INSERT INTO `keywords` (`id`, `nome`, `ano`, `total`) VALUES
(32610, 'NF-kappa B', 2019, 1);

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CREATE TABLE `papers` (
  `id` int(11) NOT NULL,
  `ano` int(11) DEFAULT NULL,
  `journal` varchar(30) DEFAULT NULL,
  `titulo` varchar(255) DEFAULT NULL,
  `autores` text,
  `afiliacao` text,
  `pais` varchar(255) DEFAULT NULL,
  `citacoes` int(11) DEFAULT NULL,
  `palavra_chave` text,
  `categorias` text,
  `doi` varchar(100) DEFAULT NULL,
  `pubmed` varchar(30) DEFAULT NULL,
  `abstract` text
) ENGINE=InnoDB DEFAULT CHARSET=latin1;

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-- Extraindo dados da tabela `papers`
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INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(68130, 2000, 'bib', 'Phylogenetic--evolutionary approaches to bioinformatics', 'Pagel, M', 'School of Animal and Microbial Sciences, University of Reading, UK. m.pagel@reading.ac.uk', 'United Kingdom', 0, 'Animals|Base Sequence|Computational Biology|DNA|Evolution, Molecular|Gene Duplication|Genetic Variation|Humans|Models, Genetic|Open Reading Frames|Phylogeny|Prions|Ribonucleases|Time Factors', 'Journal Article', '10.1093/bib/1.2.117', '11465023', 'Phylogenies of organisms are essential to investigating a range of evolutionary questions of interest to researchers in the field of bioinformatics. Phylogenies not only help to define how to study many evolutionary questions, they must also be taken into account when conducting statistical analyses. Here it is shown how phylogenies can be used to investigate variability along the sites of a gene, reconstruct ancestral states of ancient genes and proteins, identify and characterise events of parallel and convergent evolution, find events of gene duplication, analyse predictions from molecular clocks, seek evidence for correlated changes among different parts of the same gene or genome, and test theories of molecular evolution. A table of statistical and phylogenetic methods is presented.'),
(68131, 2000, 'bib', 'Getting started with Acedb', 'Kelley, S', 'The Sanger Centre, Cambridge, UK. srk@sanger.ac.uk', 'United Kingdom', 2, 'Animals|Caenorhabditis elegans|Computational Biology|Computer Systems|Databases, Factual|Genomics|Internet|Software', 'Journal Article', '10.1093/bib/1.2.131', '11465024', 'Acedb is one of the more venerable pieces of Genomics software. Acedb was originally created in 1992 by Richard Durbin and Jean Thierry-Mieg to manage the data from the Caenorhabditis elegans mapping project and subsequently the C. elegans sequencing project. From beginnings as a C. elegans-specific tool, it has been continuously developed into a flexible suite of data management, display and scripting tools providing facilities for managing and annotation mapping information and DNA and peptide sequences. This paper gives a basic overview of the Acedb suite, and step-by-step guidance on how to download and install Acedb. It is intended to take an Acedb novice to stage where they can begin to experiment and explore the facilities that are available.'),
(68132, 2000, 'bib', 'Plant genome databases: from references to inference tools', 'Dicks, J', 'John Innes Centre, Colney, Norwich, UK. jo.dicks@bbsrc.ac.uk', 'United Kingdom', 3, 'Computational Biology|Databases, Factual|Genome, Plant|Internet|Plants, Edible|User-Computer Interface', 'Comparative Study', '10.1093/bib/1.2.138', '11465025', 'Plant genome databases play an important role in the archiving and dissemination of data arising from the international genome projects. Recent developments in bioinformatics, such as new software tools, programming languages and standards, have produced better access across the Internet to the data held within them. An increasing emphasis is placed on data analysis and indeed many resources now provide tools allied to the databases, to aid in the analysis and interpretation of the data. However, a considerable wealth of information lies untapped by considering the databases as single entities and will only be exploited by linking them with a wide range of data sources. Data from research programs such as comparative mapping and germplasm studies may be used as tools, to gain additional knowledge but without additional experimentation. To date, the current plant genome databases are not yet linked comprehensively with each other or with these additional resources, although they are clearly moving toward this. Here, the current wealth of public plant genome databases is reviewed, together with an overview of initiatives underway to bind them to form a single plant genome infrastructure.'),
