isoforms.'),
(69385, 2018, 'bib', 'Trends in the development of miRNA bioinformatics tools', 'Chen, Liang|Heikkinen, Liisa|Wang, Changliang|Yang, Yang|Sun, Huiyan|Wong, Garry', 'The State key Laboratory of Reproductive Regulation and Breeding of Grassland Livestock, College of Life Sciences, Inner Mongolia University, Hohhot, China.|The State key Laboratory of Reproductive Regulation and Breeding of Grassland Livestock, College of Life Sciences, Inner Mongolia University, Hohhot, China.|The State key Laboratory of Reproductive Regulation and Breeding of Grassland Livestock, College of Life Sciences, Inner Mongolia University, Hohhot, China.|The State key Laboratory of Reproductive Regulation and Breeding of Grassland Livestock, College of Life Sciences, Inner Mongolia University, Hohhot, China.|The State key Laboratory of Reproductive Regulation and Breeding of Grassland Livestock, College of Life Sciences, Inner Mongolia University, Hohhot, China.|The State key Laboratory of Reproductive Regulation and Breeding of Grassland Livestock, College of Life Sciences, Inner Mongolia University, Hohhot, China.', 'China|China|China|China|China|China', 18, '-', 'Journal Article', '10.1093/bib/bby054', '29982332', 'MicroRNAs (miRNAs) are small noncoding RNAs that regulate gene expression via recognition of cognate sequences and interference of transcriptional, translational or epigenetic processes. Bioinformatics tools developed for miRNA study include those for miRNA prediction and discovery, structure, analysis and target prediction. We manually curated 95 review papers and xe2x88xbc1000 miRNA bioinformatics tools published since 2003. We classified and ranked them based on citation number or PageRank score, and then performed network analysis and text mining (TM) to study the miRNA tools development trends. Five key trends were observed: (1) miRNA identification and target prediction have been hot spots in the past decade, (2) manual curation and TM are the main methods for collecting miRNA knowledge from literature, (3) most early tools are well maintained and widely used, (4) classic machine learning methods retain their utility, however, novel ones have begun to emerge, (5) disease-associated miRNA tools are emerging. Our analysis yields significant insight into the past development and future directions of miRNA tools.'),
(69386, 2018, 'bib', 'ATAC-pipe: general analysis of genome-wide chromatin accessibility', 'Zuo, Zuqi|Jin, Yonghao|Zhang, Wen|Lu, Yichen|Li, Bin|Qu, Kun', 'Hefei National Laboratory for Physical Sciences at Microscale, the CAS Key Laboratory of Innate Immunity and Chronic Disease, School of Life Sciences, University of Science and Technology of China, Hefei 230027, China.|Hefei National Laboratory for Physical Sciences at Microscale, the CAS Key Laboratory of Innate Immunity and Chronic Disease, School of Life Sciences, University of Science and Technology of China, Hefei 230027, China.|Hefei National Laboratory for Physical Sciences at Microscale, the CAS Key Laboratory of Innate Immunity and Chronic Disease, School of Life Sciences, University of Science and Technology of China, Hefei 230027, China.|Hefei National Laboratory for Physical Sciences at Microscale, the CAS Key Laboratory of Innate Immunity and Chronic Disease, School of Life Sciences, University of Science and Technology of China, Hefei 230027, China.|Hefei National Laboratory for Physical Sciences at Microscale, the CAS Key Laboratory of Innate Immunity and Chronic Disease, School of Life Sciences, University of Science and Technology of China, Hefei 230027, China.|Hefei National Laboratory for Physical Sciences at Microscale, the CAS Key Laboratory of Innate Immunity and Chronic Disease, School of Life Sciences, University of Science and Technology of China, Hefei 230027, China.', 'China|China|China|China|China|China', 4, '-', 'Journal Article', '10.1093/bib/bby056', '29982337', 'https://github.com/QuKunLab/ATAC-pipe.'),
(69387, 2018, 'bib', 'Corrigendum to: How trees allocate carbon for optimal growth: insight from a game-theoretic model', '-', '-', '-', 0, '-', 'Journal Article', '10.1093/bib/bby060', '30010710', '-'),
(69388, 2018, 'bib', 'A practical guide for DNase-seq data analysis: from data management to common applications', 'Liu, Yongjing|Fu, Liangyu|Kaufmann, Kerstin|Chen, Dijun|Chen, Ming', 'Department of Bioinformatics, College of Life Sciences, Zhejiang University, Hangzhou 310058, China.|Department for Plant Cell and Molecular Biology, Institute for Biology, Humboldt-Universität zu Berlin, Berlin 10115, Germany.|Department for Plant Cell and Molecular Biology, Institute for Biology, Humboldt-Universität zu Berlin, Berlin 10115, Germany.|Department of Bioinformatics, College of Life Sciences, Zhejiang University, Hangzhou 310058, China.|Department for Plant Cell and Molecular Biology, Institute for Biology, Humboldt-Universität zu Berlin, Berlin 10115, Germany.', 'China|Germany|Germany|China|Germany', 0, '-', 'Journal Article', '10.1093/bib/bby057', '30010713', 'Deoxyribonuclease I (DNase I)-hypersensitive site sequencing (DNase-seq) has been widely used to determine chromatin accessibility and its underlying regulatory lexicon. However, exploring DNase-seq data requires sophisticated downstream bioinformatics analyses. In this study, we first review computational methods for all of the major steps in DNase-seq data analysis, including experimental design, quality control, read alignment, peak calling, annotation of cis-regulatory elements, genomic footprinting and visualization. The challenges associated with each step are highlighted. Next, we provide a practical guideline and a computational pipeline for DNase-seq data analysis by integrating some of these tools. We also discuss the competing techniques and the potential applications of this pipeline for the analysis of analogous experimental data. Finally, we discuss the integration of DNase-seq with other functional genomics techniques.'),
(69389, 2018, 'bib', 'Physiological RNA dynamics in RNA-Seq analysis', 'Xu, Zhongneng|Asakawa, Shuichi', 'Department for Plant Cell and Molecular Biology, Institute for Biology, Humboldt-Universität zu Berlin, Berlin 10115, Germany.|Department for Plant Cell and Molecular Biology, Institute for Biology, Humboldt-Universität zu Berlin, Berlin 10115, Germany.', 'Germany|Germany', 0, '-', 'Journal Article', '10.1093/bib/bby045', '30010714', 'Physiological RNA dynamics cause problems in transcriptome analysis. Physiological RNA accumulation affects the analysis of RNA quantification, and physiological RNA degradation affects the analysis of the RNA sequence length, feature site and quantification. In the present article, we review the effects of physiological degradation and accumulation of RNA on analysing RNA sequencing data. Physiological RNA accumulation and degradation probably led to such phenomena as incorrect estimations of transcription quantification, differential expressions, co-expressions, RNA decay rates, alternative splicing, boundaries of transcription, novel genes, new single-nucleotide polymorphisms, small RNAs and gene fusion. Thus, the transcriptomic data obtained up to date warrant further scrutiny. New and improved techniques and bioinformatics software are needed to produce accurate data in transcriptome research.'),
(69390, 2018, 'bib', 'A comprehensive survey of models for dissecting local ancestry deconvolution in human genome', 'Geza, Ephifania|Mugo, Jacquiline|Mulder, Nicola J|Wonkam, Ambroise|Chimusa, Emile R|Mazandu, Gaston K', 'Department for Plant Cell and Molecular Biology, Institute for Biology, Humboldt-Universität zu Berlin, Berlin 10115, Germany.|Department for Plant Cell and Molecular Biology, Institute for Biology, Humboldt-Universität zu Berlin, Berlin 10115, Germany.|Department for Plant Cell and Molecular Biology, Institute for Biology, Humboldt-Universität zu Berlin, Berlin 10115, Germany.|Department for Plant Cell and Molecular Biology, Institute for Biology, Humboldt-Universität zu Berlin, Berlin 10115, Germany.|Department for Plant Cell and Molecular Biology, Institute for Biology, Humboldt-Universität zu Berlin, Berlin 10115, Germany.|Department for Plant Cell and Molecular Biology, Institute for Biology, Humboldt-Universität zu Berlin, Berlin 10115, Germany.', 'Germany|Germany|Germany|Germany|Germany|Germany', 3, '-', 'Journal Article', '10.1093/bib/bby044', '30010715', 'Over the past decade, studies of admixed populations have increasingly gained interest in both medical and population genetics. These studies have so far shed light on the patterns of genetic variation throughout modern human evolution and have improved our understanding of the demographics and adaptive processes of human populations. To date, there exist about 20 methods or tools to deconvolve local ancestry. These methods have merits and drawbacks in estimating local ancestry in multiway admixed populations. In this article, we survey existing ancestry deconvolution methods, with special emphasis on multiway admixture, and compare these methods based on simulation results reported by different studies, computational approaches used, including mathematical and statistical models, and biological challenges related to each method. This should orient users on the choice of an appropriate method or tool for given population admixture characteristics and update researchers on current advances, challenges and opportunities behind existing ancestry deconvolution methods.'),
(69391, 2018, 'bib', 'The small peptide world in long noncoding RNAs', 'Choi, Seo-Won|Kim, Hyun-Woo|Nam, Jin-Wu', 'Department for Plant Cell and Molecular Biology, Institute for Biology, Humboldt-Universität zu Berlin, Berlin 10115, Germany.|Department for Plant Cell and Molecular Biology, Institute for Biology, Humboldt-Universität zu Berlin, Berlin 10115, Germany.|Department for Plant Cell and Molecular Biology, Institute for Biology, Humboldt-Universität zu Berlin, Berlin 10115, Germany.', 'Germany|Germany|Germany', 17, '-', 'Journal Article', '10.1093/bib/bby055', '30010717', 'Long noncoding RNAs (lncRNAs) are a group of transcripts that are longer than 200 nucleotides (nt) without coding potential. Over the past decade, tens of thousands of novel lncRNAs have been annotated in animal and plant genomes because of advanced high-throughput RNA sequencing technologies and with the aid of coding transcript classifiers. Further, a considerable number of reports have revealed the existence of stable, functional small peptides (also known as micropeptides), translated from lncRNAs. In this review, we discuss the methods of lncRNA classification, the investigations regarding their coding potential and the functional significance of the peptides they encode.'),
(69392, 2018, 'bib', 'dbCID: a manually curated resource for exploring the driver indels in human cancer', 'Yue, Zhenyu|Zhao, Le|Cheng, Na|Yan, Hua|Xia, Junfeng', 'Department for Plant Cell and Molecular Biology, Institute for Biology, Humboldt-Universität zu Berlin, Berlin 10115, Germany.|Department for Plant Cell and Molecular Biology, Institute for Biology, Humboldt-Universität zu Berlin, Berlin 10115, Germany.|Department for Plant Cell and Molecular Biology, Institute for Biology, Humboldt-Universität zu Berlin, Berlin 10115, Germany.|Department for Plant Cell and Molecular Biology, Institute for Biology, Humboldt-Universität zu Berlin, Berlin 10115, Germany.|Department for Plant Cell and Molecular Biology, Institute for Biology, Humboldt-Universität zu Berlin, Berlin 10115, Germany.', 'Germany|Germany|Germany|Germany|Germany', 1, '-', 'Journal Article', '10.1093/bib/bby059', '30016397', 'While recent advances in next-generation sequencing technologies have enabled the creation of a multitude of databases in cancer genomic research, there is no comprehensive database focusing on the annotation of driver indels (insertions and deletions) yet. Therefore, we have developed the database of Cancer driver InDels (dbCID), which is a collection of known coding indels that likely to be engaged in cancer development, progression or therapy. dbCID contains experimentally supported and putative driver indels derived from manual curation of literature and is freely available online at http://bioinfo.ahu.edu.cn: 8080/dbCID. Using the data deposited in dbCID, we summarized features of driver indels in four levels (gene, DNA, transcript and protein) through comparing with putative neutral indels. We found that most of the genes containing driver indels in dbCID are known cancer genes playing a role in tumorigenesis. Contrary to the expectation, the sequences affected by driver frameshift indels are not larger than those by neutral ones. In addition, the frameshift and inframe driver indels prefer to disrupt high-conservative regions both in DNA sequences and protein domains. Finally, we developed a computational method for discriminating cancer driver from neutral frameshift indels based on the deposited data in dbCID. The proposed method outperformed other widely used non-cancer-specific predictors on an external test set, which demonstrated the usefulness of the data deposited in dbCID. We hope dbCID will be a benchmark for improving and evaluating prediction algorithms, and the characteristics summarized here may assist with investigating the mechanism of indel-cancer association.'),
(69393, 2018, 'bib', 'A review of metrics measuring dissimilarity for rooted phylogenetic networks', 'Wang, Juan|Guo, Maozu', 'Department for Plant Cell and Molecular Biology, Institute for Biology, Humboldt-Universität zu Berlin, Berlin 10115, Germany.|Department for Plant Cell and Molecular Biology, Institute for Biology, Humboldt-Universität zu Berlin, Berlin 10115, Germany.', 'Germany|Germany', 3, '-', 'Journal Article', '10.1093/bib/bby062', '30020404', 'http://bioinformatics.imu.edu.cn/distance/.'),
(69394, 2018, 'bib', 'An efficient multi-locus mixed model framework for the detection of small and linked QTLs in F2', 'Wen, Yang-Jun|Zhang, Ya-Wen|Zhang, Jin|Feng, Jian-Ying|Dunwell, Jim M|Zhang, Yuan-Ming', 'State Key Laboratory of Crop Genetics and Germplasm Enhancement, Nanjing Agricultural University, Nanjing, China.|College of Plant Science and Technology, Huazhong Agricultural University, Wuhan, China.|State Key Laboratory of Crop Genetics and Germplasm Enhancement, Nanjing Agricultural University, Nanjing, China.|State Key Laboratory of Crop Genetics and Germplasm Enhancement, Nanjing Agricultural University, Nanjing, China.|School of Agriculture, Policy and Development, University of Reading, Reading RG6 6AR, United Kingdom.|State Key Laboratory of Crop Genetics and Germplasm Enhancement, Nanjing Agricultural University, Nanjing, China.', 'China|China|China|China|United Kingdom|China', 16, '-', 'Journal Article', '10.1093/bib/bby058', '30032279', 'In the genetic system that regulates complex traits, metabolites, gene expression levels, RNA editing levels and DNA methylation, a series of small and linked genes exist. To date, however, little is known about how to design an efficient framework for the detection of these kinds of genes. In this article, we propose a genome-wide composite interval mapping (GCIM) in F2. First, controlling polygenic background via selecting markers in the genome scanning of linkage analysis was replaced by estimating polygenic variance in a genome-wide association study. This can control large, middle and minor polygenic backgrounds in genome scanning. Then, additive and dominant effects for each putative quantitative trait locus (QTL) were separately scanned so that a negative logarithm P-value curve against genome position could be separately obtained for each kind of effect. In each curve, all the peaks were identified as potential QTLs. Thus, almost all the small-effect and linked QTLs are included in a multi-locus model. Finally, adaptive least absolute shrinkage and selection operator (adaptive lasso) was used to estimate all the effects in the multi-locus model, and all the nonzero effects were further identified by likelihood ratio test for true QTL identification. This method was used to reanalyze four rice traits. Among 25 known genes detected in this study, 16 small-effect genes were identified only by GCIM. To further demonstrate GCIM, a series of Monte Carlo simulation experiments was performed. As a result, GCIM is demonstrated to be more powerful than the widely used methods for the detection of closely linked and small-effect QTLs.'),
(69395, 2018, 'bib', 'Sequencing era methods for identifying signatures of selection in the genome', 'Horscroft, Clare|Ennis, Sarah|Pengelly, Reuben J|Sluckin, Timothy J|Collins, Andrew', 'Genetic Epidemiology and Bioinformatics, Faculty of Medicine, University of Southampton, Duthie Building (808), Tremona Road, Southampton, UK.|Genetic Epidemiology and Bioinformatics, Faculty of Medicine, University of Southampton, Duthie Building (808), Tremona Road, Southampton, UK.|Genetic Epidemiology and Bioinformatics, Faculty of Medicine, University of Southampton, Duthie Building (808), Tremona Road, Southampton, UK.|Institute for Life Sciences, University of Southampton, Life Sciences Building (85), Highfield, Southampton, UK.|Genetic Epidemiology and Bioinformatics, Faculty of Medicine, University of Southampton, Duthie Building (808), Tremona Road, Southampton, UK.', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom', 1, '-', 'Journal Article', '10.1093/bib/bby064', '30053138', 'Insights into genetic loci which are under selection and their functional roles contribute to increased understanding of the patterns of phenotypic variation we observe today. The availability of whole-genome sequence data, for humans and other species, provides opportunities to investigate adaptation and evolution at unprecedented resolution. Many analytical methods have been developed to interrogate these large data sets and characterize signatures of selection in the genome. We review here recently developed methods and consider the impact of increased computing power and data availability on the detection of selection signatures. Consideration of demography, recombination and other confounding factors is important, and use of a range of methods in combination is a powerful route to resolving different forms of selection in genome sequence data. Overall, a substantial improvement in methods for application to whole-genome sequencing is evident, although further work is required to develop robust and computationally efficient approaches which may increase reproducibility across studies.'),
(69396, 2018, 'bib', 'Review of applications of high-throughput sequencing in personalized medicine: barriers and facilitators of future progress in research and clinical application', 'Lightbody, Gaye|Haberland, Valeriia|Browne, Fiona|Taggart, Laura|Zheng, Huiru|Parkes, Eileen|Blayney, Jaine K', 'School of Computing, Ulster University, Newtownabbey, UK.|MRC Integrative Epidemiology Unit, Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK.|School of Computing, Ulster University, Newtownabbey, UK.|Almac Diagnostics, Craigavon, UK.|School of Computing, Ulster University, Newtownabbey, UK.|Centre for Cancer Research & Cell Biology, School of Medicine, Dentistry and Biomedical Sciences, Queens University, Belfast, UK.|Centre for Cancer Research & Cell Biology, School of Medicine, Dentistry and Biomedical Sciences, Queens University, Belfast, UK.', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom', 11, '-', 'Journal Article', '10.1093/bib/bby051', '30084865', 'There has been an exponential growth in the performance and output of sequencing technologies (omics data) with full genome sequencing now producing gigabases of reads on a daily basis. These data may hold the promise of personalized medicine, leading to routinely available sequencing tests that can guide patient treatment decisions. In the era of high-throughput sequencing (HTS), computational considerations, data governance and clinical translation are the greatest rate-limiting steps. To ensure that the analysis, management and interpretation of such extensive omics data is exploited to its full potential, key factors, including sample sourcing, technology selection and computational expertise and resources, need to be considered, leading to an integrated set of high-performance tools and systems. This article provides an up-to-date overview of the evolution of HTS and the accompanying tools, infrastructure and data management approaches that are emerging in this space, which, if used within in a multidisciplinary context, may ultimately facilitate the development of personalized medicine.'),
(69397, 2018, 'bib', 'Recent applications of deep learning and machine intelligence on in silico drug discovery: methods, tools and databases', 'Rifaioglu, Ahmet Sureyya|Atas, Heval|Martin, Maria Jesus|Cetin-Atalay, Rengul|Atalay, Volkan|Dogan, Tunca', 'Department of Computer Engineering, Middle East Technical University, Ankara, Turkey.|Cancer System Biology Laboratory (CanSyL), Graduate School of Informatics, Middle East Technical University, Ankara, Turkey.|European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Cambridge, Hinxton CB10 1SD, UK.|Department of Computer Engineering, Middle East Technical University, Ankara, Turkey.|Department of Computer Engineering, Middle East Technical University, Ankara, Turkey.|Cancer System Biology Laboratory (CanSyL), Graduate School of Informatics, Middle East Technical University, Ankara, Turkey and European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Cambridge, Hinxton CB10 1SD, UK.', 'Turkey|Turkey|United Kingdom|Turkey|Turkey|Turkey', 28, '-', 'Journal Article', '10.1093/bib/bby061', '30084866', 'The identification of interactions between drugs/compounds and their targets is crucial for the development of new drugs. In vitro screening experiments (i.e. bioassays) are frequently used for this purpose, however, experimental approaches are insufficient to explore novel drug-target interactions, mainly because of feasibility problems, as they are labour intensive, costly and time consuming. A computational field known as virtual screening (VS) has emerged in the past decades to aid experimental drug discovery studies by statistically estimating unknown bio-interactions between compounds and biological targets. These methods use the physico-chemical and structural properties of compounds and/or target proteins along with the experimentally verified bio-interaction information to generate predictive models. Lately, sophisticated machine learning techniques are applied in VS to elevate the predictive performance.The objective of this study is to examine and discuss the recent applications of machine learning techniques in VS, including deep learning, which became highly popular after giving rise to epochal developments in the fields of computer vision and natural language processing. The past 3 years have witnessed an unprecedented amount of research studies considering the application of deep learning in biomedicine, including computational drug discovery. In this review, we first describe the main instruments of VS methods, including compound and protein features (i.e. representations and descriptors), frequently used libraries and toolkits for VS, bioactivity databases and gold-standard data sets for system training and benchmarking. We subsequently review recent VS studies with a strong emphasis on deep learning applications. Finally, we discuss the present state of the field, including the current challenges and suggest future directions. We believe that this survey will provide insight to the researchers working in the field of computational drug discovery in terms of comprehending and developing novel bio-prediction methods.'),
(69398, 2018, 'bib', 'LncFinder: an integrated platform for long non-coding RNA identification utilizing sequence intrinsic composition, structural information and physicochemical property', 'Han, Siyu|Liang, Yanchun|Ma, Qin|Xu, Yangyi|Zhang, Yu|Du, Wei|Wang, Cankun|Li, Ying', 'College of Computer Science and Technology, Key Laboratory of Symbol Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, China.|College of Computer Science and Technology, Key Laboratory of Symbol Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, China.|Bioinformatics and Mathematical Biosciences Lab, Department of Agronomy, Horticulture and Plant Science, South Dakot State University, Brookings, SD, USA.|College of Computer Science and Technology, Key Laboratory of Symbol Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, China.|College of Computer Science and Technology, Key Laboratory of Symbol Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, China.|College of Computer Science and Technology, Key Laboratory of Symbol Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, China.|Department of Mathematics and Statistics, South Dakota State University, Brookings, SD, USA.|College of Computer Science and Technology, Key Laboratory of Symbol Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, China.', 'China|China|United States|China|China|China|United States|China', 8, '-', 'Journal Article', '10.1093/bib/bby065', '30084867', 'Discovering new long non-coding RNAs (lncRNAs) has been a fundamental step in lncRNA-related research. Nowadays, many machine learning-based tools have been developed for lncRNA identification. However, many methods predict lncRNAs using sequence-derived features alone, which tend to display unstable performances on different species. Moreover, the majority of tools cannot be re-trained or tailored by users and neither can the features be customized or integrated to meet researchers requirements. In this study, features extracted from sequence-intrinsic composition, secondary structure and physicochemical property are comprehensively reviewed and evaluated. An integrated platform named LncFinder is also developed to enhance the performance and promote the research of lncRNA identification. LncFinder includes a novel lncRNA predictor using the heterologous features we designed. Experimental results show that our method outperforms several state-of-the-art tools on multiple species with more robust and satisfactory results. Researchers can additionally employ LncFinder to extract various classic features, build classifier with numerous machine learning algorithms and evaluate classifier performance effectively and efficiently. LncFinder can reveal the properties of lncRNA and mRNA from various perspectives and further inspire lncRNA-protein interaction prediction and lncRNA evolution analysis. It is anticipated that LncFinder can significantly facilitate lncRNA-related research, especially for the poorly explored species. LncFinder is released as R package (https://CRAN.R-project.org/package=LncFinder). A web server (http://bmbl.sdstate.edu/lncfinder/) is also developed to maximize its availability.'),
(69399, 2018, 'bib', 'A brief history of bioinformatics', 'Gauthier, Jeff|Vincent, Antony T|Charette, Steve J|Derome, Nicolas', 'Institut de Biologie Intégrative et des Systèmes (IBIS), Département de Biologie, Université Laval, 1030, av. de la Médecine, Québec, Canada.|INRS-Institut Armand-Frappier, Bacterial Symbionts Evolution, 531 boul. des Prairies, Laval, QC, Canada.|Centre de Recherche de lInstitut, Universitaire de Cardiologie et de Pneumologie de Québec (CRIUCPQ), 2725 Chemin Sainte-Foy, Québec, QC, Canada.|Institut de Biologie Intégrative et des Systèmes (IBIS), Département de Biologie, Université Laval, 1030, av. de la Médecine, Québec, Canada.', 'Canada|Canada|Canada|Canada', 2, '-', 'Journal Article', '10.1093/bib/bby063', '30084940', 'It is easy for todays students and researchers to believe that modern bioinformatics emerged recently to assist next-generation sequencing data analysis. However, the very beginnings of bioinformatics occurred more than 50 years ago, when desktop computers were still a hypothesis and DNA could not yet be sequenced. The foundations of bioinformatics were laid in the early 1960s with the application of computational methods to protein sequence analysis (notably, de novo sequence assembly, biological sequence databases and substitution models). Later on, DNA analysis also emerged due to parallel advances in (i) molecular biology methods, which allowed easier manipulation of DNA, as well as its sequencing, and (ii) computer science, which saw the rise of increasingly miniaturized and more powerful computers, as well as novel software better suited to handle bioinformatics tasks. In the 1990s through the 2000s, major improvements in sequencing technology, along with reduced costs, gave rise to an exponential increase of data. The arrival of Big Data has laid out new challenges in terms of data mining and management, calling for more expertise from computer science into the field. Coupled with an ever-increasing amount of bioinformatics tools, biological Big Data had (and continues to have) profound implications on the predictive power and reproducibility of bioinformatics results. To overcome this issue, universities are now fully integrating this discipline into the curriculum of biology students. Recent subdisciplines such as synthetic biology, systems biology and whole-cell modeling have emerged from the ever-increasing complementarity between computer science and biology.'),
(69400, 2018, 'bib', 'A comparative analysis of cell-type adjustment methods for epigenome-wide association studies based on simulated and real data sets', 'Brägelmann, Johannes|Lorenzo Bermejo, Justo', 'University Hospital of Cologne, Germany.|Statistical Genetics Group, Institute of Medical Biometry and Informatics, University of Heidelberg, Germany.', 'Germany|Germany', 1, '-', 'Journal Article', '10.1093/bib/bby068', '30099476', 'Technological advances and reduced costs of high-density methylation arrays have led to an increasing number of association studies on the possible relationship between human disease and epigenetic variability. DNA samples from peripheral blood or other tissue types are analyzed in epigenome-wide association studies (EWAS) to detect methylation differences related to a particular phenotype. Since information on the cell-type composition of the sample is generally not available and methylation profiles are cell-type specific, statistical methods have been developed for adjustment of cell-type heterogeneity in EWAS.In this study we systematically compared five popular adjustment methods: the factored spectrally transformed linear mixed model (FaST-LMM-EWASher), the sparse principal component analysis algorithm ReFACTor, surrogate variable analysis (SVA), independent SVA (ISVA) and an optimized version of SVA (SmartSVA). We used real data and applied a multilayered simulation framework to assess the type I error rate, the statistical power and the quality of estimated methylation differences according to major study characteristics.While all five adjustment methods improved false-positive rates compared with unadjusted analyses, FaST-LMM-EWASher resulted in the lowest type I error rate at the expense of low statistical power. SVA efficiently corrected for cell-type heterogeneity in EWAS up to 200 cases and 200 controls, but did not control type I error rates in larger studies. Results based on real data sets confirmed simulation findings with the strongest control of type I error rates by FaST-LMM-EWASher and SmartSVA. Overall, ReFACTor, ISVA and SmartSVA showed the best comparable statistical power, quality of estimated methylation differences and runtime.'),
(69401, 2018, 'bib', 'Interpretation of differential gene expression results of RNA-seq data: review and integration', 'McDermaid, Adam|Monier, Brandon|Zhao, Jing|Liu, Bingqiang|Ma, Qin', 'Department of Mathematics and Statistics, South Dakota State University, Brookings, SD, USA.|Department of Biology and Microbiology, South Dakota State University, SD, USA.|Department of Internal Medicine, Sanford Research, University of South Dakota Sanford School of Medicine.|School of Mathematics, Shandong University.|Department of Agronomy, Horticulture, and Plant Science, Bioinformatics and Mathematical Biosciences Lab, South Dakota State University.', 'United States|United States|United States|United States|United States', 7, '-', 'Journal Article', '10.1093/bib/bby067', '30099484', 'Differential gene expression (DGE) analysis is one of the most common applications of RNA-sequencing (RNA-seq) data. This process allows for the elucidation of differentially expressed genes across two or more conditions and is widely used in many applications of RNA-seq data analysis. Interpretation of the DGE results can be nonintuitive and time consuming due to the variety of formats based on the tool of choice and the numerous pieces of information provided in these results files. Here we reviewed DGE results analysis from a functional point of view for various visualizations. We also provide an R/Bioconductor package, Visualization of Differential Gene Expression Results using R, which generates information-rich visualizations for the interpretation of DGE results from three widely used tools, Cuffdiff, DESeq2 and edgeR. The implemented functions are also tested on five real-world data sets, consisting of one human, one Malus domestica and three Vitis riparia data sets.'),
(69402, 2018, 'bib', 'Recent advances and prospects of computational methods for metabolite identification: a review with emphasis on machine learning approaches', 'Nguyen, Dai Hai|Nguyen, Canh Hao|Mamitsuka, Hiroshi', 'Department of machine learning and bioinformatics, Bioinformatics Center, Kyoto University, Uji, Japan.|Bioinformatics Center, Institute for Chemical Research, Kyoto University, Uji, Japan.|Bioinformatics Center, Institute for Chemical Research, Kyoto University, Uji, Japan.', 'Japan|Japan|Japan', 5, '-', 'Journal Article', '10.1093/bib/bby066', '30099485', 'Metabolomics involves studies of a great number of metabolites, which are small molecules present in biological systems. They play a lot of important functions such as energy transport, signaling, building block of cells and inhibition/catalysis. Understanding biochemical characteristics of the metabolites is an essential and significant part of metabolomics to enlarge the knowledge of biological systems. It is also the key to the development of many applications and areas such as biotechnology, biomedicine or pharmaceuticals. However, the identification of the metabolites remains a challenging task in metabolomics with a huge number of potentially interesting but unknown metabolites. The standard method for identifying metabolites is based on the mass spectrometry (MS) preceded by a separation technique. Over many decades, many techniques with different approaches have been proposed for MS-based metabolite identification task, which can be divided into the following four groups: mass spectra database, in silico fragmentation, fragmentation tree and machine learning. In this review paper, we thoroughly survey currently available tools for metabolite identification with the focus on in silico fragmentation, and machine learning-based approaches. We also give an intensive discussion on advanced machine learning methods, which can lead to further improvement on this task.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(69403, 2018, 'bib', 'Computational resources associating diseases with genotypes, phenotypes and exposures', 'Zhang, Wenliang|Zhang, Haiyue|Yang, Huan|Li, Miaoxin|Xie, Zhi|Li, Weizhong', 'Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou 510080, China.|Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou 510080, China.|Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou 510080, China.|Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou 510080, China.|State Key Lab of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou 500040, China.|Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou 510080, China.', 'China|China|China|China|China|China', 1, '-', 'Journal Article', '10.1093/bib/bby071', '30102366', 'The causes of a disease and its therapies are not only related to genotypes, but also associated with other factors, including phenotypes, environmental exposures, drugs and chemical molecules. Distinguishing disease-related factors from many neutral factors is critical as well as difficult. Over the past two decades, bioinformaticians have developed many computational resources to integrate the omics data and discover associations among these factors. However, researchers and clinicians are experiencing difficulties in choosing appropriate resources from hundreds of relevant databases and software tools. Here, in order to assist the researchers and clinicians, we systematically review the public computational resources of human diseases related to genotypes, phenotypes, environment factors, drugs and chemical exposures. We briefly describe the development history of these computational resources, followed by the details of the relevant databases and software tools. We finally conclude with a discussion of current challenges and future opportunities as well as prospects on this topic.'),
(69404, 2018, 'bib', 'Review and comparative assessment of similarity-based methods for prediction of drug-protein interactions in the druggable human proteome', 'Wang, Chen|Kurgan, Lukasz', 'Computer Science Department, Virginia Commonwealth University, Richmond, VA 23284, USA.|Computer Science Department, Virginia Commonwealth University, Richmond, VA 23284, USA.', 'United States|United States', 0, '-', 'Journal Article', '10.1093/bib/bby069', '30102367', 'Drug-protein interactions (DPIs) underlie the desired therapeutic actions and the adverse side effects of a significant majority of drugs. Computational prediction of DPIs facilitates research in drug discovery, characterization and repurposing. Similarity-based methods that do not require knowledge of protein structures are particularly suitable for druggable genome-wide predictions of DPIs. We review 35 high-impact similarity-based predictors that were published in the past decade. We group them based on three types of similarities and their combinations that they use. We discuss and compare key aspects of these methods including source databases, internal databases and their predictive models. Using our novel benchmark database, we perform comparative empirical analysis of predictive performance of seven types of representative predictors that utilize each type of similarity individually and all possible combinations of similarities. We assess predictive quality at the database-wide DPI level and we are the first to also include evaluation over individual drugs. Our comprehensive analysis shows that predictors that use more similarity types outperform methods that employ fewer similarities, and that the model combining all three types of similarities secures area under the receiver operating characteristic curve of 0.93. We offer a comprehensive analysis of sensitivity of predictive performance to intrinsic and extrinsic characteristics of the considered predictors. We find that predictive performance is sensitive to low levels of similarities between sequences of the drug targets and several extrinsic properties of the input drug structures, drug profiles and drug targets. The benchmark database and a webserver for the seven predictors are freely available at http://biomine.cs.vcu.edu/servers/CONNECTOR/.'),
(69405, 2018, 'bib', 'Comprehensive comparative analysis of methods and software for identifying viral integrations', 'Chen, Xun|Kost, Jason|Li, Dawei', 'Department of Microbiology and Molecular Genetics, University of Vermont, Burlington, Vermont 05405, USA.|Department of Microbiology and Molecular Genetics, University of Vermont, Burlington, Vermont 05405, USA.|Department of Microbiology and Molecular Genetics, University of Vermont, Burlington, Vermont 05405, USA.', 'United States|United States|United States', 5, '-', 'Journal Article', '10.1093/bib/bby070', '30102374', 'Many viruses are capable of integrating in the human genome, particularly viruses involved in tumorigenesis. Viral integrations can be considered genetic markers for discovering virus-caused cancers and inferring cancer cell development. Next-generation sequencing (NGS) technologies have been widely used to screen for viral integrations in cancer genomes, and a number of bioinformatics tools have been developed to detect viral integrations using NGS data. However, there has been no systematic comparison of the methods or software. In this study, we performed a comprehensive comparative analysis of the designs, performance, functionality and limitations among the existing methods and software for detecting viral integrations. We further compared the sensitivity, precision and runtime of integration detection of four representative tools. Our analyses showed that each of the existing software had its own merits, however, none of them were sufficient for parallel or accurate virome-wide detection. After carefully evaluating the limitations shared by the existing methods, we proposed strategies and directions for developing virome-wide integration detection.'),
(69406, 2018, 'bib', 'Machine learning meets genome assembly', 'Padovani de Souza, Kleber|Setubal, João Carlos|Ponce de Leon F de Carvalho, André Carlos|Oliveira, Guilherme|Chateau, Annie|Alves, Ronnie', 'Federal University of Pará, Brazil.|University of São Paulo, Brazil.|Vale Technology Institute-Sustainable Development, Brazil.|University of Montpellier, LIRMM, France.|Vale Technology Institute-Sustainable Development, Brazil.|Federal University of Pará, Brazil.', 'Brazil|Brazil|Brazil|France|Brazil|Brazil', 3, '-', 'Journal Article', '10.1093/bib/bby072', '30137230', 'With the recent advances in DNA sequencing technologies, the study of the genetic composition of living organisms has become more accessible for researchers. Several advances have been achieved because of it, especially in the health sciences. However, many challenges which emerge from the complexity of sequencing projects remain unsolved. Among them is the task of assembling DNA fragments from previously unsequenced organisms, which is classified as an NP-hard (nondeterministic polynomial time hard) problem, for which no efficient computational solution with reasonable execution time exists. However, several tools that produce approximate solutions have been used with results that have facilitated scientific discoveries, although there is ample room for improvement. As with other NP-hard problems, machine learning algorithms have been one of the approaches used in recent years in an attempt to find better solutions to the DNA fragment assembly problem, although still at a low scale.'),
(69407, 2018, 'bib', 'Impact of similarity metrics on single-cell RNA-seq data clustering', 'Kim, Taiyun|Chen, Irene Rui|Lin, Yingxin|Wang, Andy Yi-Yang|Yang, Jean Yee Hwa|Yang, Pengyi', 'School of Mathematics and Statistics, The University of Sydney, Sydney, NSW 2006, Australia.|School of Mathematics and Statistics, The University of Sydney, Sydney, NSW 2006, Australia.|School of Mathematics and Statistics, The University of Sydney, Sydney, NSW 2006, Australia.|Department of Anaesthesia, The University of Sydney Northern Clinical School, The University of Sydney, Sydney, NSW 2006, Australia.|School of Mathematics and Statistics, The University of Sydney, Sydney, NSW 2006, Australia.|School of Mathematics and Statistics, The University of Sydney, Sydney, NSW 2006, Australia.', 'Australia|Australia|Australia|Australia|Australia|Australia', 15, '-', 'Journal Article', '10.1093/bib/bby076', '30137247', 'Advances in high-throughput sequencing on single-cell gene expressions [single-cell RNA sequencing (scRNA-seq)] have enabled transcriptome profiling on individual cells from complex samples. A common goal in scRNA-seq data analysis is to discover and characterise cell types, typically through clustering methods. The quality of the clustering therefore plays a critical role in biological discovery. While numerous clustering algorithms have been proposed for scRNA-seq data, fundamentally they all rely on a similarity metric for categorising individual cells. Although several studies have compared the performance of various clustering algorithms for scRNA-seq data, currently there is no benchmark of different similarity metrics and their influence on scRNA-seq data clustering. Here, we compared a panel of similarity metrics on clustering a collection of annotated scRNA-seq datasets. Within each dataset, a stratified subsampling procedure was applied and an array of evaluation measures was employed to assess the similarity metrics. This produced a highly reliable and reproducible consensus on their performance assessment. Overall, we found that correlation-based metrics (e.g. Pearsons correlation) outperformed distance-based metrics (e.g. Euclidean distance). To test if the use of correlation-based metrics can benefit the recently published clustering techniques for scRNA-seq data, we modified a state-of-the-art kernel-based clustering algorithm (SIMLR) using Pearsons correlation as a similarity measure and found significant performance improvement over Euclidean distance on scRNA-seq data clustering. These findings demonstrate the importance of similarity metrics in clustering scRNA-seq data and highlight Pearsons correlation as a favourable choice. Further comparison on different scRNA-seq library preparation protocols suggests that they may also affect clustering performance. Finally, the benchmarking framework is available at http://www.maths.usyd.edu.au/u/SMS/bioinformatics/software.html.'),
(69408, 2018, 'bib', 'Binding site matching in rational drug design: algorithms and applications', 'Naderi, Misagh|Lemoine, Jeffrey Mitchell|Govindaraj, Rajiv Gandhi|Kana, Omar Zade|Feinstein, Wei Pan|Brylinski, Michal', 'Department of Biological Sciences, Louisiana State University, Baton Rouge, LA 70803, USA.|Department of Biological Sciences, Louisiana State University, Baton Rouge, LA 70803, USA.|Department of Biological Sciences, Louisiana State University, Baton Rouge, LA 70803, USA.|Department of Biological Sciences, Louisiana State University, Baton Rouge, LA 70803, USA.|High-Performance Computing, Louisiana State University, Baton Rouge, LA 70803, USA.|Department of Biological Sciences, Louisiana State University, Baton Rouge, LA 70803, USA.', 'United States|United States|United States|United States|United States|United States', 1, '-', 'Journal Article', '10.1093/bib/bby078', '30169563', 'Interactions between proteins and small molecules are critical for biological functions. These interactions often occur in small cavities within protein structures, known as ligand-binding pockets. Understanding the physicochemical qualities of binding pockets is essential to improve not only our basic knowledge of biological systems, but also drug development procedures. In order to quantify similarities among pockets in terms of their geometries and chemical properties, either bound ligands can be compared to one another or binding sites can be matched directly. Both perspectives routinely take advantage of computational methods including various techniques to represent and compare small molecules as well as local protein structures. In this review, we survey 12 tools widely used to match pockets. These methods are divided into five categories based on the algorithm implemented to construct binding-site alignments. In addition to the comprehensive analysis of their algorithms, test sets and the performance of each method are described. We also discuss general pharmacological applications of computational pocket matching in drug repurposing, polypharmacology and side effects. Reflecting on the importance of these techniques in drug discovery, in the end, we elaborate on the development of more accurate meta-predictors, the incorporation of protein flexibility and the integration of powerful artificial intelligence technologies such as deep learning.'),
(69409, 2018, 'bib', 'PCA-based GRS analysis enhances the effectiveness for genetic correlation detection', 'Zhao, Yan|Ning, Yujie|Zhang, Feng|Ding, Miao|Wen, Yan|Shi, Liang|Wang, Kunpeng|Lu, Mengnan|Sun, Jingyan|Wu, Menglu|Cheng, Bolun|Ma, Mei|Zhang, Lu|Cheng, Shiqiang|Shen, Hui|Tian, Qing|Guo, Xiong|Deng, Hong-Wen', 'School of Public Health, Health Science Center, Xian Jiaotong University, Xian, P. R. China.|School of Public Health, Health Science Center, Xian Jiaotong University, Xian, P. R. China.|School of Public Health, Health Science Center, Xian Jiaotong University, Xian, P. R. China.|School of Public Health, Health Science Center, Xian Jiaotong University, Xian, P. R. China.|School of Public Health, Health Science Center, Xian Jiaotong University, Xian, P. R. China.|Health Science Center, Xian Jiaotong University, Xian, P. R. China.|Health Science Center, Xian Jiaotong University, Xian, P. R. China.|Health Science Center, Xian Jiaotong University, Xian, P. R. China.|Health Science Center, Xian Jiaotong University, Xian, P. R. China.|Health Science Center, Xian Jiaotong University, Xian, P. R. China.|School of Public Health, Health Science Center, Xian Jiaotong University, Xian, P. R. China.|School of Public Health, Health Science Center, Xian Jiaotong University, Xian, P. R. China.|School of Public Health, Health Science Center, Xian Jiaotong University, Xian, P. R. China.|School of Public Health, Health Science Center, Xian Jiaotong University, Xian, P. R. China.|Center for Bioinformatics and Genomics, Department of Global Biostatistics and Data Science, School of Public Health and Tropical Medicine, Tulane University, USA.|Center for Bioinformatics and Genomics, Department of Global Biostatistics and Data Science, School of Public Health and Tropical Medicine, Tulane University, USA.|School of Public Health, Health Science Center, Xian Jiaotong University, Xian, P. R. China.|Center for Bioinformatics and Genomics, Department of Global Biostatistics and Data Science, School of Public Health and Tropical Medicine, Tulane University, USA.', 'China|China|China|China|China|China|China|China|China|China|China|China|China|China|United States|United States|China|United States', 0, '-', 'Journal Article', '10.1093/bib/bby075', '30169568', 'Genetic risk score (GRS, also known as polygenic risk score) analysis is an increasingly popular method for exploring genetic architectures and relationships of complex diseases. However, complex diseases are usually measured by multiple correlated phenotypes. Analyzing each disease phenotype.'),
(69410, 2018, 'bib', 'Breast cancer prognosis signature: linking risk stratification to disease subtypes', 'Yu, Fulong|Quan, Fei|Xu, Jinyuan|Zhang, Yan|Xie, Yi|Zhang, Jingyu|Lan, Yujia|Yuan, Huating|Zhang, Hongyi|Cheng, Shujun|Xiao, Yun|Li, Xia', 'College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang 150081, China.|College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang 150081, China.|College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang 150081, China.|College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang 150081, China.|College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang 150081, China.|College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang 150081, China.|College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang 150081, China.|College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang 150081, China.|College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang 150081, China.|College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang 150081, China.|College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang 150081, China.|College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang 150081, China.', 'China|China|China|China|China|China|China|China|China|China|China|China', 10, '-', 'Journal Article', '10.1093/bib/bby073', '30184043', 'Breast cancer is a very complex and heterogeneous disease with variable molecular mechanisms of carcinogenesis and clinical behaviors. The identification of prognostic risk factors may enable effective diagnosis and treatment of breast cancer. In particular, numerous gene-expression-based prognostic signatures were developed and some of them have already been applied into clinical trials and practice. In this study, we summarized several representative gene-expression-based signatures with significant prognostic value and separately assessed their ability of prognosis prediction in their originally targeted populations of breast cancer. Notably, many of the collected signatures were originally designed to predict the outcomes of estrogen receptor positive (ER+) patients or the whole breast cancer cohort, there are no typical signatures used for the prognostic prediction in a specific population of patients with the intrinsic subtype. We thus attempted to identify subtype-specific prognostic signatures via a computational framework for analyzing multi-omics profiles and patient survival. For both the discovery and an independent data set, we confirmed that subtype-specific signature is a strong and significant independent prognostic factor in the corresponding cohort. These results indicate that the subtype-specific prognostic signature has a much higher resolution in the risk stratification, which may lead to improved therapies and precision medicine for patients with breast cancer.'),
(69411, 2018, 'bib', 'Computational determination of gene age and characterization of evolutionary dynamics in human', 'Yin, Hongyan|Li, Mengwei|Xia, Lin|He, Chaozu|Zhang, Zhang', 'Hainan Key Laboratory for Sustainable Utilization of Tropical Bioresources, Institute of Tropical Agriculture and Forestry, Hainan University, China. Her research interests lie in computational evolutionary biology and mechanisms of plant disease resistance.|CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences. His research focuses on cancer bioinformatics and DNA methylation.|CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences. His research focuses on cancer bioinformatics and DNA methylation.|Hainan Key Laboratory for Sustainable Utilization of Tropical Bioresources, Institute of Tropical Agriculture and Forestry, Hainan University, China. Her research interests lie in computational evolutionary biology and mechanisms of plant disease resistance.|CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences. His research focuses on cancer bioinformatics and DNA methylation.', 'China|-|-|China|-', 0, '-', 'Journal Article', '10.1093/bib/bby074', '30184145', 'Genes originate at different evolutionary time scales and possess different ages, accordingly presenting diverse functional characteristics and reflecting distinct adaptive evolutionary innovations. In the past decades, progresses have been made in gene age identification by a variety of methods that are principally based on comparative genomics. Here we summarize methods for computational determination of gene age and evaluate the effectiveness of different computational methods for age identification. Our results show that improved age determination can be achieved by combining homolog clustering with phylogeny inference, which enables more accurate age identification in human genes. Accordingly, we characterize evolutionary dynamics of human genes based on an extremely long evolutionary time scale spanning ~4,000 million years from archaea/bacteria to human, revealing that young genes are clustered on certain chromosomes and that Mendelian disease genes (including monogenic disease and polygenic disease genes) and cancer genes exhibit divergent evolutionary origins. Taken together, deciphering genes ages as well as their evolutionary dynamics is of fundamental significance in unveiling the underlying mechanisms during evolution and better understanding how young or new genes become indispensable integrants coupled with novel phenotypes and biological diversity.'),
(69412, 2018, 'bib', 'TRCirc: a resource for transcriptional regulation information of circRNAs', 'Tang, Zhidong|Li, Xuecang|Zhao, Jianmei|Qian, Fengcui|Feng, Chenchen|Li, Yanyu|Zhang, Jian|Jiang, Yong|Yang, Yongsan|Wang, Qiuyu|Li, Chunquan', 'School of Medical Informatics, Daqing Campus, Harbin Medical University.|School of Medical Informatics, Daqing Campus, Harbin Medical University.|School of Medical Informatics, Daqing Campus, Harbin Medical University.|School of Medical Informatics, Daqing Campus, Harbin Medical University.|School of Medical Informatics, Daqing Campus, Harbin Medical University.|student at the School of Medical Informatics, Daqing Campus, Harbin Medical University.|School of Medical Informatics, Daqing Campus, Harbin Medical University.|School of Medical Informatics, Daqing Campus, Harbin Medical University.|School of Medical Informatics, Daqing Campus, Harbin Medical University.|School of Nursing, Daqing Campus, Harbin Medical University.|School of Medical Informatics, Daqing Campus, Harbin Medical University.', '-|-|-|-|-|-|-|-|-|-|-', 9, '-', 'Journal Article', '10.1093/bib/bby083', '30184150', 'In recent years, high-throughput genomic technologies like chromatin immunoprecipitation sequencing (ChIp-seq) and transcriptome sequencing (RNA-seq) have been becoming both more refined and less expensive, making them more accessible. Many circular RNAs (circRNAs) that originate from back-spliced exons have been identified in various cell lines across different species. However, the regulatory mechanism for transcription of circRNAs remains unclear. Therefore, there is an urgent need to construct a database detailing the transcriptional regulation of circRNAs. TRCirc (http://www.licpathway.net/TRCirc) provides a resource for efficient retrieval, browsing and visualization of transcriptional regulation information of circRNAs. The current version of TRCirc documents 92 375 circRNAs and 161 transcription factors (TFs) from more than 100 cell types and together represent more than 765 000 TF-circRNA regulatory relationships. Furthermore, TRCirc provides other regulatory information about transcription of circRNAs, including their expression, methylation levels, H3K27ac signals in regulation regions and super-enhancers associated with circRNAs. TRCirc provides a convenient, user-friendly interface to search, browse and visualize detailed information about these circRNAs.'),
(69413, 2018, 'bib', 'Twenty years of bioinformatics research for protease-specific substrate and cleavage site prediction: a comprehensive revisit and benchmarking of existing methods', 'Li, Fuyi|Wang, Yanan|Li, Chen|Marquez-Lago, Tatiana T|Leier, André|Rawlings, Neil D|Haffari, Gholamreza|Revote, Jerico|Akutsu, Tatsuya|Chou, Kuo-Chen|Purcell, Anthony W|Pike, Robert N|Webb, Geoffrey I|Ian Smith, A|Lithgow, Trevor|Daly, Roger J|Whisstock, James C|Song, Jiangning', 'Biomedicine Discovery Institute and Department of Biochemistry & Molecular Biology, Monash University, Melbourne, VIC 3800, Australia.|Biomedicine Discovery Institute and Department of Biochemistry & Molecular Biology, Monash University, Melbourne, VIC 3800, Australia.|Biomedicine Discovery Institute and Department of Biochemistry & Molecular Biology, Monash University, Melbourne, VIC 3800, Australia.|Department of Genetics and Department of Cell, Developmental and Integrative Biology, School of Medicine, University of Alabama at Birmingham, AL, USA.|Department of Genetics and Department of Cell, Developmental and Integrative Biology, School of Medicine, University of Alabama at Birmingham, AL, USA.|EMBL European Bioinformatics Institute, Wellcome Trust Genome Campus, Wellcome Trust Genome Campus,Hinxton, Cambridgeshire CB10 1SD, UK.|Monash Centre for Data Science, Faculty of Information Technology, Monash University, Melbourne, VIC 3800, Australia.|Biomedicine Discovery Institute and Department of Biochemistry & Molecular Biology, Monash University, Melbourne, VIC 3800, Australia.|Bioinformatics Center, Institute for Chemical Research, Kyoto University, Kyoto 611-0011, Japan.|Gordon Life Science Institute, Boston, MA 02478, USA.|Biomedicine Discovery Institute and Department of Biochemistry & Molecular Biology, Monash University, Melbourne, VIC 3800, Australia.|La Trobe Institute for Molecular Science, La Trobe University, Melbourne, VIC 3086, Australia.|Monash Centre for Data Science, Faculty of Information Technology, Monash University, Melbourne, VIC 3800, Australia.|Biomedicine Discovery Institute and Department of Biochemistry & Molecular Biology, Monash University, Melbourne, VIC 3800, Australia.|Biomedicine Discovery Institute and Department of Microbiology, Monash University, Melbourne, Victoria 3800, Australia.|Biomedicine Discovery Institute and Department of Biochemistry & Molecular Biology, Monash University, Melbourne, VIC 3800, Australia.|Biomedicine Discovery Institute and Department of Biochemistry & Molecular Biology, Monash University, Melbourne, VIC 3800, Australia.|Biomedicine Discovery Institute and Department of Biochemistry & Molecular Biology, Monash University, Melbourne, VIC 3800, Australia.', 'Australia|Australia|Australia|United Kingdom|United Kingdom|United Kingdom|Australia|Australia|Japan|United Kingdom|Australia|Australia|Australia|Austra', 22, '-', 'Journal Article', '10.1093/bib/bby077', '30184176', 'The roles of proteolytic cleavage have been intensively investigated and discussed during the past two decades. This irreversible chemical process has been frequently reported to influence a number of crucial biological processes (BPs), such as cell cycle, protein regulation and inflammation. A number of advanced studies have been published aiming at deciphering the mechanisms of proteolytic cleavage. Given its significance and the large number of functionally enriched substrates targeted by specific proteases, many computational approaches have been established for accurate prediction of protease-specific substrates and their cleavage sites. Consequently, there is an urgent need to systematically assess the state-of-the-art computational approaches for protease-specific cleavage site prediction to further advance the existing methodologies and to improve the prediction performance. With this goal in mind, in this article, we carefully evaluated a total of 19 computational methods (including 8 scoring function-based methods and 11 machine learning-based methods) in terms of their underlying algorithm, calculated features, performance evaluation and software usability. Then, extensive independent tests were performed to assess the robustness and scalability of the reviewed methods using our carefully prepared independent test data sets with 3641 cleavage sites (specific to 10 proteases). The comparative experimental results demonstrate that PROSPERous is the most accurate generic method for predicting eight protease-specific cleavage sites, while GPS-CCD and LabCaS outperformed other predictors for calpain-specific cleavage sites. Based on our review, we then outlined some potential ways to improve the prediction performance and ease the computational burden by applying ensemble learning, deep learning, positive unlabeled learning and parallel and distributed computing techniques. We anticipate that our study will serve as a practical and useful guide for interested readers to further advance next-generation bioinformatics tools for protease-specific cleavage site prediction.'),
(69414, 2018, 'bib', 'Polygenic approaches to detect gene-environment interactions when external information is unavailable', 'Lin, Wan-Yu|Huang, Ching-Chieh|Liu, Yu-Li|Tsai, Shih-Jen|Kuo, Po-Hsiu', 'Institute of Epidemiology and Preventive Medicine, College of Public Health, National Taiwan University, Taipei, Taiwan.|Institute of Epidemiology and Preventive Medicine, College of Public Health, National Taiwan University, Taipei, Taiwan.|Center for Neuropsychiatric Research, National Health Research Institutes, Miaoli County, Taiwan.|Department of Psychiatry, TaipeiVeterans General Hospital, Taipei, Taiwan.|Institute of Epidemiology and Preventive Medicine, College of Public Health, National Taiwan University, Taipei, Taiwan.', '-|-|-|-|-', 4, '-', 'Journal Article', '10.1093/bib/bby086', '30219835', 'The exploration of gene-environment interactions (G × E) is important for disease prediction and prevention. The scientific community usually uses external information to construct a genetic risk score (GRS), and then tests the interaction between this GRS and an environmental factor (E). However, external genome-wide association studies (GWAS) are not always available, especially for non-Caucasian ethnicity. Although GRS is an analysis tool to detect G × E in GWAS, its performance remains unclear when there is no external information. Our adaptive combination of Bayes factors method (ADABF) can aggregate G × E signals and test the significance of G × E by a polygenic test. We here explore a powerful polygenic approach for G × E when external information is unavailable, by comparing our ADABF with the GRS based on marginal effects of SNPs (GRS-M) and GRS based on SNP × E interactions (GRS-I). ADABF is the most powerful method in the absence of SNP main effects, whereas GRS-M is generally the best test when single-nucleotide polymorphisms main effects exist. GRS-I is the least powerful test due to its data-splitting strategy. Furthermore, we apply these methods to Taiwan Biobank data. ADABF and GRS-M identified gene × alcohol and gene × smoking interactions on blood pressure (BP). BP-increasing alleles elevate more BP in drinkers (smokers) than in nondrinkers (nonsmokers). This work provides guidance to choose a polygenic approach to detect G × E when external information is unavailable.'),
(69415, 2018, 'bib', 'Quick approximation of threshold values for genome-wide association studies', 'Hao, Zhiyu|Jiang, Li|Gao, Jin|Ye, Jinhua|Zhao, Jingli|Li, Shuling|Yang, Runqing', 'College of Animal Science and Technology at the Northeast Agricultural University.|Research Centre for Aquatic Biotechnology at the Chinese Academy of Fishery Sciences.|Research Centre for Aquatic Biotechnology at the Chinese Academy of Fishery Sciences.|Department of Mathematics at the Heilongjiang Bayi Agricultural University.|Research Centre for Aquatic Biotechnology at the Chinese Academy of Fishery Sciences.|College of Life Science at the Northeast Agricultural University. Her research focuses on animal behavior genetics and genomics.|Research Centre for Aquatic Biotechnology at the Chinese Academy of Fishery Sciences.', '-|-|-|-|-|-|-', 0, '-', 'Journal Article', '10.1093/bib/bby082', '30219836', 'Standard normal statistics, chi-squared statistics, Students t statistics and F statistics are used to map quantitative trait nucleotides for both small and large sample sizes. In genome-wide association studies (GWASs) of single-nucleotide polymorphisms (SNPs), the statistical distributions depend on both genetic effects and SNPs but are independent of SNPs under the null hypothesis of no genetic effects. Therefore, hypothesis testing when a nuisance parameter is present only under the alternative was introduced to quickly approximate the critical thresholds of these test statistics for GWASs. When only the statistical probabilities are available for high-throughput SNPs, the approximate critical thresholds can be estimated with chi-squared statistics, formulated by statistical probabilities with a degree of freedom of two. High similarities in the critical thresholds between the accurate and approximate estimations were demonstrated by extensive simulations and real data analysis.'),
(69416, 2018, 'bib', 'Principals about principal components in statistical genetics', 'Abegaz, Fentaw|Chaichoompu, Kridsadakorn|Génin, Emmanuelle|Fardo, David W|König, Inke R|Mahachie John, Jestinah M|Van Steen, Kristel', 'University of Liège (Belgium).|Max Planck Institute of Psychiatry (Germany).|Inserm in Brest (France).|University of Kentucky (USA).|Universität zu Lübeck, Germany.|University of Liege, Belgium.|University of Liège working on Systems Genetics.', 'Belgium|Germany|France|-|Germany|Belgium|-', 3, '-', 'Journal Article', '10.1093/bib/bby081', '30219892', 'Principal components (PCs) are widely used in statistics and refer to a relatively small number of uncorrelated variables derived from an initial pool of variables, while explaining as much of the total variance as possible. Also in statistical genetics, principal component analysis (PCA) is a popular technique. To achieve optimal results, a thorough understanding about the different implementations of PCA is required and their impact on study results, compared to alternative approaches. In this review, we focus on the possibilities, limitations and role of PCs in ancestry prediction, genome-wide association studies, rare variants analyses, imputation strategies, meta-analysis and epistasis detection. We also describe several variations of classic PCA that deserve increased attention in statistical genetics applications.'),
(69417, 2018, 'bib', 'Strategies for calibrating models of biology', 'Read, Mark N|Alden, Kieran|Timmis, Jon|Andrews, Paul S', 'University of Sydney, Australia.|University of York, UK.|University of York, UK.|SimOmics Ltd, Suite 10 IT Centre, Innovation Way, York, UK.', 'Australia|United Kingdom|United Kingdom|United Kingdom', 4, '-', 'Journal Article', '10.1093/bib/bby092', '30239570', 'Computational and mathematical modelling has become a valuable tool for investigating biological systems. Modelling enables prediction of how biological components interact to deliver system-level properties and extrapolation of biological system performance to contexts and experimental conditions where this is unknown. A models value hinges on knowing that it faithfully represents the biology under the contexts of use, or clearly ascertaining otherwise and thus motivating further model refinement. These qualities are evaluated through calibration, typically formulated as identifying model parameter values that align model and biological behaviours as measured through a metric applied to both. Calibration is critical to modelling but is often underappreciated. A failure to appropriately calibrate risks unrepresentative models that generate erroneous insights. Here, we review a suite of strategies to more rigorously challenge a models representation of a biological system. All are motivated by features of biological systems, and illustrative examples are drawn from the modelling literature. We examine the calibration of a model against distributions of biological behaviours or outcomes, not only average values. We argue for calibration even where model parameter values are experimentally ascertained. We explore how single metrics can be non-distinguishing for complex systems, with multiple-component dynamic and interaction configurations giving rise to the same metric output. Under these conditions, calibration is insufficiently constraining and the model non-identifiable: multiple solutions to the calibration problem exist. We draw an analogy to curve fitting and argue that calibrating a biological model against a single experiment or context is akin to curve fitting against a single data point. Though useful for communicating model results, we explore how metrics that quantify heavily emergent properties may not be suitable for use in calibration. Lastly, we consider the role of sensitivity and uncertainty analysis in calibration and the interpretation of model results. Our goal in this manuscript is to encourage a deeper consideration of calibration, and how to increase its capacity to either deliver faithful models or demonstrate them otherwise.'),
(69418, 2018, 'bib', 'Control principles for complex biological networks', 'Li, Min|Gao, Hao|Wang, Jianxin|Wu, Fang-Xiang', 'School of Information Science and Engineering, Central South University, Changsha, Hunan, China.|School of Information Science and Engineering, Central South University, Changsha, Hunan, China.|School of Information Science and Engineering, Central South University, Changsha, Hunan, China.|School of Information Science and Engineering, Central South University, Changsha, Hunan, China.', 'China|China|China|China', 12, '-', 'Journal Article', '10.1093/bib/bby088', '30239577', 'Networks have been widely used to model the structure of various biological systems. Currently, a series of approaches have been developed to construct reliable biological networks. However, the ultimate understanding of a biological system is to steer its states to the desired ones by imposing signals. The control process is dominated by the intrinsic structure and the dynamic propagation. To understand the underlying mechanisms behind the life process, the control theory can be applied to biological networks with specific target requirements. In this article, we first introduce the structural controllability of complex networks and discuss its advantages and disadvantages. Then, we review the effective control to meet the specific requirements for complex biological networks. Moreover, we summarize the existing methods for finding the unique minimum set of driver nodes via the optimal control for complex networks. Finally, we discuss the relationships between biological networks and structural controllability, effective control and optimal control. Moreover, potential applications of general control principles are pointed out.'),
(69419, 2018, 'bib', 'Revealing tumor heterogeneity of breast cancer by utilizing the linkage between somatic and germline mutations', 'Zou, Meng|Jin, Rui|Au, Kin Fai', 'School of Mathematics and Statistics, Huazhong University of Science and Technology.|Department of Statistics, University of Iowa.|School of Mathematics and Statistics, Huazhong University of Science and Technology.', '-|-|-', 1, '-', 'Journal Article', '10.1093/bib/bby084', '30239581', 'The intra-tumor heterogeneity is associated with cancer progression and therapeutic resistance, such as in breast cancer. While the existing methods for studying tumor heterogeneity only analyze variant allele frequency (VAF), the genotype of variant is also informative for inferring subclones, which can be detected by long reads or paired-end reads. We developed GenoClone to integrate VAF with the genotype of variant innovatively, so it showed superior performance of inferring the number of subclones, estimating the fractions of subclones and identifying somatic single-nucleotide variants composition of subclones. When GenoClone was applied to 389 TCGA breast cancer samples, it revealed extensive intra-tumor heterogeneity. We further found that a few somatic mutations were relevant to the late stage of tumor evolution, including the ones at the oncogene PIK3CA and the tumor suppress gene TP53. Moreover, 52 subclones that were identified from 167 samples shared high similarity of somatic mutations, which were clustered into three groups with the sizes of 24, 14 and 14. It is helpful for understanding the development of breast cancer in certain subgroups of people and the drug development for population level. Furthermore, GenoClone also identified the tumor heterogeneity in different aliquots of the same samples. The implementation of GenoClone is available at http://www.healthcare.uiowa.edu/labs/au/GenoClone/.'),
(69420, 2018, 'bib', 'Sequence clustering in bioinformatics: an empirical study', 'Zou, Quan|Lin, Gang|Jiang, Xingpeng|Liu, Xiangrong|Zeng, Xiangxiang', 'Tianjin University.|Tianjin University.|Central China Normal University.|Xiamen University.|Xiamen University.', '-|-|China|-|-', 53, '-', 'Journal Article', '10.1093/bib/bby090', '30239587', 'Sequence clustering is a basic bioinformatics task that is attracting renewed attention with the development of metagenomics and microbiomics. The latest sequencing techniques have decreased costs and as a result, massive amounts of DNA/RNA sequences are being produced. The challenge is to cluster the sequence data using stable, quick and accurate methods. For microbiome sequencing data, 16S ribosomal RNA operational taxonomic units are typically used. However, there is often a gap between algorithm developers and bioinformatics users. Different software tools can produce diverse results and users can find them difficult to analyze. Understanding the different clustering mechanisms is crucial to understanding the results that they produce. In this review, we selected several popular clustering tools, briefly explained the key computing principles, analyzed their characters and compared them using two independent benchmark datasets. Our aim is to assist bioinformatics users in employing suitable clustering tools effectively to analyze big sequencing data. Related data, codes and software tools were accessible at the link http://lab.malab.cn/xe2x88xbclg/clustering/.'),
(69421, 2018, 'bib', 'An evaluation of supervised methods for identifying differentially methylated regions in Illumina methylation arrays', 'Mallik, Saurav|Odom, Gabriel J|Gao, Zhen|Gomez, Lissette|Chen, Xi|Wang, Lily', 'Division of Biostatistics, Department of Public Health Sciences, University of Miami, Miller School of Medicine, Miami, FL, USA.|Division of Biostatistics, Department of Public Health Sciences, University of Miami, Miller School of Medicine, Miami, FL, USA.|Sylvester Comprehensive Cancer Center, University of Miami Miller School of Medicine, Miami, FL, USA.|Dr. John T. Macdonald Foundation, Department of Human Genetics, and John P. Hussman Institute for Human Genomics, University of Miami, Miami, FL, USA.|Division of Biostatistics, Department of Public Health Sciences, University of Miami, Miller School of Medicine, Miami, FL, USA.|Division of Biostatistics, Department of Public Health Sciences, University of Miami, Miller School of Medicine, Miami, FL, USA.', 'United States|United States|United States|United States|United States|United States', 3, '-', 'Journal Article', '10.1093/bib/bby085', '30239597', 'Epigenome-wide association studies (EWASs) have become increasingly popular for studying DNA methylation (DNAm) variations in complex diseases. The Illumina methylation arrays provide an economical, high-throughput and comprehensive platform for measuring methylation status in EWASs. A number of software tools have been developed for identifying disease-associated differentially methylated regions (DMRs) in the epigenome. However, in practice, we found these tools typically had multiple parameter settings that needed to be specified and the performance of the software tools under different parameters was often unclear. To help users better understand and choose optimal parameter settings when using DNAm analysis tools, we conducted a comprehensive evaluation of 4 popular DMR analysis tools under 60 different parameter settings. In addition to evaluating power, precision, area under precision-recall curve, Matthews correlation coefficient, F1 score and type I error rate, we also compared several additional characteristics of the analysis results, including the size of the DMRs, overlap between the methods and execution time. The results showed that none of the software tools performed best under their default parameter settings, and power varied widely when parameters were changed. Overall, the precision of these software tools were good. In contrast, all methods lacked power when effect size was consistent but small. Across all simulation scenarios, comb-p consistently had the best sensitivity as well as good control of false-positive rate.'),
(69422, 2018, 'bib', 'CPPred-FL: a sequence-based predictor for large-scale identification of cell-penetrating peptides by feature representation learning', 'Qiang, Xiaoli|Zhou, Chen|Ye, Xiucai|Du, Pu-Feng|Su, Ran|Wei, Leyi', 'Institute of Computing Science and Technology, Guangzhou University, Guangzhou, China.|School of Computer Science and Technology, Tianjin University, Tianjin, China.|Department of Computer Science, University of Tsukuba, Tsukuba, Japan.|School of Software, Tianjin University, Tianjin, China.|School of Software, Tianjin University, Tianjin, China.|School of Computer Science and Technology, Tianjin University, Tianjin, China.', 'China|China|Japan|China|China|China', 19, '-', 'Journal Article', '10.1093/bib/bby091', '30239616', 'Cell-penetrating peptides (CPPs) have been shown to be a transport vehicle for delivering cargoes into live cells, offering great potential as future therapeutics. It is essential to identify CPPs for better understanding of their functional mechanisms. Machine learning-based methods have recently emerged as a main approach for computational identification of CPPs. However, one of the main challenges and difficulties is to propose an effective feature representation model that sufficiently exploits the inner difference and relevance between CPPs and non-CPPs, in order to improve the predictive performance. In this paper, we have developed CPPred-FL, a powerful bioinformatics tool for fast, accurate and large-scale identification of CPPs. In our predictor, we introduce a new feature representation learning scheme that enables one to learn feature representations from totally 45 well-trained random forest models with multiple feature descriptors from different perspectives, such as compositional information, position-specific information and physicochemical properties, etc. We integrate class and probabilistic information into our feature representations. To improve the feature representation ability, we further remove redundant and irrelevant features by feature space optimization. Benchmarking experiments showed that CPPred-FL, using 19 informative features only, is able to achieve better performance than the state-of-the-art predictors. We anticipate that CPPred-FL will be a powerful tool for large-scale identification of CPPs, facilitating the characterization of their functional mechanisms and accelerating their applications in clinical therapy.'),
(69423, 2018, 'bib', 'Protein-peptide docking using CABS-dock and contact information', 'Blaszczyk, Maciej|Ciemny, Maciej Pawel|Kolinski, Andrzej|Kurcinski, Mateusz|Kmiecik, Sebastian', 'Biological and Chemical Research Centre, University of Warsaw.|Faculty of Physics at the University of Warsaw.|University of Warsaw.|Biological and Chemical Research Centre, University of Warsaw.|Biological and Chemical Research Centre, University of Warsaw.', '-|-|-|-|-', 4, '-', 'Journal Article', '10.1093/bib/bby080', '30247502', 'CABS-dock is a tool for flexible docking of peptides to proteins. In this article, we present a protocol for CABS-dock docking driven by information about protein-peptide contact(s). Using information on individual protein-peptide contacts allows to improve the accuracy of CABS-dock docking.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(69424, 2018, 'bib', 'Large-scale comparative assessment of computational predictors for lysine post-translational modification sites', 'Chen, Zhen|Liu, Xuhan|Li, Fuyi|Li, Chen|Marquez-Lago, Tatiana|Leier, André|Akutsu, Tatsuya|Webb, Geoffrey I|Xu, Dakang|Smith, Alexander Ian|Li, Lei|Chou, Kuo-Chen|Song, Jiangning', 'School of Basic Medical Science, Qingdao University, Dengzhou Road, Qingdao, Shandong, China.|Medicinal Chemistry, Leiden Academic Centre for Drug Research,Einsteinweg, Leiden, The Netherlands.|Biomedicine Discovery Institute and Department of Biochemistry and Molecular Biology, Faculty of Medicine, Monash University, Melbourne, VIC, Australia.|Biomedicine Discovery Institute and Department of Biochemistry and Molecular Biology, Faculty of Medicine, Monash University, Melbourne, VIC, Australia.|Department of Genetics, School of Medicine, University of Alabama at Birmingham, AL, USA.|Department of Genetics, School of Medicine, University of Alabama at Birmingham, AL, USA.|Bioinformatics Center, Institute for Chemical Research,Kyoto University, Uji, Kyoto, Japan.|Monash Centre for Data Science, Faculty of Information Technology, Monash University, Melbourne, VIC, Australia.|Faculty of Medical Laboratory Science, Ruijin Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China.|Biomedicine Discovery Institute and Department of Biochemistry and Molecular Biology, Faculty of Medicine, Monash University, Melbourne, VIC, Australia.|School of Basic Medical Science, Qingdao University, Dengzhou Road, Qingdao, Shandong, China.|Gordon Life Science Institute, Boston, MA, USA.|Biomedicine Discovery Institute and Department of Biochemistry and Molecular Biology, Faculty of Medicine, Monash University, Melbourne, VIC, Australia.', 'China|Netherlands|Australia|Australia|United States|United States|Japan|Australia|China|Australia|China|United States|Australia', 19, '-', 'Journal Article', '10.1093/bib/bby089', '30285084', 'Lysine post-translational modifications (PTMs) play a crucial role in regulating diverse functions and biological processes of proteins. However, because of the large volumes of sequencing data generated from genome-sequencing projects, systematic identification of different types of lysine PTM substrates and PTM sites in the entire proteome remains a major challenge. In recent years, a number of computational methods for lysine PTM identification have been developed. These methods show high diversity in their core algorithms, features extracted and feature selection techniques and evaluation strategies. There is therefore an urgent need to revisit these methods and summarize their methodologies, to improve and further develop computational techniques to identify and characterize lysine PTMs from the large amounts of sequence data. With this goal in mind, we first provide a comprehensive survey on a large collection of 49 state-of-the-art approaches for lysine PTM prediction. We cover a variety of important aspects that are crucial for the development of successful predictors, including operating algorithms, sequence and structural features, feature selection, model performance evaluation and software utility. We further provide our thoughts on potential strategies to improve the model performance. Second, in order to examine the feasibility of using deep learning for lysine PTM prediction, we propose a novel computational framework, termed MUscADEL (Multiple Scalable Accurate Deep Learner for lysine PTMs), using deep, bidirectional, long short-term memory recurrent neural networks for accurate and systematic mapping of eight major types of lysine PTMs in the human and mouse proteomes. Extensive benchmarking tests show that MUscADEL outperforms current methods for lysine PTM characterization, demonstrating the potential and power of deep learning techniques in protein PTM prediction. The web server of MUscADEL, together with all the data sets assembled in this study, is freely available at http://muscadel.erc.monash.edu/. We anticipate this comprehensive review and the application of deep learning will provide practical guide and useful insights into PTM prediction and inspire future bioinformatics studies in the related fields.'),
(69425, 2018, 'bib', 'Quantifying Waddingtons epigenetic landscape: a comparison of single-cell potency measures', 'Shi, Jifan|Teschendorff, Andrew E|Chen, Weiyan|Chen, Luonan|Li, Tiejun', 'School of Mathematical Sciences, Peking University, Beijing, China.|University College London.|Shanghai Institutes for Biological Sciences, Shanghai, China.|Key Lab for Systems Biology, Chinese Academy of Sciences, Shanghai, China.|School of Mathematical Sciences, Peking University, Beijing, China.', 'China|-|China|China|China', 9, '-', 'Journal Article', '10.1093/bib/bby093', '30289442', 'Estimating differentiation potency of single cells is a task of great biological and clinical significance, as it may allow identification of normal and cancer stem cell phenotypes. However, very few single-cell potency models have been proposed, and their robustness and reliability across independent studies have not yet been fully assessed.'),
(69426, 2018, 'bib', 'Human-like layout algorithms for signalling hypergraphs: outlining requirements', 'Siebenhaller, Martin|Nielsen, Sune S|McGee, Fintan|Balaur, Irina|Auffray, Charles|Mazein, Alexander', 'yWorks GmbH,Vor dem Kreuzberg 28, 72070 Tübingen, Germany.|Luxembourg Institute of Science and Technology,5 Avenue des Hauts-Fourneaux, 4362 Esch-sur-Alzette Luxembourg.|Luxembourg Institute of Science and Technology,5 Avenue des Hauts-Fourneaux, 4362 Esch-sur-Alzette Luxembourg.|European Institute for Systems Biology and Medicine, CIRI UMR5308, CNRS-ENS-UCBL-INSERM, Université de Lyon,50 Avenue Tony Garnier, Lyon, France.|European Institute for Systems Biology and Medicine, CIRI UMR5308, CNRS-ENS-UCBL-INSERM, Université de Lyon,50 Avenue Tony Garnier, Lyon, France.|European Institute for Systems Biology and Medicine, CIRI UMR5308, CNRS-ENS-UCBL-INSERM, Université de Lyon,50 Avenue Tony Garnier, Lyon, France.', 'Germany|Luxembourg|Luxembourg|France|France|France', 0, '-', 'Journal Article', '10.1093/bib/bby099', '30289443', 'The use of signalling pathway hypergraphs represented as process description diagrams is steadily becoming more pervasive in the field of biology. This makes ever more evident the necessity for an effective automated layout that can replicate high-quality manually drawn diagrams. The complexity and idiosyncrasies of these diagrams, as well as the specific tasks the end users perform with them, mean that a layout must meet many requirements beyond the simple metrics used in existing automated computational approaches. In this paper we outline these requirements, examine existing ones and describe new ones. We demonstrate state-of-the-art layout techniques enhanced with novel functionalities to meet part of the requirements. For comparatively small signalling pathways our enhanced algorithm provides results close to manually drawn layouts. In addition, we suggest technical approaches that may be suited for fulfilling the identified requirements currently not covered.'),
(69427, 2018, 'bib', 'MicroRNA-small molecule association identification: from experimental results to computational models', 'Chen, Xing|Guan, Na-Na|Sun, Ya-Zhou|Li, Jian-Qiang|Qu, Jia', 'School of Information and Control Engineering, China University of Mining and Technology, Xuzhou, China.|College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, China.|College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, China.|College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, China.|School of Information and Control Engineering, China University of Mining and Technology, Xuzhou, China.', 'China|China|China|China|China', 30, '-', 'Journal Article', '10.1093/bib/bby098', '30325405', 'Small molecule is a kind of low molecular weight organic compound with variety of biological functions. Studies have indicated that small molecules can inhibit a specific function of a multifunctional protein or disrupt protein-protein interactions and may have beneficial or detrimental effect against diseases. MicroRNAs (miRNAs) play crucial roles in cellular biology, which makes it possible to develop miRNA as diagnostics and therapeutic targets. Several drug-like compound libraries were screened successfully against different miRNAs in cellular assays further demonstrating the possibility of targeting miRNAs with small molecules. In this review, we summarized the concept and functions of small molecule and miRNAs. Especially, five aspects of miRNA functions were exhibited in detail with individual examples. In addition, four disease states that have been linked to miRNA alterations were summed up. Then, small molecules related to four important miRNAs miR-21, 122, 4644 and 27 were selected for introduction. Some important publicly accessible databases and web servers of the experimentally validated or potential small molecule-miRNA associations were discussed. Identifying small molecule targeting miRNAs has become an important goal of biomedical research. Thus, several experimental and computational models have been developed and implemented to identify novel small molecule-miRNA associations. Here, we reviewed four experimental techniques used in the past few years to search for small-molecule inhibitors of miRNAs, as well as three types of models of predicting small molecule-miRNA associations from different perspectives. Finally, we summarized the limitations of existing methods and discussed the future directions for further development of computational models.'),
(69428, 2018, 'bib', 'Algebraic shortcuts for leave-one-out cross-validation in supervised network inference', 'Stock, Michiel|Pahikkala, Tapio|Airola, Antti|Waegeman, Willem|De Baets, Bernard', 'Department of Data Analysis and Mathematical Modelling, Ghent University, Belgium.|Department of Future Technologies, University of Turku, Finland.|Department of Future Technologies, University of Turku, Finland.|Department of Data Analysis and Mathematical Modelling, Ghent University, Belgium.|Department of Data Analysis and Mathematical Modelling, Ghent University, Belgium.', 'Belgium|Finland|Finland|Belgium|Belgium', 4, '-', 'Journal Article', '10.1093/bib/bby095', '30329015', 'Supervised machine learning techniques have traditionally been very successful at reconstructing biological networks, such as protein-ligand interaction, protein-protein interaction and gene regulatory networks. Many supervised techniques for network prediction use linear models on a possibly nonlinear pairwise feature representation of edges. Recently, much emphasis has been placed on the correct evaluation of such supervised models. It is vital to distinguish between using a model to either predict new interactions in a given network or to predict interactions for a new vertex not present in the original network. This distinction matters because (i) the performance might dramatically differ between the prediction settings and (ii) tuning the model hyperparameters to obtain the best possible model depends on the setting of interest. Specific cross-validation schemes need to be used to assess the performance in such different prediction settings.In this work we discuss a state-of-the-art kernel-based network inference technique called two-step kernel ridge regression. We show that this regression model can be trained efficiently, with a time complexity scaling with the number of vertices rather than the number of edges. Furthermore, this framework leads to a series of cross-validation shortcuts that allow one to rapidly estimate the model performance for any relevant network prediction setting. This allows computational biologists to fully assess the capabilities of their models. The machine learning techniques with the algebraic shortcuts are implemented in the RLScore software package: https://github.com/aatapa/RLScore.'),
(69429, 2018, 'bib', 'Computational analysis and prediction of lysine malonylation sites by exploiting informative features in an integrative machine-learning framework', 'Zhang, Yanju|Xie, Ruopeng|Wang, Jiawei|Leier, André|Marquez-Lago, Tatiana T|Akutsu, Tatsuya|Webb, Geoffrey I|Chou, Kuo-Chen|Song, Jiangning', 'School of Computer Science and Information Security, Guilin University of Electronic Technology, Guilin 541004, China.|School of Computer Science and Information Security, Guilin University of Electronic Technology, Guilin 541004, China.|Infection and Immunity Program, Biomedicine Discovery Institute and Department of Microbiology, Monash University, VIC 3800, Australia.|Department of Genetics, School of Medicine, University of Alabama at Birmingham, AL, USA.|Department of Genetics, School of Medicine, University of Alabama at Birmingham, AL, USA.|Bioinformatics Center, Institute for Chemical Research, Kyoto University, Kyoto 611-0011, Japan.|Monash Centre for Data Science, Faculty of Information Technology, Monash University, VIC 3800, Australia.|Gordon Life Science Institute, Boston, MA 02478, USA.|Monash Centre for Data Science, Faculty of Information Technology, Monash University, VIC 3800, Australia.', 'China|China|Australia|United States|United States|Japan|Australia|United States|Australia', 21, '-', 'Journal Article', '10.1093/bib/bby079', '30351377', 'As a newly discovered post-translational modification (PTM), lysine malonylation (Kmal) regulates a myriad of cellular processes from prokaryotes to eukaryotes and has important implications in human diseases. Despite its functional significance, computational methods to accurately identify malonylation sites are still lacking and urgently needed. In particular, there is currently no comprehensive analysis and assessment of different features and machine learning (ML) methods that are required for constructing the necessary prediction models. Here, we review, analyze and compare 11 different feature encoding methods, with the goal of extracting key patterns and characteristics from residue sequences of Kmal sites. We identify optimized feature sets, with which four commonly used ML methods (random forest, support vector machines, K-nearest neighbor and logistic regression) and one recently proposed [Light Gradient Boosting Machine (LightGBM)] are trained on data from three species, namely, Escherichia coli, Mus musculus and Homo sapiens, and compared using randomized 10-fold cross-validation tests. We show that integration of the single method-based models through ensemble learning further improves the prediction performance and model robustness on the independent test. When compared to the existing state-of-the-art predictor, MaloPred, the optimal ensemble models were more accurate for all three species (AUC: 0.930, 0.923 and 0.944 for E. coli, M. musculus and H. sapiens, respectively). Using the ensemble models, we developed an accessible online predictor, kmal-sp, available at http://kmalsp.erc.monash.edu/. We hope that this comprehensive survey and the proposed strategy for building more accurate models can serve as a useful guide for inspiring future developments of computational methods for PTM site prediction, expedite the discovery of new malonylation and other PTM types and facilitate hypothesis-driven experimental validation of novel malonylated substrates and malonylation sites.'),
(69430, 2018, 'bib', 'Copy number aberrations from Affymetrix SNP 60 genotyping data-how accurate are commonly used prediction approaches?', 'Pitea, Adriana|Kondofersky, Ivan|Sass, Steffen|Theis, Fabian J|Mueller, Nikola S|Unger, Kristian', 'Institute of Computational Biology, Helmholtz Zentrum München, Neuherberg, Germany.|Institute of Computational Biology, Helmholtz Zentrum München, Neuherberg, Germany.|Institute of Computational Biology, Helmholtz Zentrum München, Neuherberg, Germany.|Institute of Computational Biology, Helmholtz Zentrum München, Neuherberg, Germany.|Institute of Computational Biology, Helmholtz Zentrum München, Neuherberg, Germany.|Research Unit Radiation Cytogenetics, Helmholtz Zentrum München, Neuherberg, Germany.', 'Germany|Germany|Germany|Germany|Germany|Germany', 2, '-', 'Journal Article', '10.1093/bib/bby096', '30351397', 'Copy number aberrations (CNAs) are known to strongly affect oncogenes and tumour suppressor genes. Given the critical role CNAs play in cancer research, it is essential to accurately identify CNAs from tumour genomes. One particular challenge in finding CNAs is the effect of confounding variables. To address this issue, we assessed how commonly used CNA identification algorithms perform on SNP 6.0 genotyping data in the presence of confounding variables. We simulated realistic synthetic data with varying levels of three confounding variables-the tumour purity, the length of a copy number region and the CNA burden (the percentage of CNAs present in a profiled genome)-and evaluated the performance of OncoSNP, ASCAT, GenoCNA, GISTIC and CGHcall. Furthermore, we implemented and assessed CGHcall*, an adjusted version of CGHcall accounting for high CNA burden. Our analysis on synthetic data indicates that tumour purity and the CNA burden strongly influence the performance of all the algorithms. No algorithm can correctly find lost and gained genomic regions across all tumour purities. The length of CNA regions influenced the performance of ASCAT, CGHcall and GISTIC. OncoSNP, GenoCNA and CGHcall* showed little sensitivity. Overall, CGHcall* and OncoSNP showed reasonable performance, particularly in samples with high tumour purity. Our analysis on the HapMap data revealed a good overlap between CGHcall, CGHcall* and GenoCNA results and experimentally validated data. Our exploratory analysis on the TCGA HNSCC data revealed plausible results of CGHcall, CGHcall* and GISTIC in consensus HNSCC CNA regions. Code is available at https://github.com/adspit/PASCAL.'),
(69431, 2018, 'bib', 'Comprehensive assessment of nine docking programs on type II kinase inhibitors: prediction accuracy of sampling power, scoring power and screening power', 'Shen, Chao|Wang, Zhe|Yao, Xiaojun|Li, Youyong|Lei, Tailong|Wang, Ercheng|Xu, Lei|Zhu, Feng|Li, Dan|Hou, Tingjun', 'College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, Zhejiang, P. R. China.|College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, Zhejiang, P. R. China.|State Key Laboratory of Quality Research in Chinese Medicine, Macau Institute for Applied Research in Medicine and Health,Macau University of Science and Technology, Avenida Wai Long, Taipa, Macau (SAR), P. R. China.|Institute of Functional Nano and Soft Materials (FUNSOM), Soochow University, Suzhou, Jiangsu, P. R. China.|College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, Zhejiang, P. R. China.|College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, Zhejiang, P. R. China.|Institute of Bioinformatics and Medical Engineering, School of Electrical and Information Engineering, Jiangsu University of Technology, Changzhou, P. R. China.|College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, Zhejiang, P. R. China.|College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, Zhejiang, P. R. China.|College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, Zhejiang, P. R. China.', 'China|China|China|China|China|China|China|China|China|China', 6, '-', 'Journal Article', '10.1093/bib/bby103', '30379986', 'Protein kinases have been regarded as important therapeutic targets for many diseases. Currently, a total of 41 kinase inhibitors have been approved by the Food and Drug Administration, along with a large number of kinase inhibitors being evaluated in clinical and preclinical trials. Among all, allosteric inhibitors, such as type II kinase inhibitors, have attracted extensive attention owing to their potential high selectivity. Nowadays, molecular docking has become a powerful tool to search for novel kinase inhibitors. However, as for type II kinase inhibitors, their allosteric characteristics may exert a deep influence on docking accuracy. In this study, a comprehensive assessment was conducted to evaluate the effectiveness of nine docking algorithms towards type II kinase inhibitors. The calculation results showed that most tested docking programs, especially Glide with XP scoring, LeDock and Surflex-Dock, succeeded in the accurate identification of near-native binding poses, with the success rates ranging from 0.80 to 0.90, and the scoring functions in GOLD and LeDock outperformed the others in the prediction of relative binding affinities. In terms of the P-values, areas under the curve and enrichment factors, Glide with XP scoring, Surflex-Dock, GOLD with Astex Statistical Potential scoring and LeDock had better screening power to discriminate between active compounds and decoys. However, the screening power is sensitive to different initial conformations of the same target. It is expected that our study can provide some guidance for docking-based virtual screening to discover novel type II kinase inhibitors, as well as other allosteric inhibitors.'),