(68133, 2000, 'bib', 'Farm animal genome databases', 'Law, A S|Archibald, A L', 'Roslin Institute Edinburgh, Midlothian, UK. andy.law@bbsrc.ac.uk|Roslin Institute Edinburgh, Midlothian, UK. andy.law@bbsrc.ac.uk', 'United Kingdom|United Kingdom', 3, 'Animals|Animals, Domestic|Chromosome Mapping|Computational Biology|Databases, Factual|Genetic Linkage|Genome|Internet', 'Comparative Study', '10.1093/bib/1.2.151', '11465026', 'The requirements for bioinformatics resources to support genome research in farm animals is reviewed. The resources developed to meet these needs are described. Resource databases and associated tools have been developed to handle experimental data. Several of these systems serve the needs of multinational collaborations. Genome databases have been established to provide contemporary summaries of the status of genome maps in a range of farm and domestic animals along with experimental details and citations. New resources and tools will be required to address the informatics needs of emerging technologies such as microarrays. However, continued investment is also required to maintain the currency and utility of the current systems, especially the genome databases.'),
(68134, 2000, 'bib', 'Human sequence variation and mutation databases', 'Lehväslaiho, H', 'EMBL Outstation EBI, Hinxton, Cambridge, UK. heikki@ebi.ac.uk', 'United Kingdom', 2, 'Computational Biology|Databases, Factual|Genetic Variation|Genome, Human|Humans|Internet|Molecular Sequence Data|Mutation', 'Journal Article', '10.1093/bib/1.2.161', '11465027', '-'),
(68135, 2000, 'bib', 'Sensitive protein comparisons with profiles and hidden Markov models', 'Hofmann, K', 'Bioinformatics & Gene Discovery Group, MEMOREC Stoffel GmbH, Köln, Germany. Kay.hoffmann@memorec.com', 'Germany', 34, 'Amino Acid Sequence|Computational Biology|Databases, Factual|Humans|Markov Chains|Molecular Sequence Data|Proteins|Sensitivity and Specificity|Sequence Alignment', 'Comparative Study', '10.1093/bib/1.2.167', '11465028', 'Sequence database searches have become an important tool for the life sciences in general and for gene discovery-driven biotechnology in particular. Both the functional assignment of newly found proteins and the mining of genome databases for functional candidates are equally important tasks typically addressed by database searches. Sensitivity and reliability of the search methods are of crucial importance. The overall performance of sequence alignments and database searches can be enhanced considerably, when profiles or hidden Markov models (HMMs) derived from protein families are used as query objects instead of single sequences. This review discusses the concept of profiles, generalised profiles and profile-HMMs, the methods how they are constructed and the scope of possible applications in gene discovery and gene functional assignment.'),
(68136, 2000, 'bib', 'Epitope scanning using virtual matrix-based algorithms', 'Raddrizzani, L|Hammer, J', 'Department of Genomics and Information Sciences, Hoffmann-La Roche, Nutley, NJ 07110, USA. laura.raddrizzani@roche.com|Department of Genomics and Information Sciences, Hoffmann-La Roche, Nutley, NJ 07110, USA. laura.raddrizzani@roche.com', 'United States|United States', 1, 'Algorithms|Amino Acid Sequence|Antigen Presentation|Computational Biology|Databases, Factual|Epitopes|HLA Antigens|Humans|Internet|Molecular Sequence Data|Proteins|Sequence Alignment|Software|T-Lymphocytes|User-Computer Interface', 'Journal Article', '10.1093/bib/1.2.179', '11465029', 'Protein sequence and expression databases (transcriptomes) contain the information required to identify epitopes capable of generating protective immune responses in humans. A key event in the initiation of an immune response against disease is the presentation of antigenic peptide epitopes to T cells by human leukocyte antigen (HLA) molecules. Computational filtering tools that allow the prediction of HLA/epitope interaction can be applied to sequence databases to select for candidate epitopes, thus minimising the subsequent amount of laboratory work. Here, the basic principles of epitope prediction and a summary of the available prediction approaches are presented, with a particular emphasis on the use of algorithms based on virtual HLA-II quantitative matrices, capable of predicting promiscuous HLA-II ligands.'),
(68137, 2000, 'bib', 'Data warehousing in molecular biology', 'Schönbach, C|Kowalski-Saunders, P|Brusic, V', 'Bioinformatics Center, Research Unit, Kent Ridge Digital Labs, Singapore. schoen@krdl.org.sg|Bioinformatics Center, Research Unit, Kent Ridge Digital Labs, Singapore. schoen@krdl.org.sg|Bioinformatics Center, Research Unit, Kent Ridge Digital Labs, Singapore. schoen@krdl.org.sg', 'Singapore|Singapore|Singapore', 28, 'Computational Biology|Computer Simulation|Databases, Factual|Information Storage and Retrieval|Internet|Molecular Biology', 'Journal Article', '10.1093/bib/1.2.190', '11465030', 'In the business and healthcare sectors data warehousing has provided effective solutions for information usage and knowledge discovery from databases. However, data warehousing applications in the biological research and development (R&D) sector are lagging far behind. The fuzziness and complexity of biological data represent a major challenge in data warehousing for molecular biology. By combining experiences in other domains with our findings from building a model database, we have defined the requirements for data warehousing in molecular biology.'),