(69432, 2018, 'bib', 'Landscape of SNPs-mediated lncRNA structural variations and their implication in human complex diseases', 'Wang, Hong|Lu, Xiaoyan|Chen, Fukun|Ding, Yu|Zheng, Hewei|Wang, Lianzong|Zhang, Guosi|Yang, Jiaxin|Bai, Yu|Li, Jing|Wu, Jingqi|Zhou, Meng|Xu, Liangde', 'Harbin Medical University.|Harbin Medical University.|Harbin Medical University.|Harbin Medical University.|Harbin Medical University.|Harbin Medical University.|Harbin Medical University.|Harbin Medical University.|Harbin Medical University.|Harbin Medical University.|Harbin Medical University.|Wenzhou Medical University.|Wenzhou Medical University.', '-|-|-|-|-|-|-|-|-|-|-|-|-', 3, '-', 'Journal Article', '10.1093/bib/bby102', '30379995', 'An increasing number of functional studies shows that long noncoding RNAs (lncRNAs) are involved in many aspects of cellular physiology and fulfills a wide variety of regulatory roles at almost every stage of gene expression. A major feature of lncRNAs is the highly folded modular domains in transcripts. With improved modeling and definition, it is now feasible to explore and gain novel insights into the structural-functional relationship of lncRNAs and their association with complex human diseases. In this study, we utilized an automatic computational pipeline to scan lncRNA architecture at the genome-wide scale and to obtain a landscape of functional domains. An accurate alignment algorithm was performed to identify 40 triple pairs between single-nucleotide polymorphisms (SNPs), lncRNAs and diseases. In order to detect the potential contribution of a lncRNAs modular character, we estimated and evaluated structural rearrangements, which were derived from disease-associated SNPs. In addition, we focused on annotating and comparing the global and local heterogeneity of the wild-type and mutant lncRNAs. Assessing lncRNA architecture has yielded how variations in structured regions impact the molecular mechanisms of lncRNAs and how SNPs disturb binding and recruiting ability. These observations are the first glimpse of the lncRNA structurome and make it possible to robustly explore and assemble intricate space conformation and their stress variation. This result also successfully demonstrates that lncRNA transcripts contain a complex structural landscape and highlights the proposed contribution of lncRNA structure in controlling RNA functions and disease mechanisms.'),
(69433, 2018, 'bib', 'dbCPM: a manually curated database for exploring the cancer passenger mutations', 'Yue, Zhenyu|Zhao, Le|Xia, Junfeng', 'Institute of Physical Science and Information Technology, School of Computer Science and Technology, Anhui University, Hefei, Anhui, China.|Institute of Physical Science and Information Technology, School of Computer Science and Technology, Anhui University, Hefei, Anhui, China.|Institute of Physical Science and Information Technology, School of Computer Science and Technology, Anhui University, Hefei, Anhui, China.', 'China|China|China', 0, '-', 'Journal Article', '10.1093/bib/bby105', '30379998', 'While recently emergent driver mutation data sets are available for developing computational methods to predict cancer mutation effects, benchmark sets focusing on passenger mutations are largely missing. Here, we developed a comprehensive literature-based database of Cancer Passenger Mutations (dbCPM), which contains 941 experimentally supported and 978 putative passenger mutations derived from a manual curation of the literature. Using the missense mutation data, the largest group in the dbCPM, we explored patterns of missense passenger mutations by comparing them with the missense driver mutations and assessed the performance of four cancer-focused mutation effect predictors. We found that the missense passenger mutations showed significant differences with drivers at multiple levels, and several appeared in both the passenger and driver categories, showing pleiotropic functions depending on the tumor context. Although all the predictors displayed good true positive rates, their true negative rates were relatively low due to the lack of negative training samples with experimental evidence, which suggests that a suitable negative data set for developing a more robust methodology is needed. We hope that the dbCPM will be a benchmark data set for improving and evaluating prediction algorithms and serve as a valuable resource for the cancer research community. dbCPM is freely available online at http://bioinfo.ahu.edu.cn:8080/dbCPM.'),
(69434, 2018, 'bib', 'CytoPred: 7-gene pair metric for AML cytogenetic risk prediction', 'Salunkhe, Sameer|Chandran, Naren|Chandrani, Pratik|Dutt, Amit|Dutt, Shilpee', 'Shilpee Dutt laboratory, Tata Memorial Centre, Advanced Centre for Treatment, Research and Education in Cancer, Navi Mumbai, India.|Shilpee Dutt laboratory, Tata Memorial Centre, Advanced Centre for Treatment, Research and Education in Cancer, Navi Mumbai, India.|Integrated Genomics Laboratory, Advanced Centre for Treatment Research Education in Cancer (ACTREC).|Integrated Genomics Laboratory, Advanced Centre for Treatment Research Education in Cancer (ACTREC).|Shilpee Dutt laboratory, Tata Memorial Centre, Advanced Centre for Treatment, Research and Education in Cancer, Navi Mumbai, India.', 'India|India|-|-|India', 1, '-', 'Journal Article', '10.1093/bib/bby100', '30380003', 'Cytogenetic-based subjective prognostication of acute myeloid leukemia (AML) patients is a cumbersome process. Top scoring pair (TSP)-based decision tree using a robust analytical algorithm with statistical rigor offers a promising alternative. We describe CytoPred as a 7-gene pair signature based on the analysis of 2547 AML patient sample gene expression data using a modified TSP algorithm to estimate cytogenetic risk. The essential modification in TSP that helped computational encumbrance includes the filtration of gene pairs above random weighted guessers as well as sampling the gene pairs from the original gene pair pool to reduce overfitting issue. The CytoPred classifies AML cohort into clinically relevant `good and `Int_poor prognosis groups with distinct survival differences. The 7-gene pair was derived using 1248 AML patient samples in training set and 675 samples used for internal testing of the algorithm. The finest classifier 7-gene pair was picked from an initial pool size of 6.1 × 107 gene pairs that generated 57 687 decision trees. Further, for unbiased evaluation of CytoPred performance, we did an independent validation in 624 AML patient cohort. The CytoPred well qualifies the cutoffs for diagnostic application with 98.27% sensitivity and 99.27% specificity to predictive value in Int_poor class while 97.09% sensitivity and 91.74% specificity to predictive value for good class. Furthermore, CytoPred predicts almost identical survival probabilities like cytogenetics and its performance is not much influenced by various recurrent mutations as well as individual French-American-British (FAB) subtypes. In summary, we present a robust 7-gene pair-based metric to clinically prognosticate AML patients.'),
(69435, 2018, 'bib', 'Comparative analysis and prediction of quorum-sensing peptides using feature representation learning and machine learning algorithms', 'Wei, Leyi|Hu, Jie|Li, Fuyi|Song, Jiangning|Su, Ran|Zou, Quan', 'School of Computer Science and Technology, Tianjin University, Tianjin, China.|School of Computer Science and Technology, Tianjin University, Tianjin, China.|Infection and Immunity Program, Biomedicine Discovery Institute and Department of Biochemistry and Molecular Biology, Monash University, Clayton, VIC, Australia.|Infection and Immunity Program, Biomedicine Discovery Institute and Department of Biochemistry and Molecular Biology, Monash University, Clayton, VIC, Australia.|School of Computer Software, Tianjin University, Tianjin, China.|School of Computer Science and Technology, Tianjin University, Tianjin, China.', 'China|China|Australia|Australia|China|China', 9, '-', 'Journal Article', '10.1093/bib/bby107', '30383239', 'Quorum-sensing peptides (QSPs) are the signal molecules that are closely associated with diverse cellular processes, such as cell-cell communication, and gene expression regulation in Gram-positive bacteria. It is therefore of great importance to identify QSPs for better understanding and in-depth revealing of their functional mechanisms in physiological processes. Machine learning algorithms have been developed for this purpose, showing the great potential for the reliable prediction of QSPs. In this study, several sequence-based feature descriptors for peptide representation and machine learning algorithms are comprehensively reviewed, evaluated and compared. To effectively use existing feature descriptors, we used a feature representation learning strategy that automatically learns the most discriminative features from existing feature descriptors in a supervised way. Our results demonstrate that this strategy is capable of effectively capturing the sequence determinants to represent the characteristics of QSPs, thereby contributing to the improved predictive performance. Furthermore, wrapping this feature representation learning strategy, we developed a powerful predictor named QSPred-FL for the detection of QSPs in large-scale proteomic data. Benchmarking results with 10-fold cross validation showed that QSPred-FL is able to achieve better performance as compared to the state-of-the-art predictors. In addition, we have established a user-friendly webserver that implements QSPred-FL, which is currently available at http://server.malab.cn/QSPred-FL. We expect that this tool will be useful for the high-throughput prediction of QSPs and the discovery of important functional mechanisms of QSPs.'),
(69436, 2018, 'bib', 'HITS-PR-HHblits: protein remote homology detection by combining PageRank and Hyperlink-Induced Topic Search', 'Liu, Bin|Jiang, Shuangyan|Zou, Quan', 'School of Computer Science and Technology, Harbin Institute of Technology, Shenzhen, China.|School of Computer Science and Technology, Harbin Institute of Technology, Shenzhen, China.|Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu, China.', 'China|China|China', 22, '-', 'Journal Article', '10.1093/bib/bby104', '30403770', 'As one of the most important fundamental problems in protein sequence analysis, protein remote homology detection is critical for both theoretical research (protein structure and function studies) and real world applications (drug design). Although several computational predictors have been proposed, their detection performance is still limited. In this study, we treat protein remote homology detection as a document retrieval task, where the proteins are considered as documents and its aim is to find the highly related documents with the query documents in a database. A protein similarity network was constructed based on the true labels of proteins in the database, and the query proteins were then connected into the network based on the similarity scores calculated by three ranking methods, including PSI-BLAST, Hmmer and HHblits. The PageRank algorithm and Hyperlink-Induced Topic Search (HITS) algorithm were respectively performed on this network to move the homologous proteins of query proteins to the neighbors of the query proteins in the network. Finally, PageRank and HITS algorithms were combined, and a predictor called HITS-PR-HHblits was proposed to further improve the predictive performance. Tested on the SCOP and SCOPe benchmark datasets, the experimental results showed that the proposed protocols outperformed other state-of-the-art methods. For the convenience of the most experimental scientists, a web server for HITS-PR-HHblits was established at http://bioinformatics.hitsz.edu.cn/HITS-PR-HHblits, by which the users can easily get the results without the need to go through the mathematical details. The HITS-PR-HHblits predictor is a protocol for protein remote homology detection using different sets of programs, which will become a very useful computational tool for proteome analysis.'),
(69437, 2018, 'bib', 'gmRAD: an integrated SNP calling pipeline for genetic mapping with RADseq across a hybrid population', 'Yao, Dan|Wu, Hainan|Chen, Yuhua|Yang, Wenguo|Gao, Hua|Tong, Chunfa', 'Southern Modern Forestry Collaborative Innovation Center, College of Forestry, Nanjing Forestry University, Nanjing, China.|Southern Modern Forestry Collaborative Innovation Center, College of Forestry, Nanjing Forestry University, Nanjing, China.|Southern Modern Forestry Collaborative Innovation Center, College of Forestry, Nanjing Forestry University, Nanjing, China.|Southern Modern Forestry Collaborative Innovation Center, College of Forestry, Nanjing Forestry University, Nanjing, China.|Southern Modern Forestry Collaborative Innovation Center, College of Forestry, Nanjing Forestry University, Nanjing, China.|Southern Modern Forestry Collaborative Innovation Center, College of Forestry, Nanjing Forestry University, Nanjing, China.', 'China|China|China|China|China|China', 1, '-', 'Journal Article', '10.1093/bib/bby114', '30445432', 'Restriction site-associated DNA sequencing (RADseq) is a powerful technology that has been extensively applied in population genetics, phylogenetics and genetic mapping. Although many software packages are available for ecological and evolutionary studies, a few effective tools are available for extracting genotype data with RADseq for genetic mapping, a prerequisite for quantitative trait locus mapping, comparative genomics and genome scaffold assembly. Here, we present an integrated pipeline called gmRAD for generating single nucleotide polymorphism (SNP) genotypes from RADseq data, de novo, across a genetic mapping population derived by crossing two parents. As an analytical strategy, the software takes five steps to implement the whole algorithms, including clustering the first (forward) reads of each parent, building two parental references, generating parental SNP catalogs, calling SNP genotypes across all individuals and filtering the genotype data for genetic linkage mapping. All the steps can be completed with a simple command line, but they can be also performed optionally if prerequisite files are available. To validate its application, we also performed a real data analysis with RADseq data from an F1 hybrid population derived by crossing Populus deltoides and Prunus simonii. The software gmRAD is freely available at https://github.com/tongchf/gmRAD.'),
(69438, 2018, 'bib', 'Characteristics of plant circular RNAs', 'Chu, Qinjie|Bai, Panpan|Zhu, Xintian|Zhang, Xingchen|Mao, Lingfeng|Zhu, Qian-Hao|Fan, Longjiang|Ye, Chu-Yu', 'Institute of Crop Science, Zhejiang University, Hangzhou, China.|Institute of Crop Science, Zhejiang University, Hangzhou, China.|Institute of Crop Science, Zhejiang University, Hangzhou, China.|Institute of Crop Science, Zhejiang University, Hangzhou, China.|Institute of Crop Science, Zhejiang University, Hangzhou, China.|CSIRO Agriculture and Food, Canberra, Australia.|Institute of Crop Science, Zhejiang University, Hangzhou, China.|Institute of Crop Science, Zhejiang University, Hangzhou, China.', 'China|China|China|China|China|Australia|China|China', 14, '-', 'Journal Article', '10.1093/bib/bby111', '30445438', 'Circular RNA (circRNA) is a kind of covalently closed single-stranded RNA molecules that have been proved to play important roles in transcriptional regulation of genes in diverse species. With the rapid development of bioinformatics tools, a huge number (95143) of circRNAs have been identified from different plant species, providing an opportunity for uncovering the overall characteristics of plant circRNAs. Here, based on publicly available circRNAs, we comprehensively analyzed characteristics of plant circRNAs with the help of various bioinformatics tools as well as in-house scripts and workflows, including the percentage of coding genes generating circRNAs, the frequency of alternative splicing events of circRNAs, the non-canonical splicing signals of circRNAs and the networks involving circRNAs, miRNAs and mRNAs. All this information has been integrated into an upgraded online database, PlantcircBase 3.0 (http://ibi.zju.edu.cn/plantcircbase/). In this database, we provided browse, search and visualization tools as well as a web-based blast tool, BLASTcirc, for prediction of circRNAs from query sequences based on searching against plant genomes and transcriptomes.'),
(69439, 2018, 'bib', 'The landscape of miRNA-related ceRNA networks for marking different renal cell carcinoma subtypes', 'Qin, Liu|Liu, Yanhong|Li, Menglong|Pu, Xuemei|Guo, Yanzhi', 'College of Chemistry, Sichuan University, Chengdu, Sichuan, P.R. China.|College of Chemistry, Sichuan University, Chengdu, Sichuan, P.R. China.|College of Chemistry, Sichuan University, Chengdu, Sichuan, P.R. China.|College of Chemistry, Sichuan University, Chengdu, Sichuan, P.R. China.|College of Chemistry, Sichuan University, Chengdu, Sichuan, P.R. China.', 'China|China|China|China|China', 1, '-', 'Journal Article', '10.1093/bib/bby101', '30452527', 'We know that different types of cancers usually have different responses to the same treatment. Therefore, it is important to understand the similarities and differences across subtypes of cancers, so as to provide a basis for the individualized treatments. Until now, no comprehensive investigation on competing endogenous RNAs (ceRNAs) has been reported for the three main subtypes of renal cell carcinoma (RCC), so the regulation characteristics of ceRNAs in three subtypes are not well revealed. This paper firstly describes a comparative analysis of ceRNA-ceRNA interaction networks for all the three subtypes of RCC based on differential microRNAs (miRNAs). We comprehensively summarized all miRNA and messenger RNAdata of RCC from 126 matched tumor-normal tissues in The Cancer Genome Atlas, systematically analyzed a total of more than 80 000 ceRNA interactions and highlighted the common and specific properties among them, aiming to identify critical genes to classify them for providing supplementary help in the precise diagnosis of RCC. From three aspects, including common or specific ceRNAs, upregulated or downregulated and classifications across the three subtypes, we highlighted the common and specific properties for the three subtypes and also explored the classification of RCC by combining the specific ceRNAs with differential regulations. Moreover, for the most major subtype of clear cell renal cell carcinoma (KIRC), three critical genes were screened out from KIRC ceRNA network and further demonstrated to be the potential biomarkers of KIRC by performing biological experiments at the transcriptional level.'),
(69440, 2018, 'bib', 'Bioinformatics methodologies for coeliac disease and its comorbidities', 'Del Prete, Eugenio|Facchiano, Angelo|Liò, Pietro', 'Department of Sciences, University of Basilicata,Via dellAteneo Lucano, Potenza, Italy.|National Research Council, Institute of Food Science (CNR-ISA),Via Roma 64, Avellino, Italy.|Computer Laboratory, University of Cambridge, JJ Thomson Ave., Cambridge, UK.', 'Italy|Italy|United Kingdom', 0, '-', 'Journal Article', '10.1093/bib/bby109', '30452543', 'Coeliac disease (CD) is a complex, multifactorial pathology caused by different factors, such as nutrition, immunological response and genetic factors. Many autoimmune diseases are comorbidities for CD, and a comprehensive and integrated analysis with bioinformatics approaches can help in evaluating the interconnections among all the selected pathologies. We first performed a detailed survey of gene expression data available in public repositories on CD and less commonly considered comorbidities. Then we developed an innovative pipeline that integrates gene expression, cell-type data and online resources (e.g. a list of comorbidities from the literature), using bioinformatics methods such as gene set enrichment analysis and semantic similarity. Our pipeline is written in R language, available at the following link: http://bioinformatica.isa.cnr.it/COELIAC_DISEASE/SCRIPTS/. We found a list of common differential expressed genes, gene ontology terms and pathways among CD and comorbidities and the closeness among the selected pathologies by means of disease ontology terms. Physicians and other researchers, such as molecular biologists, systems biologists and pharmacologists can use it to analyze pathology in detail, from differential expressed genes to ontologies, performing a comparison with the pathology comorbidities or with other diseases.'),
(69441, 2018, 'bib', 'A comprehensive review of computational prediction of genome-wide features', 'Xu, Tianlei|Zheng, Xiaoqi|Li, Ben|Jin, Peng|Qin, Zhaohui|Wu, Hao', 'Department of Mathematics and Computer Science, Emory University, Atlanta, GA, USA.|Department of Mathematics, Shanghai Normal University, Shanghai, China.|Department of Biostatistics and Bioinformatics, Rollins School of Public Health, Emory University, Atlanta, GA, USA.|Department of Human Genetics, Rollins School of Public Health, Emory University, Atlanta, GA, USA.|Department of Biostatistics and Bioinformatics, Rollins School of Public Health, Emory University, Atlanta, GA, USA.|Department of Biostatistics and Bioinformatics, Rollins School of Public Health, Emory University, Atlanta, GA, USA.', 'United States|China|United States|United States|United States|United States', 2, '-', 'Journal Article', '10.1093/bib/bby110', '30462144', 'There are significant correlations among different types of genetic, genomic and epigenomic features within the genome. These correlations make the in silico feature prediction possible through statistical or machine learning models. With the accumulation of a vast amount of high-throughput data, feature prediction has gained significant interest lately, and a plethora of papers have been published in the past few years. Here we provide a comprehensive review on these published works, categorized by the prediction targets, including protein binding site, enhancer, DNA methylation, chromatin structure and gene expression. We also provide discussions on some important points and possible future directions.'),
(69442, 2018, 'bib', 'Comprehensive and critical evaluation of individualized pathway activity measurement tools on pan-cancer data', 'Lim, Sangsoo|Lee, Sangseon|Jung, Inuk|Rhee, Sungmin|Kim, Sun', 'Interdisciplinary Program in Bioinformatics, Seoul National University, Seoul, Korea.|Department of Computer Science and Engineering, Seoul National University, Seoul, Korea.|Bioinformatics Institute, Seoul National University, Seoul, Korea.|Department of Computer Science and Engineering, Seoul National University, Seoul, Korea.|Interdisciplinary Program in Bioinformatics, Seoul National University, Seoul, Korea.', '-|-|-|-|-', 5, '-', 'Journal Article', '10.1093/bib/bby097', '30462155', ': Biological pathways are extensively used for the analysis of transcriptome data to characterize biological mechanisms underlying various phenotypes. There are a number of computational tools that summarize transcriptome data at the pathway level. However, there is no comparative study on how well these tools produce useful information at the cohort level, enabling comparison of many samples or patients.'),
(69443, 2018, 'bib', 'Dating admixture events is unsolved problem in multi-way admixed populations', 'Chimusa, Emile R|Defo, Joel|Thami, Prisca K|Awany, Denis|Mulisa, Delesa D|Allali, Imane|Ghaza, Hassan|Moussa, Ahmed|Mazandu, Gaston K', 'Division of Human Genetics, Department of Pathology, Institute of Infectious Disease and Molecular Medicine,Faculty of Health Sciences, University of Cape Town, Observatory, Cape Town, South Africa.|Division of Human Genetics, Department of Pathology, Institute of Infectious Disease and Molecular Medicine,Faculty of Health Sciences, University of Cape Town, Observatory, Cape Town, South Africa.|Division of Human Genetics, Department of Pathology, Institute of Infectious Disease and Molecular Medicine,Faculty of Health Sciences, University of Cape Town, Observatory, Cape Town, South Africa.|Division of Human Genetics, Department of Pathology, Institute of Infectious Disease and Molecular Medicine,Faculty of Health Sciences, University of Cape Town, Observatory, Cape Town, South Africa.|Division of Human Genetics, Department of Pathology, Institute of Infectious Disease and Molecular Medicine,Faculty of Health Sciences, University of Cape Town, Observatory, Cape Town, South Africa.|Division of Computational Biology, Department of Biomedical Sciences, Institute of Infectious Disease and Molecular Medicine,Faculty of Health Sciences, University of Cape Town, Observatory, Cape Town, South Africa.|University Mohammed First,B.P.524 Oujda, Morocco.|Abdelmalek Essaadi University ENSA, Tangier, Morocco.|Division of Human Genetics, Department of Pathology, Institute of Infectious Disease and Molecular Medicine,Faculty of Health Sciences, University of Cape Town, Observatory, Cape Town, South Africa.', 'South Africa|South Africa|South Africa|South Africa|South Africa|South Africa|Morocco|Morocco|South ', 0, '-', 'Journal Article', '10.1093/bib/bby112', '30462157', 'Advances in human sequencing technologies, coupled with statistical and computational tools, have fostered the development of methods for dating admixture events. These methods have merits and drawbacks in estimating admixture events in multi-way admixed populations. Here, we first provide a comprehensive review and comparison of current methods pertinent to dating admixture events. Second, we assess various admixture dating tools. We do so by performing various simulations. Third, we apply the top two assessed methods to real data of a uniquely admixed population from South Africa. Results reveal that current dating admixture models are not sufficiently equipped to estimate ancient admixtures events and to identify multi-faceted admixture events in complex multi-way admixed populations. We conclude with a discussion of research areas where further work on dating admixture-based methods is needed.'),
(69444, 2018, 'bib', 'The application of Hadoop in structural bioinformatics', 'Alnasir, Jamie J|Shanahan, Hugh P', 'Institute of Cancer Research, Old Brompton Road, London, United Kingdom.|Department of Computer Science, Royal Holloway, University of London, Egham, Surrey, United Kingdom.', 'United Kingdom|United Kingdom', 1, '-', 'Journal Article', '10.1093/bib/bby106', '30462158', 'The paper reviews the use of the Hadoop platform in structural bioinformatics applications. For structural bioinformatics, Hadoop provides a new framework to analyse large fractions of the Protein Data Bank that is key for high-throughput studies of, for example, protein-ligand docking, clustering of protein-ligand complexes and structural alignment. Specifically we review in the literature a number of implementations using Hadoop of high-throughput analyses and their scalability. We find that these deployments for the most part use known executables called from MapReduce rather than rewriting the algorithms. The scalability exhibits a variable behaviour in comparison with other batch schedulers, particularly as direct comparisons on the same platform are generally not available. Direct comparisons of Hadoop with batch schedulers are absent in the literature but we note there is some evidence that Message Passing Interface implementations scale better than Hadoop. A significant barrier to the use of the Hadoop ecosystem is the difficulty of the interface and configuration of a resource to use Hadoop. This will improve over time as interfaces to Hadoop, e.g. Spark improve, usage of cloud platforms (e.g. Azure and Amazon Web Services (AWS)) increases and standardised approaches such as Workflow Languages (i.e. Workflow Definition Language, Common Workflow Language and Nextflow) are taken up.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(69445, 2019, 'bib', 'Harmonizing semantic annotations for computational models in biology', 'Neal, Maxwell Lewis|Kxc3xb6nig, Matthias|Nickerson, David|Mxc4xb1sxc4xb1rlxc4xb1, Gxc3xb6ksel|Kalbasi, Reza|Drxc3xa4ger, Andreas|Atalag, Koray|Chelliah, Vijayalakshmi|Cooling, Michael T|Cook, Daniel L|Crook, Sharon|de Alba, Miguel|Friedman, Samuel H|Garny, Alan|Gennari, John H|Gleeson, Padraig|Golebiewski, Martin|Hucka, Michael|Juty, Nick|Myers, Chris|Olivier, Brett G|Sauro, Herbert M|Scharm, Martin|Snoep, Jacky L|Tourxc3xa9, Vasundra|Wipat, Anil|Wolkenhauer, Olaf|Waltemath, Dagmar', 'Seattle Childrens Research Institute, Center for Global Infectious Disease Research, Seattle, USA.|Department of Biology, Humboldt-University Berlin, Institute for Theoretical Biology, Berlin, Germany.|Auckland Bioengineering Institute, University of Auckland, Auckland, NZ.|School of Computing and Mathematics, Keele University, Keele, UK.|Auckland Bioengineering Institute, University of Auckland, Auckland, NZ.|Computational Systems Biology of Infection and Antimicrobial-Resistant Pathogens, Center for Bioinformatics Txc3xbcbingen (ZBIT), University of Txc3xbcbingen, Txc3xbcbingen, Germany.|Auckland Bioengineering Institute, University of Auckland, Auckland, NZ.|European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridge, UK.|Auckland Bioengineering Institute, University of Auckland, Auckland, NZ.|Department of Physiology and Biophysics, University of Washington, Seattle, WA, USA.|School of Mathematical and Statistical Sciences, Arizona State University, Tempe, USA.|German Federal Institute for Risk Assessment, Berlin, Germany.|Opto-Knowledge Systems, Inc. (OKSI), Torrance, CA, USA.|Auckland Bioengineering Institute, University of Auckland, Auckland, NZ.|Department of Biomedical Informatics and Medical Education, University of Washington, Seattle, WA, USA.|Department of Neuroscience, Physiology and Pharmacology, University College London, London, UK.|Heidelberg Institute for Theoretical Studies (HITS gGmbH), Heidelberg, Germany.|Department of Computing and Mathematical Sciences, California Institute of Technology, Pasadena, CA, USA.|European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridge, UK.|Department of Electrical and Computer Engineering, University of Utah, Salt Lake City, UT, USA.|Systems Bioinformatics, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands.|Department of Bioengineering, University of Washington, Seattle, WA, USA.|Department of Systems Biology and Bioinformatics, University of Rostock, Rostock, Germany.|Department of Biochemistry, Stellenbosch University, Matieland, South Africa.|Department of Biology, Norwegian University of Science and Technology, Trondheim, Norway.|School of Computing Science, Newcastle University, Newcastle upon Tyne, UK.|Department of Systems Biology and Bioinformatics, University of Rostock, Rostock, Germany.|Department of Systems Biology and Bioinformatics, University of Rostock, Rostock, Germany.', 'United Kingdom|Germany|United Kingdom|United Kingdom|United Kingdom|Germany|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|Germany|United Kingdom|United Kingdom|United Kingdom|United Kingdom|Germany|United Kingdom|United Kingdo', 3, 'Biological Science Disciplines|Computational Biology|Computer Simulation|Databases, Factual|Humans|Semantics|Software', 'Journal Article', '10.1093/bib/bby087', '30462164', 'Life science researchers use computational models to articulate and test hypotheses about the behavior of biological systems. Semantic annotation is a critical component for enhancing the interoperability and reusability of such models as well as for the integration of the data needed for model parameterization and validation. Encoded as machine-readable links to knowledge resource terms, semantic annotations describe the computational or biological meaning of what models and data represent. These annotations help researchers find and repurpose models, accelerate model composition and enable knowledge integration across model repositories and experimental data stores. However, realizing the potential benefits of semantic annotation requires the development of model annotation standards that adhere to a community-based annotation protocol. Without such standards, tool developers must account for a variety of annotation formats and approaches, a situation that can become prohibitively cumbersome and which can defeat the purpose of linking model elements to controlled knowledge resource terms. Currently, no consensus protocol for semantic annotation exists among the larger biological modeling community. Here, we report on the landscape of current annotation practices among the COmputational Modeling in BIology NEtwork community and provide a set of recommendations for building a consensus approach to semantic annotation.'),
(69446, 2018, 'bib', 'Cancer-specific expression quantitative loci are affected by expression dysregulation', 'Sheng, Quanhu|Samuels, David C|Yu, Hui|Ness, Scott|Zhao, Ying-Yong|Guo, Yan', 'Department of Biostatistics, Vanderbilt University Medical Center, Nashville, TN, USA.|Vanderbilt Genetics Institute, Dept. of Molecular Physiology and Biophysics, Vanderbilt University Medical School, Nashville, TN, USA.|Department of Internal Medicine, University of New Mexico, Albuquerque, NM, USA.|Department of Internal Medicine, University of New Mexico, Albuquerque, NM, USA.|Key Laboratory of Resource Biology and Biotechnology in Western China, School of Life Sciences, Northwest University, Xian, Shaanxi, China.|Department of Internal Medicine, University of New Mexico, Albuquerque, NM, USA.', 'United States|United States|Mexico|Mexico|China|Mexico', 0, '-', 'Journal Article', '10.1093/bib/bby108', '30475999', 'Expression quantitative trait loci (eQTLs) have been touted as the missing piece that can bridge the gap between genetic variants and phenotypes. Over the past decade, we have witnessed a sharp rise of effort in the identification and application of eQTLs. The successful application of eQTLs relies heavily on their reproducibility. The current eQTL databases such as Genotype-Tissue Expression (GTEx) were populated primarily with eQTLs deriving from germline single nucleotide polymorphisms and normal tissue gene expression. The novel scenarios that employ eQTL models for prediction purposes often involve disease phenotypes characterized by altered gene expressions. To evaluate eQTL reproducibility across diverse data sources and the effect of disease-specific gene expression alteration on eQTL identification, we conducted an eQTL study using 5178 samples from The Cancer Genome Atlas (TCGA). We found that the reproducibility of eQTLs between normal and tumor tissues was low in terms of the number of shared eQTLs. However, among the shared eQTLs, the effect directions were generally concordant. This suggests that the source of the gene expression (normal or tumor tissue) has a strong effect on the detectable eQTLs and the effect direction of the eQTLs. Additional analyses demonstrated good directional concordance of eQTLs between GTEx and TCGA. Furthermore, we found that multi-tissue eQTLs may exert opposite effects across multiple tissue types. In summary, our results suggest that eQTL prediction models need to carefully address tissue and disease dependency of eQTLs. Tissue-disease-specific eQTL databases can afford more accurate prediction models for future studies.'),
(69447, 2018, 'bib', 'Subcellular locations of potential cell wall proteins in plants: predictors, databases and cross-referencing', 'Wu, Xiaolin|Zhang, Qinbin|Wu, Zhaokun|Tai, Fuju|Wang, Wei', 'College of Life Sciences, Henan Agricultural University (HAU), China.|College of Life Sciences, HAU.|College of Life Sciences, HAU.|College of Life Sciences, HAU.|HAU.', 'China|-|-|-|-', 1, 'Cell Wall|Databases, Protein|Plant Proteins|Proteome|Subcellular Fractions', 'Journal Article', '10.1093/bib/bbx050', '30481282', 'The cell wall is the most striking feature that distinguishes plant cells from animal cells. It plays an essential role in cell shape, stability, growth and protection. Despite being present in small amounts, cell wall proteins (CWPs) are crucial components of the cell wall. The cell wall proteome generally consists of sensu stricto CWPs, apoplast proteins and extracellular secreted proteins. Currently, there is a need for the bioinformatics analysis of a tremendous number of protein sequences that have been generated from genomic, transcriptomic and proteomics research. Compared with intracellular proteins, the location prediction of CWPs is challenging because many aspects of these proteins have not been experimentally characterized, and there are no CWP-trained, specific predictors available. By introducing the biological relevance (particularly molecular aspects) of the cell wall and CWPs, we critically evaluated the accuracy of 16 state-of-the-art predictors and classical predictors for the prediction of CWPs using an independent database of Arabidopsis and rice proteins. All experimentally verified CWPs and non-CWPs were retrieved from the UniProt Knowledgebase. Based on the evaluation, we currently recommend the predictors mGOASVM, HybridGO-Loc and FUEL-mLoc for CWPs. Furthermore, we outlined the public databases that can be used to cross-reference the subcellular location of CWPs. We illustrate a flowchart of the subcellular location prediction and a cross-reference of possible CWPs. Finally, we discuss challenges and perspectives in the bioinformatics analysis of CWPs. It is hoped that this article will provide practical guidance regarding CWPs for nonspecialists and provide insight for bioinformatics experts to develop computational tools for CWPs.'),
(69448, 2018, 'bib', 'AIMMS suite: a web server dedicated for prediction of drug resistance on protein mutation', 'Wu, Feng-Xu|Wang, Fan|Yang, Jing-Fang|Jiang, Wen|Wang, Meng-Yao|Jia, Chen-Yang|Hao, Ge-Fei|Yang, Guang-Fu', 'Key Laboratory of Pesticide & Chemical Biology, Ministry of Education, College of Chemistry, Central China Normal University, Wuhan, P.R. China.|Key Laboratory of Pesticide & Chemical Biology, Ministry of Education, College of Chemistry, Central China Normal University, Wuhan, P.R. China.|Key Laboratory of Pesticide & Chemical Biology, Ministry of Education, College of Chemistry, Central China Normal University, Wuhan, P.R. China.|Key Laboratory of Pesticide & Chemical Biology, Ministry of Education, College of Chemistry, Central China Normal University, Wuhan, P.R. China.|Key Laboratory of Pesticide & Chemical Biology, Ministry of Education, College of Chemistry, Central China Normal University, Wuhan, P.R. China.|Key Laboratory of Pesticide & Chemical Biology, Ministry of Education, College of Chemistry, Central China Normal University, Wuhan, P.R. China.|Key Laboratory of Pesticide & Chemical Biology, Ministry of Education, College of Chemistry, Central China Normal University, Wuhan, P.R. China.|Key Laboratory of Pesticide & Chemical Biology, Ministry of Education, College of Chemistry, Central China Normal University, Wuhan, P.R. China.', 'China|China|China|China|China|China|China|China', 0, '-', 'Journal Article', '10.1093/bib/bby113', '30496338', 'Drug resistance is one of the most intractable issues for successful treatment in current clinical practice. Although many mutations contributing to drug resistance have been identified, the relationship between the mutations and the related pharmacological profile of drug candidates has yet to be fully elucidated, which is valuable both for the molecular dissection of drug resistance mechanisms and for suggestion of promising treatment strategies to counter resistant. Hence, effective prediction approach for estimating the sensitivity of mutations to agents is a new opportunity that counters drug resistance and creates a high interest in pharmaceutical research. However, this task is always hampered by limited known resistance training samples and accurately estimation of binding affinity. Upon this challenge, we successfully developed Auto In Silico Macromolecular Mutation Scanning (AIMMS), a web server for computer-aided de novo drug resistance prediction for any ligand-protein systems. AIMMS can qualitatively estimate the free energy consequences of any mutations through a fast mutagenesis scanning calculation based on a single molecular dynamics trajectory, which is differentiated with other web services by a statistical learning system. AIMMS suite is available at http://chemyang.ccnu.edu.cn/ccb/server/AIMMS/.'),
(69449, 2018, 'bib', 'Multilevel heterogeneous omics data integration with kernel fusion', 'Yang, Haitao|Cao, Hongyan|He, Tao|Wang, Tong|Cui, Yuehua', 'Department of Epidemiology and Health Statistics, School of Public Health, and Hebei Province Key Laboratory of Environment and Human Health, Hebei Medical University, Shijiazhuang, PR China.|Division of Health Statistics, School of Public Health, Shanxi Medical University, Taiyuan, PR China.|Department of Mathematics, San Francisco State University, San Francisco, CA, USA.|Division of Health Statistics, School of Public Health, Shanxi Medical University, Taiyuan, PR China.|Division of Health Statistics, School of Public Health, Shanxi Medical University, Taiyuan, PR China.', 'China|China|United States|China|China', 0, '-', 'Journal Article', '10.1093/bib/bby115', '30496340', 'High-throughput omics data are generated almost with no limit nowadays. It becomes increasingly important to integrate different omics data types to disentangle the molecular machinery of complex diseases with the hope for better disease prevention and treatment. Since the relationship among different omics data features are typically unknown, a supervised learning model assuming a particular distribution with a specific structure will not serve the purpose to capture the underlying complex relationship between multiple features and a disease phenotype. In this work, we briefly reviewed methods for kernel fusion (KF) based on support vector machine and kernel partial least squares (KPLS) algorithms. We then proposed a fused KPLS (fKPLS) model for disease classification and prediction with multilevel omics data. The fused kernel can deal with effect heterogeneity in which different omic data types may have different effect contribution to the trait of interest, with the purpose to improve the prediction performance. We proposed to optimize the kernel parameters and kernel weights with the genetic algorithm (GA). The proposed GA-fKPLS model can substantially improve disease classification performance by integrating multiple omics data types, demonstrated via extensive simulations and real data analysis. With properly defined fitness functions during GA optimization, the proposed KF method can be extended to other kernel-based analyses such as in kernel association analysis with common or rare variants.'),
(69450, 2018, 'bib', 'Comprehensive review of the identification of essential genes using computational methods: focusing on feature implementation and assessment', 'Dong, Chuan|Jin, Yan-Ting|Hua, Hong-Li|Wen, Qing-Feng|Luo, Sen|Zheng, Wen-Xin|Guo, Feng-Biao', 'School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China.|School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China.|School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China.|School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China.|School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China.|School of Biomedical Engineering, Capital Medical University, Beijing, China.|School of Life Science and Technology, Center for Informational Biology, Intelligent Learning Institute for Science and Application, University of Electronic Science and Technology of China, Chengdu, China.', 'China|China|China|China|China|China|China', 1, '-', 'Journal Article', '10.1093/bib/bby116', '30496347', 'Essential genes have attracted increasing attention in recent years due to the important functions of these genes in organisms. Among the methods used to identify the essential genes, accurate and efficient computational methods can make up for the deficiencies of expensive and time-consuming experimental technologies. In this review, we have collected researches on essential gene predictions in prokaryotes and eukaryotes and summarized the five predominant types of features used in these studies. The five types of features include evolutionary conservation, domain information, network topology, sequence component and expression level. We have described how to implement the useful forms of these features and evaluated their performance based on the data of Escherichia coli MG1655, Bacillus subtilis 168 and human. The prerequisite and applicable range of these features is described. In addition, we have investigated the techniques used to weight features in various models. To facilitate researchers in the field, two available online tools, which are accessible for free and can be directly used to predict gene essentiality in prokaryotes and humans, were referred. This article provides a simple guide for the identification of essential genes in prokaryotes and eukaryotes.'),
(69451, 2018, 'bib', 'Corrigendum to: Comprehensive and critical evaluation of individualized pathway activity measurement tools on pan-cancer data', '-', '-', '-', 0, '-', 'Journal Article', '10.1093/bib/bby125', '30534977', '-'),
(69452, 2018, 'bib', 'Network embedding in biomedical data science', 'Su, Chang|Tong, Jie|Zhu, Yongjun|Cui, Peng|Wang, Fei', 'Department of Healthcare Policy and Research, Weill Cornell Medicine at Cornell University, New York, NY, USA.|Department of Mechanical and Aerospace Engineering at New York University, New York, NY, USA.|Department of Library and Information Science, Sungkyunkwan University, Seoul, South Korea.|Department of Computer Science and Technology, Tsinghua University, Beijing, China.|Department of Healthcare Policy and Research, Weill Cornell Medicine at Cornell University, New York, NY, USA.', 'United States|United States|United States|China|United States', 2, '-', 'Journal Article', '10.1093/bib/bby117', '30535359', 'Owning to the rapid development of computer technologies, an increasing number of relational data have been emerging in modern biomedical research. Many network-based learning methods have been proposed to perform analysis on such data, which provide people a deep understanding of topology and knowledge behind the biomedical networks and benefit a lot of applications for human healthcare. However, most network-based methods suffer from high computational and space cost. There remain challenges on handling high dimensionality and sparsity of the biomedical networks. The latest advances in network embedding technologies provide new effective paradigms to solve the network analysis problem. It converts network into a low-dimensional space while maximally preserves structural properties. In this way, downstream tasks such as link prediction and node classification can be done by traditional machine learning methods. In this survey, we conduct a comprehensive review of the literature on applying network embedding to advance the biomedical domain. We first briefly introduce the widely used network embedding models. After that, we carefully discuss how the network embedding approaches were performed on biomedical networks as well as how they accelerated the downstream tasks in biomedical science. Finally, we discuss challenges the existing network embedding applications in biomedical domains are faced with and suggest several promising future directions for a better improvement in human healthcare.'),
(69453, 2018, 'bib', 'Interactive visual analysis of drug-target interaction networks using Drug Target Profiler, with applications to precision medicine and drug repurposing', 'Tanoli, Ziaurrehman|Alam, Zaid|Ianevski, Aleksandr|Wennerberg, Krister|Vähä-Koskela, Markus|Aittokallio, Tero', 'Institute for Molecular Medicine Finland, University of Helsinki, Helsinki, Finland.|Institute for Molecular Medicine Finland, University of Helsinki, Helsinki, Finland.|Institute for Molecular Medicine Finland, University of Helsinki, Helsinki, Finland.|University of Copenhagen, Biotech Research & Innovation Centre (BRIC).|Institute for Molecular Medicine Finland, University of Helsinki, Helsinki, Finland.|Institute for Molecular Medicine Finland, University of Helsinki, Helsinki, Finland.', 'Finland|Finland|Finland|-|Finland|Finland', 5, '-', 'Journal Article', '10.1093/bib/bby119', '30566623', 'Knowledge of the full target space of drugs (or drug-like compounds) provides important insights into the potential therapeutic use of the agents to modulate or avoid their various on- and off-targets in drug discovery and precision medicine. However, there is a lack of consolidated databases and associated data exploration tools that allow for systematic profiling of drug target-binding potencies of both approved and investigational agents using a network-centric approach. We recently initiated a community-driven platform, Drug Target Commons (DTC), which is an open-data crowdsourcing platform designed to improve the management, reproducibility and extended use of compound-target bioactivity data for drug discovery and repurposing, as well as target identification applications. In this work, we demonstrate an integrated use of the rich bioactivity data from DTC and related drug databases using Drug Target Profiler (DTP), an open-source software and web tool for interactive exploration of drug-target interaction networks. DTP was designed for network-centric modeling of mode-of-action of multi-targeting anticancer compounds, especially for precision oncology applications. DTP enables users to construct an interaction network based on integrated bioactivity data across selected chemical compounds and their protein targets, further customizable using various visualization and filtering options, as well as cross-links to several drug and protein databases to provide comprehensive information of the network nodes and interactions. We demonstrate here the operation of the DTP tool and its unique features by several use cases related to both drug discovery and drug repurposing applications, using examples of anticancer drugs with shared target profiles. DTP is freely accessible at http://drugtargetprofiler.fimm.fi/.'),
(69454, 2018, 'bib', 'Fuzzy Petri nets for modelling of uncertain biological systems', 'Liu, Fei|Heiner, Monika|Gilbert, David', 'School of Software Engineering, South China University of Technology, Guangzhou, P. R. China.|Department of Computer Science, Brandenburg University of Technology Cottbus-Senftenberg, Cottbus, Germany.|Department of Computer Science, Brunel University London, Middlesex, UK.', 'China|Germany|United Kingdom', 0, '-', 'Journal Article', '10.1093/bib/bby118', '30590430', 'The modelling of biological systems is accompanied with epistemic uncertainties that range from structural uncertainty to parametric uncertainty due to such limitations as insufficient understanding of the underlying mechanism and incomplete measurement data of a system. Fuzzy logic approaches such as fuzzy Petri nets (FPNs) are effective in addressing these issues. In this paper, we review FPNs that have been used for modelling uncertain biological systems, which we classify in three categories: basic fuzzy Petri nets, fuzzy quantitative Petri nets and Petri nets with fuzzy kinetic parameters. For each category of these FPNs, we summarize its modelling capabilities and current applications, discuss its merits and drawbacks and give suggestions for further research. This understanding on how to use FPNs for modelling uncertain biological systems will assist readers in selecting appropriate FPN classes for specific modelling circumstances. This review may also promote the extensive research and application of FPNs in the systems biology area.'),
(69455, 2018, 'bib', 'Homeolog expression quantification methods for allopolyploids', 'Kuo, Tony C Y|Hatakeyama, Masaomi|Tameshige, Toshiaki|Shimizu, Kentaro K|Sese, Jun', 'Artificial Intelligence Research Center, AIST, 2-3-26 Aomi, Koto-ku, Tokyo 135-0064, Japan.|Department of Evolutionary Biology and Environmental Studies, University of Zurich, Winterthurerstrasse 190, Zurich CH-8057, Switzerland.|Kihara Institute for Biological Research, Yokohama City University, 641-12, Maioka, Totsuka-ku, Yokohama 244-0813, Japan.|Department of Evolutionary Biology and Environmental Studies, University of Zurich, Winterthurerstrasse 190, Zurich CH-8057, Switzerland.|Artificial Intelligence Research Center, AIST, 2-3-26 Aomi, Koto-ku, Tokyo 135-0064, Japan.', 'Japan|Switzerland|Japan|Switzerland|Japan', 0, '-', 'Journal Article', '10.1093/bib/bby121', '30590436', 'Genome duplication with hybridization, or allopolyploidization, occurs in animals, fungi and plants, and is especially common in crop plants. There is an increasing interest in the study of allopolyploids because of advances in polyploid genome assembly, however, the high level of sequence similarity in duplicated gene copies (homeologs) poses many challenges. Here we compared standard RNA-seq expression quantification approaches used currently for diploid species against subgenome-classification approaches which maps reads to each subgenome separately. We examined mapping error using our previous and new RNA-seq data in which a subgenome is experimentally added (synthetic allotetraploid Arabidopsis kamchatica) or reduced (allohexaploid wheat Triticum aestivum versus extracted allotetraploid) as ground truth. The error rates in the two species were very similar. The standard approaches showed higher error rates (>10% using pseudo-alignment with Kallisto) while subgenome-classification approaches showed much lower error rates (<1% using EAGLE-RC, <2% using HomeoRoq). Although downstream analysis may partly mitigate mapping errors, the difference in methods was substantial in hexaploid wheat, where Kallisto appeared to have systematic differences relative to other methods. Only approximately half of the differentially expressed homeologs detected using Kallisto overlapped with those by any other method in wheat. In general, disagreement in low-expression genes was responsible for most of the discordance between methods, which is consistent with known biases in Kallisto. We also observed that there exist uncertainties in genome sequences and annotation which can affect each method differently. Overall, subgenome-classification approaches tend to perform better than standard approaches with EAGLE-RC having the highest precision.'),