(68138, 2000, 'bib', 'ARTEMIS: a tool for displaying and annotating DNA sequence', 'Mural, R J', 'Chromosome Team, Celera Genomics, Rockville, MD, USA.', 'United States', 15, 'Computational Biology|DNA|Sequence Analysis, DNA|Software', 'Evaluation Studies', '10.1093/bib/1.2.199', '11465031', '-'),
(68139, 2000, 'bib', 'Human genome sequencing project', 'Bishop, M', 'Chromosome Team, Celera Genomics, Rockville, MD, USA.', 'United States', 0, 'Animals|Computational Biology|Genome|Human Genome Project|Humans', 'Editorial', '10.1093/bib/1.3.212', '11465032', '-'),
(68140, 2000, 'bib', 'Comparing the success of different prediction software in sequence analysis: a review', 'Bajixc4x87, V B', 'Centre for Engineering Research, Technikon Natal, Durban, South Africa. bajic.v@cer.co.za', 'South Africa', 36, 'Computational Biology|Quality Control|Sequence Analysis|Software', 'Comparative Study', '10.1093/bib/1.3.214', '11465033', 'The abundance of computer software for different types of prediction in DNA and protein sequence analyses raises the problem of adequate ranking of prediction program quality. A single measure of success of predictor software, which adequately ranks the predictors, does not exist. A typical example of such an incomplete measure is the so-called correlation coefficient. This paper provides an overview and short analysis of several different measures of prediction quality. Frequently, some of these measures give results contradictory to each other even when they relate to the same prediction scores. This may lead to confusion. In order to overcome some of the problems, a few new measures are proposed including some variants of a generalised distance from the ideal predictor score, these are based on topological properties, rather than on statistics. In order to provide a sort of a balanced ranking, the averaged score measure (ASM) is introduced. The ASM provides a possibility for the selection of the predictor that probably has the best overall performance. The method presented in the paper applies to the ranking problem of any prediction software whose results can be properly represented in a true positive-false positive framework, thus providing a natural set-up for linear biological sequence analysis.'),
(68141, 2000, 'bib', 'Information resources for the bioinformatician', 'Ison, J C|OLeary, S B|Bleasby, A J|Moss, D', 'Bioinformatics Applications Group, UK MRC Human Genome Mapping Project Resource Centre, Hinxton, Cambridge, UK. jison@hgmp.mrc.ac.uk|Bioinformatics Applications Group, UK MRC Human Genome Mapping Project Resource Centre, Hinxton, Cambridge, UK. jison@hgmp.mrc.ac.uk|Bioinformatics Applications Group, UK MRC Human Genome Mapping Project Resource Centre, Hinxton, Cambridge, UK. jison@hgmp.mrc.ac.uk|Bioinformatics Applications Group, UK MRC Human Genome Mapping Project Resource Centre, Hinxton, Cambridge, UK. jison@hgmp.mrc.ac.uk', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom', 0, 'Computational Biology|Internet|United Kingdom', 'Journal Article', '10.1093/bib/1.3.229', '11465034', 'The collaborative computing project in biosequence and structure analysis (CCPII) was established to foster bioinformatics in the broad community and the UK research community in particular. A World-Wide Web site called The Bioinformatics Resource has been created containing a comprehensive set of information resources of use to the bioinformatician. The activities of CCPII are complementary to other providers of molecular biology information such as the BIOSCI electronic communication forum, which was established to facilitate communication between professionals in the biological sciences.'),
(68142, 2000, 'bib', 'The untranslated regions of eukaryotic mRNAs: structure, function, evolution and bioinformatic tools for their analysis', 'Pesole, G|Grillo, G|Larizza, A|Liuni, S', 'Dipartimento di Fisiologia e Biochimica Generali, Università di Milano, Italy. graziano.pesole@unimi.it|Dipartimento di Fisiologia e Biochimica Generali, Università di Milano, Italy. graziano.pesole@unimi.it|Dipartimento di Fisiologia e Biochimica Generali, Università di Milano, Italy. graziano.pesole@unimi.it|Dipartimento di Fisiologia e Biochimica Generali, Università di Milano, Italy. graziano.pesole@unimi.it', 'Italy|Italy|Italy|Italy', 55, 'Animals|Base Sequence|Computational Biology|Databases, Factual|Eukaryotic Cells|Evolution, Molecular|Humans|Internet|Molecular Sequence Data|Protein Biosynthesis|RNA Stability|RNA, Messenger|Untranslated Regions', 'Journal Article', '10.1093/bib/1.3.236', '11465035', 'The crucial role of the non-coding portion of genomes is now widely