(69456, 2018, 'bib', 'Computational prediction and analysis of species-specific fungi phosphorylation via feature optimization strategy', 'Cao, Man|Chen, Guodong|Yu, Jialin|Shi, Shaoping', 'Department of Mathematics and Numerical Simulation and High-Performance Computing Laboratory, School of Sciences, Nanchang University, Nanchang, China.|Department of Mathematics and Numerical Simulation and High-Performance Computing Laboratory, School of Sciences, Nanchang University, Nanchang, China.|Department of Mathematics and Numerical Simulation and High-Performance Computing Laboratory, School of Sciences, Nanchang University, Nanchang, China.|Department of Mathematics and Numerical Simulation and High-Performance Computing Laboratory, School of Sciences, Nanchang University, Nanchang, China.', 'China|China|China|China', 3, '-', 'Journal Article', '10.1093/bib/bby122', '30590490', 'Protein phosphorylation is a reversible and ubiquitous post-translational modification that primarily occurs at serine, threonine and tyrosine residues and regulates a variety of biological processes. In this paper, we first briefly summarized the current progresses in computational prediction of eukaryotic protein phosphorylation sites, which mainly focused on animals and plants, especially on human, with a less extent on fungi. Since the number of identified fungi phosphorylation sites has greatly increased in a wide variety of organisms and their roles in pathological physiology still remain largely unknown, more attention has been paid on the identification of fungi-specific phosphorylation. Here, experimental fungi phosphorylation sites data were collected and most of the sites were classified into different types to be encoded with various features and trained via a two-step feature optimization method. A novel method for prediction of species-specific fungi phosphorylation-PreSSFP was developed, which can identify fungi phosphorylation in seven species for specific serine, threonine and tyrosine residues (http://computbiol.ncu.edu.cn/PreSSFP). Meanwhile, we critically evaluated the performance of PreSSFP and compared it with other existing tools. The satisfying results showed that PreSSFP is a robust predictor. Feature analyses exhibited that there have some significant differences among seven species. The species-specific prediction via two-step feature optimization method to mine important features for training could considerably improve the prediction performance. We anticipate that our study provides a new lead for future computational analysis of fungi phosphorylation.'),
(69457, 2019, 'bib', 'Investigating the role of Simpsons paradox in the analysis of top-ranked features in high-dimensional bioinformatics datasets', 'Freitas, Alex A', 'University of Kent, Kent, UK.', 'United Kingdom', 0, '-', 'Journal Article', '10.1093/bib/bby126', '30629111', 'An important problem in bioinformatics consists of identifying the most important features (or predictors), among a large number of features in a given classification dataset. This problem is often addressed by using a machine learning-based feature ranking method to identify a small set of top-ranked predictors (i.e. the most relevant features for classification). The large number of studies in this area has, however, an important limitation: they ignore the possibility that the top-ranked predictors occur in an instance of Simpsons paradox, where the positive or negative association between a predictor and a class variable reverses sign upon conditional on each of the values of a third (confounder) variable. In this work, we review and investigate the role of Simpsons paradox in the analysis of top-ranked predictors in high-dimensional bioinformatics datasets, in order to avoid the potential danger of misinterpreting an association between a predictor and the class variable. We perform computational experiments using four well-known feature ranking methods from the machine learning field and five high-dimensional datasets of ageing-related genes, where the predictors are Gene Ontology terms. The results show that occurrences of Simpsons paradox involving top-ranked predictors are much more common for one of the feature ranking methods.'),
(69458, 2019, 'bib', 'Graph- and rule-based learning algorithms: a comprehensive review of their applications for cancer type classification and prognosis using genomic data', 'Mallik, Saurav|Zhao, Zhongming', 'Center for Precision Health, School of Biomedical Informatics, The University of Texas Health Science Center, Houston.|Center for Precision Health, School of Biomedical Informatics, The University of Texas Health Science Center, Houston.', '-|-', 5, '-', 'Journal Article', '10.1093/bib/bby120', '30649169', 'Cancer is well recognized as a complex disease with dysregulated molecular networks or modules. Graph- and rule-based analytics have been applied extensively for cancer classification as well as prognosis using large genomic and other data over the past decade. This article provides a comprehensive review of various graph- and rule-based machine learning algorithms that have been applied to numerous genomics data to determine the cancer-specific gene modules, identify gene signature-based classifiers and carry out other related objectives of potential therapeutic value. This review focuses mainly on the methodological design and features of these algorithms to facilitate the application of these graph- and rule-based analytical approaches for cancer classification and prognosis. Based on the type of data integration, we divided all the algorithms into three categories: model-based integration, pre-processing integration and post-processing integration. Each category is further divided into four sub-categories (supervised, unsupervised, semi-supervised and survival-driven learning analyses) based on learning style. Therefore, a total of 11 categories of methods are summarized with their inputs, objectives and description, advantages and potential limitations. Next, we briefly demonstrate well-known and most recently developed algorithms for each sub-category along with salient information, such as data profiles, statistical or feature selection methods and outputs. Finally, we summarize the appropriate use and efficiency of all categories of graph- and rule mining-based learning methods when input data and specific objective are given. This review aims to help readers to select and use the appropriate algorithms for cancer classification and prognosis study.'),
(69459, 2019, 'bib', 'Empirical comparison and analysis of web-based cell-penetrating peptide prediction tools', 'Su, Ran|Hu, Jie|Zou, Quan|Manavalan, Balachandran|Wei, Leyi', 'College of Intelligence and Computing, Tianjin University, Tianjin, China.|College of Intelligence and Computing, Tianjin University, Tianjin, China.|Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu, China.|Department of Physiology, Ajou University School of Medicine, Suwon, Republic of Korea.|College of Intelligence and Computing, Tianjin University, Tianjin, China.', 'China|China|China|-|China', 19, '-', 'Journal Article', '10.1093/bib/bby124', '30649170', 'Cell-penetrating peptides (CPPs) facilitate the delivery of therapeutically relevant molecules, including DNA, proteins and oligonucleotides, into cells both in vitro and in vivo. This unique ability explores the possibility of CPPs as therapeutic delivery and its potential applications in clinical therapy. Over the last few decades, a number of machine learning (ML)-based prediction tools have been developed, and some of them are freely available as web portals. However, the predictions produced by various tools are difficult to quantify and compare. In particular, there is no systematic comparison of the web-based prediction tools in performance, especially in practical applications. In this work, we provide a comprehensive review on the biological importance of CPPs, CPP database and existing ML-based methods for CPP prediction. To evaluate current prediction tools, we conducted a comparative study and analyzed a total of 12 models from 6 publicly available CPP prediction tools on 2 benchmark validation sets of CPPs and non-CPPs. Our benchmarking results demonstrated that a model from the KELM-CPPpred, namely KELM-hybrid-AAC, showed a significant improvement in overall performance, when compared to the other 11 prediction models. Moreover, through a length-dependency analysis, we find that existing prediction tools tend to more accurately predict CPPs and non-CPPs with the length of 20-25 residues long than peptides in other length ranges.'),
(69460, 2019, 'bib', 'ANPELA: analysis and performance assessment of the label-free quantification workflow for metaproteomic studies', 'Tang, Jing|Fu, Jianbo|Wang, Yunxia|Li, Bo|Li, Yinghong|Yang, Qingxia|Cui, Xuejiao|Hong, Jiajun|Li, Xiaofeng|Chen, Yuzong|Xue, Weiwei|Zhu, Feng', 'College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, China.|College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, China.|College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, China.|School of Pharmaceutical Sciences and Collaborative Innovation Center for Brain Science, Chongqing University, Chongqing, China.|College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, China.|College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, China.|College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, China.|College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, China.|College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, China.|Bioinformatics and Drug Design Group, Department of Pharmacy, National University of Singapore, Singapore, Singapore.|School of Pharmaceutical Sciences and Collaborative Innovation Center for Brain Science, Chongqing University, Chongqing, China.|College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, China.', 'China|China|China|China|China|China|China|China|China|Singapore|China|China', 4, '-', 'Journal Article', '10.1093/bib/bby127', '30649171', 'Label-free quantification (LFQ) with a specific and sequentially integrated workflow of acquisition technique, quantification tool and processing method has emerged as the popular technique employed in metaproteomic research to provide a comprehensive landscape of the adaptive response of microbes to external stimuli and their interactions with other organisms or host cells. The performance of a specific LFQ workflow is highly dependent on the studied data. Hence, it is essential to discover the most appropriate one for a specific data set. However, it is challenging to perform such discovery due to the large number of possible workflows and the multifaceted nature of the evaluation criteria. Herein, a web server ANPELA (https://idrblab.org/anpela/) was developed and validated as the first tool enabling performance assessment of whole LFQ workflow (collective assessment by five well-established criteria with distinct underlying theories), and it enabled the identification of the optimal LFQ workflow(s) by a comprehensive performance ranking. ANPELA not only automatically detects the diverse formats of data generated by all quantification tools but also provides the most complete set of processing methods among the available web servers and stand-alone tools. Systematic validation using metaproteomic benchmarks revealed ANPELAs capabilities in 1 discovering well-performing workflow(s), (2) enabling assessment from multiple perspectives and (3) validating LFQ accuracy using spiked proteins. ANPELA has a unique ability to evaluate the performance of whole LFQ workflow and enables the discovery of the optimal LFQs by the comprehensive performance ranking of all 560 workflows. Therefore, it has great potential for applications in metaproteomic and other studies requiring LFQ techniques, as many features are shared among proteomic studies.'),
(69461, 2019, 'bib', 'Corrigendum to: gmRAD: an integrated SNP calling pipeline for genetic mapping with RADseq across a hybrid population', '-', '-', '-', 0, '-', 'Journal Article', '10.1093/bib/bbz002', '30649178', '-'),
(69462, 2019, 'bib', 'Structure-based prediction of post-translational modification cross-talk within proteins using complementary residue- and residue pair-based features', 'Liu, Hui-Fang|Liu, Rong', 'Hubei Key Laboratory of Agricultural Bioinformatics, College of Informatics, Huazhong Agricultural University, Wuhan, P. R. China.|Hubei Key Laboratory of Agricultural Bioinformatics, College of Informatics, Huazhong Agricultural University, Wuhan, P. R. China.', 'China|China', 0, '-', 'Journal Article', '10.1093/bib/bby123', '30649184', 'Post-translational modification (PTM)-based regulation can be mediated not only by the modification of a single residue but also by the interplay of different modifications. Accurate prediction of PTM cross-talk is a highly challenging issue and is in its infant stage. Especially, less attention has been paid to the structural preferences (except intrinsic disorder and spatial proximity) of cross-talk pairs and the characteristics of individual residues involved in cross-talk, which may restrict the improvement of the prediction accuracy. Here we report a structure-based algorithm called PCTpred to improve the PTM cross-talk prediction. The comprehensive residue- and residue pair-based features were designed for paired PTM sites at the sequence and structural levels. Through feature selection, we reserved 23 newly introduced descriptors and 3 traditional descriptors to develop a sequence-based predictor PCTseq and a structure-based predictor PCTstr, both of which were integrated to construct our final prediction model. According to pair- and protein-based evaluations, PCTpred yielded area under the curve values of approximately 0.9 and 0.8, respectively. Even when removing the distance preference of samples or using the input of modeled structures, our prediction performance was maintained or moderately reduced. PCTpred displayed stable and reliable improvements over the state-of-the-art methods based on various evaluations. The source code and data set are freely available at https://github.com/Liulab-HZAU/PCTpred or http://liulab.hzau.edu.cn/PCTpred/.'),
(69463, 2019, 'bib', 'Corrigendum to: Dating admixture events is unsolved problem in multi-way admixed populations', '-', '-', '-', 0, '-', 'Journal Article', '10.1093/bib/bbz001', '30649186', '-'),
(69464, 2019, 'bib', 'miR+Pathway: the integration and visualization of miRNA and KEGG pathways', 'Pian, Cong|Zhang, Guangle|Gao, Libin|Fan, Xiaodan|Li, Fei', 'Ministry of Agriculture Key Lab of Agricultural Entomology, Institute of Insect Sciences, College of Agriculture and Biotechnology, Zhejiang University, Hangzhou, China.|Department of Mathematics, College of Science, Nanjing Agricultural University, Nanjing, Jiangsu, China.|Ministry of Agriculture Key Lab of Agricultural Entomology, Institute of Insect Sciences, College of Agriculture and Biotechnology, Zhejiang University, Hangzhou, China.|Department of Statistics, The Chinese University of Hong Kong, Hong Kong, Special Administrative Region, China.|Ministry of Agriculture Key Lab of Agricultural Entomology, Institute of Insect Sciences, College of Agriculture and Biotechnology, Zhejiang University, Hangzhou, China.', 'China|China|China|China|China', 1, '-', 'Journal Article', '10.1093/bib/bby128', '30649247', 'miRNAs represent a type of noncoding small molecule RNA. Many studies have shown that miRNAs are widely involved in the regulation of various pathways. The key to fully understanding the regulatory function of miRNAs is the determination of the pathways in which the miRNAs participate. However, the major pathway databases such as KEGG only include information regarding protein-coding genes. Here, we redesigned a pathway database (called miR+Pathway) by integrating and visualizing the 8882 human experimentally validated miRNA-target interactions (MTIs) and 150 KEGG pathways. This database is freely accessible at http://www.insect-genome.com/miR-pathway. Researchers can intuitively determine the pathways and the genes in the pathways that are regulated by miRNAs as well as the miRNAs that target the pathways. To determine the pathways in which targets of a certain miRNA or multiple miRNAs are enriched, we performed a KEGG analysis miRNAs by using the hypergeometric test. In addition, miR+Pathway provides information regarding MTIs, PubMed IDs and the experimental verification method. Users can retrieve pathways regulated by an miRNA or a gene by inputting its names.'),
(69465, 2019, 'bib', 'Long non-coding RNA transcriptome of uncharacterized samples can be accurately imputed using protein-coding genes', 'Nath, Aritro|Geeleher, Paul|Huang, R Stephanie', 'Department of Experimental and Clinical Pharmacology, University of Minnesota, Minneapolis, MN, USA.|Department of Medicine, The University of Chicago, Chicago, IL, USA.|Department of Experimental and Clinical Pharmacology, University of Minnesota, Minneapolis, MN, USA.', 'United States|United States|United States', 0, '-', 'Journal Article', '10.1093/bib/bby129', '30657858', 'Long non-coding RNAs (lncRNAs) play an important role in gene regulation and are increasingly being recognized as crucial mediators of disease pathogenesis. However, the vast majority of published transcriptome datasets lack high-quality lncRNA profiles compared to protein-coding genes (PCGs). Here we propose a framework to harnesses the correlative expression patterns between lncRNA and PCGs to impute unknown lncRNA profiles. The lncRNA expression imputation (LEXI) framework enables characterization of lncRNA transcriptome of samples lacking any lncRNA data using only their PCG profiles. We compare various machine learning and missing value imputation algorithms to implement LEXI and demonstrate the feasibility of this approach to impute lncRNA transcriptome of normal and cancer tissues. Additionally, we determine the factors that influence imputation accuracy and provide guidelines for implementing this approach.'),
(69466, 2019, 'bib', 'Principal component analysis of binary genomics data', 'Song, Yipeng|Westerhuis, Johan A|Aben, Nanne|Michaut, Magali|Wessels, Lodewyk F A|Smilde, Age K', 'Swammerdam Institute for Life Sciences, University of Amsterdam, Amsterdam, The Netherlands.|Swammerdam Institute for Life Sciences, University of Amsterdam, Amsterdam, The Netherlands.|Division of Molecular Carcinogenesis, The Netherlands Cancer Institute, Amsterdam, The Netherlands.|Division of Molecular Carcinogenesis, The Netherlands Cancer Institute, Amsterdam, The Netherlands.|Division of Molecular Carcinogenesis, The Netherlands Cancer Institute, Amsterdam, The Netherlands.|Swammerdam Institute for Life Sciences, University of Amsterdam, Amsterdam, The Netherlands.', 'Netherlands|Netherlands|Netherlands|Netherlands|Netherlands|Netherlands', 2, 'Algorithms|Computational Biology|Computer Simulation|DNA Copy Number Variations|DNA Methylation|Databases, Genetic|Genomics|Humans|Logistic Models|Machine Learning|Neoplasms|Nonlinear Dynamics|Principal Component Analysis|Software|Statistics, Nonparametric', 'Journal Article', '10.1093/bib/bbx119', '30657888', 'Genome-wide measurements of genetic and epigenetic alterations are generating more and more high-dimensional binary data. The special mathematical characteristics of binary data make the direct use of the classical principal component analysis (PCA) model to explore low-dimensional structures less obvious. Although there are several PCA alternatives for binary data in the psychometric, data analysis and machine learning literature, they are not well known to the bioinformatics community. Results: In this article, we introduce the motivation and rationale of some parametric and nonparametric versions of PCA specifically geared for binary data. Using both realistic simulations of binary data as well as mutation, CNA and methylation data of the Genomic Determinants of Sensitivity in Cancer 1000 (GDSC1000), the methods were explored for their performance with respect to finding the correct number of components, overfit, finding back the correct low-dimensional structure, variable importance, etc. The results show that if a low-dimensional structure exists in the data, that most of the methods can find it. When assuming a probabilistic generating process is underlying the data, we recommend to use the parametric logistic PCA model, while when such an assumption is not valid and the data are considered as given, the nonparametric Gifi model is recommended.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(69467, 2019, 'bib', 'A comprehensive review and comparison of existing computational methods for intrinsically disordered protein and region prediction', 'Liu, Yumeng|Wang, Xiaolong|Liu, Bin', 'School of Computer Science and Technology, Harbin Institute of Technology Shenzhen Graduate School, China.|School of Computer Science and Technology, Harbin Institute of Technology Shenzhen Graduate School, China.|School of Computer Science and Technology, Harbin Institute of Technology Shenzhen Graduate School, China.', 'China|China|China', 6, 'Algorithms|Amino Acid Sequence|Chemical Phenomena|Computational Biology|Databases, Protein|Humans|Intrinsically Disordered Proteins|Machine Learning|Models, Molecular|Protein Structural Elements', 'Journal Article', '10.1093/bib/bbx126', '30657889', 'Intrinsically disordered proteins and regions are widely distributed in proteins, which are associated with many biological processes and diseases. Accurate prediction of intrinsically disordered proteins and regions is critical for both basic research (such as protein structure and function prediction) and practical applications (such as drug development). During the past decades, many computational approaches have been proposed, which have greatly facilitated the development of this important field. Therefore, a comprehensive and updated review is highly required. In this regard, we give a review on the computational methods for intrinsically disordered protein and region prediction, especially focusing on the recent development in this field. These computational approaches are divided into four categories based on their methodologies, including physicochemical-based method, machine-learning-based method, template-based method and meta method. Furthermore, their advantages and disadvantages are also discussed. The performance of 40 state-of-the-art predictors is directly compared on the target proteins in the task of disordered region prediction in the 10th Critical Assessment of protein Structure Prediction. A more comprehensive performance comparison of 45 different predictors is conducted based on seven widely used benchmark data sets. Finally, some open problems and perspectives are discussed.'),
(69468, 2019, 'bib', 'Advanced bioinformatics methods for practical applications in proteomics', 'Goh, Wilson Wen Bin|Wong, Limsoon', 'School of Biological Sciences, Nanyang Technological University.|National University of Singapore.', '-|Singapore', 0, 'Algorithms|Computational Biology|Databases, Protein|Humans|Peptides|Proteins|Proteomics|Software|Tandem Mass Spectrometry', 'Journal Article', '10.1093/bib/bbx128', '30657890', 'Mass spectrometry (MS)-based proteomics has undergone rapid advancements in recent years, creating challenging problems for bioinformatics. We focus on four aspects where bioinformatics plays a crucial role (and proteomics is needed for clinical application): peptide-spectra matching (PSM) based on the new data-independent acquisition (DIA) paradigm, resolving missing proteins (MPs), dealing with biological and technical heterogeneity in data and statistical feature selection (SFS). DIA is a brute-force strategy that provides greater width and depth but, because it indiscriminately captures spectra such that signal from multiple peptides is mixed, getting good PSMs is difficult. We consider two strategies: simplification of DIA spectra to pseudo-data-dependent acquisition spectra or, alternatively, brute-force search of each DIA spectra against known reference libraries. The MP problem arises when proteins are never (or inconsistently) detected by MS. When observed in at least one sample, imputation methods can be used to guess the approximate protein expression level. If never observed at all, network/protein complex-based contextualization provides an independent prediction platform. Data heterogeneity is a difficult problem with two dimensions: technical (batch effects), which should be removed, and biological (including demography and disease subpopulations), which should be retained. Simple normalization is seldom sufficient, while batch effect-correction algorithms may create errors. Batch effect-resistant normalization methods are a viable alternative. Finally, SFS is vital for practical applications. While many methods exist, there is no best method, and both upstream (e.g. normalization) and downstream processing (e.g. multiple-testing correction) are performance confounders. We also discuss signal detection when class effects are weak.'),
(69469, 2019, 'bib', 'Clinical trials, progression-speed differentiating features and swiftness rule of the innovative targets of first-in-class drugs', 'Li, Ying Hong|Li, Xiao Xu|Hong, Jia Jun|Wang, Yun Xia|Fu, Jian Bo|Yang, Hong|Yu, Chun Yan|Li, Feng Cheng|Hu, Jie|Xue, Wei Wei|Jiang, Yu Yang|Chen, Yu Zong|Zhu, Feng', 'Lab of Innovative Drug Research and Bioinformatics, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, China.|Lab of Innovative Drug Research and Bioinformatics, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, China.|Lab of Innovative Drug Research and Bioinformatics, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, China.|Lab of Innovative Drug Research and Bioinformatics, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, China.|Lab of Innovative Drug Research and Bioinformatics, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, China.|Innovative Drug Research and Bioinformatics Group, School of Pharmaceutical Sciences and Collaborative Innovation Center for Brain Science, Chongqing University, Chongqing, China.|Innovative Drug Research and Bioinformatics Group, School of Pharmaceutical Sciences and Collaborative Innovation Center for Brain Science, Chongqing University, Chongqing, China.|Lab of Innovative Drug Research and Bioinformatics, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, China.|School of International Studies, Zhejiang University, Hangzhou, China.|Innovative Drug Research and Bioinformatics Group, School of Pharmaceutical Sciences and Collaborative Innovation Center for Brain Science, Chongqing University, Chongqing, China.|State Key Laboratory of Chemical Oncogenomics, Key Laboratory of Chemical Biology, The Graduate School at Shenzhen, Tsinghua University, Shenzhen, Guangdong, China.|Bioinformatics and Drug Design Group, Department of Pharmacy, National University of Singapore, Singapore, Singapore.|Lab of Innovative Drug Research and Bioinformatics, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, China.', 'China|China|China|China|China|China|China|China|China|China|China|Singapore|China', 2, '-', 'Journal Article', '10.1093/bib/bby130', '30689717', 'Drugs produce their therapeutic effects by modulating specific targets, and there are 89 innovative targets of first-in-class drugs approved in 2004-17, each with information about drug clinical trial dated back to 1984. Analysis of the clinical trial timelines of these targets may reveal the trial-speed differentiating features for facilitating target assessment. Here we present a comprehensive analysis of all these 89 targets, following the earlier studies for prospective prediction of clinical success of the targets of clinical trial drugs. Our analysis confirmed the literature-reported common druggability characteristics for clinical success of these innovative targets, exposed trial-speed differentiating features associated to the on-target and off-target collateral effects in humans and further revealed a simple rule for identifying the speedy human targets through clinical trials (from the earliest phase I to the 1st drug approval within 8 years). This simple rule correctly identified 75.0% of the 28 speedy human targets and only unexpectedly misclassified 13.2% of 53 non-speedy human targets. Certain extraordinary circumstances were also discovered to likely contribute to the misclassification of some human targets by this simple rule. Investigation and knowledge of trial-speed differentiating features enable prioritized drug discovery and development.'),
(69470, 2019, 'bib', 'Cancer Target Gene Screening: a web application for breast cancer target gene screening using multi-omics data analysis', 'Kim, Hyung-Yong|Choi, Hee-Joo|Lee, Jeong-Yeon|Kong, Gu', 'Department of Pathology, College of Medicine, Hanyang University, Seoul, Republic of Korea.|Department of Pathology, College of Medicine, Hanyang University, Seoul, Republic of Korea.|Department of Medicine, College of Medicine, Hanyang University, Seoul, Republic of Korea.|Department of Pathology, College of Medicine, Hanyang University, Seoul, Republic of Korea.', '-|-|-|-', 1, '-', 'Journal Article', '10.1093/bib/bbz003', '30698638', 'Breast cancer comprises several molecular subtypes with distinct clinical features and treatment responses, and a substantial portion of each subtype remains incurable. A comprehensive analysis of multi-omics data and clinical profiles is required in order to better understand the biological complexity of this cancer type and to identify new prognostic and therapeutic markers. Thus, there arises a need for useful analytical tools to assist in the investigation and clinical management of the disease. We developed Cancer Target Gene Screening (CTGS), a web application that provides rapid and user-friendly analysis of multi-omics data sets from a large number of primary breast tumors. It allows the investigation of genomic and epigenomic aberrations, evaluation of transcriptomic profiles and performance of survival analyses and of bivariate correlations between layers of omics data. Notably, the genome-wide screening function of CTGS prioritizes candidate genes of clinical and biological significance among genes with copy number alteration, DNA methylation and dysregulated expression by the integrative analysis of different types of omics data in customized subgroups of breast cancer patients. These features may help in the identification of druggable cancer driver genes in a specific subtype or the clinical condition of human breast cancer. CTGS is available at http://ctgs.biohackers.net.'),
(69471, 2019, 'bib', 'Disentangling the complexity of low complexity proteins', 'Mier, Pablo|Paladin, Lisanna|Tamana, Stella|Petrosian, Sophia|Hajdu-Soltész, Borbála|Urbanek, Annika|Gruca, Aleksandra|Plewczynski, Dariusz|Grynberg, Marcin|Bernadó, Pau|Gáspári, Zoltán|Ouzounis, Christos A|Promponas, Vasilis J|Kajava, Andrey V|Hancock, John M|Tosatto, Silvio C E|Dosztanyi, Zsuzsanna|Andrade-Navarro, Miguel A', 'Institute of Organismic and Molecular Evolution, Johannes Gutenberg University of Mainz, Mainz, Germany.|Department of Biomedical Science, University of Padova, Padova, Italy.|Bioinformatics Research Laboratory, Department of Biological Sciences, University of Cyprus, Nicosia, Cyprus.|Biological Computation and Process Laboratory, Chemical Process & Energy Resources Institute, Centre for Research & Technology Hellas, Thessalonica, Greece.|MTA-ELTE Lendület Bioinformatics Research Group, Department of Biochemistry, Eötvös Loránd University, Budapest, Hungary.|Centre de Biochimie Structurale, INSERM, CNRS, Université de Montpellier, Montpellier, France.|Institute of Informatics, Silesian University of Technology, Gliwice, Poland.|Center of New Technologies, University of Warsaw, Warsaw, Poland.|Institute of Biochemistry and Biophysics, Warsaw, Poland.|Centre de Biochimie Structurale, INSERM, CNRS, Université de Montpellier, Montpellier, France.|Faculty of Information Technology and Bionics, Pázmány Péter Catholic University, Budapest, Hungary.|Biological Computation and Process Laboratory, Chemical Process & Energy Resources Institute, Centre for Research & Technology Hellas, Thessalonica, Greece.|Bioinformatics Research Laboratory, Department of Biological Sciences, University of Cyprus, Nicosia, Cyprus.|Centre de Recherche en Biologie Cellulaire de Montpellier, CNRS-UMR, Institut de Biologie Computationnelle, Universite de Montpellier, Montpellier, France.|Earlham Institute, Norwich, UK.|Department of Biomedical Science, University of Padova, Padova, Italy.|MTA-ELTE Lendület Bioinformatics Research Group, Department of Biochemistry, Eötvös Loránd University, Budapest, Hungary.|Institute of Organismic and Molecular Evolution, Johannes Gutenberg University of Mainz, Mainz, Germany.', 'Germany|Italy|Cyprus|Greece|Hungary|France|Poland|Poland|Poland|France|Hungary|Greece|Cyprus|France|', 5, '-', 'Journal Article', '10.1093/bib/bbz007', '30698641', 'There are multiple definitions for low complexity regions (LCRs) in protein sequences, with all of them broadly considering LCRs as regions with fewer amino acid types compared to an average composition. Following this view, LCRs can also be defined as regions showing composition bias. In this critical review, we focus on the definition of sequence complexity of LCRs and their connection with structure. We present statistics and methodological approaches that measure low complexity (LC) and related sequence properties. Composition bias is often associated with LC and disorder, but repeats, while compositionally biased, might also induce ordered structures. We illustrate this dichotomy, and more generally the overlaps between different properties related to LCRs, using examples. We argue that statistical measures alone cannot capture all structural aspects of LCRs and recommend the combined usage of a variety of predictive tools and measurements. While the methodologies available to study LCRs are already very advanced, we foresee that a more comprehensive annotation of sequences in the databases will enable the improvement of predictions and a better understanding of the evolution and the connection between structure and function of LCRs. This will require the use of standards for the generation and exchange of data describing all aspects of LCRs.'),
(69472, 2019, 'bib', 'Computational medicine: quantitative modeling of complex diseases', 'Tiwary, Basant K', 'Centre for Bioinformatics, School of Life Sciences, Pondicherry University, Pondicherry, India.', 'India', 0, '-', 'Journal Article', '10.1093/bib/bbz005', '30698665', 'Biological complex systems are composed of numerous components that interact within and across different scales. The ever-increasing generation of high-throughput biomedical data has given us an opportunity to develop a quantitative model of nonlinear biological systems having implications in health and diseases. Multidimensional molecular data can be modeled using various statistical methods at different scales of biological organization, such as genome, transcriptome and proteome. I will discuss recent advances in the application of computational medicine in complex diseases such as network-based studies, genome-scale metabolic modeling, kinetic modeling and support vector machines with specific examples in the field of cancer, psychiatric disorders and type 2 diabetes. The recent advances in translating these computational models in diagnosis and identification of drug targets of complex diseases are discussed, as well as the challenges researchers and clinicians are facing in taking computational medicine from the bench to bedside.'),
(69473, 2019, 'bib', 'Evaluation of ontology structural metrics based on public repository data', 'Franco, Manuel|Vivo, Juana María|Quesada-Martínez, Manuel|Duque-Ramos, Astrid|Fernández-Breis, Jesualdo Tomás', 'Departamento de Estadística e Investigación Operativa, Universidad de Murcia, Murcia, Spain.|Departamento de Estadística e Investigación Operativa, Universidad de Murcia, Murcia, Spain.|Center of Operations Research (CIO), Miguel Hernández University of Elche, Elche, Spain.|Departamento de Sistemas, Facultad de Ingenierías, Universidad de Antioquia, Medellín, Colombia.|Departamento de Informática y Sistemas, Universidad de Murcia, IMIB-Arrixaca, Murcia, Spain.', 'Spain|Spain|Spain|Colombia|Spain', 1, '-', 'Journal Article', '10.1093/bib/bbz009', '30715146', 'The development and application of biological ontologies have increased significantly in recent years. These ontologies can be retrieved from different repositories, which do not provide much information about quality aspects of the ontologies. In the past years, some ontology structural metrics have been proposed, but their validity as measurement instrument has not been sufficiently studied to date. In this work, we evaluate a set of reproducible and objective ontology structural metrics. Given the lack of standard methods for this purpose, we have applied an evaluation method based on the stability and goodness of the classifications of ontologies produced by each metric on an ontology corpus. The evaluation has been done using ontology repositories as corpora. More concretely, we have used 119 ontologies from the OBO Foundry repository and 78 ontologies from AgroPortal. First, we study the correlations between the metrics. Second, we study whether the clusters for a given metric are stable and have a good structure. The results show that the existing correlations are not biasing the evaluation, there are no metrics generating unstable clusterings and all the metrics evaluated provide at least reasonable clustering structure. Furthermore, our work permits to review and suggest the most reliable ontology structural metrics in terms of stability and goodness of their classifications.Availability: http://sele.inf.um.es/ontology-metrics.'),
(69474, 2019, 'bib', 'Decoding competing endogenous RNA networks for cancer biomarker discovery', 'Qi, Xin|Lin, Yuxin|Chen, Jiajia|Shen, Bairong', 'Center for Systems Biology, Soochow University, Suzhou, China.|Center for Systems Biology, Soochow University, Suzhou, China.|School of Chemistry, Biology and Material Engineering, Suzhou University of Science and Technology, Suzhou, China.|Institutes for Systems Genetics, West China Hospital, Sichuan University, Chengdu, China.', 'China|China|China|China', 6, '-', 'Journal Article', '10.1093/bib/bbz006', '30715152', 'Crosstalk between competing endogenous RNAs (ceRNAs) is mediated by shared microRNAs (miRNAs) and plays important roles both in normal physiology and tumorigenesis, thus, it is attractive for systems-level decoding of gene regulation. As ceRNA networks link the function of miRNAs with that of transcripts sharing the same miRNA response elements (MREs), e.g. pseudogenes, competing mRNAs, long non-coding RNAs, and circular RNAs, the perturbation of crucial interactions in ceRNA networks may contribute to carcinogenesis by affecting the balance of cellular regulatory system. Therefore, discovering biomarkers that indicate cancer initiation, development, and/or therapeutic responses via reconstructing and analyzing ceRNA networks is of clinical significance. In this review, the regulatory function of ceRNAs in cancer and crucial determinants of ceRNA crosstalk are firstly discussed to gain a global understanding of ceRNA-mediated carcinogenesis. Then, computational and experimental approaches for ceRNA network reconstruction and ceRNA validation, respectively, are described from a systems biology perspective. We focus on strategies for biomarker identification based on analyzing ceRNA networks and highlight the translational applications of ceRNA biomarkers for cancer management. This article will shed light on the significance of miRNA-mediated ceRNA interactions and provide important clues for discovering ceRNA network-based biomarker in cancer biology, thereby accelerating the pace of precision medicine and healthcare for cancer patients.'),
(69475, 2019, 'bib', 'The global dissemination of bacterial infections necessitates the study of reverse genomic epidemiology', 'Ruan, Zhi|Yu, Yunsong|Feng, Ye', 'Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China.|Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China.|Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China.', 'China|China|China', 1, '-', 'Journal Article', '10.1093/bib/bbz010', '30715167', 'Whole genome sequencing (WGS) has revolutionized the genotyping of bacterial pathogens and is expected to become the new gold standard for tracing the transmissions of bacterial infectious diseases for public health purposes. Traditional genomic epidemiology often uses WGS as a verification tool, namely, when a common source or epidemiological link is suspected, the collected isolates are sequenced for the determination of clonal relationships. However, increasingly frequent international travel and food transportation, and the associated potential for the cross-border transmission of bacterial pathogens, often lead to an absence of information on bacterial transmission routes. Here we introduce the concept of reverse genomic epidemiology, i.e. when isolates are inspected by genome comparisons to be sufficiently similar to one another, they are assumed to be a consequence of infection from a common source. Through BacWGSTdb (http://bacdb.org/BacWGSTdb/), a database we have developed for bacterial genome typing and source tracking, we have found that almost the entire analyzed 20 bacterial species exhibit the phenomenon of cross-border clonal dissemination. Five networks were further identified in which isolates sharing nearly identical genomes were collected from at least five different countries. Three of these have been documented as real infectious disease outbreaks, therefore demonstrating the feasibility and authority of reverse genomic epidemiology. Our survey and proposed strategy would be of potential value in establishing a global surveillance system for tracing bacterial transmissions and outbreaks, the related database and techniques require urgent standardization.'),
(69476, 2019, 'bib', 'Performance of gene expression-based single sample predictors for assessment of clinicopathological subgroups and molecular subtypes in cancers: a case comparison study in non-small cell lung cancer', 'Cirenajwis, Helena|Lauss, Martin|Planck, Maria|Vallon-Christersson, Johan|Staaf, Johan', 'Division of Oncology and Pathology, Department of Clinical Sciences Lund, Lund University, Medicon Village, Lund, Sweden.|Division of Oncology and Pathology, Department of Clinical Sciences Lund, Lund University, Medicon Village, Lund, Sweden.|Division of Oncology and Pathology, Department of Clinical Sciences Lund, Lund University, Medicon Village, Lund, Sweden.|Division of Oncology and Pathology, Department of Clinical Sciences Lund, Lund University, Medicon Village, Lund, Sweden.|Division of Oncology and Pathology, Department of Clinical Sciences Lund, Lund University, Medicon Village, Lund, Sweden.', 'Sweden|Sweden|Sweden|Sweden|Sweden', 2, '-', 'Journal Article', '10.1093/bib/bbz008', '30721923', 'The development of multigene classifiers for cancer prognosis, treatment prediction, molecular subtypes or clinicopathological groups has been a cornerstone in transcriptomic analyses of human malignancies for nearly two decades. However, many reported classifiers are critically limited by different preprocessing needs like normalization and data centering. In response, a new breed of classifiers, single sample predictors (SSPs), has emerged. SSPs classify samples in an N-of-1 fashion, relying on, e.g. gene rules comparing expression values within a sample. To date, several methods have been reported, but there is a lack of head-to-head performance comparison for typical cancer classification problems, representing an unmet methodological need in cancer bioinformatics. To resolve this need, we performed an evaluation of two SSPs [k-top-scoring pair classifier (kTSP) and absolute intrinsic molecular subtyping (AIMS)] for two case examples of different magnitude of difficulty in non-small cell lung cancer: gene expression-based classification of (i) tumor histology and (ii) molecular subtype. Through the analysis of ~2000 lung cancer samples for each case example (n = 1918 and n = 2106, respectively), we compared the performance of the methods for different sample compositions, training data set sizes, gene expression platforms and gene rule selections. Three main conclusions are drawn from the comparisons: both methods are platform independent, they select largely overlapping gene rules associated with actual underlying tumor biology and, for large training data sets, they behave interchangeably performance-wise. While SSPs like AIMS and kTSP offer new possibilities to move gene expression signatures/predictors closer to a clinical context, they are still importantly limited by the difficultness of the classification problem at hand.'),
(69477, 2019, 'bib', 'A comparison of deterministic and stochastic approaches for sensitivity analysis in computational systems biology', 'Simoni, Giulia|Vo, Hong Thanh|Priami, Corrado|Marchetti, Luca', 'The Microsoft Research - University of Trento Centre for Computational and Systems Biology (COSBI), Piazza Manifattura, Rovereto (TN), Italy.|The Microsoft Research - University of Trento Centre for Computational and Systems Biology (COSBI), Piazza Manifattura, Rovereto (TN), Italy.|The Microsoft Research - University of Trento Centre for Computational and Systems Biology (COSBI), Piazza Manifattura, Rovereto (TN), Italy.|The Microsoft Research - University of Trento Centre for Computational and Systems Biology (COSBI), Piazza Manifattura, Rovereto (TN), Italy.', 'Italy|Italy|Italy|Italy', 1, '-', 'Journal Article', '10.1093/bib/bbz014', '30753281', 'With the recent rising application of mathematical models in the field of computational systems biology, the interest in sensitivity analysis methods had increased. The stochastic approach, based on chemical master equations, and the deterministic approach, based on ordinary differential equations (ODEs), are the two main approaches for analyzing mathematical models of biochemical systems. In this work, the performance of these approaches to compute sensitivity coefficients is explored in situations where stochastic and deterministic simulation can potentially provide different results (systems with unstable steady states, oscillators with population extinction and bistable systems). We consider two methods in the deterministic approach, namely the direct differential method and the finite difference method, and five methods in the stochastic approach, namely the Girsanov transformation, the independent random number method, the common random number method, the coupled finite difference method and the rejection-based finite difference method. The reviewed methods are compared in terms of sensitivity values and computational time to identify differences in outcome that can highlight conditions in which one approach performs better than the other.'),
(69478, 2019, 'bib', 'Computational methods for identifying the critical nodes in biological networks', 'Liu, Xiangrong|Hong, Zengyan|Liu, Juan|Lin, Yuan|Rodríguez-Patón, Alfonso|Zou, Quan|Zeng, Xiangxiang', 'Department of Computer Science, Xiamen University, China.|Department of Computer Science, Xiamen University, China.|Department of Computer Science, Xiamen University, China.|ITOP Section, DNB Bank ASA, Solheimsgaten, Bergen, Norway.|Universidad Politécnica de Madrid (UPM) Campus Montegancedo s/n, Boadilla del Monte, Madrid, Spain.|Department of Computer Science, Xiamen University, China.|Department of Computer Science, Xiamen University, China.', 'China|China|China|Norway|Spain|China|China', 12, '-', 'Journal Article', '10.1093/bib/bbz011', '30753282', 'A biological network is complex. A group of critical nodes determines the quality and state of such a network. Increasing studies have shown that diseases and biological networks are closely and mutually related and that certain diseases are often caused by errors occurring in certain nodes in biological networks. Thus, studying biological networks and identifying critical nodes can help determine the key targets in treating diseases. The problem is how to find the critical nodes in a network efficiently and with low cost. Existing experimental methods in identifying critical nodes generally require much time, manpower and money. Accordingly, many scientists are attempting to solve this problem by researching efficient and low-cost computing methods. To facilitate calculations, biological networks are often modeled as several common networks. In this review, we classify biological networks according to the network types used by several kinds of common computational methods and introduce the computational methods used by each type of network.'),
(69479, 2019, 'bib', 'In silico drug repositioning based on drug-miRNA associations', 'Zhou, Xu|Dai, Enyu|Song, Qian|Ma, Xueyan|Meng, Qianqian|Jiang, Yongshuai|Jiang, Wei', 'College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, P. R. China.|College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, P. R. China.|College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, P. R. China.|College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, P. R. China.|College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, P. R. China.|College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, P. R. China.|Department of Biomedical Engineering, College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, P. R. China.', 'China|China|China|China|China|China|China', 0, '-', 'Journal Article', '10.1093/bib/bbz012', '30753359', 'Drug repositioning has become a prevailing tactic as this strategy is efficient, economical and low risk for drug discovery. Meanwhile, recent studies have confirmed that small-molecule drugs can modulate the expression of disease-related miRNAs, which indicates that miRNAs are promising therapeutic targets for complex diseases. In this study, we put forward and verified the hypothesis that drugs with similar miRNA profiles may share similar therapeutic properties. Furthermore, a comprehensive drug-drug interaction network was constructed based on curated drug-miRNA associations. Through random network comparison, topological structure analysis and network module extraction, we found that the closely linked drugs in the network tend to treat the same diseases. Additionally, the curated drug-disease relationships (from the CTD) and random walk with restarts algorithm were utilized on the drug-drug interaction network to identify the potential drugs for a given disease. Both internal validation (leave-one-out cross-validation) and external validation (independent drug-disease data set from the ChEMBL) demonstrated the effectiveness of the proposed approach. Finally, by integrating drug-miRNA and miRNA-disease information, we also explain the modes of action of drugs in the view of miRNA regulation. In summary, our work could determine novel and credible drug indications and offer novel insights and valuable perspectives for drug repositioning.'),
(69480, 2019, 'bib', 'Ensuring privacy and security of genomic data and functionalities', 'Mohammed Yakubu, Abukari|Chen, Yi-Ping Phoebe', 'Department of Computer Science and Information Technology, La Trobe University, Melbourne, VIC, Australia.|Department of Computer Science and Information Technology, La Trobe University, Melbourne, VIC, Australia.', 'Australia|Australia', 0, '-', 'Journal Article', '10.1093/bib/bbz013', '30759195', 'In recent times, the reduced cost of DNA sequencing has resulted in a plethora of genomic data that is being used to advance biomedical research and improve clinical procedures and healthcare delivery. These advances are revolutionizing areas in genome-wide association studies (GWASs), diagnostic testing, personalized medicine and drug discovery. This, however, comes with security and privacy challenges as the human genome is sensitive in nature and uniquely identifies an individual. In this article, we discuss the genome privacy problem and review relevant privacy attacks, classified into identity tracing, attribute disclosure and completion attacks, which have been used to breach the privacy of an individual. We then classify state-of-the-art genomic privacy-preserving solutions based on their application and computational domains (genomic aggregation, GWASs and statistical analysis, sequence comparison and genetic testing) that have been proposed to mitigate these attacks and compare them in terms of their underlining cryptographic primitives, security goals and complexities-computation and transmission overheads. Finally, we identify and discuss the open issues, research challenges and future directions in the field of genomic privacy. We believe this article will provide researchers with the current trends and insights on the importance and challenges of privacy and security issues in the area of genomics.'),
(69481, 2019, 'bib', 'Delivering blended bioinformatics training in resource-limited settings: a case study on the University of Khartoum H3ABioNet node', 'Ahmed, Azza E|Awadallah, Ayah A|Tagelsir, Mawada|Suliman, Maram A|Eltigani, Atheer|Elsafi, Hassan|Hamdelnile, Basil D|Mukhtar, Mohamed A|Fadlelmola, Faisal M', 'Center for Bioinformatics and Systems Biology, Faculty of Science, University of Khartoum, Khartoum, Sudan.|Department of Zoology, Faculty of Science, University of Khartoum, Khartoum, Sudan.|Department of Haematology and Immunohaematology, Faculty of Medical Laboratory Sciences, Ibn Sina University, Khartoum, Sudan.|Department of Biology, Faculty of Medicine, Ibn Sina University, Khartoum, Sudan.|Department of Medical Biotechnology, Commission for Biotechnology and Genetic Engineering, National Centre for Research, Khartoum, Sudan.|Medicinal, Aromatic Plants and Traditional Medicine Research Institute, National Centre for Research, Khartoum, Sudan.|Center for Bioinformatics and Systems Biology, Faculty of Science, University of Khartoum, Khartoum, Sudan.|Faculty of Pharmacy, Karary University, Omdurman, Sudan.|Center for Bioinformatics and Systems Biology, Faculty of Science, University of Khartoum, Khartoum, Sudan.', 'Sudan|Sudan|Sudan|Sudan|Sudan|Sudan|Sudan|Sudan|Sudan', 0, '-', 'Journal Article', '10.1093/bib/bbz004', '30773584', 'Delivering high-quality distance-based courses in resource-limited settings is a challenging task. Besides the needed infrastructure and expertise, effective delivery of a bioinformatics course could benefit from hands-on sessions, interactivity and problem-based learning approaches.'),
(69482, 2019, 'bib', 'Network-based methods for predicting essential genes or proteins: a survey', 'Li, Xingyi|Li, Wenkai|Zeng, Min|Zheng, Ruiqing|Li, Min', 'School of Computer Science and Engineering, Central South University, Changsha, Hunan, China.|School of Computer Science and Engineering, Central South University, Changsha, Hunan, China.|School of Computer Science and Engineering, Central South University, Changsha, Hunan, China.|School of Computer Science and Engineering, Central South University, Changsha, Hunan, China.|School of Computer Science and Engineering, Central South University, Changsha, Hunan, China.', 'China|China|China|China|China', 2, '-', 'Journal Article', '10.1093/bib/bbz017', '30776072', 'Genes that are thought to be critical for the survival of organisms or cells are called essential genes. The prediction of essential genes and their products (essential proteins) is of great value in exploring the mechanism of complex diseases, the study of the minimal required genome for living cells and the development of new drug targets. As laboratory methods are often complicated, costly and time-consuming, a great many of computational methods have been proposed to identify essential genes/proteins from the perspective of the network level with the in-depth understanding of network biology and the rapid development of biotechnologies. Through analyzing the topological characteristics of essential genes/proteins in protein-protein interaction networks (PINs), integrating biological information and considering the dynamic features of PINs, network-based methods have been proved to be effective in the identification of essential genes/proteins. In this paper, we survey the advanced methods for network-based prediction of essential genes/proteins and present the challenges and directions for future research.'),
(69483, 2019, 'bib', 'CAFU: a Galaxy framework for exploring unmapped RNA-Seq data', 'Chen, Siyuan|Ren, Chengzhi|Zhai, Jingjing|Yu, Jiantao|Zhao, Xuyang|Li, Zelong|Zhang, Ting|Ma, Wenlong|Han, Zhaoxue|Ma, Chuang', 'State Key Laboratory of Crop Stress Biology for Arid Areas, Center of Bioinformatics, College of Life Sciences, Northwest Agriculture and Forestry University.|State Key Laboratory of Crop Stress Biology for Arid Areas, Center of Bioinformatics, College of Life Sciences, Northwest Agriculture and Forestry University.|State Key Laboratory of Crop Stress Biology for Arid Areas, Center of Bioinformatics, College of Life Sciences, Northwest Agriculture and Forestry University.|College of Information Engineering, Northwest Agriculture and Forestry University.|College of Information Engineering, Northwest Agriculture and Forestry University.|State Key Laboratory of Crop Stress Biology for Arid Areas, Center of Bioinformatics, College of Life Sciences, Northwest Agriculture and Forestry University.|State Key Laboratory of Crop Stress Biology for Arid Areas, Center of Bioinformatics, College of Life Sciences, Northwest Agriculture and Forestry University.|State Key Laboratory of Crop Stress Biology for Arid Areas, Center of Bioinformatics, College of Life Sciences, Northwest Agriculture and Forestry University.|State Key Laboratory of Crop Stress Biology for Arid Areas, Center of Bioinformatics, College of Life Sciences, Northwest Agriculture and Forestry University.|State Key Laboratory of Crop Stress Biology for Arid Areas, Center of Bioinformatics, College of Life Sciences, Northwest Agriculture and Forestry University.', '-|-|-|-|-|-|-|-|-|-', 0, '-', 'Journal Article', '10.1093/bib/bbz018', '30815667', 'A widely used approach in transcriptome analysis is the alignment of short reads to a reference genome. However, owing to the deficiencies of specially designed analytical systems, short reads unmapped to the genome sequence are usually ignored, resulting in the loss of significant biological information and insights. To fill this gap, we present Comprehensive Assembly and Functional annotation of Unmapped RNA-Seq data (CAFU), a Galaxy-based framework that can facilitate the large-scale analysis of unmapped RNA sequencing (RNA-Seq) reads from single- and mixed-species samples. By taking advantage of machine learning techniques, CAFU addresses the issue of accurately identifying the species origin of transcripts assembled using unmapped reads from mixed-species samples. CAFU also represents an innovation in that it provides a comprehensive collection of functions required for transcript confidence evaluation, coding potential calculation, sequence and expression characterization and function annotation. These functions and their dependencies have been integrated into a Galaxy framework that provides access to CAFU via a user-friendly interface, dramatically simplifying complex exploration tasks involving unmapped RNA-Seq reads. CAFU has been validated with RNA-Seq data sets from wheat and Zea mays (maize) samples. CAFU is freely available via GitHub: https://github.com/cma2015/CAFU.'),
(69484, 2019, 'bib', 'New approaches for metagenome assembly with short reads', 'Ayling, Martin|Clark, Matthew D|Leggett, Richard M', 'Earlham Institute, Norwich Research Park, Norwich, UK.|Natural History Museum, London, UK.|Earlham Institute, Norwich Research Park, Norwich, UK.', 'United Kingdom|United Kingdom|United Kingdom', 13, '-', 'Journal Article', '10.1093/bib/bbz020', '30815668', 'In recent years, the use of longer range read data combined with advances in assembly algorithms has stimulated big improvements in the contiguity and quality of genome assemblies. However, these advances have not directly transferred to metagenomic data sets, as assumptions made by the single genome assembly algorithms do not apply when assembling multiple genomes at varying levels of abundance. The development of dedicated assemblers for metagenomic data was a relatively late innovation and for many years, researchers had to make do using tools designed for single genomes. This has changed in the last few years and we have seen the emergence of a new type of tool built using different principles. In this review, we describe the challenges inherent in metagenomic assemblies and compare the different approaches taken by these novel assembly tools.'),
(69485, 2019, 'bib', 'MCAM abnormal expression and clinical outcome associations are highly cancer dependent as revealed through pan-cancer analysis', 'An, Yunxia|Wei, Nan|Cheng, Xiangsong|Li, Ying|Liu, Haiyang|Wang, Jia|Xu, Zhiwei|Sun, Zhifu|Zhang, Xiaoju', 'Department of Respiratory Medicine, Henan Provincial Peoples Hospital, Peoples Hospital of Zhengzhou University, Zhengzhou, Henan, China.|Department of Respiratory Medicine, Peoples Hospital of Zhengzhou University, Zhengzhou, Henan, China.|Department of Respiratory Medicine, Henan Provincial Peoples Hospital, Peoples Hospital of Zhengzhou University, Zhengzhou, Henan, China.|Department of Respiratory Medicine, Henan Provincial Peoples Hospital, Peoples Hospital of Zhengzhou University, Zhengzhou, Henan, China.|Department of Respiratory Medicine, Henan Provincial Peoples Hospital, Peoples Hospital of Zhengzhou University, Zhengzhou, Henan, China.|Department of Respiratory Medicine, Henan Provincial Peoples Hospital, Peoples Hospital of Zhengzhou University, Zhengzhou, Henan, China.|Department of Respiratory Medicine, Henan Provincial Peoples Hospital, Peoples Hospital of Zhengzhou University, Zhengzhou, Henan, China.|Department of Health Sciences Research, Mayo Clinic, Rochester, MN, USA.|Department of Respiratory Medicine, Henan Provincial Peoples Hospital, Peoples Hospital of Zhengzhou University, Zhengzhou, Henan, China.', 'China|China|China|China|China|China|China|United States|China', 0, '-', 'Journal Article', '10.1093/bib/bbz019', '30815677', 'MCAM (CD146) is a cell surface adhesion molecule that has been reported to promote cancer development, progression and metastasis and is considered as a potential tumor biomarker and therapeutic target. However, inconsistent reports exist, and its clinical value is yet to be confirmed. Here we took advantage of several large genomic data collections (Genotype-Tissue Expression, The Cancer Genome Atlas and Cancer Cell Line Encyclopedia) and comprehensively analyzed MCAM expression in thousands of normal and cancer samples and cell lines along with their clinical phenotypes and drug response information. Our results show that MCAM is very highly expressed in large vessel tissues while majority of tissues have low or minimal expression. Its expression is dramatically increased in a few tumors but significantly decreased in most other tumors relative to their pairing normal tissues. Increased MCAM expression is associated with a higher tumor stage and worse patient survival for some less common tumors but not for major ones. Higher MCAM expression in primary tumors may be complicated by tumor-associated or normal stromal blood vessels yet its significance may differ from the one from cancer cells. MCAM expression is weakly associated with the response to a few small molecular drugs and the association with targeted anti-BRAF agents suggests its involvement in that pathway which warrants further investigation.'),
(69486, 2019, 'bib', 'MinE-RFE: determine the optimal subset from RFE by minimizing the subset-accuracy-defined energy', 'Su, Ran|Liu, Xinyi|Wei, Leyi', 'School of Computer Software, College of Intelligence and Computing, Tianjin University, Tianjin, China.|School of Computer Science and Technology, College of Intelligence and Computing, Tianjin University, Tianjin, China.|School of Computer Science and Technology, College of Intelligence and Computing, Tianjin University, Tianjin, China.', 'China|China|China', 5, '-', 'Journal Article', '10.1093/bib/bbz021', '30860571', 'Recursive feature elimination (RFE), as one of the most popular feature selection algorithms, has been extensively applied to bioinformatics. During the training, a group of candidate subsets are generated by iteratively eliminating the least important features from the original features. However, how to determine the optimal subset from them still remains ambiguous. Among most current studies, either overall accuracy or subset size (SS) is used to select the most predictive features. Using which one or both and how they affect the prediction performance are still open questions. In this study, we proposed MinE-RFE, a novel RFE-based feature selection approach by sufficiently considering the effect of both factors. Subset decision problem was reflected into subset-accuracy space and became an energy-minimization problem. We also provided a mathematical description of the relationship between the overall accuracy and SS using Gaussian Mixture Models together with spline fitting. Besides, we comprehensively reviewed a variety of state-of-the-art applications in bioinformatics using RFE. We compared their approaches of deciding the final subset from all the candidate subsets with MinE-RFE on diverse bioinformatics data sets. Additionally, we also compared MinE-RFE with some well-used feature selection algorithms. The comparative results demonstrate that the proposed approach exhibits the best performance among all the approaches. To facilitate the use of MinE-RFE, we further established a user-friendly web server with the implementation of the proposed approach, which is accessible at http://qgking.wicp.net/MinE/. We expect this web server will be a useful tool for research community.'),
(69487, 2019, 'bib', 'Assessment of metagenomic assemblers based on hybrid reads of real and simulated metagenomic sequences', 'Wang, Ziye|Wang, Ying|Fuhrman, Jed A|Sun, Fengzhu|Zhu, Shanfeng', 'School of Mathematical Sciences and the Institute of Science and Technology for Brain-inspired Intelligence, Fudan University, Shanghai, China.|Department of Automation, Xiamen University, Xiamen, China.|Department of Biological Sciences and Wrigley Institute for Environmental Studies, University of Southern California, Los Angeles, California, United States of America.|Department of Biological Sciences, University of Southern California, Los Angeles, California, United States of America.|Shanghai Key Lab of Intelligent Information Processing, the School of Computer Science and the Institute of Science and Technology for Brain-inspired Intelligence, Fudan University, Shanghai, China.', 'China|China|United States|United States|China', 1, '-', 'Journal Article', '10.1093/bib/bbz025', '30860572', 'In metagenomic studies of microbial communities, the short reads come from mixtures of genomes. Read assembly is usually an essential first step for the follow-up studies in metagenomic research. Understanding the power and limitations of various read assembly programs in practice is important for researchers to choose which programs to use in their investigations. Many studies evaluating different assembly programs used either simulated metagenomes or real metagenomes with unknown genome compositions. However, the simulated datasets may not reflect the real complexities of metagenomic samples and the estimated assembly accuracy could be misleading due to the unknown genomes in real metagenomes. Therefore, hybrid strategies are required to evaluate the various read assemblers for metagenomic studies. In this paper, we benchmark the metagenomic read assemblers by mixing reads from real metagenomic datasets with reads from known genomes and evaluating the integrity, contiguity and accuracy of the assembly using the reads from the known genomes. We selected four advanced metagenome assemblers, MEGAHIT, MetaSPAdes, IDBA-UD and Faucet, for evaluation. We showed the strengths and weaknesses of these assemblers in terms of integrity, contiguity and accuracy for different variables, including the genetic difference of the real genomes with the genome sequences in the real metagenomic datasets and the sequencing depth of the simulated datasets. Overall, MetaSPAdes performs best in terms of integrity and continuity at the species-level, followed by MEGAHIT. Faucet performs best in terms of accuracy at the cost of worst integrity and continuity, especially at low sequencing depth. MEGAHIT has the highest genome fractions at the strain-level and MetaSPAdes has the overall best performance at the strain-level. MEGAHIT is the most efficient in our experiments. Availability: The source code is available at https://github.com/ziyewang/MetaAssemblyEval.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(69488, 2019, 'bib', 'Optimal selection of genetic variants for adjustment of population stratification in European association studies', 'Brinster, Regina|Scherer, Dominique|Lorenzo Bermejo, Justo', 'Institute of Medical Biometry and Informatics, University of Heidelberg, Im Neuenheimer Feld 130.3, Heidelberg, Germany.|Institute of Medical Biometry and Informatics, University of Heidelberg, Im Neuenheimer Feld 130.3, Heidelberg, Germany.|Institute of Medical Biometry and Informatics, University of Heidelberg, Im Neuenheimer Feld 130.3, Heidelberg, Germany.', 'Germany|Germany|Germany', 1, '-', 'Journal Article', '10.1093/bib/bbz023', '30863848', 'Population stratification is usually corrected relying on principal component analysis (PCA) of genome-wide genotype data, even in populations considered genetically homogeneous, such as Europeans. The need to genotype only a small number of genetic variants that show large differences in allele frequency among subpopulations-so-called ancestry-informative markers (AIMs)-instead of the whole genome for stratification adjustment could represent an advantage for replication studies and candidate gene/pathway studies. Here we compare the correction performance of classical and robust principal components (PCs) with the use of AIMs selected according to four different methods: the informativeness for assignment measure ($IN$-AIMs), the combination of PCA and F-statistics, PCA-correlated measurement and the PCA weighted loadings for each genetic variant. We used real genotype data from the Population Reference Sample and The Cancer Genome Atlas to simulate European genetic association studies and to quantify type I error rate and statistical power in different case-control settings. In studies with the same numbers of cases and controls per country and control-to-case ratios reflecting actual rates of disease prevalence, no adjustment for population stratification was required. The unnecessary inclusion of the country of origin, PCs or AIMs as covariates in the regression models translated into increasing type I error rates. In studies with cases and controls from separate countries, no investigated method was able to adequately correct for population stratification. The first classical and the first two robust PCs achieved the lowest (although inflated) type I error, followed at some distance by the first eight $IN$-AIMs.'),
(69489, 2019, 'bib', 'Meta-GDBP: a high-level stacked regression model to improve anticancer drug response prediction', 'Su, Ran|Liu, Xinyi|Xiao, Guobao|Wei, Leyi', 'School of Computer Software, College of Intelligence and Computing, Tianjin University, China.|School of Computer Software, College of Intelligence and Computing, Tianjin University, China.|Fujian Provincial Key Laboratory of Information Processing and Intelligent Control, College of Computer and Control Engineering, Minjiang University, Fuzhou, China.|School of Computer Science and Technology, College of Intelligence and Computing, Tianjin University, China.', 'China|China|China|China', 5, '-', 'Journal Article', '10.1093/bib/bbz022', '30868164', 'Anticancer drug response prediction plays an important role in personalized medicine. In particular, precisely predicting drug response in specific cancer types and patients is still a challenge problem. Here we propose Meta-GDBP, a novel anticancer drug-response model, which involves two levels. At the first level of Meta-GDBP, we build four optimized base models (BMs) using genetic information, chemical properties and biological context with an ensemble optimization strategy, while at the second level, we construct a weighted model to integrate the four BMs. Notably, the weights of the models are learned upstream, thus the parameter cost is significantly reduced compared to previous methods. We evaluate the Meta-GDBP on Genomics of Drug Sensitivity in Cancer (GDSC) and the Cancer Cell Line Encyclopedia (CCLE) data sets. Benchmarking results demonstrate that compared to other methods, the Meta-GDBP achieves a much higher correlation between the predicted and the observed responses for almost all the drugs. Moreover, we apply the Meta-GDBP to predict the GDSC-missing drug response and use the CCLE-known data to validate the performance. The results show quite a similar tendency between these two response sets. Particularly, we here for the first time introduce a biological context-based frequency matrix (BCFM) to associate the biological context with the drug response. It is encouraging that the proposed BCFM is biologically meaningful and consistent with the reported biological mechanism, further demonstrating its efficacy for predicting drug response. The R implementation for the proposed Meta-GDBP is available at https://github.com/RanSuLab/Meta-GDBP.'),
(69490, 2019, 'bib', 'Evaluation of drug efficacy based on the spatial position comparison of drug-target interaction centers', 'Ding, Yu|Wang, Hong|Zheng, Hewei|Wang, Lianzong|Zhang, Guosi|Yang, Jiaxin|Lu, Xiaoyan|Bai, Yu|Zhang, Haotian|Li, Jing|Gao, Wenyan|Chen, Fukun|Hu, Shui|Wu, Jingqi|Xu, Liangde', 'Harbin Medical University, Harbin, P. R. China.|College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, P. R. China.|Harbin Medical University, Harbin, P. R. China.|Harbin Medical University, Harbin, P. R. China.|Harbin Medical University, Harbin, P. R. China.|Harbin Medical University, Harbin, P. R. China.|Harbin Medical University, Harbin, P. R. China.|Harbin Medical University, Harbin, P. R. China.|Harbin Medical University, Harbin, P. R. China.|Harbin Medical University, Harbin, P. R. China.|Harbin Medical University, Harbin, P. R. China.|Harbin Medical University, Harbin, P. R. China.|Harbin Medical University, Harbin, P. R. China.|Harbin Medical University, Harbin, P. R. China.|School of Ophthalmology & Optometry and Eye Hospital, School of Biomedical Engineering, Wenzhou Medical University, Wenzhou.', 'China|China|China|China|China|China|China|China|China|China|China|China|China|China|-', 0, '-', 'Journal Article', '10.1093/bib/bbz024', '30868167', 'The spatial position and interaction of drugs and their targets is the most important characteristics for understanding a drugs pharmacological effect, and it could help both in finding new and more precise treatment targets for diseases and in exploring the targeting effects of the new drugs. In this work, we develop a computational pipeline to confirm the spatial interaction relationship of the drugs and their targets and compare the drugs efficacies based on the interaction centers. First, we produce a 100-sample set to reconstruct a stable docking model of the confirmed drug-target pairs. Second, we set 5.5 Å as the maximum distance threshold for the drug-amino acid residue atom interaction and construct 3-dimensional interaction surface models. Third, by calculating the spatial position of the 3-dimensional interaction surface center, we develop a comparison strategy for estimating the efficacy of different drug-target pairs. For the 1199 drug-target interactions of the 649 drugs and 355 targets, the drugs that have similar interaction center positions tend to have similar efficacies in disease treatment, especially in the analysis of the 37 targeted relationships between the 15 known anti-cancer drugs and 10 target molecules. Furthermore, the analysis of the unpaired anti-cancer drug and target molecules suggests that there is a potential application for discovering new drug actions using the sampling molecular docking and analyzing method. The comparison of the drug-target interaction center spatial position method better reflect the drug-target interaction situations and could support the discovery of new efficacies among the known anti-cancer drugs.'),
(69491, 2019, 'bib', 'Shaping the nebulous enhancer in the era of high-throughput assays and genome editing', 'Ho, Edwin Yu-Kiu|Cao, Qin|Gu, Mengting|Chan, Ricky Wai-Lun|Wu, Qiong|Gerstein, Mark|Yip, Kevin Y', 'Department of Biomedical Engineering.|Department of Computer Science and Engineering, The Chinese University of Hong Kong, Hong Kong.|Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, Connecticut, USA.|Department of Computer Science and Engineering, The Chinese University of Hong Kong, Hong Kong.|Department of Computer Science and Engineering, The Chinese University of Hong Kong, Hong Kong.|Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, Connecticut, USA.|Department of Biomedical Engineering.', 'United States|Hong Kong|United States|Hong Kong|Hong Kong|United States|United States', 0, '-', 'Journal Article', '10.1093/bib/bbz030', '30895290', 'Since the 1st discovery of transcriptional enhancers in 1981, their textbook definition has remained largely unchanged in the past 37 years. With the emergence of high-throughput assays and genome editing, which are switching the paradigm from bottom-up discovery and testing of individual enhancers to top-down profiling of enhancer activities genome-wide, it has become increasingly evidenced that this classical definition has left substantial gray areas in different aspects. Here we survey a representative set of recent research articles and report the definitions of enhancers they have adopted. The results reveal that a wide spectrum of definitions is used usually without the definition stated explicitly, which could lead to difficulties in data interpretation and downstream analyses. Based on these findings, we discuss the practical implications and suggestions for future studies.'),
(69492, 2019, 'bib', 'Comparing enrichment analysis and machine learning for identifying gene properties that discriminate between gene classes', 'Fabris, Fabio|Palmer, Daniel|de Magalhães, João Pedro|Freitas, Alex A', 'School of Computing, University of Kent, Kent, CT2 7NF, UK.|Integrative Genomics of Ageing Group, Institute of Ageing and Chronic Disease, University of Liverpool, Liverpool, UK.|Integrative Genomics of Ageing Group, Institute of Ageing and Chronic Disease, University of Liverpool, Liverpool, UK.|School of Computing, University of Kent, Kent, CT2 7NF, UK.', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom', 1, '-', 'Journal Article', '10.1093/bib/bbz028', '30895300', 'Biologists very often use enrichment methods based on statistical hypothesis tests to identify gene properties that are significantly over-represented in a given set of genes of interest, by comparison with a background set of genes. These enrichment methods, although based on rigorous statistical foundations, are not always the best single option to identify patterns in biological data. In many cases, one can also use classification algorithms from the machine-learning field. Unlike enrichment methods, classification algorithms are designed to maximize measures of predictive performance and are capable of analysing combinations of gene properties, instead of one property at a time. In practice, however, the majority of studies use either enrichment or classification methods (rather than both), and there is a lack of literature discussing the pros and cons of both types of method. The goal of this paper is to compare and contrast enrichment and classification methods, offering two contributions. First, we discuss the (to some extent complementary) advantages and disadvantages of both types of methods for identifying gene properties that discriminate between gene classes. Second, we provide a set of high-level recommendations for using enrichment and classification methods. Overall, by highlighting the strengths and the weaknesses of both types of methods we argue that both should be used in bioinformatics analyses.'),
(69493, 2019, 'bib', 'Platform-independent approach for cancer detection from gene expression profiles of peripheral blood cells', 'Yang, Yadong|Zhang, Tao|Xiao, Rudan|Hao, Xiaopeng|Zhang, Huiqiang|Qu, Hongzhu|Xie, Bingbing|Wang, Tao|Fang, Xiangdong', 'CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing, China.|BIG Data Center, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing, China.|CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing, China.|Breast Oncology Department, Affiliated Hospital, Academy of Military Medical Sciences, Beijing, China.|Breast Oncology Department, Affiliated Hospital, Academy of Military Medical Sciences, Beijing, China.|CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing, China.|CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing, China.|Breast Oncology Department, Affiliated Hospital, Academy of Military Medical Sciences, Beijing, China.|CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing, China.', 'China|China|China|China|China|China|China|China|China', 1, '-', 'Journal Article', '10.1093/bib/bbz027', '30895303', 'Peripheral blood gene expression intensity-based methods for distinguishing healthy individuals from cancer patients are limited by sensitivity to batch effects and data normalization and variability between expression profiling assays. To improve the robustness and precision of blood gene expression-based tumour detection, it is necessary to perform molecular diagnostic tests using a more stable approach. Taking breast cancer as an example, we propose a machine learning-based framework that distinguishes breast cancer patients from healthy subjects by pairwise rank transformation of gene expression intensity in each sample. We showed the diagnostic potential of the method by performing RNA-seq for 37 peripheral blood samples from breast cancer patients and by collecting RNA-seq data from healthy donors in Genotype-Tissue Expression project and microarray mRNA expression datasets in Gene Expression Omnibus. The framework was insensitive to experimental batch effects and data normalization, and it can be simultaneously applied to new sample prediction.'),
(69494, 2019, 'bib', 'Sensitivity and specificity of information criteria', 'Dziak, John J|Coffman, Donna L|Lanza, Stephanie T|Li, Runze|Jermiin, Lars S', 'Methodology Center at Penn State.|Department of Epidemiology and Biostatistics at Temple University.|Department of Biobehavioral Health and a principal investigator at the Methodology Center.|Department of Statistics and a principal investigator in the Methodology Center at Penn State.|Research School of Biology at the Australian National University and a visiting researcher at the Earth Institute and School of Biology and Environmental Science, University College Dublin.', '-|-|-|-|Australia', 15, '-', 'Journal Article', '10.1093/bib/bbz016', '30895308', 'Information criteria (ICs) based on penalized likelihood, such as Akaikes information criterion (AIC), the Bayesian information criterion (BIC) and sample-size-adjusted versions of them, are widely used for model selection in health and biological research. However, different criteria sometimes support different models, leading to discussions about which is the most trustworthy. Some researchers and fields of study habitually use one or the other, often without a clearly stated justification. They may not realize that the criteria may disagree. Others try to compare models using multiple criteria but encounter ambiguity when different criteria lead to substantively different answers, leading to questions about which criterion is best. In this paper we present an alternative perspective on these criteria that can help in interpreting their practical implications. Specifically, in some cases the comparison of two models using ICs can be viewed as equivalent to a likelihood ratio test, with the different criteria representing different alpha levels and BIC being a more conservative test than AIC. This perspective may lead to insights about how to interpret the ICs in more complex situations. For example, AIC or BIC could be preferable, depending on the relative importance one assigns to sensitivity versus specificity. Understanding the differences and similarities among the ICs can make it easier to compare their results and to use them to make informed decisions.'),
(69495, 2019, 'bib', 'Degrees of freedom analysis in educational research and decision-making: leveraging qualitative data to promote excellence in bioinformatics training and education', 'Tractenberg, Rochelle E', 'Georgetown University Medical Center, Building D, Suite, Reservoir Rd. NW, Washington, DC, USA.', 'United States', 4, 'Biomedical Research|Computational Biology|Curriculum|Data Mining|Decision Making|Evidence-Based Practice|Humans|Learning|Teaching', 'Journal Article', '10.1093/bib/bbx106', '30908585', 'Qualitative data are commonly collected in higher, graduate and postgraduate education, however, perhaps especially in the quantitative sciences, utilization of these qualitative data for decision-making can be challenging. A method for the analysis of qualitative data is the degrees of freedom analysis (DoFA), published in 1975. Given its origins in political science and its application in mainly business contexts, the DoFA method is unlikely to be discoverable or used to understand survey or other educational data obtained from teaching, training or evaluation. This article therefore introduces and demonstrates the DoFA with modifications specifically to support educational research and decision-making with examples in bioinformatics. DoFA identifies and aligns theoretical or applied principles with qualitative evidence. The demonstrations include two hypothetical examples, and a case study of the role of scaffolding in an independent project (capstone) of a graduate course in biostatistics. Included to promote inquiry, inquiry-based learning and the development of research skills, the capstone is often scaffolded (instructor-supported and therefore, formative), although it is intended to be summative. The case analysis addresses the question of whether the scaffolding provided for a capstone assignment affects its utility for formative or summative assessment. The DoFA is also used to evaluate the relative efficacies of other models for scaffolding the capstone project. These examples are intended to both explain this method and to demonstrate how it can be used to make decisions within a curriculum or for bioinformatics training.'),
(69496, 2019, 'bib', 'Integration of network models and evolutionary analysis into high-throughput modeling of protein dynamics and allosteric regulation: theory, tools and applications', 'Liang, Zhongjie|Verkhivker, Gennady M|Hu, Guang', 'School of Biology and Basic Medical Sciences, Soochow University, Suzhou, China.|Department of Computational and Data Sciences, Schmid College of Science and Technology, Chapman University, Orange, CA, USA.|School of Biology and Basic Medical Sciences, Soochow University, Suzhou, China.', 'China|United States|China', 7, '-', 'Journal Article', '10.1093/bib/bbz029', '30911759', 'Proteins are dynamical entities that undergo a plethora of conformational changes, accomplishing their biological functions. Molecular dynamics simulation and normal mode analysis methods have become the gold standard for studying protein dynamics, analyzing molecular mechanism and allosteric regulation of biological systems. The enormous amount of the ensemble-based experimental and computational data on protein structure and dynamics has presented a major challenge for the high-throughput modeling of protein regulation and molecular mechanisms. In parallel, bioinformatics and systems biology approaches including genomic analysis, coevolution and network-based modeling have provided an array of powerful tools that complemented and enriched biophysical insights by enabling high-throughput analysis of biological data and dissection of global molecular signatures underlying mechanisms of protein function and interactions in the cellular environment. These developments have provided a powerful interdisciplinary framework for quantifying the relationships between protein dynamics and allosteric regulation, allowing for high-throughput modeling and engineering of molecular mechanisms. Here, we review fundamental advances in protein dynamics, network theory and coevolutionary analysis that have provided foundation for rapidly growing computational tools for modeling of allosteric regulation. We discuss recent developments in these interdisciplinary areas bridging computational biophysics and network biology, focusing on promising applications in allosteric regulations, including the investigation of allosteric communication pathways, protein-DNA/RNA interactions and disease mutations in genomic medicine. We conclude by formulating and discussing future directions and potential challenges facing quantitative computational investigations of allosteric regulatory mechanisms in protein systems.'),
(69497, 2019, 'bib', 'Molecular Biology Information Service: an innovative medical library-based bioinformatics support service for biomedical researchers', 'Chattopadhyay, Ansuman|Iwema, Carrie L|Epstein, Barbara A|Lee, Adrian V|Levine, Arthur S', 'University of Pittsburgh, Health Sciences Library System.|University of Pittsburgh, Health Sciences Library System.|University of Pittsburgh, Health Sciences Library System.|University of Pittsburgh, Health Sciences Library System.|University of Pittsburgh, Health Sciences Library System.', '-|-|-|-|-', 0, '-', 'Journal Article', '10.1093/bib/bbz035', '30949666', 'Biomedical researchers are increasingly reliant on obtaining bioinformatics training in order to conduct their research. Here we present a model that academic institutions may follow to provide such training for their researchers, based on the Molecular Biology Information Service (MBIS) of the Health Sciences Library System, University of Pittsburgh (Pitt). The MBIS runs a four-facet service with the following goals: (1) identify, procure and implement commercially licensed bioinformatics software, (2) teach hands-on workshops using bioinformatics tools to solve research questions, (3) provide in-person and email consultations on software/databases and (4) maintain a web portal providing overall guidance on the access and use of bioinformatics resources and MBIS-created webtools. This paper describes these facets of MBIS activities from 2006 to 2018, including outcomes from a survey measuring attitudes of Pitt researchers about MBIS service and performance.'),
(69498, 2019, 'bib', 'Identifying psychiatric disorder-associated gut microbiota using microbiota-related gene set enrichment analysis', 'Cheng, Shiqiang|Han, Bei|Ding, Miao|Wen, Yan|Ma, Mei|Zhang, Lu|Qi, Xin|Cheng, Bolun|Li, Ping|Kafle, Om Prakash|Liang, Xiao|Liu, Li|Du, Yanan|Zhao, Yan|Zhang, Feng', 'School of Public Health, Health Science Center, Xian Jiaotong University, Xian, China.|School of Public Health, Health Science Center, Xian Jiaotong University, Xian, China.|School of Public Health, Health Science Center, Xian Jiaotong University, Xian, China.|School of Public Health, Health Science Center, Xian Jiaotong University, Xian, China.|School of Public Health, Health Science Center, Xian Jiaotong University, Xian, China.|School of Public Health, Health Science Center, Xian Jiaotong University, Xian, China.|School of Public Health, Health Science Center, Xian Jiaotong University, Xian, China.|School of Public Health, Health Science Center, Xian Jiaotong University, Xian, China.|School of Public Health, Health Science Center, Xian Jiaotong University, Xian, China.|School of Public Health, Health Science Center, Xian Jiaotong University, Xian, China.|School of Public Health, Health Science Center, Xian Jiaotong University, Xian, China.|School of Public Health, Health Science Center, Xian Jiaotong University, Xian, China.|School of Public Health, Health Science Center, Xian Jiaotong University, Xian, China.|School of Public Health, Health Science Center, Xian Jiaotong University, Xian, China.|School of Public Health, Health Science Center, Xian Jiaotong University, Xian, China.', 'China|China|China|China|China|China|China|China|China|China|China|China|China|China|China', 7, '-', 'Journal Article', '10.1093/bib/bbz034', '30953055', 'Psychiatric disorders are a group of complex psychological syndromes with high prevalence. It has been reported that gut microbiota has a dominant influence on the risks of psychiatric disorders through gut microbiota-brain axis. We extended the classic gene set enrichment analysis (GSEA) approach to detect the association between gut microbiota and complex diseases using published genome-wide association study (GWAS) and GWAS of gut microbiota summary data. We applied our approach to real GWAS data sets of five psychiatric disorders, including attention deficiency/hyperactive disorder (ADHD), autism spectrum disorder (AUT), bipolar disorder (BD), schizophrenia (SCZ) and major depressive disorder (MDD). To evaluate the performance of our approach, we also tested the genetic correlations of obesity and type 2 diabetes with gut microbiota. We identified several significant associations between psychiatric disorders and gut microbiota, such as ADHD and genus Desulfovibrio (Pxc2xa0=xe2x80x890.031), order Clostridiales (Pxc2xa0=xe2x80x890.034). For AUT, association signals were observed for genera Bacteroides (Pxc2xa0=xe2x80x890.012) and Desulfovibrio (Pxc2xa0=xe2x80x890.033). Genus Desulfovibrio (Pxc2xa0=xe2x80x890.005) appeared to be associated with BD. For MDD, association signals were observed for genus Desulfovibrio (Pxc2xa0=xe2x80x890.003), order Clostridiales (Pxc2xa0=xe2x80x890.004), family Lachnospiraceae (Pxc2xa0=xe2x80x890.007) and genus Bacteroides (Pxc2xa0=xe2x80x890.007). Genus Desulfovibrio (Pxc2xa0=xe2x80x890.012) and genus Bacteroides (Pxc2xa0=xe2x80x890.038) appeared to be associated with SCZ. Our study results provide novel clues for revealing the roles of gut microbiota in psychiatric disorders. This study also illustrated the good performance of GSEA approach for exploring the relationships between gut microbiota and complex diseases.'),
(69499, 2019, 'bib', 'MicroRNAs and nervous system diseases: network insights and computational challenges', 'Wang, Jianjian|Cao, Yuze|Lu, Xiaoyu|Wang, Tianfeng|Li, Shuang|Kong, Xiaotong|Bo, Chunrui|Li, Jie|Wang, Xiaolong|Ma, Heping|Li, Lei|Zhang, Huixue|Ning, Shangwei|Wang, Lihua', 'Department of Neurology, The Second Affiliated Hospital of Harbin Medical University, Harbin, China.|Department of Neurology, The Second Affiliated Hospital of Harbin Medical University, Harbin, China.|Department of Neurology, The Second Affiliated Hospital of Harbin Medical University, Harbin, China.|Department of Neurology, The Second Affiliated Hospital of Harbin Medical University, Harbin, China.|Department of Neurology, The Second Affiliated Hospital of Harbin Medical University, Harbin, China.|Department of Neurology, The Second Affiliated Hospital of Harbin Medical University, Harbin, China.|Department of Neurology, The Second Affiliated Hospital of Harbin Medical University, Harbin, China.|Department of Neurology, The Second Affiliated Hospital of Harbin Medical University, Harbin, China.|Department of Orthopedics, Harbin Medical University Cancer Hospital, Harbin, China.|Department of Physiology, Emory University School of Medicine, Atlanta, GA, USA.|Department of Neurology, The Second Affiliated Hospital of Harbin Medical University, Harbin, China.|Department of Neurology, The Second Affiliated Hospital of Harbin Medical University, Harbin, China.|College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China.|Department of Neurology, The Second Affiliated Hospital of Harbin Medical University, Harbin, China.', 'China|China|China|China|China|China|China|China|China|United States|China|China|China|China', 0, '-', 'Journal Article', '10.1093/bib/bbz032', '30953059', 'The nervous system is one of the most complex biological systems, and nervous system disease (NSD) is a major cause of disability and mortality. Extensive evidence indicates that numerous dysregulated microRNAs (miRNAs) are involved in a broad spectrum of NSDs. A comprehensive review of miRNA-mediated regulatory will facilitate our understanding of miRNA dysregulation mechanisms in NSDs. In this work, we summarized currently available databases on miRNAs and NSDs, star NSD miRNAs, NSD spectrum width, miRNA spectrum width and the distribution of miRNAs in NSD sub-categories by reviewing approximately 1000 studies. In addition, we characterized miRNA-miRNA and NSD-NSD interactions from a network perspective based on miRNA-NSD benchmarking data sets. Furthermore, we summarized the regulatory principles of miRNAs in NSDs, including miRNA synergistic regulation in NSDs, miRNA modules and NSD modules. We also discussed computational challenges for identifying novel miRNAs in NSDs. Elucidating the roles of miRNAs in NSDs from a network perspective would not only improve our understanding of the precise mechanism underlying these complex diseases, but also provide novel insight into the development, diagnosis and treatment of NSDs.'),
(69500, 2019, 'bib', 'A feature-based approach to predict hot spots in protein-DNA binding interfaces', 'Zhang, Sijia|Zhao, Le|Zheng, Chun-Hou|Xia, Junfeng', 'Institutes of Physical Science and Information Technology, School of Computer Science and Technology, Anhui University, Hefei, Anhui, China.|Institutes of Physical Science and Information Technology, School of Computer Science and Technology, Anhui University, Hefei, Anhui, China.|Institutes of Physical Science and Information Technology, School of Computer Science and Technology, Anhui University, Hefei, Anhui, China.|Institutes of Physical Science and Information Technology, School of Computer Science and Technology, Anhui University, Hefei, Anhui, China.', 'China|China|China|China', 2, '-', 'Journal Article', '10.1093/bib/bbz037', '30957840', 'DNA-binding hot spot residues of proteins are dominant and fundamental interface residues that contribute most of the binding free energy of protein-DNA interfaces. As experimental methods for identifying hot spots are expensive and time consuming, computational approaches are urgently required in predicting hot spots on a large scale. In this work, we systematically assessed a wide variety of 114 features from a combination of the protein sequence, structure, network and solvent accessible information and their combinations along with various feature selection strategies for hot spot prediction. We then trained and compared four commonly used machine learning models, namely, support vector machine (SVM), random forest, Naïve Bayes and k-nearest neighbor, for the identification of hot spots using 10-fold cross-validation and the independent test set. Our results show that (1) features based on the solvent accessible surface area have significant effect on hot spot prediction, (2) different but complementary features generally enhance the prediction performance, and (3) SVM outperforms other machine learning methods on both training and independent test sets. In an effort to improve predictive performance, we developed a feature-based method, namely, PrPDH (Prediction of Protein-DNA binding Hot spots), for the prediction of hot spots in protein-DNA binding interfaces using SVM based on the selected 10 optimal features. Comparative results on benchmark data sets indicate that our predictor is able to achieve generally better performance in predicting hot spots compared to the state-of-the-art predictors. A user-friendly web server for PrPDH is well established and is freely available at http://bioinfo.ahu.edu.cn:8080/PrPDH.'),
(69501, 2019, 'bib', '100 Years of evolving gene-disease complexities and scientific debutants', 'Zeeshan, Saman|Xiong, Ruoyun|Liang, Bruce T|Ahmed, Zeeshan', 'The Jackson Laboratory for Genomic Medicine, 10 Discovery Drive, Farmington, CT, USA.|Department of Genetics and Genome Sciences, School of Medicine, University of Connecticut Health Center, Farmington Ave, Farmington, CT, USA.|Department of Genetics and Genome Sciences, School of Medicine, University of Connecticut Health Center, Farmington Ave, Farmington, CT, USA.|Department of Genetics and Genome Sciences, School of Medicine, University of Connecticut Health Center, Farmington Ave, Farmington, CT, USA.', 'United States|United States|United States|United States', 0, '-', 'Journal Article', '10.1093/bib/bbz038', '30972412', 'Its been over 100 years since the word `gene is around and progressively evolving in several scientific directions. Time-to-time technological advancements have heavily revolutionized the field of genomics, especially when its about, e.g. triple code development, gene number proposition, genetic mapping, data banks, gene-disease maps, catalogs of human genes and genetic disorders, CRISPR/Cas9, big data and next generation sequencing, etc. In this manuscript, we present the progress of genomics from pea plant genetics to the human genome project and highlight the molecular, technical and computational developments. Studying genome and epigenome led to the fundamentals of development and progression of human diseases, which includes chromosomal, monogenic, multifactorial and mitochondrial diseases. World Health Organization has classified, standardized and maintained all human diseases, when many academic and commercial online systems are sharing information about genes and linking to associated diseases. To efficiently fathom the wealth of this biological data, there is a crucial need to generate appropriate gene annotation repositories and resources. Our focus has been how many gene-disease databases are available worldwide and which sources are authentic, timely updated and recommended for research and clinical purposes. In this manuscript, we have discussed and compared 43 such databases and bioinformatics applications, which enable users to connect, explore and, if possible, download gene-disease data.'),
(69502, 2019, 'bib', 'iLearn: an integrated platform and meta-learner for feature engineering, machine-learning analysis and modeling of DNA, RNA and protein sequence data', 'Chen, Zhen|Zhao, Pei|Li, Fuyi|Marquez-Lago, Tatiana T|Leier, André|Revote, Jerico|Zhu, Yan|Powell, David R|Akutsu, Tatsuya|Webb, Geoffrey I|Chou, Kuo-Chen|Smith, A Ian|Daly, Roger J|Li, Jian|Song, Jiangning', 'School of Basic Medical Science, Qingdao University, Dengzhou Road, Qingdao, Shandong, China.|State Key Laboratory of Cotton Biology, Institute of Cotton Research of Chinese Academy of Agricultural Sciences (CAAS), Anyang, China.|Biomedicine Discovery Institute and Department of Biochemistry and Molecular Biology, Monash University, Melbourne, VIC, Australia.|Department of Genetics, School of Medicine, University of Alabama at Birmingham, USA.|Department of Genetics, School of Medicine, University of Alabama at Birmingham, USA.|Biomedicine Discovery Institute and Department of Biochemistry and Molecular Biology, Monash University, Melbourne, VIC, Australia.|Biomedicine Discovery Institute and Department of Microbiology, Monash University, Melbourne, VIC, Australia.|Biomedicine Discovery Institute and Department of Microbiology, Monash University, Melbourne, VIC, Australia.|Bioinformatics Center, Institute for Chemical Research, Kyoto University, Kyoto, Japan.|Monash Centre for Data Science, Faculty of Information Technology, Monash University, Melbourne, VIC, Australia.|Gordon Life Science Institute, Boston, MA, USA.|Biomedicine Discovery Institute and Department of Biochemistry and Molecular Biology, Monash University, Melbourne, VIC, Australia.|Biomedicine Discovery Institute and Department of Biochemistry and Molecular Biology, Monash University, Melbourne, VIC, Australia.|Biomedicine Discovery Institute and Department of Microbiology, Monash University, Melbourne, VIC, Australia.|Biomedicine Discovery Institute and Department of Biochemistry and Molecular Biology, Monash University, Melbourne, VIC, Australia.', 'China|China|Australia|United States|United States|Australia|Australia|Australia|Japan|Australia|United States|Australia|Australia|Austra', 0, '-', 'Journal Article', '10.1093/bib/bbz041', '31067315', 'With the explosive growth of biological sequences generated in the post-genomic era, one of the most challenging problems in bioinformatics and computational biology is to computationally characterize sequences, structures and functions in an efficient, accurate and high-throughput manner. A number of online web servers and stand-alone tools have been developed to address this to date, however, all these tools have their limitations and drawbacks in terms of their effectiveness, user-friendliness and capacity. Here, we present iLearn, a comprehensive and versatile Python-based toolkit, integrating the functionality of feature extraction, clustering, normalization, selection, dimensionality reduction, predictor construction, best descriptor/model selection, ensemble learning and results visualization for DNA, RNA and protein sequences. iLearn was designed for users that only want to upload their data set and select the functions they need calculated from it, while all necessary procedures and optimal settings are completed automatically by the software. iLearn includes a variety of descriptors for DNA, RNA and proteins, and four feature output formats are supported so as to facilitate direct output usage or communication with other computational tools. In total, iLearn encompasses 16 different types of feature clustering, selection, normalization and dimensionality reduction algorithms, and five commonly used machine-learning algorithms, thereby greatly facilitating feature analysis and predictor construction. iLearn is made freely available via an online web server and a stand-alone toolkit.'),
(69503, 2019, 'bib', 'Transcription factors-DNA interactions in rice: identification and verification', 'Shen, Zijie|Lin, Yuan|Zou, Quan', 'Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu, China.|Department of System Integration, Sparebanken Vest, Bergen, Norway.|Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu, China.', 'China|Norway|China', 0, '-', 'Journal Article', '10.1093/bib/bbz045', '31091308', 'The completion of the rice genome sequence paved the way for rice functional genomics research. Additionally, the functional characterization of transcription factors is currently a popular and crucial objective among researchers. Transcription factors are one of the groups of proteins that bind to either enhancer or promoter regions of genes to regulate expression. On the basis of several typical examples of transcription factor analyses, we herein summarize selected research strategies and methods and introduce their advantages and disadvantages. This review may provide some theoretical and technical guidelines for future investigations of transcription factors, which may be helpful to develop new rice varieties with ideal traits.'),
(69504, 2019, 'bib', 'Bioinformatics: scalability, capabilities and training in the data-driven era', 'Schneider, Maria Victoria', 'University of Melbourne, Australia.', 'Australia', 1, '-', 'Editorial', '10.1093/bib/bbz053', '31100108', '-'),
(69505, 2019, 'bib', 'In silico signaling modeling to understand cancer pathways and treatment responses', 'Kunz, Meik|Jeromin, Julian|Fuchs, Maximilian|Christoph, Jan|Veronesi, Giulia|Flentje, Michael|Nietzer, Sarah|Dandekar, Gudrun|Dandekar, Thomas', 'Chair of Medical Informatics, Friedrich-Alexander University of Erlangen-Nürnberg, Erlangen, Germany.|Functional Genomics and Systems Biology Group, Department of Bioinformatics, University of Würzburg, Würzburg, Germany.|Functional Genomics and Systems Biology Group, Department of Bioinformatics, University of Würzburg, Würzburg, Germany.|Chair of Medical Informatics, Friedrich-Alexander University of Erlangen-Nürnberg, Erlangen, Germany.|Humanitas Research Hospital, Rozzano (Milan), Italy.|Department of Radiation Oncology, University Hospital of Würzburg, Würzburg, Germany.|Chair of Tissue Engineering and Regenerative Medicine, University Hospital Wuerzburg, Roentgenring, Wuerzburg.|Chair of Tissue Engineering and Regenerative Medicine, University Hospital Wuerzburg, Roentgenring, Wuerzburg.|Functional Genomics and Systems Biology Group, Department of Bioinformatics, University of Würzburg, Würzburg, Germany.', 'Germany|Germany|Germany|Germany|Italy|Germany|-|-|Germany', 0, '-', 'Journal Article', '10.1093/bib/bbz033', '31117120', 'Precision medicine has changed thinking in cancer therapy, highlighting a better understanding of the individual clinical interventions. But what role do the drivers and pathways identified from pan-cancer genome analysis play in the tumor? In this letter, we will highlight the importance of in silico modeling in precision medicine. In the current era of big data, tumor engines and pathways derived from pan-cancer analysis should be integrated into in silico models to understand the mutational tumor status and individual molecular pathway mechanism at a deeper level. This allows to pre-evaluate the potential therapy response and develop optimal patient-tailored treatment strategies which pave the way to support precision medicine in the clinic of the future.'),
(69506, 2019, 'bib', 'Graph convolutional networks for computational drug development and discovery', 'Sun, Mengying|Zhao, Sendong|Gilvary, Coryandar|Elemento, Olivier|Zhou, Jiayu|Wang, Fei', 'Department of Computer Science and Engineering, Michigan State University, East Lansing, MI USA.|Department of Healthcare Policy and Research, Weill Cornell Medicine, New York, NY, USA.|Institute for Computational Biomedicine and the Tri-I Program in Computational Biology & Medicine at Weill Cornell Medicine at Cornell University, New York, NY, USA.|Department of Physiology and Biophysics, Weill Cornell Medicine, Weill Cornell Medicine, New York, NY, USA.|Department of Computer Science and Engineering, Michigan State University, East Lansing, MI USA.|Department of Healthcare Policy and Research, Weill Cornell Medicine, New York, NY, USA.', 'United States|United States|United States|United States|United States|United States', 0, '-', 'Journal Article', '10.1093/bib/bbz042', '31155636', 'Despite the fact that deep learning has achieved remarkable success in various domains over the past decade, its application in molecular informatics and drug discovery is still limited. Recent advances in adapting deep architectures to structured data have opened a new paradigm for pharmaceutical research. In this survey, we provide a systematic review on the emerging field of graph convolutional networks and their applications in drug discovery and molecular informatics. Typically we are interested in why and how graph convolution networks can help in drug-related tasks. We elaborate the existing applications through four perspectives: molecular property and activity prediction, interaction prediction, synthesis prediction and de novo drug design. We briefly introduce the theoretical foundations behind graph convolutional networks and illustrate various architectures based on different formulations. Then we summarize the representative applications in drug-related problems. We also discuss the current challenges and future possibilities of applying graph convolutional networks to drug discovery.'),
(69507, 2019, 'bib', 'Characterization and identification of antimicrobial peptides with different functional activities', 'Chung, Chia-Ru|Kuo, Ting-Rung|Wu, Li-Ching|Lee, Tzong-Yi|Horng, Jorng-Tzong', 'Department of Computer Science and Information Engineering, National Central University, Taoyuan, Taiwan.|Department of Computer Science and Information Engineering, National Central University, Taoyuan, Taiwan.|Department of Biomedical Sciences and Engineering, National Central University, Taoyuan, Taiwan.|School of Science and Engineering, The Chinese University of Hong Kong, Shenzhen, China.|Department of Computer Science and Information Engineering, National Central University, Taoyuan, Taiwan.', '-|-|-|China|-', 1, '-', 'Journal Article', '10.1093/bib/bbz043', '31155657', 'In recent years, antimicrobial peptides (AMPs) have become an emerging area of focus when developing therapeutics hot spot residues of proteins are dominant against infections. Importantly, AMPs are produced by virtually all known living organisms and are able to target a wide range of pathogenic microorganisms, including viruses, parasites, bacteria and fungi. Although several studies have proposed different machine learning methods to predict peptides as being AMPs, most do not consider the diversity of AMP activities. On this basis, we specifically investigated the sequence features of AMPs with a range of functional activities, including anti-parasitic, anti-viral, anti-cancer and anti-fungal activities and those that target mammals, Gram-positive and Gram-negative bacteria. A new scheme is proposed to systematically characterize and identify AMPs and their functional activities. The 1st stage of the proposed approach is to identify the AMPs, while the 2nd involves further characterization of their functional activities. Sequential forward selection was employed to extract potentially informative features that are possibly associated with the functional activities of the AMPs. These features include hydrophobicity, the normalized van der Waals volume, polarity, charge and solvent accessibility-all of which are essential attributes in classifying between AMPs and non-AMPs. The results revealed the 1st stage AMP classifier was able to achieve an area under the receiver operating characteristic curve (AUC) value of 0.9894. During the 2nd stage, we found pseudo amino acid composition to be an informative attribute when differentiating between AMPs in terms of their functional activities. The independent testing results demonstrated that the AUCs of the multi-class models were 0.7773, 0.9404, 0.8231, 0.8578, 0.8648, 0.8745 and 0.8672 for anti-parasitic, anti-viral, anti-cancer, anti-fungal AMPs and those that target mammals, Gram-positive and Gram-negative bacteria, respectively. The proposed scheme helps facilitate biological experiments related to the functional analysis of AMPs. Additionally, it was implemented as a user-friendly web server (AMPfun, http://fdblab.csie.ncu.edu.tw/AMPfun/index.html) that allows individuals to explore the antimicrobial functions of peptides of interest.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(69508, 2019, 'bib', 'A comprehensive overview of oncogenic pathways in human cancer', 'Li, Feng|Wu, Tan|Xu, Yanjun|Dong, Qun|Xiao, Jing|Xu, Yingqi|Li, Qian|Zhang, Chunlong|Gao, Jianxia|Liu, Liqiu|Hu, Xiaoxu|Huang, Jian|Li, Xia|Zhang, Yunpeng', 'College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China.|College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China.|College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China.|College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China.|College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China.|College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China.|College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China.|College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China.|College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China.|College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China.|College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China.|Center for Informational Biology, University of Electronic Science and Technology of China.|College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China.|College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China.', 'China|China|China|China|China|China|China|China|China|China|China|China|China|China', 1, '-', 'Journal Article', '10.1093/bib/bbz046', '31155677', 'Alterations of biological pathways can lead to oncogenesis. An overview of these oncogenic pathways would be highly valuable for researchers to reveal the pathogenic mechanism and develop novel therapeutic approaches for cancers. Here, we reviewed approximately 8500 literatures and documented experimentally validated cancer-pathway associations as benchmarking data set. This data resource includes 4709 manually curated relationships between 1557 paths and 49 cancers with 2427 upstream regulators in 7 species. Based on this resource, we first summarized the cancer-pathway associations and revealed some commonly deregulated pathways across tumor types. Then, we systematically analyzed these oncogenic pathways by integrating TCGA pan-cancer data sets. Multi-omics analysis showed oncogenic pathways may play different roles across tumor types under different omics contexts. We also charted the survival relevance landscape of oncogenic pathways in 26 tumor types, identified dominant omics features and found survival relevance for oncogenic pathways varied in tumor types and omics levels. Moreover, we predicted upstream regulators and constructed a hierarchical network model to understand the pathogenic mechanism of human cancers underlying oncogenic pathway context. Finally, we developed `CPAD (freely available at http://bio-bigdata.hrbmu.edu.cn/CPAD/), an online resource for exploring oncogenic pathways in human cancers, that integrated manually curated cancer-pathway associations, TCGA pan-cancer multi-omics data sets, drug-target data, drug sensitivity and multi-omics data for cancer cell lines. In summary, our study provides a comprehensive characterization of oncogenic pathways and also presents a valuable resource for investigating the pathogenesis of human cancer.'),
(69509, 2019, 'bib', 'Consistent gene signature of schizophrenia identified by a novel feature selection strategy from comprehensive sets of transcriptomic data', 'Yang, Qingxia|Li, Bo|Tang, Jing|Cui, Xuejiao|Wang, Yunxia|Li, Xiaofeng|Hu, Jie|Chen, Yuzong|Xue, Weiwei|Lou, Yan|Qiu, Yunqing|Zhu, Feng', 'Innovative Drug Research and Bioinformatics Group, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, China.|Innovative Drug Research and Bioinformatics Group, School of Pharmaceutical Sciences and Collaborative Innovation Center for Brain Science, Chongqing University, Chongqing, China.|Innovative Drug Research and Bioinformatics Group, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, China.|Innovative Drug Research and Bioinformatics Group, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, China.|Innovative Drug Research and Bioinformatics Group, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, China.|Innovative Drug Research and Bioinformatics Group, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, China.|Innovative Drug Research and Bioinformatics Group, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, China.|Bioinformatics and Drug Design Group, Department of Pharmacy, and Center for Computational Science and Engineering, National University of Singapore, Singapore, Singapore.|Innovative Drug Research and Bioinformatics Group, School of Pharmaceutical Sciences and Collaborative Innovation Center for Brain Science, Chongqing University, Chongqing, China.|Zhejiang Provincial Key Laboratory for Drug Clinical Research and Evaluation, The First Affiliated Hospital, Zhejiang University, Hangzhou, Zhejiang, China.|Zhejiang Provincial Key Laboratory for Drug Clinical Research and Evaluation, The First Affiliated Hospital, Zhejiang University, Hangzhou, Zhejiang, China.|Innovative Drug Research and Bioinformatics Group, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, China.', 'China|China|China|China|China|China|China|Singapore|China|China|China|China', 14, '-', 'Journal Article', '10.1093/bib/bbz049', '31157371', 'The etiology of schizophrenia (SCZ) is regarded as one of the most fundamental puzzles in current medical research, and its diagnosis is limited by the lack of objective molecular criteria. Although plenty of studies were conducted, SCZ gene signatures identified by these independent studies are found highly inconsistent. As one of the most important factors contributing to this inconsistency, the feature selection methods used currently do not fully consider the reproducibility among the signatures discovered from different datasets. Therefore, it is crucial to develop new bioinformatics tools of novel strategy for ensuring a stable discovery of gene signature for SCZ. In this study, a novel feature selection strategy (1) integrating repeated random sampling with consensus scoring and (2) evaluating the consistency of gene rank among different datasets was constructed. By systematically assessing the identified SCZ signature comprising 135 differentially expressed genes, this newly constructed strategy demonstrated significantly enhanced stability and better differentiating ability compared with the feature selection methods popular in current SCZ research. Based on a first-ever assessment on methods reproducibility cross-validated by independent datasets from three representative studies, the new strategy stood out among the popular methods by showing superior stability and differentiating ability. Finally, 2 novel and 17 previously reported transcription factors were identified and showed great potential in revealing the etiology of SCZ. In sum, the SCZ signature identified in this study would provide valuable clues for discovering diagnostic molecules and potential targets for SCZ.'),
(69510, 2019, 'bib', 'MENDA: a comprehensive curated resource of metabolic characterization in depression', 'Pu, Juncai|Yu, Yue|Liu, Yiyun|Tian, Lu|Gui, Siwen|Zhong, Xiaogang|Fan, Chu|Xu, Shaohua|Song, Xuemian|Liu, Lanxiang|Yang, Lining|Zheng, Peng|Chen, Jianjun|Cheng, Ke|Zhou, Chanjuan|Wang, Haiyang|Xie, Peng', 'Department of Neurology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China.|College of Medical Informatics, Chongqing Medical University, Chongqing, China.|Department of Neurology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China.|Institute of Neuroscience and the Collaborative Innovation Center for Brain Science, Chongqing Medical University, Chongqing, China.|Institute of Neuroscience and the Collaborative Innovation Center for Brain Science, Chongqing Medical University, Chongqing, China.|Institute of Neuroscience and the Collaborative Innovation Center for Brain Science, Chongqing Medical University, Chongqing, China.|College of Medical Informatics, Chongqing Medical University, Chongqing, China.|Institute of Neuroscience and the Collaborative Innovation Center for Brain Science, Chongqing Medical University, Chongqing, China.|Institute of Neuroscience and the Collaborative Innovation Center for Brain Science, Chongqing Medical University, Chongqing, China.|Department of Neurology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China.|Department of Neurology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China.|Department of Neurology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China.|Institute of Neuroscience and the Collaborative Innovation Center for Brain Science, Chongqing Medical University, Chongqing, China.|Institute of Neuroscience and the Collaborative Innovation Center for Brain Science, Chongqing Medical University, Chongqing, China.|Institute of Neuroscience and the Collaborative Innovation Center for Brain Science, Chongqing Medical University, Chongqing, China.|Institute of Neuroscience and the Collaborative Innovation Center for Brain Science, Chongqing Medical University, Chongqing, China.|Department of Neurology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China.', 'China|China|China|China|China|China|China|China|China|China|China|China|China|China|China|China|Chin', 0, '-', 'Journal Article', '10.1093/bib/bbz055', '31157825', 'Depression is a seriously disabling psychiatric disorder with a significant burden of disease. Metabolic abnormalities have been widely reported in depressed patients and animal models. However, there are few systematic efforts that integrate meaningful biological insights from these studies. Herein, available metabolic knowledge in the context of depression was integrated to provide a systematic and panoramic view of metabolic characterization. After screening more than 10 000 citations from five electronic literature databases and five metabolomics databases, we manually curated 5675 metabolite entries from 464 studies, including human, rat, mouse and non-human primate, to develop a new metabolite-disease association database, called MENDA (http://menda.cqmu.edu.cn:8080/index.php). The standardized data extraction process was used for data collection, a multi-faceted annotation scheme was developed, and a user-friendly search engine and web interface were integrated for database access. To facilitate data analysis and interpretation based on MENDA, we also proposed a systematic analytical framework, including data integration and biological function analysis. Case studies were provided that identified the consistently altered metabolites using the vote-counting method, and that captured the underlying molecular mechanism using pathway and network analyses. Collectively, we provided a comprehensive curation of metabolic characterization in depression. Our model of a specific psychiatry disorder may be replicated to study other complex diseases.'),
(69511, 2019, 'bib', 'Evaluation of different computational methods on 5-methylcytosine sites identification', 'Lv, Hao|Zhang, Zi-Mei|Li, Shi-Hao|Tan, Jiu-Xin|Chen, Wei|Lin, Hao', 'Key Laboratory for NeuroInformation of Ministry of Education, School of Life Science and Technology and Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, China.|Key Laboratory for NeuroInformation of Ministry of Education, School of Life Science and Technology and Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, China.|Key Laboratory for NeuroInformation of Ministry of Education, School of Life Science and Technology and Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, China.|Key Laboratory for NeuroInformation of Ministry of Education, School of Life Science and Technology and Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, China.|Key Laboratory for NeuroInformation of Ministry of Education, School of Life Science and Technology and Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, China.|Key Laboratory for NeuroInformation of Ministry of Education, School of Life Science and Technology and Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, China.', 'China|China|China|China|China|China', 25, '-', 'Journal Article', '10.1093/bib/bbz048', '31157855', '5-Methylcytosine (m5C) plays an extremely important role in the basic biochemical process. With the great increase of identified m5C sites in a wide variety of organisms, their epigenetic roles become largely unknown. Hence, accurate identification of m5C site is a key step in understanding its biological functions. Over the past several years, more attentions have been paid on the identification of m5C sites in multiple species. In this work, we firstly summarized the current progresses in computational prediction of m5C sites and then constructed a more powerful and reliable model for identifying m5C sites. To train the model, we collected experimentally confirmed m5C data from Homo sapiens, Mus musculus, Saccharomyces cerevisiae and Arabidopsis thaliana, and compared the performances of different feature extraction methods and classification algorithms for optimizing prediction model. Based on the optimal model, a novel predictor called iRNA-m5C was developed for the recognition of m5C sites. Finally, we critically evaluated the performance of iRNA-m5C and compared it with existing methods. The result showed that iRNA-m5C could produce the best prediction performance. We hope that this paper could provide a guide on the computational identification of m5C site and also anticipate that the proposed iRNA-m5C will become a powerful tool for large scale identification of m5C sites.'),
(69512, 2019, 'bib', 'Comparison and integration of computational methods for deleterious synonymous mutation prediction', 'Cheng, Na|Li, Menglu|Zhao, Le|Zhang, Bo|Yang, Yuhua|Zheng, Chun-Hou|Xia, Junfeng', 'Institutes of Physical Science and Information Technology, Anhui University.|School of Computer Science and Technology, Anhui University.|School of Computer Science and Technology, Anhui University.|School of Computer Science and Technology, Anhui University.|School of Computer Science and Technology, Anhui University.|School of Computer Science and Technology, Anhui University.|Institutes of Physical Science and Information Technology, Anhui University.', '-|-|-|-|-|-|-', 3, '-', 'Journal Article', '10.1093/bib/bbz047', '31157880', 'Synonymous mutations do not change the encoded amino acids but may alter the structure or function of an mRNA in ways that impact gene function. Advances in next generation sequencing technologies have detected numerous synonymous mutations in the human genome. Several computational models have been proposed to predict deleterious synonymous mutations, which have greatly facilitated the development of this important field. Consequently, there is an urgent need to assess the state-of-the-art computational methods for deleterious synonymous mutation prediction to further advance the existing methodologies and to improve performance. In this regard, we systematically compared a total of 10 computational methods (including specific method for deleterious synonymous mutation and general method for single nucleotide mutation) in terms of the algorithms used, calculated features, performance evaluation and software usability. In addition, we constructed two carefully curated independent test datasets and accordingly assessed the robustness and scalability of these different computational methods for the identification of deleterious synonymous mutations. In an effort to improve predictive performance, we established an ensemble model, named Prediction of Deleterious Synonymous Mutation (PrDSM), which averages the ratings generated by the three most accurate predictors. Our benchmark tests demonstrated that the ensemble model PrDSM outperformed the reviewed tools for the prediction of deleterious synonymous mutations. Using the ensemble model, we developed an accessible online predictor, PrDSM, available at http://bioinfo.ahu.edu.cn:8080/PrDSM/. We hope that this comprehensive survey and the proposed strategy for building more accurate models can serve as a useful guide for inspiring future developments of computational methods for deleterious synonymous mutation prediction.'),
(69513, 2019, 'bib', 'PRISMOID: a comprehensive 3D structure database for post-translational modifications and mutations with functional impact', 'Li, Fuyi|Fan, Cunshuo|Marquez-Lago, Tatiana T|Leier, André|Revote, Jerico|Jia, Cangzhi|Zhu, Yan|Smith, A Ian|Webb, Geoffrey I|Liu, Quanzhong|Wei, Leyi|Li, Jian|Song, Jiangning', 'Biomedicine Discovery Institute and Department of Biochemistry and Molecular Biology, Monash University, Melbourne, Victoria, Australia.|College of Information Engineering, Northwest A&F University, Yangling, China.|Department of Genetics and Department of Cell, Developmental and Integrative Biology, School of Medicine, University of Alabama at Birmingham, AL, USA.|Department of Genetics and Department of Cell, Developmental and Integrative Biology, School of Medicine, University of Alabama at Birmingham, AL, USA.|Biomedicine Discovery Institute and Department of Biochemistry and Molecular Biology, Monash University, Melbourne, Victoria, Australia.|College of Science, Dalian Maritime University, Dalian, China.|Biomedicine Discovery Institute and Department of Microbiology, Monash University, Melbourne, Victoria, Australia.|Biomedicine Discovery Institute and Department of Biochemistry and Molecular Biology, Monash University, Melbourne, Victoria, Australia.|Monash Centre for Data Science, Faculty of Information Technology, Monash University, Melbourne, VIC, Australia.|College of Information Engineering, Northwest A&F University, Yangling, China.|School of Computer Science and Technology, College of Intelligence and Computing, Tianjin University, Tianjin, China.|Biomedicine Discovery Institute and Department of Biochemistry and Molecular Biology, Monash University, Melbourne, Victoria, Australia.|Biomedicine Discovery Institute and Department of Biochemistry and Molecular Biology, Monash University, Melbourne, Victoria, Australia.', 'Australia|China|United States|United States|Australia|China|Australia|Australia|Australia|China|China|Australia|Australia', 0, '-', 'Journal Article', '10.1093/bib/bbz050', '31161204', 'Post-translational modifications (PTMs) play very important roles in various cell signaling pathways and biological process. Due to PTMs extremely important roles, many major PTMs have been studied, while the functional and mechanical characterization of major PTMs is well documented in several databases. However, most currently available databases mainly focus on protein sequences, while the real 3D structures of PTMs have been largely ignored. Therefore, studies of PTMs 3D structural signatures have been severely limited by the deficiency of the data. Here, we develop PRISMOID, a novel publicly available and free 3D structure database for a wide range of PTMs. PRISMOID represents an up-to-date and interactive online knowledge base with specific focus on 3D structural contexts of PTMs sites and mutations that occur on PTMs and in the close proximity of PTM sites with functional impact. The first version of PRISMOID encompasses 17 145 non-redundant modification sites on 3919 related protein 3D structure entries pertaining to 37 different types of PTMs. Our entry web page is organized in a comprehensive manner, including detailed PTM annotation on the 3D structure and biological information in terms of mutations affecting PTMs, secondary structure features and per-residue solvent accessibility features of PTM sites, domain context, predicted natively disordered regions and sequence alignments. In addition, high-definition JavaScript packages are employed to enhance information visualization in PRISMOID. PRISMOID equips a variety of interactive and customizable search options and data browsing functions, these capabilities allow users to access data via keyword, ID and advanced options combination search in an efficient and user-friendly way. A download page is also provided to enable users to download the SQL file, computational structural features and PTM sites data. We anticipate PRISMOID will swiftly become an invaluable online resource, assisting both biologists and bioinformaticians to conduct experiments and develop applications supporting discovery efforts in the sequence-structural-functional relationship of PTMs and providing important insight into mutations and PTM sites interaction mechanisms. The PRISMOID database is freely accessible at http://prismoid.erc.monash.edu/. The database and web interface are implemented in MySQL, JSP, JavaScript and HTML with all major browsers supported.'),
(69514, 2019, 'bib', 'Sepsis in the era of data-driven medicine: personalizing risks, diagnoses, treatments and prognoses', 'Liu, Andrew C|Patel, Krishna|Vunikili, Ramya Dhatri|Johnson, Kipp W|Abdu, Fahad|Belman, Shivani Kamath|Glicksberg, Benjamin S|Tandale, Pratyush|Fontanez, Roberto|Mathew, Oommen K|Kasarskis, Andrew|Mukherjee, Priyabrata|Subramanian, Lakshminarayanan|Dudley, Joel T|Shameer, Khader', 'Department of Information Services, Northwell Health, New Hyde Park, NY, USA.|Department of Information Services, Northwell Health, New Hyde Park, NY, USA.|Center for Research Informatics and Innovation, Northwell Health, New Hyde Park, NY, USA.|Department of Genetics and Genomic Sciences, Mount Sinai Health System, New York, NY, USA.|Center for Research Informatics and Innovation, Northwell Health, New Hyde Park, NY, USA.|Center for Research Informatics and Innovation, Northwell Health, New Hyde Park, NY, USA.|Department of Genetics and Genomic Sciences, Mount Sinai Health System, New York, NY, USA.|Center for Research Informatics and Innovation, Northwell Health, New Hyde Park, NY, USA.|Department of Information Services, Northwell Health, New Hyde Park, NY, USA.|TBC, B3, JKR, Aluva, Kerala, India.|Department of Genetics and Genomic Sciences, Mount Sinai Health System, New York, NY, USA.|University of Oklahoma Health Sciences Center, Oklahoma City, OK, USA.|Courant Institute of Mathematical Sciences, New York University, New York, NY, USA.|Department of Genetics and Genomic Sciences, Mount Sinai Health System, New York, NY, USA.|Department of Information Services, Northwell Health, New Hyde Park, NY, USA.', 'United States|United States|United States|United States|United States|United States|United States|United States|United States|India|United States|United States|United States|United States|United States', 0, '-', 'Journal Article', '10.1093/bib/bbz059', '31190075', 'Sepsis is a series of clinical syndromes caused by the immunological response to infection. The clinical evidence for sepsis could typically attribute to bacterial infection or bacterial endotoxins, but infections due to viruses, fungi or parasites could also lead to sepsis. Regardless of the etiology, rapid clinical deterioration, prolonged stay in intensive care units and high risk for mortality correlate with the incidence of sepsis. Despite its prevalence and morbidity, improvement in sepsis outcomes has remained limited. In this comprehensive review, we summarize the current landscape of risk estimation, diagnosis, treatment and prognosis strategies in the setting of sepsis and discuss future challenges. We argue that the advent of modern technologies such as in-depth molecular profiling, biomedical big data and machine intelligence methods will augment the treatment and prevention of sepsis. The volume, variety, veracity and velocity of heterogeneous data generated as part of healthcare delivery and recent advances in biotechnology-driven therapeutics and companion diagnostics may provide a new wave of approaches to identify the most at-risk sepsis patients and reduce the symptom burden in patients within shorter turnaround times. Developing novel therapies by leveraging modern drug discovery strategies including computational drug repositioning, cell and gene-therapy, clustered regularly interspaced short palindromic repeats -based genetic editing systems, immunotherapy, microbiome restoration, nanomaterial-based therapy and phage therapy may help to develop treatments to target sepsis. We also provide empirical evidence for potential new sepsis targets including FER and STARD3NL. Implementing data-driven methods that use real-time collection and analysis of clinical variables to trace, track and treat sepsis-related adverse outcomes will be key. Understanding the root and route of sepsis and its comorbid conditions that complicate treatment outcomes and lead to organ dysfunction may help to facilitate identification of most at-risk patients and prevent further deterioration. To conclude, leveraging the advances in precision medicine, biomedical data science and translational bioinformatics approaches may help to develop better strategies to diagnose and treat sepsis in the next decade.'),
(69515, 2019, 'bib', 'Systematic analysis of supervised machine learning as an effective approach to predicate xcexb2-lactam resistance phenotype in Streptococcus pneumoniae', 'Zhang, Chaodong|Ju, Yingjiao|Tang, Na|Li, Yun|Zhang, Gang|Song, Yuqin|Fang, Hailing|Yang, Liang|Feng, Jie', 'State Key Laboratory of Microbial Resources, Institute of Microbiology, Chinese Academy of Sciences, Beijing, China.|State Key Laboratory of Microbial Resources, Institute of Microbiology, Chinese Academy of Sciences, Beijing, China.|State Key Laboratory of Microbial Resources, Institute of Microbiology, Chinese Academy of Sciences, Beijing, China.|Institute of Clinical Pharmacology, Peking University First Hospital, Beijing, China.|State Key Laboratory of Microbial Resources, Institute of Microbiology, Chinese Academy of Sciences, Beijing, China.|State Key Laboratory of Microbial Resources, Institute of Microbiology, Chinese Academy of Sciences, Beijing, China.|State Key Laboratory of Microbial Resources, Institute of Microbiology, Chinese Academy of Sciences, Beijing, China.|School of Medicine, Southern University of Science and Technology, Shenzhen, Guangdong Province, China.|State Key Laboratory of Microbial Resources, Institute of Microbiology, Chinese Academy of Sciences, Beijing, China.', 'China|China|China|China|China|China|China|China|China', 0, '-', 'Journal Article', '10.1093/bib/bbz056', '31192359', 'Streptococcus pneumoniae is the most common human respiratory pathogen, and xcexb2-lactam antibiotics have been employed to treat infections caused by S. pneumoniae for decades. xcexb2-lactam resistance is steadily increasing in pneumococci and is mainly associated with the alteration in penicillin-binding proteins (PBPs) that reduce binding affinity of antibiotics to PBPs. However, the high variability of PBPs in clinical isolates and their mosaic gene structure hamper the predication of resistance level according to the PBP gene sequences. In this study, we developed a systematic strategy for applying supervised machine learning to predict S. pneumoniae antimicrobial susceptibility to xcexb2-lactam antibiotics. We combined published PBP sequences with minimum inhibitory concentration (MIC) values as labelled data and the sequences from NCBI database without MIC values as unlabelled data to develop an approach, using only a fragment from pbp2x (750 bp) and a fragment from pbp2b (750 bp) to predicate the cefuroxime and amoxicillin resistance. We further validated the performance of the supervised learning model by constructing mutants containing the randomly selected pbps and testing more clinical strains isolated from Chinese hospital. In addition, we established the association between resistance phenotypes and serotypes and sequence type of S. pneumoniae using our approach, which facilitate the understanding of the worldwide epidemiology of S. pneumonia.'),
(69516, 2019, 'bib', 'A critical assessment of the feature selection methods used for biomarker discovery in current metaproteomics studies', 'Tang, Jing|Wang, Yunxia|Fu, Jianbo|Zhou, Ying|Luo, Yongchao|Zhang, Ying|Li, Bo|Yang, Qingxia|Xue, Weiwei|Lou, Yan|Qiu, Yunqing|Zhu, Feng', 'College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, China.|College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, China.|College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, China.|College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, China.|College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, China.|College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, China.|School of Pharmaceutical Sciences, Chongqing University, Chongqing, China.|College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, China.|School of Pharmaceutical Sciences, Chongqing University, Chongqing, China.|Zhejiang Provincial Key Laboratory for Drug Clinical Research and Evaluation, The First Affiliated Hospital, Zhejiang University, Hangzhou, Zhejiang, China.|Zhejiang Provincial Key Laboratory for Drug Clinical Research and Evaluation, The First Affiliated Hospital, Zhejiang University, Hangzhou, Zhejiang, China.|College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, China.', 'China|China|China|China|China|China|China|China|China|China|China|China', 5, '-', 'Journal Article', '10.1093/bib/bbz061', '31197323', 'Microbial community (MC) has great impact on mediating complex disease indications, biogeochemical cycling and agricultural productivities, which makes metaproteomics powerful technique for quantifying diverse and dynamic composition of proteins or peptides. The key role of biostatistical strategies in MC study is reported to be underestimated, especially the appropriate application of feature selection method (FSM) is largely ignored. Although extensive efforts have been devoted to assessing the performance of FSMs, previous studies focused only on their classification accuracy without considering their ability to correctly and comprehensively identify the spiked proteins. In this study, the performances of 14 FSMs were comprehensively assessed based on two key criteria (both sample classification and spiked protein discovery) using a variety of metaproteomics benchmarks. First, the classification accuracies of those 14 FSMs were evaluated. Then, their abilities in identifying the proteins of different spiked concentrations were assessed. Finally, seven FSMs (FC, LMEB, OPLS-DA, PLS-DA, SAM, SVM-RFE and T-Test) were identified as performing consistently superior or good under both criteria with the PLS-DA performing consistently superior. In summary, this study served as comprehensive analysis on the performances of current FSMs and could provide a valuable guideline for researchers in metaproteomics.'),
(69517, 2019, 'bib', 'iCircDA-MF: identification of circRNA-disease associations based on matrix factorization', 'Wei, Hang|Liu, Bin', 'School of Computer Science and Technology, Harbin Institute of Technology, Shenzhen, Guangdong, China.|School of Computer Science and Technology, Harbin Institute of Technology, Shenzhen, Guangdong, China.', 'China|China', 6, '-', 'Journal Article', '10.1093/bib/bbz057', '31197324', 'Circular RNAs (circRNAs) are a group of novel discovered non-coding RNAs with closed-loop structure, which play critical roles in various biological processes. Identifying associations between circRNAs and diseases is critical for exploring the complex disease mechanism and facilitating disease-targeted therapy. Although several computational predictors have been proposed, their performance is still limited. In this study, a novel computational method called iCircDA-MF is proposed. Because the circRNA-disease associations with experimental validation are very limited, the potential circRNA-disease associations are calculated based on the circRNA similarity and disease similarity extracted from the disease semantic information and the known associations of circRNA-gene, gene-disease and circRNA-disease. The circRNA-disease interaction profiles are then updated by the neighbour interaction profiles so as to correct the false negative associations. Finally, the matrix factorization is performed on the updated circRNA-disease interaction profiles to predict the circRNA-disease associations. The experimental results on a widely used benchmark dataset showed that iCircDA-MF outperforms other state-of-the-art predictors and can identify new circRNA-disease associations effectively.'),
(69518, 2019, 'bib', 'A comprehensive review and performance evaluation of bioinformatics tools for HLA class I peptide-binding prediction', 'Mei, Shutao|Li, Fuyi|Leier, André|Marquez-Lago, Tatiana T|Giam, Kailin|Croft, Nathan P|Akutsu, Tatsuya|Smith, A Ian|Li, Jian|Rossjohn, Jamie|Purcell, Anthony W|Song, Jiangning', 'Biomedicine Discovery Institute and Department of Biochemistry & Molecular Biology, Monash University, Melbourne, VIC, Australia.|Biomedicine Discovery Institute and Department of Biochemistry & Molecular Biology, Monash University, Melbourne, VIC, Australia.|Department of Genetics and Department of Cell, Developmental and Integrative Biology, School of Medicine, University of Alabama at Birmingham, AL, USA.|Department of Genetics and Department of Cell, Developmental and Integrative Biology, School of Medicine, University of Alabama at Birmingham, AL, USA.|Department of Immunology, Kings College London, London, UK.|Biomedicine Discovery Institute and Department of Biochemistry & Molecular Biology, Monash University, Melbourne, VIC, Australia.|Bioinformatics Centre, Institute for Chemical Research, Kyoto University, Kyoto, Japan.|Biomedicine Discovery Institute and Department of Biochemistry & Molecular Biology, Monash University, Melbourne, VIC, Australia.|Biomedicine Discovery Institute and Department of Microbiology, Monash University, Melbourne, VIC, Australia.|Biomedicine Discovery Institute and Department of Biochemistry & Molecular Biology, Monash University, Melbourne, VIC, Australia.|Biomedicine Discovery Institute and Department of Biochemistry & Molecular Biology, Monash University, Melbourne, VIC, Australia.|Biomedicine Discovery Institute and Department of Biochemistry & Molecular Biology, Monash University, Melbourne, VIC, Australia.', 'Australia|Australia|United Kingdom|United Kingdom|United Kingdom|Australia|Japan|Australia|Australia|Australia|Australia|Australia', 0, '-', 'Journal Article', '10.1093/bib/bbz051', '31204427', 'Human leukocyte antigen class I (HLA-I) molecules are encoded by major histocompatibility complex (MHC) class I loci in humans. The binding and interaction between HLA-I molecules and intracellular peptides derived from a variety of proteolytic mechanisms play a crucial role in subsequent T-cell recognition of target cells and the specificity of the immune response. In this context, tools that predict the likelihood for a peptide to bind to specific HLA class I allotypes are important for selecting the most promising antigenic targets for immunotherapy. In this article, we comprehensively review a variety of currently available tools for predicting the binding of peptides to a selection of HLA-I allomorphs. Specifically, we compare their calculation methods for the prediction score, employed algorithms, evaluation strategies and software functionalities. In addition, we have evaluated the prediction performance of the reviewed tools based on an independent validation data set, containing 21 101 experimentally verified ligands across 19 HLA-I allotypes. The benchmarking results show that MixMHCpred 2.0.1 achieves the best performance for predicting peptides binding to most of the HLA-I allomorphs studied, while NetMHCpan 4.0 and NetMHCcons 1.1 outperform the other machine learning-based and consensus-based tools, respectively. Importantly, it should be noted that a peptide predicted with a higher binding score for a specific HLA allotype does not necessarily imply it will be immunogenic. That said, peptide-binding predictors are still very useful in that they can help to significantly reduce the large number of epitope candidates that need to be experimentally verified. Several other factors, including susceptibility to proteasome cleavage, peptide transport into the endoplasmic reticulum and T-cell receptor repertoire, also contribute to the immunogenicity of peptide antigens, and some of them can be considered by some predictors. Therefore, integrating features derived from these additional factors together with HLA-binding properties by using machine-learning algorithms may increase the prediction accuracy of immunogenic peptides. As such, we anticipate that this review and benchmarking survey will assist researchers in selecting appropriate prediction tools that best suit their purposes and provide useful guidelines for the development of improved antigen predictors in the future.'),
(69519, 2019, 'bib', 'An overview of diffusion models for intracellular dynamics analysis', 'Briane, Vincent|Vimond, Myriam|Kervrann, Charles', 'Inria, Centre Rennes-Bretagne Atlantique, SERPICO Project Team, Rennes, France.|CREST (Ensai, Université Bretagne Loire), Bruz, France.|Inria, Centre Rennes-Bretagne Atlantique, SERPICO Project Team, Rennes, France.', 'France|France|France', 1, '-', 'Journal Article', '10.1093/bib/bbz052', '31204428', 'We present an overview of diffusion models commonly used for quantifying the dynamics of intracellular particles (e.g. biomolecules) inside eukaryotic living cells. It is established that inference on the modes of mobility of molecules is central in cell biology since it reflects interactions between structures and determines functions of biomolecules in the cell. In that context, Brownian motion is a key component in short distance transportation (e.g. connectivity for signal transduction). Another dynamical process that has been heavily studied in the past decade is the motor-mediated transport (e.g. dynein, kinesin and myosin) of molecules. Primarily supported by actin filament and microtubule network, it ensures spatial organization and temporal synchronization in the intracellular mechanisms and structures. Nevertheless, the complexity of internal structures and molecular processes in the living cell influence the molecular dynamics and prevent the systematic application of pure Brownian or directed motion modeling. On the one hand, cytoskeleton density will hinder the free displacement of the particle, a phenomenon called subdiffusion. On the other hand, the cytoskeleton elasticity combined with thermal bending can contribute a phenomenon called superdiffusion. This paper discusses the basics of diffusion modes observed in eukariotic cells, by introducing the essential properties of these processes. Applications of diffusion models include protein trafficking and transport and membrane diffusion.'),
(69520, 2019, 'bib', 'Mitochondrial DNA enrichment reduced NUMT contamination in porcine NGS analyses', 'Wang, Dan|Xiang, Hai|Ning, Chao|Liu, Hao|Liu, Jian-Feng|Zhao, Xingbo', 'National Engineering Laboratory for Animal Breeding, Ministry of Agricultural Key Laboratory of Animal Genetics, Breeding and Reproduction, College of Animal Science and Technology, China Agricultural University, Beijing, China.|College of Life Science and Engineering, Foshan University, Foshan, Guangdong 528231, China.|National Engineering Laboratory for Animal Breeding, Ministry of Agricultural Key Laboratory of Animal Genetics, Breeding and Reproduction, College of Animal Science and Technology, China Agricultural University, Beijing, China.|National Engineering Laboratory for Animal Breeding, Ministry of Agricultural Key Laboratory of Animal Genetics, Breeding and Reproduction, College of Animal Science and Technology, China Agricultural University, Beijing, China.|National Engineering Laboratory for Animal Breeding, Ministry of Agricultural Key Laboratory of Animal Genetics, Breeding and Reproduction, College of Animal Science and Technology, China Agricultural University, Beijing, China.|National Engineering Laboratory for Animal Breeding, Ministry of Agricultural Key Laboratory of Animal Genetics, Breeding and Reproduction, College of Animal Science and Technology, China Agricultural University, Beijing, China.', 'China|China|China|China|China|China', 0, '-', 'Journal Article', '10.1093/bib/bbz060', '31204429', 'Genetic associations between mitochondrial DNA (mtDNA) and economic traits have been widely reported for pigs, which indicate the importance of mtDNA. However, studies on mtDNA heteroplasmy in pigs are rare. Next generation sequencing (NGS) methodologies have emerged as a promising genomic approach for detection of mitochondrial heteroplasmy. Due to the short reads, flexible bioinformatic analyses and the contamination of nuclear mitochondrial sequences (NUMTs), NGS was expected to increase false-positive detection of heteroplasmy. In this study, Sanger sequencing was performed as a gold standard to detect heteroplasmy with a detection sensitivity of 5% in pigs and then one whole-genome sequencing method (WGS) and two mtDNA enrichment sequencing methods (Capture and LongPCR) were carried out. The aim of this study was to determine whether mitochondrial heteroplasmy identification from NGS data was affected by NUMTs. We find that WGS generated more false intra-individual polymorphisms and less mapping specificity than the two enrichment sequencing methods, suggesting NUMTs indeed led to false-positive mitochondrial heteroplasmies from NGS data. In addition, to accurately detect mitochondrial diversity, three commonly used tools-SAMtools, VarScan and GATK-with different parameter values were compared. VarScan achieved the best specificity and sensitivity when considering the base alignment quality re-computation and the minimum variant frequency of 0.25. It also suggested bioinformatic workflow interfere in the identification of mtDNA SNPs. In conclusion, intra-individual polymorphism in pig mitochondria from NGS data was confused with NUMTs, and mtDNA-specific enrichment is essential before high-throughput sequencing in the detection of mitochondrial genome sequences.'),
(69521, 2019, 'bib', 'RNA-mediated translation regulation in viral genomes: computational advances in the recognition of sequences and structures', 'Gupta, Asmita|Bansal, Manju', 'Molecular Biophysics Unit, Indian Institute of Science, Bangalore, India.|Molecular Biophysics Unit, Indian Institute of Science, Bangalore, India.', 'India|India', 0, '-', 'Journal Article', '10.1093/bib/bbz054', '31204430', 'RNA structures are widely distributed across all life forms. The global conformation of these structures is defined by a variety of constituent structural units such as helices, hairpin loops, kissing-loop motifs and pseudoknots, which often behave in a modular way. Their ubiquitous distribution is associated with a variety of functions in biological processes. The location of these structures in the genomes of RNA viruses is often coordinated with specific processes in the viral life cycle, where the presence of the structure acts as a checkpoint for deciding the eventual fate of the process. These structures have been found to adopt complex conformations and exert their effects by interacting with ribosomes, multiple host translation factors and small RNA molecules like miRNA. A number of such RNA structures have also been shown to regulate translation in viruses at the level of initiation, elongation or termination. The role of various computational studies in the preliminary identification of such sequences and/or structures and subsequent functional analysis has not been fully appreciated. This review aims to summarize the processes in which viral RNA structures have been found to play an active role in translational regulation, their global conformational features and the bioinformatics/computational tools available for the identification and prediction of these structures.'),
(69522, 2019, 'bib', 'Evaluation of integrative clustering methods for the analysis of multi-omics data', 'Chauvel, Cécile|Novoloaca, Alexei|Veyre, Pierre|Reynier, Frédéric|Becker, Jérémie', 'BIOASTER Research Institute, avenue Tony Garnier, Lyon, France.|BIOASTER Research Institute, avenue Tony Garnier, Lyon, France.|BIOASTER Research Institute, avenue Tony Garnier, Lyon, France.|BIOASTER Research Institute, avenue Tony Garnier, Lyon, France.|BIOASTER Research Institute, avenue Tony Garnier, Lyon, France.', 'France|France|France|France|France', 3, '-', 'Journal Article', '10.1093/bib/bbz015', '31220206', 'Recent advances in sequencing, mass spectrometry and cytometry technologies have enabled researchers to collect large-scale omics data from the same set of biological samples. The joint analysis of multiple omics offers the opportunity to uncover coordinated cellular processes acting across different omic layers. In this work, we present a thorough comparison of a selection of recent integrative clustering approaches, including Bayesian (BCC and MDI) and matrix factorization approaches (iCluster, moCluster, JIVE and iNMF). Based on simulations, the methods were evaluated on their sensitivity and their ability to recover both the correct number of clusters and the simulated clustering at the common and data-specific levels. Standard non-integrative approaches were also included to quantify the added value of integrative methods. For most matrix factorization methods and one Bayesian approach (BCC), the shared and specific structures were successfully recovered with high and moderate accuracy, respectively. An opposite behavior was observed on non-integrative approaches, i.e. high performances on specific structures only. Finally, we applied the methods on the Cancer Genome Atlas breast cancer data set to check whether results based on experimental data were consistent with those obtained in the simulations.'),
(69523, 2019, 'bib', 'Validation strategies for target prediction methods', 'Mathai, Neann|Chen, Ya|Kirchmair, Johannes', 'Department of Chemistry, University of Bergen, Bergen, Norway.|Center for Bioinformatics (ZBH), Department of Computer Science, Faculty of Mathematics, Informatics and Natural Sciences, Universität Hamburg, Hamburg, Germany.|Department of Chemistry, University of Bergen, Bergen, Norway.', 'Norway|Germany|Norway', 7, '-', 'Journal Article', '10.1093/bib/bbz026', '31220208', 'Computational methods for target prediction, based on molecular similarity and network-based approaches, machine learning, docking and others, have evolved as valuable and powerful tools to aid the challenging task of mode of action identification for bioactive small molecules such as drugs and drug-like compounds. Critical to discerning the scope and limitations of a target prediction method is understanding how its performance was evaluated and reported. Ideally, large-scale prospective experiments are conducted to validate the performance of a model, however, this expensive and time-consuming endeavor is often not feasible. Therefore, to estimate the predictive power of a method, statistical validation based on retrospective knowledge is commonly used. There are multiple statistical validation techniques that vary in rigor. In this review we discuss the validation strategies employed, highlighting the usefulness and constraints of the validation schemes and metrics that are employed to measure and describe performance. We address the limitations of measuring only generalized performance, given that the underlying bioactivity and structural data are biased towards certain small-molecule scaffolds and target families, and suggest additional aspects of performance to consider in order to produce more detailed and realistic estimates of predictive power. Finally, we describe the validation strategies that were employed by some of the most thoroughly validated and accessible target prediction methods.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(69524, 2019, 'bib', 'DNA methylation analysis in plants: review of computational tools and future perspectives', 'Omony, Jimmy|Nussbaumer, Thomas|Gutzat, Ruben', 'Plant Genome and Systems Biology, Helmholtz Center Munich-German Research Center for Environmental Health, Neuherberg, Germany.|Institute of Network Biology, Department of Environmental Science, Helmholtz Center Munich, Neuherberg, Germany.|Gregor Mendel Institute of Molecular Plant Biology, Austrian Academy of Sciences, Vienna BioCenter (VBC), Vienna, Austria.', 'Germany|Germany|Austria', 0, '-', 'Journal Article', '10.1093/bib/bbz039', '31220217', 'Genome-wide DNA methylation studies have quickly expanded due to advances in next-generation sequencing techniques along with a wealth of computational tools to analyze the data. Most of our knowledge about DNA methylation profiles, epigenetic heritability and the function of DNA methylation in plants derives from the model species Arabidopsis thaliana. There are increasingly many studies on DNA methylation in plants-uncovering methylation profiles and explaining variations in different plant tissues. Additionally, DNA methylation comparisons of different plant tissue types and dynamics during development processes are only slowly emerging but are crucial for understanding developmental and regulatory decisions. Translating this knowledge from plant model species to commercial crops could allow the establishment of new varieties with increased stress resilience and improved yield. In this review, we provide an overview of the most commonly applied bioinformatics tools for the analysis of DNA methylation data (particularly bisulfite sequencing data). The performances of a selection of the tools are analyzed for computational time and agreement in predicted methylated sites for A. thaliana, which has a smaller genome compared to the hexaploid bread wheat. The performance of the tools was benchmarked on five plant genomes. We give examples of applications of DNA methylation data analysis in crops (with a focus on cereals) and an outlook for future developments for DNA methylation status manipulations and data integration.'),
(69525, 2019, 'bib', 'Comparative assessment of long-read error correction software applied to Nanopore RNA-sequencing data', 'Lima, Leandro|Marchet, Camille|Caboche, Ségolène|Da Silva, Corinne|Istace, Benjamin|Aury, Jean-Marc|Touzet, Hélène|Chikhi, Rayan', 'Univ Lyon, Université Lyon 1, CNRS, Laboratoire de Biométrie et Biologie Evolutive UMR Villeurbanne, France.|CNRS, Université de Lille, CRIStAL UMR, Lille, France.|Université de Lille, CNRS, Inserm, CHU Lille, Institut Pasteur de Lille, UMR, Center for Infection and Immunity of Lille, Lille, France.|Genoscope, Institut de biologie Francois-Jacob, Commissariat à lEnergie Atomique (CEA), Université Paris-Saclay, Evry, France.|Genoscope, Institut de biologie Francois-Jacob, Commissariat à lEnergie Atomique (CEA), Université Paris-Saclay, Evry, France.|Genoscope, Institut de biologie Francois-Jacob, Commissariat à lEnergie Atomique (CEA), Université Paris-Saclay, Evry, France.|CNRS, Université de Lille, CRIStAL UMR, Lille, France.|CNRS, Université de Lille, CRIStAL UMR, Lille, France.', 'France|France|France|France|France|France|France|France', 1, '-', 'Journal Article', '10.1093/bib/bbz058', '31232449', 'Nanopore long-read sequencing technology offers promising alternatives to high-throughput short read sequencing, especially in the context of RNA-sequencing. However this technology is currently hindered by high error rates in the output data that affect analyses such as the identification of isoforms, exon boundaries, open reading frames and creation of gene catalogues. Due to the novelty of such data, computational methods are still actively being developed and options for the error correction of Nanopore RNA-sequencing long reads remain limited.'),
(69526, 2019, 'bib', 'Machine learning and statistical methods for clustering single-cell RNA-sequencing data', 'Petegrosso, Raphael|Li, Zhuliu|Kuang, Rui', 'CREST (Ensai, Université Bretagne Loire), Bruz, France.|CREST (Ensai, Université Bretagne Loire), Bruz, France.|CREST (Ensai, Université Bretagne Loire), Bruz, France.', 'France|France|France', 0, '-', 'Journal Article', '10.1093/bib/bbz063', '31243426', 'Single-cell RNAsequencing (scRNA-seq) technologies have enabled the large-scale whole-transcriptome profiling of each individual single cell in a cell population. A core analysis of the scRNA-seq transcriptome profiles is to cluster the single cells to reveal cell subtypes and infer cell lineages based on the relations among the cells. This article reviews the machine learning and statistical methods for clustering scRNA-seq transcriptomes developed in the past few years. The review focuses on how conventional clustering techniques such as hierarchical clustering, graph-based clustering, mixture models, $k$-means, ensemble learning, neural networks and density-based clustering are modified or customized to tackle the unique challenges in scRNA-seq data analysis, such as the dropout of low-expression genes, low and uneven read coverage of transcripts, highly variable total mRNAs from single cells and ambiguous cell markers in the presence of technical biases and irrelevant confounding biological variations. We review how cell-specific normalization, the imputation of dropouts and dimension reduction methods can be applied with new statistical or optimization strategies to improve the clustering of single cells. We will also introduce those more advanced approaches to cluster scRNA-seq transcriptomes in time series data and multiple cell populations and to detect rare cell types. Several software packages developed to support the cluster analysis of scRNA-seq data are also reviewed and experimentally compared to evaluate their performance and efficiency. Finally, we conclude with useful observations and possible future directions in scRNA-seq data analytics.'),
(69527, 2019, 'bib', 'Data-driven rational biosynthesis design: from molecules to cell factories', 'Chen, Fu|Yuan, Le|Ding, Shaozhen|Tian, Yu|Hu, Qian-Nan', 'College of Biotechnology, Tianjin University of Science and Technology, Tianjin, Peoples Republic of China.|Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin, Peoples Republic of China.|CAS Key Laboratory of Computational Biology, CAS-MPG Partner Institute for Computational Biology, Shanghai Institute of Nutrition and Health, Shanghai Institutes for Biological Sciences, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, Peoples Republic of China.|Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin, Peoples Republic of China.|CAS Key Laboratory of Computational Biology, CAS-MPG Partner Institute for Computational Biology, Shanghai Institute of Nutrition and Health, Shanghai Institutes for Biological Sciences, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, Peoples Republic of China.', 'China|China|China|China|China', 0, '-', 'Journal Article', '10.1093/bib/bbz065', '31243440', 'A proliferation of chemical, reaction and enzyme databases, new computational methods and software tools for data-driven rational biosynthesis design have emerged in recent years. With the coming of the era of big data, particularly in the bio-medical field, data-driven rational biosynthesis design could potentially be useful to construct target-oriented chassis organisms. Engineering the complicated metabolic systems of chassis organisms to biosynthesize target molecules from inexpensive biomass is the main goal of cell factory design. The process of data-driven cell factory design could be divided into several parts: (1) target molecule selection, (2) metabolic reaction and pathway design, (3) prediction of novel enzymes based on protein domain and structure transformation of biosynthetic reactions, (4) construction of large-scale DNA for metabolic pathways, and (5) DNA assembly methods and visualization tools. The construction of a one-stop cell factory system could achieve automated design from the molecule level to the chassis level. In this article, we outline data-driven rational biosynthesis design steps and provide an overview of related tools in individual steps.'),
(69528, 2019, 'bib', 'Implementing the FAIR Data Principles in precision oncology: review of supporting initiatives', 'Vesteghem, Charles|Brøndum, Rasmus Froberg|Sønderkær, Mads|Sommer, Mia|Schmitz, Alexander|Bødker, Julie Støve|Dybkær, Karen|El-Galaly, Tarec Christoffer|Bøgsted, Martin', 'Department of Clinical Medicine, Aalborg University, Denmark.|Department of Haematology, Aalborg University Hospital, Denmark.|Department of Haematology, Aalborg University Hospital, Denmark.|Department of Clinical Medicine, Aalborg University, Denmark.|Department of Haematology, Aalborg University Hospital, Denmark.|Department of Haematology, Aalborg University Hospital, Denmark.|Department of Clinical Medicine, Aalborg University, Denmark.|Department of Clinical Medicine, Aalborg University, Denmark.|Department of Clinical Medicine, Aalborg University, Denmark.', 'Denmark|Denmark|Denmark|Denmark|Denmark|Denmark|Denmark|Denmark|Denmark', 0, '-', 'Journal Article', '10.1093/bib/bbz044', '31263868', 'Compelling research has recently shown that cancer is so heterogeneous that single research centres cannot produce enough data to fit prognostic and predictive models of sufficient accuracy. Data sharing in precision oncology is therefore of utmost importance. The Findable, Accessible, Interoperable and Reusable (FAIR) Data Principles have been developed to define good practices in data sharing. Motivated by the ambition of applying the FAIR Data Principles to our own clinical precision oncology implementations and research, we have performed a systematic literature review of potentially relevant initiatives. For clinical data, we suggest using the Genomic Data Commons model as a reference as it provides a field-tested and well-documented solution. Regarding classification of diagnosis, morphology and topography and drugs, we chose to follow the World Health Organization standards, i.e. ICD10, ICD-O-3 and Anatomical Therapeutic Chemical classifications, respectively. For the bioinformatics pipeline, the Genome Analysis ToolKit Best Practices using Docker containers offer a coherent solution and have therefore been selected. Regarding the naming of variants, we follow the Human Genome Variation Societys standard. For the IT infrastructure, we have built a centralized solution to participate in data sharing through federated solutions such as the Beacon Networks.'),
(69529, 2019, 'bib', 'A survey on identification and quantification of alternative polyadenylation sites from RNA-seq data', 'Chen, Moliang|Ji, Guoli|Fu, Hongjuan|Lin, Qianmin|Ye, Congting|Ye, Wenbin|Su, Yaru|Wu, Xiaohui', 'Department of Automation, Xiamen University, Xiamen 361005, China.|Department of Automation, Xiamen University, Xiamen 361005, China.|Department of Automation, Xiamen University, Xiamen 361005, China.|Xiang an hospital of Xiamen university, Xiamen 361005, China.|Key Laboratory of the Ministry of Education for Coastal and Wetland Ecosystems, College of the Environment and Ecology, Xiamen University, Xiamen, Fujian 361102, China.|Department of Automation, Xiamen University, Xiamen 361005, China.|College of Mathematics and Computer Science, Fuzhou University, Fuzhou 350116, China.|Department of Automation, Xiamen University, Xiamen 361005, China.', 'China|China|China|China|China|China|China|China', 1, '-', 'Journal Article', '10.1093/bib/bbz068', '31267126', 'Alternative polyadenylation (APA) has been implicated to play an important role in post-transcriptional regulation by regulating mRNA abundance, stability, localization and translation, which contributes considerably to transcriptome diversity and gene expression regulation. RNA-seq has become a routine approach for transcriptome profiling, generating unprecedented data that could be used to identify and quantify APA site usage. A number of computational approaches for identifying APA sites and/or dynamic APA events from RNA-seq data have emerged in the literature, which provide valuable yet preliminary results that should be refined to yield credible guidelines for the scientific community. In this review, we provided a comprehensive overview of the status of currently available computational approaches. We also conducted objective benchmarking analysis using RNA-seq data sets from different species (human, mouse and Arabidopsis) and simulated data sets to present a systematic evaluation of 11 representative methods. Our benchmarking study showed that the overall performance of all tools investigated is moderate, reflecting that there is still lot of scope to improve the prediction of APA site or dynamic APA events from RNA-seq data. Particularly, prediction results from individual tools differ considerably, and only a limited number of predicted APA sites or genes are common among different tools. Accordingly, we attempted to give some advice on how to assess the reliability of the obtained results. We also proposed practical recommendations on the appropriate method applicable to diverse scenarios and discussed implications and future directions relevant to profiling APA from RNA-seq data.'),
(69530, 2019, 'bib', 'Data imbalance in CRISPR off-target prediction', 'Gao, Yuli|Chuai, Guohui|Yu, Weichuan|Qu, Shen|Liu, Qi', 'Department of Endocrinology & Metabolism, Shanghai Tenth Peoples Hospital, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai, 20009, China.|Department of Endocrinology & Metabolism, Shanghai Tenth Peoples Hospital, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai, 20009, China.|Department of Electronic and Computer Engineering, The Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong, China.|Department of Endocrinology & Metabolism, Shanghai Tenth Peoples Hospital, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai, 20009, China.|Department of Endocrinology & Metabolism, Shanghai Tenth Peoples Hospital, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai, 20009, China.', 'China|China|China|China|China', 0, '-', 'Journal Article', '10.1093/bib/bbz069', '31267129', 'For genome-wide CRISPR off-target cleavage sites (OTS) prediction, an important issue is data imbalance-the number of true OTS recognized by whole-genome off-target detection techniques is much smaller than that of all possible nucleotide mismatch loci, making the training of machine learning model very challenging. Therefore, computational models proposed for OTS prediction and scoring should be carefully designed and properly evaluated in order to avoid bias. In our study, two tools are taken as examples to further emphasize the data imbalance issue in CRISPR off-target prediction to achieve better sensitivity and specificity for optimized CRISPR gene editing. We would like to indicate that (1) the benchmark of CRISPR off-target prediction should be properly evaluated and not overestimated by considering data imbalance issue, (2) incorporation of efficient computational techniques (including ensemble learning and data synthesis techniques) can help to address the data imbalance issue and improve the performance of CRISPR off-target prediction. Taking together, we call for more efforts to address the data imbalance issue in CRISPR off-target prediction to facilitate clinical utility of CRISPR-based gene editing techniques.'),
(69531, 2019, 'bib', 'Clustering and classification methods for single-cell RNA-sequencing data', 'Qi, Ren|Ma, Anjun|Ma, Qin|Zou, Quan', 'School of Computer Science and Technology, College of Intelligence and Computing, Tianjin University, Tianjin, China.|Department of Biomedical Informatics, College of Medicine, The Ohio State University, Columbus, USA.|Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu, China.|Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, China.', 'China|United States|China|China', 6, '-', 'Journal Article', '10.1093/bib/bbz062', '31271412', 'Appropriate ways to measure the similarity between single-cell RNA-sequencing (scRNA-seq) data are ubiquitous in bioinformatics, but using single clustering or classification methods to process scRNA-seq data is generally difficult. This has led to the emergence of integrated methods and tools that aim to automatically process specific problems associated with scRNA-seq data. These approaches have attracted a lot of interest in bioinformatics and related fields. In this paper, we systematically review the integrated methods and tools, highlighting the pros and cons of each approach. We not only pay particular attention to clustering and classification methods but also discuss methods that have emerged recently as powerful alternatives, including nonlinear and linear methods and descending dimension methods. Finally, we focus on clustering and classification methods for scRNA-seq data, in particular, integrated methods, and provide a comprehensive description of scRNA-seq data and download URLs.'),
(69532, 2019, 'bib', 'A critical review of five machine learning-based algorithms for predicting protein stability changes upon mutation', 'Fang, Jianwen', 'Computational & Systems Biology Branch, Biometric Research Program, Division of Cancer Treatment and Diagnosis, National Cancer Institute, 9609 Medical Center Drive, Rockville, MD 20850, USA.', 'United States', 2, '-', 'Journal Article', '10.1093/bib/bbz071', '31273374', 'A number of machine learning (ML)-based algorithms have been proposed for predicting mutation-induced stability changes in proteins. In this critical review, we used hypothetical reverse mutations to evaluate the performance of five representative algorithms and found all of them suffer from the problem of overfitting. This approach is based on the fact that if a wild-type protein is more stable than a mutant protein, then the same mutant is less stable than the wild-type protein. We analyzed the underlying issues and suggest that the main causes of the overfitting problem include that the numbers of training cases were too small, and the features used in the models were not sufficiently informative for the task. We make recommendations on how to avoid overfitting in this important research area and improve the reliability and robustness of ML-based algorithms in general.'),
(69533, 2019, 'bib', 'Closing the gap between formats for storing layout information in systems biology', 'Hoksza, David|Gawron, Piotr|Ostaszewski, Marek|Hausenauer, Jan|Schneider, Reinhard', 'Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, 6, avenue du Swing L-4367 Belvaux, Luxembourg.|Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, 6, avenue du Swing L-4367 Belvaux, Luxembourg.|Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, 6, avenue du Swing L-4367 Belvaux, Luxembourg.|Institute of Computational Biology, Helmholtz Zentrum München - German Research Center for Environmental Health, Ingolstädter Landstr. 1, 85764, Neuherberg, Germany.|Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, 6, avenue du Swing L-4367 Belvaux, Luxembourg.', 'Luxembourg|Luxembourg|Luxembourg|Germany|Luxembourg', 0, '-', 'Journal Article', '10.1093/bib/bbz067', '31273380', 'The understanding of complex biological networks often relies on both a dedicated layout and a topology. Currently, there are three major competing layout-aware systems biology formats, but there are no software tools or software libraries supporting all of them. This complicates the management of molecular network layouts and hinders their reuse and extension. In this paper, we present a high-level overview of the layout formats in systems biology, focusing on their commonalities and differences, review their support in existing software tools, libraries and repositories and finally introduce a new conversion module within the MINERVA platform. The module is available via a REST API and offers, besides the ability to convert between layout-aware systems biology formats, the possibility to export layouts into several graphical formats. The module enables conversion of very large networks with thousands of elements, such as disease maps or metabolic reconstructions, rendering it widely applicable in systems biology.'),
(69534, 2019, 'bib', 'Consistency and overfitting of multi-omics methods on experimental data', 'McCabe, Sean D|Lin, Dan-Yu|Love, Michael I', 'Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA.|Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA.|Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA.', 'United States|United States|United States', 0, '-', 'Journal Article', '10.1093/bib/bbz070', '31281919', 'Knowledge on the relationship between different biological modalities (RNA, chromatin, etc.) can help further our understanding of the processes through which biological components interact. The ready availability of multi-omics datasets has led to the development of numerous methods for identifying sources of common variation across biological modalities. However, evaluation of the performance of these methods, in terms of consistency, has been difficult because most methods are unsupervised. We present a comparison of sparse multiple canonical correlation analysis (Sparse mCCA), angle-based joint and individual variation explained (AJIVE) and multi-omics factor analysis (MOFA) using a cross-validation approach to assess overfitting and consistency. Both large and small-sample datasets were used to evaluate performance, and a permuted null dataset was used to identify overfitting through the application of our framework and approach. In the large-sample setting, we found that all methods demonstrated consistency and lack of overfitting, however, in the small-sample size setting, AJIVE provided the most stable results. We provide an R package so that our framework and approach can be applied to evaluate other methods and datasets.'),
(69535, 2019, 'bib', 'Genome-wide functional association networks: background, data & state-of-the-art resources', 'Guala, Dimitri|Ogris, Christoph|Müller, Nikola|Sonnhammer, Erik L L', 'Science for Life Laboratory, Stockholm Bioinformatics Center, Department of Biochemistry and Biophysics, Stockholm University, Box 1031, 17121 Solna, Sweden.|Computational Cell Maps, Institute of Computational Biology, Helmholtz Center Munich, Ingolstädter Landstr. 1, 85764 Neuherberg, Germany.|Computational Cell Maps, Institute of Computational Biology, Helmholtz Center Munich, Ingolstädter Landstr. 1, 85764 Neuherberg, Germany.|Science for Life Laboratory, Stockholm Bioinformatics Center, Department of Biochemistry and Biophysics, Stockholm University, Box 1031, 17121 Solna, Sweden.', 'Sweden|Germany|Germany|Sweden', 0, '-', 'Journal Article', '10.1093/bib/bbz064', '31281921', 'The vast amount of experimental data from recent advances in the field of high-throughput biology begs for integration into more complex data structures such as genome-wide functional association networks. Such networks have been used for elucidation of the interplay of intra-cellular molecules to make advances ranging from the basic science understanding of evolutionary processes to the more translational field of precision medicine. The allure of the field has resulted in rapid growth of the number of available network resources, each with unique attributes exploitable to answer different biological questions. Unfortunately, the high volume of network resources makes it impossible for the intended user to select an appropriate tool for their particular research question. The aim of this paper is to provide an overview of the underlying data and representative network resources as well as to mention methods of integration, allowing a customized approach to resource selection. Additionally, this report will provide a primer for researchers venturing into the field of network integration.'),
(69536, 2019, 'bib', 'Simultaneous Enrichment Analysis of all Possible Gene-sets: Unifying Self-Contained and Competitive Methods', 'Ebrahimpoor, Mitra|Spitali, Pietro|Hettne, Kristina|Tsonaka, Roula|Goeman, Jelle', 'Medical statistics, Department of Biomedical Data Science, Leiden University Medical Center, Leiden, The Netherlands.|Department of Human Genetics, Leiden University Medical Center, Leiden, The Netherlands.|Medical statistics, Department of Biomedical Data Science, Leiden University Medical Center, Leiden, The Netherlands.|Medical statistics, Department of Biomedical Data Science, Leiden University Medical Center, Leiden, The Netherlands.|Medical statistics, Department of Biomedical Data Science, Leiden University Medical Center, Leiden, The Netherlands.', 'Netherlands|Netherlands|Netherlands|Netherlands|Netherlands', 1, '-', 'Journal Article', '10.1093/bib/bbz074', '31297505', 'Studying sets of genomic features is increasingly popular in genomics, proteomics and metabolomics since analyzing at set level not only creates a natural connection to biological knowledge but also offers more statistical power. Currently, there are two gene-set testing approaches, self-contained and competitive, both of which have their advantages and disadvantages, but neither offers the final solution. We introduce simultaneous enrichment analysis (SEA), a new approach for analysis of feature sets in genomics and other omics based on a new unified null hypothesis, which includes the self-contained and competitive null hypotheses as special cases. We employ closed testing using Simes tests to test this new hypothesis. For every feature set, the proportion of active features is estimated, and a confidence bound is provided. Also, for every unified null hypotheses, a $P$-value is calculated, which is adjusted for family-wise error rate. SEA does not need to assume that the features are independent. Moreover, users are allowed to choose the feature set(s) of interest after observing the data. We develop a novel pipeline and apply it on RNA-seq data of dystrophin-deficient mdx mice, showcasing the flexibility of the method. Finally, the power properties of the method are evaluated through simulation studies.'),
(69537, 2019, 'bib', 'Some comments on certain statistical aspects of the study of the microbiome', 'Ferreira, José A|Fuentes, Susana', 'Department of Statistics and Informatics, RIVM. National Institute for Public Health and the Environment (RIVM). Antonie van Leeuwenhoeklaan MA Bilthoven The Netherlands.|Centre for Infectious Disease Control, RIVM. National Institute for Public Health and the Environment (RIVM). Antonie van Leeuwenhoeklaan MA Bilthoven The Netherlands.', 'Netherlands|Netherlands', 0, '-', 'Journal Article', '10.1093/bib/bbz077', '31298267', 'This note complements and clarifies part of the work of Hawinkel et al. recently published in the journal and suggests some more or less standard tools and methods for carrying out association studies of the microbiome.'),
(69538, 2019, 'bib', 'Genome-wide identification and analysis of the eQTL lncRNAs in multiple sclerosis based on RNA-seq data', 'Han, Zhijie|Xue, Weiwei|Tao, Lin|Lou, Yan|Qiu, Yunqing|Zhu, Feng', 'Zhejiang Provincial Key Laboratory for Drug Clinical Research and Evaluation, The First Affiliated Hospital, Zhejiang University, Hangzhou, Zhejiang, China.|School of Pharmaceutical Sciences and Collaborative Innovation Center for Brain Science, Chongqing University, Chongqing, China.|Key Laboratory of Elemene Class Anti-cancer Chinese Medicine of Zhejiang Province, School of Medicine, Hangzhou Normal University, Hangzhou, China.|Zhejiang Provincial Key Laboratory for Drug Clinical Research and Evaluation, The First Affiliated Hospital, Zhejiang University, Hangzhou, Zhejiang, China.|Zhejiang Provincial Key Laboratory for Drug Clinical Research and Evaluation, The First Affiliated Hospital, Zhejiang University, Hangzhou, Zhejiang, China.|Zhejiang Provincial Key Laboratory for Drug Clinical Research and Evaluation, The First Affiliated Hospital, Zhejiang University, Hangzhou, Zhejiang, China.', 'China|China|China|China|China|China', 1, '-', 'Journal Article', '10.1093/bib/bbz036', '31323688', 'The pathogenesis of multiple sclerosis (MS) is significantly regulated by long noncoding RNAs (lncRNAs), the expression of which is substantially influenced by a number of MS-associated risk single nucleotide polymorphisms (SNPs). It is thus hypothesized that the dysregulation of lncRNA induced by genomic variants may be one of the key molecular mechanisms for the pathology of MS. However, due to the lack of sufficient data on lncRNA expression and SNP genotypes of the same MS patients, such molecular mechanisms underlying the pathology of MS remain elusive. In this study, a bioinformatics strategy was applied to obtain lncRNA expression and SNP genotype data simultaneously from 142 samples (51 MS patients and 91 controls) based on RNA-seq data, and an expression quantitative trait loci (eQTL) analysis was conducted. In total, 2383 differentially expressed lncRNAs were identified as specifically expressing in brain-related tissues, and 517 of them were affected by SNPs. Then, the functional characterization, secondary structure changes and tissue and disease specificity of the cis-eQTL SNPs and lncRNA were assessed. The cis-eQTL SNPs were substantially and specifically enriched in neurological disease and intergenic region, and the secondary structure was altered in 17.6% of all lncRNAs in MS. Finally, the weighted gene coexpression network and gene set enrichment analyses were used to investigate how the influence of SNPs on lncRNAs contributed to the pathogenesis of MS. As a result, the regulation of lncRNAs by SNPs was found to mainly influence the antigen processing/presentation and mitogen-activated protein kinases (MAPK) signaling pathway in MS. These results revealed the effectiveness of the strategy proposed in this study and give insight into the mechanism (SNP-mediated modulation of lncRNAs) underlying the pathology of MS.'),
(69539, 2019, 'bib', 'Comparison of haplotype-based tests for detecting gene-environment interactions with rare variants', 'Papachristou, Charalampos|Biswas, Swati', 'Department of Mathematical Sciences, Rowan University, NJ, USA.|Department of Mathematical Sciences, University of Texas at Dallas, Richardson, TX, USA.', 'United States|United States', 1, '-', 'Journal Article', '10.1093/bib/bbz031', '31329820', 'Dissecting the genetic mechanism underlying a complex disease hinges on discovering gene-environment interactions (GXE). However, detecting GXE is a challenging problem especially when the genetic variants under study are rare. Haplotype-based tests have several advantages over the so-called collapsing tests for detecting rare variants as highlighted in recent literature. Thus, it is of practical interest to compare haplotype-based tests for detecting GXE including the recent ones developed specifically for rare haplotypes. We compare the following methods: haplo.glm, hapassoc, HapReg, Bayesian hierarchical generalized linear model (BhGLM) and logistic Bayesian LASSO (LBL). We simulate data under different types of association scenarios and levels of gene-environment dependence. We find that when the type I error rates are controlled to be the same for all methods, LBL is the most powerful method for detecting GXE. We applied the methods to a lung cancer data set, in particular, in region 15q25.1 as it has been suggested in the literature that it interacts with smoking to affect the lung cancer susceptibility and that it is associated with smoking behavior. LBL and BhGLM were able to detect a rare haplotype-smoking interaction in this region. We also analyzed the sequence data from the Dallas Heart Study, a population-based multi-ethnic study. Specifically, we considered haplotype blocks in the gene ANGPTL4 for association with trait serum triglyceride and used ethnicity as a covariate. Only LBL found interactions of haplotypes with race (Hispanic). Thus, in general, LBL seems to be the best method for detecting GXE among the ones we studied here. Nonetheless, it requires the most computation time.'),
(69540, 2019, 'bib', 'Single-cell transcriptome-based multilayer network biomarker for predicting prognosis and therapeutic response of gliomas', 'Zhang, Ji|Guan, Meige|Wang, Qianliang|Zhang, Jiajun|Zhou, Tianshou|Sun, Xiaoqiang', 'Department of Neurosurgery, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-Sen University Cancer Center, Guangzhou, China.|School of Mathematics, Sun Yat-Sen University, Guangzhou, China.|School of Life Science, Sun Yat-Sen University, Guangzhou, China.|School of Mathematics, Sun Yat-Sen University, Guangzhou, China.|School of Mathematics, Sun Yat-Sen University, Guangzhou, China.|School of Mathematics, Sun Yat-Sen University, Guangzhou, China.', 'China|China|China|China|China|China', 5, '-', 'Journal Article', '10.1093/bib/bbz040', '31329830', 'Occurrence and development of cancers are governed by complex networks of interacting intercellular and intracellular signals. The technology of single-cell RNA sequencing (scRNA-seq) provides an unprecedented opportunity for dissecting the interplay between the cancer cells and the associated microenvironment. Here we combined scRNA-seq data with clinical bulk gene expression data to develop a computational pipeline for identifying the prognostic and predictive signature that connects cancer cells and microenvironmental cells. The pipeline was applied to glioma scRNA-seq data and revealed a tumor-associated microglia/macrophage-mediated EGFR/ERBB2 feedback-crosstalk signaling module, which was defined as a multilayer network biomarker (MNB) to predict survival outcome and therapeutic response of glioma patients. We used publicly available clinical data sets from large cohorts of glioma patients to examine the prognostic significance and predictive accuracy of the MNB, which outperformed conventional gene biomarkers and other methods. Additionally, the MNB was found to be predictive of the sensitivity or resistance of glioma patients to molecularly targeted therapeutics. Moreover, the MNB was an independent and the strongest prognostic factor when adjusted for clinicopathologic risk factors and other existing gene signatures. The robustness of the MNB was further tested on additional data sets. Our study presents a promising scRNA-seq transcriptome-based multilayer network approach to elucidate the interactions between tumor cell and tumor-associated microenvironment and to identify prognostic and predictive signatures of cancer patients. The proposed MNB method may facilitate the design of more effective biomarkers for predicting prognosis and therapeutic resistance of cancer patients.'),
(69541, 2019, 'bib', 'HiFreSP: A novel high-frequency sub-pathway mining approach to identify robust prognostic gene signatures', 'Li, Meng|Zhao, Jianmei|Li, Xuecang|Chen, Yang|Feng, Chenchen|Qian, Fengcui|Liu, Yuejuan|Zhang, Jian|He, Jianzhong|Ai, Bo|Ning, Ziyu|Liu, Wei|Bai, Xuefeng|Han, Xiaole|Wu, Zhiyong|Xu, Xiue|Tang, Zhidong|Pan, Qi|Xu, Liyan|Li, Chunquan|Wang, Qiuyu|Li, Enmin', 'School of Medical Informatics, Daqing Campus, Harbin Medical University, Daqing, China.|School of Medical Informatics, Daqing Campus, Harbin Medical University, Daqing, China.|School of Medical Informatics, Daqing Campus, Harbin Medical University, Daqing, China.|Key Laboratory of Molecular Biology for High Cancer Incidence Coastal Chaoshan Area, Shantou University Medical College, Shantou, China.|School of Medical Informatics, Daqing Campus, Harbin Medical University, Daqing, China.|School of Medical Informatics, Daqing Campus, Harbin Medical University, Daqing, China.|School of Medical Informatics, Daqing Campus, Harbin Medical University, Daqing, China.|School of Medical Informatics, Daqing Campus, Harbin Medical University, Daqing, China.|Institute of Oncologic Pathology, Shantou University Medical College.|School of Medical Informatics, Daqing Campus, Harbin Medical University, Daqing, China.|School of Medical Informatics, Daqing Campus, Harbin Medical University, Daqing, China.|Key Laboratory of Molecular Biology for High Cancer Incidence Coastal Chaoshan Area, Shantou University Medical College, Shantou, China.|School of Medical Informatics, Daqing Campus, Harbin Medical University, Daqing, China.|School of Medical Informatics, Daqing Campus, Harbin Medical University, Daqing, China.|Departments of Oncology Surgery, Shantou Central Hospital, Affiliated Shantou Hospital of Sun Yat-Sen University.|Institute of Oncologic Pathology, Shantou University Medical College.|School of Medical Informatics, Daqing Campus, Harbin Medical University, Daqing, China.|School of Medical Informatics, Daqing Campus, Harbin Medical University, Daqing, China.|Key Laboratory of Molecular Biology for High Cancer Incidence Coastal Chaoshan Area, Shantou University Medical College, Shantou, China.|School of Medical Informatics, Daqing Campus, Harbin Medical University, Daqing, China.|School of Medical Informatics, Daqing Campus, Harbin Medical University, Daqing, China.|Key Laboratory of Molecular Biology for High Cancer Incidence Coastal Chaoshan Area, Shantou University Medical College, Shantou, China.', 'China|China|China|China|China|China|China|China|-|China|China|China|China|China|-|-|China|China|Chin', 0, '-', 'Journal Article', '10.1093/bib/bbz078', '31350847', 'With the increasing awareness of heterogeneity in cancers, better prediction of cancer prognosis is much needed for more personalized treatment. Recently, extensive efforts have been made to explore the variations in gene expression for better prognosis. However, the prognostic gene signatures predicted by most existing methods have little robustness among different datasets of the same cancer. To improve the robustness of the gene signatures, we propose a novel high-frequency sub-pathways mining approach (HiFreSP), integrating a randomization strategy with gene interaction pathways. We identified a six-gene signature (CCND1, CSF3R, E2F2, JUP, RARA and TCF7) in esophageal squamous cell carcinoma (ESCC) by HiFreSP. This signature displayed a strong ability to predict the clinical outcome of ESCC patients in two independent datasets (log-rank test, Pxe2x80x89=xe2x80x890.0045 and 0.0087). To further show the predictive performance of HiFreSP, we applied it to two other cancers: pancreatic adenocarcinoma and breast cancer. The identified signatures show high predictive power in all testing datasets of the two cancers. Furthermore, compared with the two popular prognosis signature predicting methods, the least absolute shrinkage and selection operator penalized Cox proportional hazards model and the random survival forest, HiFreSP showed better predictive accuracy and generalization across all testing datasets of the above three cancers. Lastly, we applied HiFreSP to 8137 patients involving 20 cancer types in the TCGA database and found high-frequency prognosis-associated pathways in many cancers. Taken together, HiFreSP shows higher prognostic capability and greater robustness, and the identified signatures provide clinical guidance for cancer prognosis. HiFreSP is freely available via GitHub: https://github.com/chunquanlipathway/HiFreSP.'),
(69542, 2019, 'bib', 'Location deviations of DNA functional elements affected SNP mapping in the published databases and references', 'Zheng, Hewei|Zhao, Xueying|Wang, Hong|Ding, Yu|Lu, Xiaoyan|Zhang, Guosi|Yang, Jiaxin|Wang, Lianzong|Zhang, Haotian|Bai, Yu|Li, Jing|Wu, Jingqi|Jiang, Yongshuai|Xu, Liangde', 'Harbin Medical University and Wenzhou Medical University.|Institute of Hematology and Blood Diseases Hospital, Chinese Academy of Medical Sciences.|College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, P R China.|Harbin Medical University and Wenzhou Medical University.|Harbin Medical University and Wenzhou Medical University.|Harbin Medical University and Wenzhou Medical University.|Harbin Medical University and Wenzhou Medical University.|Harbin Medical University and Wenzhou Medical University.|Harbin Medical University and Wenzhou Medical University.|Harbin Medical University and Wenzhou Medical University.|Harbin Medical University and Wenzhou Medical University.|Harbin Medical University and Wenzhou Medical University.|College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, P. R. China.|School of Ophthalmology & Optometry and Eye Hospital, School of Biomedical Engineering, Wenzhou Medical University, Wenzhou 325027, and Training Center for Students Innovation and Entrepreneurship Education, Harbin Medical University, Harbin 150081, P. R. China.', '-|-|China|-|-|-|-|-|-|-|-|-|China|China', 0, '-', 'Journal Article', '10.1093/bib/bbz073', '31392334', 'The recent extensive application of next-generation sequencing has led to the rapid accumulation of multiple types of data for functional DNA elements. With the advent of precision medicine, the fine-mapping of risk loci based on these elements has become of paramount importance. In this study, we obtained the human reference genome (GRCh38) and the main DNA sequence elements, including protein-coding genes, miRNAs, lncRNAs and single nucleotide polymorphism flanking sequences, from different repositories. We then realigned these elements to identify their exact locations on the genome. Overall, 5%-20% of all sequence element locations deviated among databases, on the scale of kilobase-pair to megabase-pair. These deviations even affected the selection of genome-wide association study risk-associated genes. Our results implied that the location information for functional DNA elements may deviate among public databases. Researchers should take care when using cross-database sources and should perform pilot sequence alignments before element location-based studies.'),
(69543, 2019, 'bib', 'Circulating microRNA trafficking and regulation: computational principles and practice', 'Cui, Juan|Shu, Jiang', 'Systems Biology and Biomedical Informatics Laboratory, Department of Computer Science and Engineering, University of Nebraska-Lincoln, Lincoln, NE, USA.|Systems Biology and Biomedical Informatics Laboratory, Department of Computer Science and Engineering, University of Nebraska-Lincoln, Lincoln, NE, USA.', 'United States|United States', 0, '-', 'Journal Article', '10.1093/bib/bbz079', '31504144', 'Rapid advances in genomics discovery tools and a growing realization of microRNAs implication in intercellular communication have led to a proliferation of studies of circulating microRNA sorting and regulation across cells and different species. Although sometimes, reaching controversial scientific discoveries and conclusions, these studies have yielded new insights in the functional roles of circulating microRNA and a plethora of analytical methods and tools. Here, we consider this body of work in light of key computational principles underpinning discovery of circulating microRNAs in terms of their sorting and targeting, with the goal of providing practical guidance for applications that is focused on the design and analysis of circulating microRNAs and their context-dependent regulation. We survey a broad range of informatics methods and tools that are available to the researcher, discuss their key features, applications and various unsolved problems and close this review with prospects and broader implication of this field.'),
(69544, 2019, 'bib', 'Protein functional annotation of simultaneously improved stability, accuracy and false discovery rate achieved by a sequence-based deep learning', 'Hong, Jiajun|Luo, Yongchao|Zhang, Yang|Ying, Junbiao|Xue, Weiwei|Xie, Tian|Tao, Lin|Zhu, Feng', 'Key Laboratory of Elemene Class Anti-cancer Chinese Medicine of Zhejiang Province, School of Medicine, Hangzhou Normal University, Hangzhou, China.|College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, China.|College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, China.|College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, China.|School of Pharmaceutical Sciences, Chongqing University, Chongqing, China.|Key Laboratory of Elemene Class Anti-cancer Chinese Medicine of Zhejiang Province, School of Medicine, Hangzhou Normal University, Hangzhou, China.|Key Laboratory of Elemene Class Anti-cancer Chinese Medicine of Zhejiang Province, School of Medicine, Hangzhou Normal University, Hangzhou, China.|Key Laboratory of Elemene Class Anti-cancer Chinese Medicine of Zhejiang Province, School of Medicine, Hangzhou Normal University, Hangzhou, China.', 'China|China|China|China|China|China|China|China', 0, '-', 'Journal Article', '10.1093/bib/bbz081', '31504150', 'Functional annotation of protein sequence with high accuracy has become one of the most important issues in modern biomedical studies, and computational approaches of significantly accelerated analysis process and enhanced accuracy are greatly desired. Although a variety of methods have been developed to elevate protein annotation accuracy, their ability in controlling false annotation rates remains either limited or not systematically evaluated. In this study, a protein encoding strategy, together with a deep learning algorithm, was proposed to control the false discovery rate in protein function annotation, and its performances were systematically compared with that of the traditional similarity-based and de novo approaches. Based on a comprehensive assessment from multiple perspectives, the proposed strategy and algorithm were found to perform better in both prediction stability and annotation accuracy compared with other de novo methods. Moreover, an in-depth assessment revealed that it possessed an improved capacity of controlling the false discovery rate compared with traditional methods. All in all, this study not only provided a comprehensive analysis on the performances of the newly proposed strategy but also provided a tool for the researcher in the fields of protein function annotation.'),
(69545, 2019, 'bib', 'New insights on human essential genes based on integrated analysis and the construction of the HEGIAP web-based platform', 'Chen, Hebing|Zhang, Zhuo|Jiang, Shuai|Li, Ruijiang|Li, Wanying|Zhao, Chenghui|Hong, Hao|Huang, Xin|Li, Hao|Bo, Xiaochen', 'Beijing Institute of Radiation Medicine, Beijing 100850, China.|Beijing Institute of Radiation Medicine, Beijing 100850, China.|Beijing Institute of Radiation Medicine, Beijing 100850, China.|Beijing Institute of Radiation Medicine, Beijing 100850, China.|Beijing Institute of Radiation Medicine, Beijing 100850, China.|Beijing Institute of Radiation Medicine, Beijing 100850, China.|Beijing Institute of Radiation Medicine, Beijing 100850, China.|Beijing Institute of Radiation Medicine, Beijing 100850, China.|Beijing Institute of Radiation Medicine, Beijing 100850, China.|Beijing Institute of Radiation Medicine, Beijing 100850, China.', 'China|China|China|China|China|China|China|China|China|China', 2, '-', 'Journal Article', '10.1093/bib/bbz072', '31504171', 'Essential genes are those whose loss of function compromises organism viability or results in profound loss of fitness. Recent gene-editing technologies have provided new opportunities to characterize essential genes. Here, we present an integrated analysis that comprehensively and systematically elucidates the genetic and regulatory characteristics of human essential genes. First, we found that essential genes act as hubs in protein-protein interaction networks, chromatin structure and epigenetic modification. Second, essential genes represent conserved biological processes across species, although gene essentiality changes differently among species. Third, essential genes are important for cell development due to their discriminate transcription activity in embryo development and oncogenesis. In addition, we developed an interactive web server, the Human Essential Genes Interactive Analysis Platform (http://sysomics.com/HEGIAP/), which integrates abundant analytical tools to enable global, multidimensional interpretation of gene essentiality. Our study provides new insights that improve the understanding of human essential genes.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(69546, 2019, 'bib', 'Heterogeneous information network and its application to human health and disease', 'Ding, Pingjian|Ouyang, Wenjue|Luo, Jiawei|Kwoh, Chee-Keong', 'School of Computer Science, University of South China, Hengyang, China.|College of Computer Science and Electronic Engineering, Hunan University, Changsha, China.|College of Computer Science and Electronic Engineering, Hunan University, Changsha, China.|School of Computer Science and Engineering, Nanyang Technological University, Singapore, Singapore.', 'China|China|China|Singapore', 1, '-', 'Journal Article', '10.1093/bib/bbz091', '31566212', 'The molecular components with the functional interdependencies in human cell form complicated biological network. Diseases are mostly caused by the perturbations of the composite of the interaction multi-biomolecules, rather than an abnormality of a single biomolecule. Furthermore, new biological functions and processes could be revealed by discovering novel biological entity relationships. Hence, more and more biologists focus on studying the complex biological system instead of the individual biological components. The emergence of heterogeneous information network (HIN) offers a promising way to systematically explore complicated and heterogeneous relationships between various molecules for apparently distinct phenotypes. In this review, we first present the basic definition of HIN and the biological system considered as a complex HIN. Then, we discuss the topological properties of HIN and how these can be applied to detect network motif and functional module. Afterwards, methodologies of discovering relationships between disease and biomolecule are presented. Useful insights on how HIN aids in drug development and explores human interactome are provided. Finally, we analyze the challenges and opportunities for uncovering combinatorial patterns among pharmacogenomics and cell-type detection based on single-cell genomic data.'),
(69547, 2019, 'bib', 'A comprehensive method protocol for annotation and integrated functional understanding of lncRNAs', 'Kunz, Meik|Wolf, Beat|Fuchs, Maximilian|Christoph, Jan|Xiao, Ke|Thum, Thomas|Atlan, David|Prokosch, Hans-Ulrich|Dandekar, Thomas', 'Chair of Medical Informatics, Friedrich-Alexander University of Erlangen-Nürnberg, Erlangen, Germany.|University of Applied Sciences and Arts of Western Switzerland, Perolles 80, 1700 Fribourg, Switzerland.|Functional Genomics and Systems Biology Group, Department of Bioinformatics, University of Würzburg, Germany.|Chair of Medical Informatics, Friedrich-Alexander University of Erlangen-Nürnberg, Erlangen, Germany.|Institute of Molecular and Translational Therapeutic Strategies (IMTTS), Hannover Medical School, Hannover, Germany.|Institute of Molecular and Translational Therapeutic Strategies (IMTTS), Hannover Medical School, Hannover, Germany.|Phenosystems SA, 137 Rue de Tubize, 1440 Braine le Château, Belgium.|Chair of Medical Informatics, Friedrich-Alexander University of Erlangen-Nürnberg, Erlangen, Germany.|Functional Genomics and Systems Biology Group, Department of Bioinformatics, University of Würzburg, Germany.', 'Germany|Switzerland|Germany|Germany|Germany|Germany|Belgium|Germany|Germany', 0, '-', 'Journal Article', '10.1093/bib/bbz066', '31578571', 'Long non-coding RNAs (lncRNAs) are of fundamental biological importance, however, their functional role is often unclear or loosely defined as experimental characterization is challenging and bioinformatic methods are limited. We developed a novel integrated method protocol for the annotation and detailed functional characterization of lncRNAs within the genome. It combines annotation, normalization and gene expression with sequence-structure conservation, functional interactome and promoter analysis. Our protocol allows an analysis based on the tissue and biological context, and is powerful in functional characterization of experimental and clinical RNA-Seq datasets including existing lncRNAs. This is demonstrated on the uncharacterized lncRNA GATA6-AS1 in dilated cardiomyopathy.'),
(69548, 1999, 'bio', 'Imagene: an integrated computer environment for sequence annotation and analysis', 'Médigue, C|Rechenmann, F|Danchin, A|Viari, A', 'Institut Pasteur, REG, 28 rue du Docteur Roux, 75724 Paris Cedex 15, France.imagene@abi.snv.jussieu.fr|Institut Pasteur, REG, 28 rue du Docteur Roux, 75724 Paris Cedex 15, France.imagene@abi.snv.jussieu.fr|Institut Pasteur, REG, 28 rue du Docteur Roux, 75724 Paris Cedex 15, France.imagene@abi.snv.jussieu.fr|Institut Pasteur, REG, 28 rue du Docteur Roux, 75724 Paris Cedex 15, France.imagene@abi.snv.jussieu.fr', 'France|France|France|France', 39, 'Bacillus subtilis|CD-ROM|Computer Simulation|Computer Systems|Data Display|Databases, Factual|Genome|Humans|Information Storage and Retrieval|Models, Genetic|Sequence Analysis|Software|Systems Integration|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/15.1.2', '10068688', 'To be fully and efficiently exploited, data coming from sequencing projects together with specific sequence analysis tools need to be integrated within reliable data management systems. Systems designed to manage genome data and analysis tend to give a greater importance either to the data storage or to the methodological aspect, but lack a complete integration of both components.'),
(69549, 1999, 'bio', 'Stem Trace: an interactive visual tool for comparative RNA structure analysis', 'Kasprzak, W|Shapiro, B', 'Intramural Research Support Program, SAIC Frederick, National Cancer Institute/FCRDC, Bldg 469, Rm 150C, MD 21702, USA. kasprzak@ncifcrf.gov|Intramural Research Support Program, SAIC Frederick, National Cancer Institute/FCRDC, Bldg 469, Rm 150C, MD 21702, USA. kasprzak@ncifcrf.gov', 'United States|United States', 26, 'Algorithms|Computer Graphics|Computer Simulation|Data Display|Models, Molecular|Nucleic Acid Conformation|Protein Folding|RNA|Sequence Analysis|Software|Software Design|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/15.1.16', '10068689', 'Stem Trace is one of the latest tools available in STRUCTURELAB, an RNA structure analysis computer workbench. The paradigm used in STRUCTURELAB views RNA structure determination as a problem of dealing with a database of a large number of computationally generated structures. Stem Trace provides the capability to analyze this data set in a novel, visually driven, interactive and exploratory way. In addition to providing graphs at a high level of ion, it is also connected with complementary visualization tools which provide orthogonal views of the same data, as well as drawing of structures represented by a stem trace. Thus, on top of being an analysis tool, Stem Trace is a graphical user interface to an RNA structural information database.'),
(69550, 1999, 'bio', 'RNA movies: visualizing RNA secondary structure spaces', 'Evers, D|Giegerich, R', 'Technische Fakultät, Universität Bielefeld, Postfach 100 131, 33501 Bielefeld, Germany.dirk@TechFak.Uni-Bielefeld.DE|Technische Fakultät, Universität Bielefeld, Postfach 100 131, 33501 Bielefeld, Germany.dirk@TechFak.Uni-Bielefeld.DE', 'Germany|Germany', 31, 'Algorithms|Data Display|Multimedia|Nucleic Acid Conformation|Programming Languages|RNA|RNA, Bacterial', 'Journal Article', '10.1093/bioinformatics/15.1.32', '10068690', 'RNA Movies is a system for the visualization of RNA secondary structure spaces. Its input is a script consisting of primary and secondary structure information. From this script, the system fully automatically generates animated graphical structure representations. In this way, it creates the impression of an RNA molecule exploring its own two-dimensional structure space.'),
(69551, 1999, 'bio', 'Bayesian inference on biopolymer models', 'Liu, J S|Lawrence, C E', 'Department of Statistics, Stanford University, Stanford, CA, USA. jliu@stat.stabford.edu|Department of Statistics, Stanford University, Stanford, CA, USA. jliu@stat.stabford.edu', 'United States|United States', 107, 'Bayes Theorem|Computational Biology|Mathematical Computing|Models, Statistical|Monte Carlo Method|Probability|Software', 'Journal Article', '10.1093/bioinformatics/15.1.38', '10068691', 'Most existing bioinformatics methods are limited to making point estimates of one variable, e.g. the optimal alignment, with fixed input values for all other variables, e.g. gap penalties and scoring matrices. While the requirement to specify parameters remains one of the more vexing issues in bioinformatics, it is a reflection of a larger issue: the need to broaden the view on statistical inference in bioinformatics.'),
(69552, 1999, 'bio', 'Using imperfect secondary structure predictions to improve molecular structure computations', 'Chen, C C|Singh, J P|Altman, R B', 'Electrical Engineering Department, Stanford University, Stanford, CA 94305, USA.|Electrical Engineering Department, Stanford University, Stanford, CA 94305, USA.|Electrical Engineering Department, Stanford University, Stanford, CA 94305, USA.', 'United States|United States|United States', 0, 'Algorithms|Least-Squares Analysis|Models, Molecular|Models, Statistical|Probability|Protein Structure, Secondary|Stress, Mechanical', 'Journal Article', '10.1093/bioinformatics/15.1.53', '10068692', 'Until ab initio structure prediction methods are perfected, the estimation of structure for protein molecules will depend on combining multiple sources of experimental and theoretical data. Secondary structure predictions are a particularly useful source of structural information, but are currently only approximately 70% correct, on average. Structure computation algorithms which incorporate secondary structure information must therefore have methods for dealing with predictions that are imperfect. EXPERIMENTS PERFORMED: We have modified our algorithm for probabilistic least squares structural computations to accept disjunctive constraints, in which a constraint is provided as a set of possible values, each weighted with a probability. Thus, when a helix is predicted, the distances associated with a helix are given most of the weight, but some weights can be allocated to the other possibilities (strand and coil). We have tested a variety of strategies for this weighting scheme in conjunction with a baseline synthetic set of sparse distance data, and compared it with strategies which do not use disjunctive constraints.'),
(69553, 1999, 'bio', 'A fast, stochastic threading algorithm for proteins', 'Crawford, O H', 'Life Sciences Division, Oak Ridge National Laboratory, Oak Ridge, TN 37831-6480, USA. crawfordoh@ornl.gov', 'United States', 0, 'Algorithms|Amino Acid Sequence|Models, Molecular|Peptide Library|Protein Conformation|Protein Folding|Protein Structure, Tertiary|Proteins|Stochastic Processes|Templates, Genetic', 'Journal Article', '10.1093/bioinformatics/15.1.66', '10068693', 'Sequences for new proteins are being determined at a rapid rate, as a result of the Human Genome Project, and related genome research. The ability to predict the three-dimensional structure of proteins from sequence alone would be useful in discovering and understanding their function. Threading, or fold recognition, aims to predict the tertiary structure of a protein by aligning its amino acid sequence with a large number of structures, and finding the best fit. This approach depends on obtaining good performance from both the scoring function, which simulates the free energy for given trial alignments, and the threading algorithm, which searches for the lowest-score alignment. It appears that current scoring functions and threading algorithms need improvement.'),
(69554, 1999, 'bio', 'E-CELL: software environment for whole-cell simulation', 'Tomita, M|Hashimoto, K|Takahashi, K|Shimizu, T S|Matsuzaki, Y|Miyoshi, F|Saito, K|Tanida, S|Yugi, K|Venter, J C|Hutchison, C A', 'Laboratory for Bioinformatics, Keio University, 5322 Endo, Fujisawa, 252, Japan.mt@sfc.keio.ac.jp|Laboratory for Bioinformatics, Keio University, 5322 Endo, Fujisawa, 252, Japan.mt@sfc.keio.ac.jp|Laboratory for Bioinformatics, Keio University, 5322 Endo, Fujisawa, 252, Japan.mt@sfc.keio.ac.jp|Laboratory for Bioinformatics, Keio University, 5322 Endo, Fujisawa, 252, Japan.mt@sfc.keio.ac.jp|Laboratory for Bioinformatics, Keio University, 5322 Endo, Fujisawa, 252, Japan.mt@sfc.keio.ac.jp|Laboratory for Bioinformatics, Keio University, 5322 Endo, Fujisawa, 252, Japan.mt@sfc.keio.ac.jp|Laboratory for Bioinformatics, Keio University, 5322 Endo, Fujisawa, 252, Japan.mt@sfc.keio.ac.jp|Laboratory for Bioinformatics, Keio University, 5322 Endo, Fujisawa, 252, Japan.mt@sfc.keio.ac.jp|Laboratory for Bioinformatics, Keio University, 5322 Endo, Fujisawa, 252, Japan.mt@sfc.keio.ac.jp|Laboratory for Bioinformatics, Keio University, 5322 Endo, Fujisawa, 252, Japan.mt@sfc.keio.ac.jp|Laboratory for Bioinformatics, Keio University, 5322 Endo, Fujisawa, 252, Japan.mt@sfc.keio.ac.jp', 'Japan|Japan|Japan|Japan|Japan|Japan|Japan|Japan|Japan|Japan|Japan', 507, 'Adenosine Triphosphate|Artificial Intelligence|Cell Physiological Phenomena|Cells|Computer Graphics|Computer Simulation|Data Display|Enzymes|Gene Expression|Genetic Engineering|Models, Biological|Protein Binding|Software|Transcription, Genetic|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/15.1.72', '10068694', 'Genome sequencing projects and further systematic functional analyses of complete gene sets are producing an unprecedented mass of molecular information for a wide range of model organisms. This provides us with a detailed account of the cell with which we may begin to build models for simulating intracellular molecular processes to predict the dynamic behavior of living cells. Previous work in biochemical and genetic simulation has isolated well-characterized pathways for detailed analysis, but methods for building integrative models of the cell that incorporate gene regulation, metabolism and signaling have not been established. We, therefore, were motivated to develop a software environment for building such integrative models based on gene sets, and running simulations to conduct experiments in silico.'),
(69555, 1999, 'bio', 'Genetic Simulation Library', 'Conery, J|Lynch, M', 'Computational Science Institute, University of Oregon, Eugene, OR 97403, USA.|Computational Science Institute, University of Oregon, Eugene, OR 97403, USA.', 'United States|United States', 3, 'Computer Simulation|Gene Library|Genotype|Internet|Models, Molecular|Software', 'Journal Article', '10.1093/bioinformatics/15.1.85', '10068695', 'The Genetic Simulation Library (GSL) is a set of C++ programs and classes that can be used in individual-based simulation models of plant and animal populations. The classes in this library build representations of genes for a wide variety of different types of organisms, from asexually reproducing single-cell organisms to chromosome-structured sexually reproducing species.'),
(69556, 1999, 'bio', 'BAliBASE: a benchmark alignment database for the evaluation of multiple alignment programs', 'Thompson, J D|Plewniak, F|Poch, O', 'Institut de Génétique et de Biologie Moleculaire et Cellulaire (CNRS/INSERM/ULP), BP 163, 67404 Illkirch Cedex, France. julie@igbmc.u-strasbg.fr|Institut de Génétique et de Biologie Moleculaire et Cellulaire (CNRS/INSERM/ULP), BP 163, 67404 Illkirch Cedex, France. julie@igbmc.u-strasbg.fr|Institut de Génétique et de Biologie Moleculaire et Cellulaire (CNRS/INSERM/ULP), BP 163, 67404 Illkirch Cedex, France. julie@igbmc.u-strasbg.fr', 'France|France|France', 357, 'Algorithms|Benchmarking|Data Display|Sequence Alignment|Software', 'Journal Article', '10.1093/bioinformatics/15.1.87', '10068696', 'BAliBASE is a database of manually refined multiple sequence alignments categorized by core blocks of conservation sequence length, similarity, and the presence of insertions and N/C-terminal extensions.'),
(69557, 1999, 'bio', 'Application of distance geometry to 3D visualization of sequence relationships', 'Forster, M|Heath, A|Afzal, M', 'National Institute for Biological Standards and Control, Blanche Lane, South Mimms, Herts. EN6 3QG, UK. mforster@nibsc.ac.uk|National Institute for Biological Standards and Control, Blanche Lane, South Mimms, Herts. EN6 3QG, UK. mforster@nibsc.ac.uk|National Institute for Biological Standards and Control, Blanche Lane, South Mimms, Herts. EN6 3QG, UK. mforster@nibsc.ac.uk', 'United Kingdom|United Kingdom|United Kingdom', 16, 'Algorithms|Animals|Computer Simulation|DNA, Complementary|DNA, Viral|Internet|Mathematical Computing|Models, Molecular|Molecular Conformation|Mumps virus|Prions|RNA|Rabbits|Rats|Sequence Alignment', 'Journal Article', '10.1093/bioinformatics/15.1.89', '10068697', 'We describe the application of distance geometry methods to the three-dimensional visualization of sequence relationships, with examples for mumps virus SH gene cDNA and prion protein sequences. Sequence-sequence distance measures may be obtained from either a multiple sequence alignment or from sets of pairwise alignments.'),
(69558, 1999, 'bio', 'A computer-driven approach to PCR-based differential screening, alternative to differential display', 'Consalez, G G|Cabibbo, A|Corradi, A|Alli, C|Sardella, M|Sitia, R|Fesce, R', 'Department of Biological and Technological Research (DIBIT), San Raffaele Scientific Institute (HSR), Via Olgettina 58, 20132 Milano, Italy.|Department of Biological and Technological Research (DIBIT), San Raffaele Scientific Institute (HSR), Via Olgettina 58, 20132 Milano, Italy.|Department of Biological and Technological Research (DIBIT), San Raffaele Scientific Institute (HSR), Via Olgettina 58, 20132 Milano, Italy.|Department of Biological and Technological Research (DIBIT), San Raffaele Scientific Institute (HSR), Via Olgettina 58, 20132 Milano, Italy.|Department of Biological and Technological Research (DIBIT), San Raffaele Scientific Institute (HSR), Via Olgettina 58, 20132 Milano, Italy.|Department of Biological and Technological Research (DIBIT), San Raffaele Scientific Institute (HSR), Via Olgettina 58, 20132 Milano, Italy.|Department of Biological and Technological Research (DIBIT), San Raffaele Scientific Institute (HSR), Via Olgettina 58, 20132 Milano, Italy.', 'Italy|Italy|Italy|Italy|Italy|Italy|Italy', 14, 'Base Composition|Base Sequence|Computer Simulation|DNA Primers|Humans|RNA|RNA, Messenger|Reverse Transcriptase Polymerase Chain Reaction|Software', 'Journal Article', '10.1093/bioinformatics/15.2.93', '10089194', 'Polymerase chain reaction (PCR)-based RNA fingerprinting is a powerful tool for the isolation of differentially expressed genes in studies of neoplasia, differentiation or development. Arbitrarily primed RNA fingerprinting is capable of targeting coding regions of genes, as opposed to differential display techniques, which target 3 non-coding cDNA. In order to be of general use and to permit a systematic survey of differential gene expression, RNA fingerprinting has to be standardized and a number of highly efficient and selective arbitrary primers must be identified.'),
(69559, 1999, 'bio', 'Establishing a method of vector contamination identification in database sequences', 'Seluja, G A|Farmer, A|McLeod, M|Harger, C|Schad, P A', 'National Center for Genome Resources, 1800-A Old Pecos Trail, Santa Fe, NM 87505, USA.|National Center for Genome Resources, 1800-A Old Pecos Trail, Santa Fe, NM 87505, USA.|National Center for Genome Resources, 1800-A Old Pecos Trail, Santa Fe, NM 87505, USA.|National Center for Genome Resources, 1800-A Old Pecos Trail, Santa Fe, NM 87505, USA.|National Center for Genome Resources, 1800-A Old Pecos Trail, Santa Fe, NM 87505, USA.', 'United States|United States|United States|United States|United States', 21, 'Animals|Base Sequence|Cloning, Molecular|DNA|Databases, Factual|Genetic Vectors|Humans|Sequence Analysis, DNA', 'Journal Article', '10.1093/bioinformatics/15.2.106', '10089195', 'The nucleotide sequence databases are invaluable tools both for the private and the academic research communities, from the retrieval of sequences to homology searching. Several issues related to data quality, such as the existence of sequencing artifacts and errors, are facing the databases. We investigated a major source of these errors, i.e. the presence of vector-contaminated sequences.'),
(69560, 1999, 'bio', 'A RAPID algorithm for sequence database comparisons: application to the identification of vector contamination in the EMBL databases', 'Miller, C|Gurd, J|Brass, A', 'School of Biological Sciences, 2.205 The Stopford Building, University of Manchester, Oxford Road, Manchester M13 9PT, UK.|School of Biological Sciences, 2.205 The Stopford Building, University of Manchester, Oxford Road, Manchester M13 9PT, UK.|School of Biological Sciences, 2.205 The Stopford Building, University of Manchester, Oxford Road, Manchester M13 9PT, UK.', 'United Kingdom|United Kingdom|United Kingdom', 27, 'Algorithms|Bacteriophage lambda|Base Sequence|DNA|DNA, Viral|Databases, Factual|Evaluation Studies as Topic|Expressed Sequence Tags|Genetic Vectors|Molecular Sequence Data|ROC Curve|Sequence Alignment', 'Comparative Study', '10.1093/bioinformatics/15.2.111', '10089196', 'Word-matching algorithms such as BLAST are routinely used for sequence comparison. These algorithms typically use areas of matching words to seed alignments which are then used to assess the degree of sequence similarity. In this paper, we show that by formally separating the word-matching and sequence-alignment process, and using information about word frequencies to generate alignments and similarity scores, we can create a new sequence-comparison algorithm which is both fast and sensitive. The formal split between word searching and alignment allows users to select an appropriate alignment method without affecting the underlying similarity search. The algorithm has been used to develop software for identifying entries in DNA sequence databases which are contaminated with vector sequence.'),
(69561, 1999, 'bio', 'Combining many multiple alignments in one improved alignment', 'Bucka-Lassen, K|Caprani, O|Hein, J', 'Object Oriented Ltd, 6004 Luzern, Switzerland, Department of Computer Science and Department of Ecology and Genetics, University of Aarhus, 8000 Aarhus C, Denmark.|Object Oriented Ltd, 6004 Luzern, Switzerland, Department of Computer Science and Department of Ecology and Genetics, University of Aarhus, 8000 Aarhus C, Denmark.|Object Oriented Ltd, 6004 Luzern, Switzerland, Department of Computer Science and Department of Ecology and Genetics, University of Aarhus, 8000 Aarhus C, Denmark.', 'Switzerland|Switzerland|Switzerland', 27, 'Algorithms|Base Sequence|Computer Simulation|DNA|Molecular Sequence Data|RNA, Ribosomal, 5S|Sequence Alignment|Software', 'Journal Article', '10.1093/bioinformatics/15.2.122', '10089197', 'The fact that the multiple sequence alignment problem is of high complexity has led to many different heuristic algorithms attempting to find a solution in what would be considered a reasonable amount of computation time and space. Very few of these heuristics produce results that are guaranteed always to lie within a certain distance of an optimal solution (given a measure of quality, e.g. parsimony). Most practical heuristics cannot guarantee this, but nevertheless perform well for certain cases. An alignment, obtained with one of these heuristics and with a bad overall score, is not unusable though, it might contain important information on how substrings should be aligned. This paper presents a method that extracts qualitatively good sub-alignments from a set of multiple alignments and combines these into a new, often improved alignment. The algorithm is implemented as a variant of the traditional dynamic programming technique.'),
(69562, 1999, 'bio', 'FORESST: fold recognition from secondary structure predictions of proteins', 'Di Francesco, V|Munson, P J|Garnier, J', 'Analytical Biostatistics Section, Mathematical and Statistical Computing Laboratory, The Institute, Center for Information Technology, National Institutes of Health, Bethesda, MD 20892-5626, USA.|Analytical Biostatistics Section, Mathematical and Statistical Computing Laboratory, The Institute, Center for Information Technology, National Institutes of Health, Bethesda, MD 20892-5626, USA.|Analytical Biostatistics Section, Mathematical and Statistical Computing Laboratory, The Institute, Center for Information Technology, National Institutes of Health, Bethesda, MD 20892-5626, USA.', 'United States|United States|United States', 43, 'Computer Simulation|Databases, Factual|Markov Chains|Protein Folding|Protein Structure, Secondary|Proteins|Reproducibility of Results|Software|Stochastic Processes', 'Journal Article', '10.1093/bioinformatics/15.2.131', '10089198', 'A method for recognizing the three-dimensional fold from the protein amino acid sequence based on a combination of hidden Markov models (HMMs) and secondary structure prediction was recently developed for proteins in the Mainly-Alpha structural class. Here, this methodology is extended to Mainly-Beta and Alpha-Beta class proteins. Compared to other fold recognition methods based on HMMs, this approach is novel in that only secondary structure information is used. Each HMM is trained from known secondary structure sequences of proteins having a similar fold. Secondary structure prediction is performed for the amino acid sequence of a query protein. The predicted fold of a query protein is the fold described by the model fitting the predicted sequence the best.'),
(69563, 1999, 'bio', 'The hydrophobic cores of proteins predicted by wavelet analysis', 'Hirakawa, H|Muta, S|Kuhara, S', 'Graduate School of Genetic Resources Technology, Kyushu University, Hakozaki, Higashi-ku, Fukuoka 812-8581, Japan.|Graduate School of Genetic Resources Technology, Kyushu University, Hakozaki, Higashi-ku, Fukuoka 812-8581, Japan.|Graduate School of Genetic Resources Technology, Kyushu University, Hakozaki, Higashi-ku, Fukuoka 812-8581, Japan.', 'Japan|Japan|Japan', 50, 'Computer Simulation|Models, Molecular|Protein Conformation|Proteins|Reproducibility of Results|Sequence Alignment|Software', 'Comparative Study', '10.1093/bioinformatics/15.2.141', '10089199', 'In the process of protein construction, buried hydrophobic residues tend to assemble in a core of a protein. Methods used to predict these cores involve use or no use of sequential alignment. In the case of a close homology, prediction was more accurate if sequential alignment was used. If the homology was weak, predictions would be unreliable. A hydrophobicity plot involving the hydropathy index is useful for purposes of prediction, and smoothing is essential. However, the proposed methods are insufficient. We attempted to predict hydrophobic cores with a low frequency extracted from the hydrophobicity plot, using wavelet analysis.'),
(69564, 1999, 'bio', 'HICLAS: a taxonomic database system for displaying and comparing biological classification and phylogenetic trees', 'Zhong, Y|Luo, Y|Pramanik, S|Beaman, J H', 'Department of Computer Science and Department of Botany and Plant Pathology, Michigan State University, East Lansing, MI 48824, USA.|Department of Computer Science and Department of Botany and Plant Pathology, Michigan State University, East Lansing, MI 48824, USA.|Department of Computer Science and Department of Botany and Plant Pathology, Michigan State University, East Lansing, MI 48824, USA.|Department of Computer Science and Department of Botany and Plant Pathology, Michigan State University, East Lansing, MI 48824, USA.', 'United States|United States|United States|United States', 8, 'Classification|Database Management Systems|Databases, Factual|Phylogeny|Software', 'Comparative Study', '10.1093/bioinformatics/15.2.149', '10089200', 'Numerous database management systems have been developed for processing various taxonomic data bases on biological classification or phylogenetic information. In this paper, we present an integrated system to deal with interacting classifications and phylogenies concerning particular taxonomic groups.'),
(69565, 1999, 'bio', 'A proposal for a standard CORBA interface for genome maps', 'Barillot, E|Leser, U|Lijnzaad, P|Cussat-Blanc, C|Jungfer, K|Guyon, F|Vaysseix, G|Helgesen, C|Rodriguez-Tomé, P', 'GIS Infobiogen, 7 rue Guy Môquet - BP 8, 94801 Villejuif cedex, France.|GIS Infobiogen, 7 rue Guy Môquet - BP 8, 94801 Villejuif cedex, France.|GIS Infobiogen, 7 rue Guy Môquet - BP 8, 94801 Villejuif cedex, France.|GIS Infobiogen, 7 rue Guy Môquet - BP 8, 94801 Villejuif cedex, France.|GIS Infobiogen, 7 rue Guy Môquet - BP 8, 94801 Villejuif cedex, France.|GIS Infobiogen, 7 rue Guy Môquet - BP 8, 94801 Villejuif cedex, France.|GIS Infobiogen, 7 rue Guy Môquet - BP 8, 94801 Villejuif cedex, France.|GIS Infobiogen, 7 rue Guy Môquet - BP 8, 94801 Villejuif cedex, France.|GIS Infobiogen, 7 rue Guy Môquet - BP 8, 94801 Villejuif cedex, France.', 'France|France|France|France|France|France|France|France|France', 12, 'Animals|Chromosome Mapping|Computer Systems|Databases, Factual|Genome|Human Genome Project|Humans|Programming Languages|Software|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/15.2.157', '10089201', 'The scientific community urgently needs to standardize the exchange of biological data. This is helped by the use of a common protocol and the definition of shared data structures. We have based our standardization work on CORBA, a technology that has become a standard in the past years and allows interoperability between distributed objects.'),
(69566, 1999, 'bio', 'An extensive list of genes that produce alternative transcripts in the mouse', 'Mangan, M E|Frazer, K S', 'The Jackson Laboratory, 600 Main Street, Bar Harbor, ME 04609, USA.|The Jackson Laboratory, 600 Main Street, Bar Harbor, ME 04609, USA.', 'United States|United States', 5, 'Alternative Splicing|Animals|Databases, Factual|Internet|Mice', 'Journal Article', '10.1093/bioinformatics/15.2.170', '10089202', 'A single gene can generate multiple transcribed gene products. An extensive list of alternatively transcribed mouse genes has been generated, and is publicly available from http://www.informatics.jax.org/report.html.'),
(69567, 1999, 'bio', 'TargetFinder: searching annotated sequence databases for target genes of transcription factors', 'Lavorgna, G|Guffanti, A|Borsani, G|Ballabio, A|Boncinelli, E', 'DIBIT, HSR Via Olgettina 60, 20132 Milano, Centro per lo studio della Farmacologia Cellulare e Molecolare, CNR, Via Vanvitelli 32, 20129 Milano, Italy.|DIBIT, HSR Via Olgettina 60, 20132 Milano, Centro per lo studio della Farmacologia Cellulare e Molecolare, CNR, Via Vanvitelli 32, 20129 Milano, Italy.|DIBIT, HSR Via Olgettina 60, 20132 Milano, Centro per lo studio della Farmacologia Cellulare e Molecolare, CNR, Via Vanvitelli 32, 20129 Milano, Italy.|DIBIT, HSR Via Olgettina 60, 20132 Milano, Centro per lo studio della Farmacologia Cellulare e Molecolare, CNR, Via Vanvitelli 32, 20129 Milano, Italy.|DIBIT, HSR Via Olgettina 60, 20132 Milano, Centro per lo studio della Farmacologia Cellulare e Molecolare, CNR, Via Vanvitelli 32, 20129 Milano, Italy.', 'Italy|Italy|Italy|Italy|Italy', 0, 'Binding Sites|Databases, Factual|Genes|Sequence Alignment|Software|Transcription Factors', 'Journal Article', '10.1093/bioinformatics/15.2.172', '10089203', 'TargetFinder is a new software tool to search a database of annotated sequences for transcription factor binding sites located in context with other important transcription regulatory signals and regions, like the TATA element, the promoter, and so on, thereby greatly reducing the background usually associated with this kind of search.'),
(69568, 1999, 'bio', 'DnaSP version 3: an integrated program for molecular population genetics and molecular evolution analysis', 'Rozas, J|Rozas, R', 'Departament de Genètica, Facultat de Biologia, Universitat de Barcelona, Diagonal 645, 08071 Barcelona, Spain.|Departament de Genètica, Facultat de Biologia, Universitat de Barcelona, Diagonal 645, 08071 Barcelona, Spain.', 'Spain|Spain', 0, 'Computer Simulation|DNA|Evolution, Molecular|Genetics, Population|Models, Genetic|Molecular Biology|Polymorphism, Genetic|Software', 'Journal Article', '10.1093/bioinformatics/15.2.174', '10089204', 'DnaSP is a Windows integrated software package for the analysis of the DNA polymorphism from nucleotide sequence data. DnaSP version 3 incorporates several methods for estimating the amount and pattern of DNA polymorphism and divergence, and for conducting neutrality tests.'),
(69569, 1999, 'bio', 'PredAcc: prediction of solvent accessibility', 'Mucchielli-Giorgi, M H|Hazout, S|Tufféry, P', 'Equipe de Bioinformatique Moléculaire, INSERM U155, Université Paris 7, case 7113, 2, place Jussieu, 75251 Paris cedex 05, France.|Equipe de Bioinformatique Moléculaire, INSERM U155, Université Paris 7, case 7113, 2, place Jussieu, 75251 Paris cedex 05, France.|Equipe de Bioinformatique Moléculaire, INSERM U155, Université Paris 7, case 7113, 2, place Jussieu, 75251 Paris cedex 05, France.', 'France|France|France', 47, 'Amino Acids|Computer Simulation|Logistic Models|Models, Chemical|Protein Conformation|Proteins|Software|Solvents', 'Journal Article', '10.1093/bioinformatics/15.2.176', '10089205', 'PredAcc is a tool for predicting the solvent accessibility of protein residues from the sequence at different relative accessibility levels (0-55%). The prediction rate varies between 70. 7% (for 25% relative accessibility) and 85.7% (for 0% relative accessibility). Amino acids are predicted in four categories: almost certainly hidden and almost certainly exposed with a given a posteriori prediction error, probably hidden and probably exposed otherwise.'),
(69570, 1999, 'bio', 'Functional promoter modules can be detected by formal models independent of overall nucleotide sequence similarity', 'Klingenhoff, A|Frech, K|Quandt, K|Werner, T', 'Institute of Mammalian Genetics, GSF-National Research Center for Environment and Health, Ingolstaedter Landstrasse 1, D-85764 Neuherberg, Germany.|Institute of Mammalian Genetics, GSF-National Research Center for Environment and Health, Ingolstaedter Landstrasse 1, D-85764 Neuherberg, Germany.|Institute of Mammalian Genetics, GSF-National Research Center for Environment and Health, Ingolstaedter Landstrasse 1, D-85764 Neuherberg, Germany.|Institute of Mammalian Genetics, GSF-National Research Center for Environment and Health, Ingolstaedter Landstrasse 1, D-85764 Neuherberg, Germany.', 'Germany|Germany|Germany|Germany', 0, 'Algorithms|Base Sequence|Binding Sites|DNA|Databases, Factual|HLA Antigens|Humans|Interferon-beta|Models, Genetic|Molecular Sequence Data|Promoter Regions, Genetic|Sequence Alignment|Sequence Homology, Nucleic Acid|Software|Transcription Factors|beta 2-Microglobulin', 'Comparative Study', '10.1093/bioinformatics/15.3.180', '10222404', 'Gene regulation often depends on functional modules which feature a detectable internal organization. Overall sequence similarity of these modules is often insufficient for detection by general search methods like FASTA or even Gapped BLAST. However, it is of interest to evaluate whether modules, often known from experimental analysis of single sequences, are present in other regulatory sequences.'),
(69571, 1999, 'bio', '10-11 bp periodicities in complete genomes reflect protein structure and DNA folding', 'Herzel, H|Weiss, O|Trifonov, E N', 'Institute for Theoretical Biology, Humboldt University, Invalidenstr. 43, D-10115 Berlin, Germany. h.herzel@biologie.hu-berlin.de|Institute for Theoretical Biology, Humboldt University, Invalidenstr. 43, D-10115 Berlin, Germany. h.herzel@biologie.hu-berlin.de|Institute for Theoretical Biology, Humboldt University, Invalidenstr. 43, D-10115 Berlin, Germany. h.herzel@biologie.hu-berlin.de', 'Germany|Germany|Germany', 137, 'Archaea|Bacteria|DNA|DNA, Archaeal|DNA, Bacterial|DNA, Fungal|DNA, Superhelical|Databases, Factual|Fungal Proteins|Genome|Genome, Fungal|Nucleic Acid Conformation|Nucleosomes|Protein Conformation|Repetitive Sequences, Nucleic Acid|Software|Yeasts', 'Journal Article', '10.1093/bioinformatics/15.3.187', '10222405', 'Completely sequenced genomes allow for detection and analysis of the relatively weak periodicities of 10-11 basepairs (bp). Two sources contribute to such signals: correlations in the corresponding protein sequences due to the amphipatic character of alpha-helices and the folding of DNA (nucleosomal patterns, DNA supercoiling). Since the topological state of genomic DNA is of importance for its replication, recombination and transcription, there is an immediate interest to obtain information about the supercoiled state from sequence periodicities.'),
(69572, 1999, 'bio', 'Transformation distances: a family of dissimilarity measures based on movements of segments', 'Varré, J S|Delahaye, J P|Rivals, E', 'Laboratoire dInformatique Fondamentale de Lille (LIFL), UFR IEEA - Bât M3, 59655 Villeneuve dAscq Cedex, France.|Laboratoire dInformatique Fondamentale de Lille (LIFL), UFR IEEA - Bât M3, 59655 Villeneuve dAscq Cedex, France.|Laboratoire dInformatique Fondamentale de Lille (LIFL), UFR IEEA - Bât M3, 59655 Villeneuve dAscq Cedex, France.', 'France|France|France', 43, 'Algorithms|Base Sequence|Evolution, Molecular|Gene Rearrangement|Molecular Sequence Data|Mutation|Plants, Toxic|RNA|Retroelements|Sequence Alignment|Sequence Homology, Nucleic Acid|Software|Terminal Repeat Sequences|Tobacco|Transformation, Genetic', 'Journal Article', '10.1093/bioinformatics/15.3.194', '10222406', 'Evolution acts in several ways on DNA: either by mutating a base, or by inserting, deleting or copying a segment of the sequence (Ruddle, 1997, Russell, 1994, Li and Grauer, 1991). Classical alignment methods deal with point mutations (Waterman, 1995), genome-level mutations are studied using genome rearrangement distances (Bafna and Pevzner, 1993, 1995, Kececioglu and Sankoff, 1994, Kececioglu and Ravi, 1995). The latter distances generally operate, not on the sequences, but on an ordered list of genes. To our knowledge, no measure of distance attempts to compare sequences using a general set of segment-based operations.'),
(69573, 1999, 'bio', 'An exact solution for the segment-to-segment multiple sequence alignment problem', 'Lenhof, H P|Morgenstern, B|Reinert, K', 'MPI für Informatik, Im Stadtwald, 66123 Saarbrücken, Germany.|MPI für Informatik, Im Stadtwald, 66123 Saarbrücken, Germany.|MPI für Informatik, Im Stadtwald, 66123 Saarbrücken, Germany.', 'Germany|Germany|Germany', 0, 'Algorithms|Amino Acid Sequence|Globins|Molecular Sequence Data|Prions|Sequence Alignment|Sequence Homology, Amino Acid|Software', 'Journal Article', '10.1093/bioinformatics/15.3.203', '10222407', 'In molecular biology, sequence alignment is a crucial tool in studying the structure and function of molecules, as well as the evolution of species. In the segment-to-segment variation of the multiple alignment problem, the input can be seen as a set of non-gapped segment pairs (diagonals). Given a weight function that assigns a weight score to every possible diagonal, the goal is to choose a consistent set of diagonals of maximum weight. We show that the segment-to-segment multiple alignment problem is equivalent to a novel formulation of the Maximum Trace problem: the Generalized Maximum Trace (GMT) problem. Solving this problem to optimality, therefore, may improve upon the previous greedy strategies that are used for solving the segment-to-segment multiple sequence alignment problem. We show that the GMT can be stated in terms of an integer linear program and then solve the integer linear program using methods from polyhedral combinatorics. This leads to a branch-and-cut algorithm for segment-to-segment multiple sequence alignment.'),
(69574, 1999, 'bio', 'DIALIGN 2: improvement of the segment-to-segment approach to multiple sequence alignment', 'Morgenstern, B', 'GSF - National Research Center for Environment and Health, Institute of Biomathematics and Biometry, Ingolstädter Landstrasse 1, 85764 Neuherberg, Germany. burkhard.morgenstern@rp-rorer.co.uk', 'Germany', 0, 'Algorithms|Amino Acid Sequence|Molecular Sequence Data|Proteins|Sequence Alignment|Sequence Homology, Amino Acid|Software', 'Comparative Study', '10.1093/bioinformatics/15.3.211', '10222408', 'The performance and time complexity of an improved version of the segment-to-segment approach to multiple sequence alignment is discussed. In this approach, alignments are composed from gap-free segment pairs, and the score of an alignment is defined as the sum of so-called weights of these segment pairs.'),
(69575, 1999, 'bio', 'EDITtoTrEMBL: a distributed approach to high-quality automated protein sequence annotation', 'Möller, S|Leser, U|Fleischmann, W|Apweiler, R', 'European Bioinformatics Institute, Hinxton, UK. moeller@ebi.ac.uk|European Bioinformatics Institute, Hinxton, UK. moeller@ebi.ac.uk|European Bioinformatics Institute, Hinxton, UK. moeller@ebi.ac.uk|European Bioinformatics Institute, Hinxton, UK. moeller@ebi.ac.uk', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom', 0, 'Algorithms|Amino Acid Sequence|Databases, Factual|Humans|Molecular Sequence Data|Proteins|Sequence Analysis|Software', 'Comparative Study', '10.1093/bioinformatics/15.3.219', '10222409', 'Many databases in molecular biology face the problem that the ever increasing rate of data production can no longer be handled by traditional methods, especially human curation. Therefore, a number of projects are currently investigating methods for automated sequence annotation. This paper describes the EBIs approach to this problem for protein sequences by integration of arbitrary analysis programs into a distributed and highly flexible environment. Our software framework allows an individual treatment of sequences depending on their particular properties, which is achieved through a high-level description of the preconditions and capabilities of analysing modules. This not only improves the overall performance of the annotation process, as unnecessary steps are avoided, but also enhances its quality since dependencies between different modules are taken into account. We have implemented a prototype and use it in the production of TrEMBL releases.'),
(69576, 1999, 'bio', 'A novel method for automatic functional annotation of proteins', 'Fleischmann, W|Möller, S|Gateau, A|Apweiler, R', 'The EMBL Outstation - The European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK. fleischmann@ebi.ac.uk|The EMBL Outstation - The European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK. fleischmann@ebi.ac.uk|The EMBL Outstation - The European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK. fleischmann@ebi.ac.uk|The EMBL Outstation - The European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK. fleischmann@ebi.ac.uk', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom', 69, 'Algorithms|Databases, Factual|Proteins|Reproducibility of Results|Software', 'Journal Article', '10.1093/bioinformatics/15.3.228', '10222410', 'To cope with the increasing amount of sequence data, reliable automatic annotation tools are required. The TrEMBL database contains together with SWISS-PROT nearly all publicly available protein sequences, but in contrast to SWISS-PROT only limited functional annotation. To improve this situation, we had to develop a method of automatic annotation that produces highly reliable functional prediction using the language and the syntax of SWISS-PROT.'),
(69577, 1999, 'bio', 'Application of constraint programming techniques for structure prediction of lattice proteins with extended alphabets', 'Backofen, R|Will, S|Bornberg-Bauer, E', 'Institut für Informatik, LMU München, Oettingenstrasse 67, D-80538 München, Germany. backofen@informatik.uni-muenchen.de|Institut für Informatik, LMU München, Oettingenstrasse 67, D-80538 München, Germany. backofen@informatik.uni-muenchen.de|Institut für Informatik, LMU München, Oettingenstrasse 67, D-80538 München, Germany. backofen@informatik.uni-muenchen.de', 'Germany|Germany|Germany', 34, 'Algorithms|Amino Acid Sequence|Computer Simulation|Models, Molecular|Molecular Sequence Data|Protein Conformation|Proteins|Software|Thermodynamics', 'Journal Article', '10.1093/bioinformatics/15.3.234', '10222411', 'Predicting the ground state of biopolymers is a notoriously hard problem in biocomputing. Model systems, such as lattice proteins, are simple tools and valuable to test and improve new methods. Best known are models with sequences composed from a binary (hydrophobic and polar) alphabet. The major drawback is the degeneracy, i.e. the number of different ground state conformations.'),
(69578, 1999, 'bio', 'Docking of hydrophobic ligands with interaction-based matching algorithms', 'Rarey, M|Kramer, B|Lengauer, T', 'German National Research Center for Information Technology (GMD), Institute for Algorithms and Scientific Computing (SCAI), Schloss Birlinghoven, 53754 Sankt Augustin, Germany. Rarey@gmd.de|German National Research Center for Information Technology (GMD), Institute for Algorithms and Scientific Computing (SCAI), Schloss Birlinghoven, 53754 Sankt Augustin, Germany. Rarey@gmd.de|German National Research Center for Information Technology (GMD), Institute for Algorithms and Scientific Computing (SCAI), Schloss Birlinghoven, 53754 Sankt Augustin, Germany. Rarey@gmd.de', 'Germany|Germany|Germany', 73, 'Algorithms|Binding Sites|Databases, Factual|Ligands|Models, Molecular|Protein Binding|Protein Conformation|Proteins|Software', 'Journal Article', '10.1093/bioinformatics/15.3.243', '10222412', 'Matching of chemical interacting groups is a common concept for docking and fragment placement algorithms in computer-aided drug design. These algorithms have been proven to be reliable and fast if at least a certain number of hydrogen bonds or salt bridges occur. However, the algorithms typically run into problems if hydrophobic fragments or ligands should be placed. In order to dock hydrophobic fragments without significant loss of computational efficiency, we have extended the interaction model and placement algorithms in our docking tool FlexX. The concept of multi-level interactions is introduced into the algorithms for automatic selection and placement of base fragments.'),
(69579, 1999, 'bio', 'METATOOL: for studying metabolic networks', 'Pfeiffer, T|Sánchez-Valdenebro, I|Nuño, J C|Montero, F|Schuster, S', 'Humboldt University Berlin, Institute of Biology, Invalidenstrasse 42, D-10115 Berlin, Germany. Tpfeiffer@bp.biologie.hu-berlin.de|Humboldt University Berlin, Institute of Biology, Invalidenstrasse 42, D-10115 Berlin, Germany. Tpfeiffer@bp.biologie.hu-berlin.de|Humboldt University Berlin, Institute of Biology, Invalidenstrasse 42, D-10115 Berlin, Germany. Tpfeiffer@bp.biologie.hu-berlin.de|Humboldt University Berlin, Institute of Biology, Invalidenstrasse 42, D-10115 Berlin, Germany. Tpfeiffer@bp.biologie.hu-berlin.de|Humboldt University Berlin, Institute of Biology, Invalidenstrasse 42, D-10115 Berlin, Germany. Tpfeiffer@bp.biologie.hu-berlin.de', 'Germany|Germany|Germany|Germany|Germany', 0, 'Algorithms|Carbohydrate Metabolism|Enzymes|Glycolysis|Metabolism|Models, Biological|Pentose Phosphate Pathway|Software', 'Journal Article', '10.1093/bioinformatics/15.3.251', '10222413', 'To reconstruct metabolic pathways from biochemical and/or genome sequence data, the stoichiometric and thermodynamic feasibility of the pathways has to be tested. This is achieved by characterizing the admissible region of flux distributions in steady state. This region is spanned by what can be called a convex basis. The concept of elementary flux modes provides a mathematical tool to define all metabolic routes that are feasible in a given metabolic network. In addition, we define enzyme subsets to be groups of enzymes that operate together in fixed flux proportions in all steady states of the system.'),
(69580, 1999, 'bio', 'Removing redundancy in SWISS-PROT and TrEMBL', 'ODonovan, C|Martin, M J|Glemet, E|Codani, J J|Apweiler, R', 'EMBL Outstation - The European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK. odonovan@ebi.ac.uk|EMBL Outstation - The European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK. odonovan@ebi.ac.uk|EMBL Outstation - The European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK. odonovan@ebi.ac.uk|EMBL Outstation - The European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK. odonovan@ebi.ac.uk|EMBL Outstation - The European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK. odonovan@ebi.ac.uk', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom', 0, 'Algorithms|Databases, Factual|Genetic Variation|Mutation|Polymorphism, Genetic|Proteins|Sequence Analysis|Software', 'Journal Article', '10.1093/bioinformatics/15.3.258', '10222414', 'One of the distinguishing criteria of the SWISS-PROT protein sequence data bank is minimal redundancy. The introduction of TrEMBL as a supplementary database ensured the comprehensiveness of SWISS-PROT and TrEMBL but introduced some degree of redundancy. We developed a strategy to identify the redundancy present within and between SWISS-PROT and TrEMBL and its subsequent removal.');