acknowledged. In particular, mRNA untranslated regions are involved in many post-transcriptional regulatory pathways that control mRNA localisation, stability and translation efficiency. A review is given of the most recent research works on the functional characterisation of eukaryotic mRNA untranslated regions. In order to make possible a systematic and detailed sequence analysis of mRNA untranslated regions (UTRs), a non-redundant database of metazoan mRNA untranslated sequences annotated for the occurrence of specific functional elements, UTRdb, was devised. These elements, whose consensus structure has been devised on the basis of experimental assays and of comparative analyses, have been collected in the UTRsite database. A suitable pattern-matching software has been devised to search UTRsite patterns in user-submitted sequences, also assessing their statistical significance. Structural, compositional and evolutionary features of untranslated sequences of metazoan mRNAs have been investigated showing peculiar intra- and interspecific patterns.'),
(68143, 2000, 'bib', 'Proteomic databases and software on the web', 'Wojcik, J|Schächter, V', 'Hybrigenics SA, Daumesnil, Paris, France. jwojcik@hybrigenics.fr|Hybrigenics SA, Daumesnil, Paris, France. jwojcik@hybrigenics.fr', 'France|France', 17, 'Computational Biology|Databases, Factual|Electrophoresis, Gel, Two-Dimensional|Internet|Mass Spectrometry|Proteome|Software', 'Journal Article', '10.1093/bib/1.3.250', '11465036', 'In the wake of sequencing projects, protein function analysis is evolving fast, from the careful design of assays that address specific questions to large-scale proteomics technologies that yield proteome-wide maps of protein expression or interaction. As these new technologies depend heavily on information storage, representation and analysis, existing databases and software tools are being adapted, while new resources are emerging. This paper describes the proteomics databases and software available through the World-Wide Web, focusing on their present use and applicability. As the resource situation is highly transitory, trends and probable evolutions are discussed whenever applicable.'),
(68144, 2000, 'bib', 'Prediction and phylogenetic analysis of mammalian short interspersed elements (SINEs)', 'Rogozin, I B|Mayorov, V I|Lavrentieva, M V|Milanesi, L|Adkison, L R', 'National Center for Biotechnology Information National Library of Medicine, National Institutes of Health, Bethesda, MD 20894, USA. rogozin@ncbi.nlm.nih.gov|National Center for Biotechnology Information National Library of Medicine, National Institutes of Health, Bethesda, MD 20894, USA. rogozin@ncbi.nlm.nih.gov|National Center for Biotechnology Information National Library of Medicine, National Institutes of Health, Bethesda, MD 20894, USA. rogozin@ncbi.nlm.nih.gov|National Center for Biotechnology Information National Library of Medicine, National Institutes of Health, Bethesda, MD 20894, USA. rogozin@ncbi.nlm.nih.gov|National Center for Biotechnology Information National Library of Medicine, National Institutes of Health, Bethesda, MD 20894, USA. rogozin@ncbi.nlm.nih.gov', 'United States|United States|United States|United States|United States', 10, 'Animals|Base Sequence|Computational Biology|Databases, Factual|Evolution, Molecular|Genetic Markers|Humans|Mammals|Models, Genetic|Molecular Sequence Data|Phylogeny|Sequence Alignment|Sequence Analysis|Short Interspersed Nucleotide Elements|Software', 'Journal Article', '10.1093/bib/1.3.260', '11465037', 'The presence of repetitive elements can create serious problems for sequence analysis, especially in the case of homology searches in nucleotide sequence databases. Repetitive elements should be treated carefully by using special programs and databases. In this paper, various aspects of SINE (short interspersed repetitive element) identification, analysis and evolution are discussed.'),
(68145, 2000, 'bib', 'Protein structure prediction methods for drug design', 'Lengauer, T|Zimmer, R', 'Institute for Algorithms and Scientific Computing, GMD, National Research Center for Information Technology, Sankt Augustin, Germany. lengauer@gmd.de|Institute for Algorithms and Scientific Computing, GMD, National Research Center for Information Technology, Sankt Augustin, Germany. lengauer@gmd.de', 'Germany|Germany', 9, 'Computational Biology|Computer Simulation|Computer-Aided Design|Drug Design|Molecular Structure|Proteins|Sequence Alignment|Software', 'Journal Article', '10.1093/bib/1.3.275', '11465038', 'Along the long path from genomic data to a new drug, the knowledge of three-dimensional protein structure can be of significant help in several places. This paper points out such places, discusses the virtues of protein structure knowledge and reviews bioinformatics methods for gaining such knowledge on the protein structure.'),
(68146, 2000, 'bib', 'Tuberculosis vaccine design: influence of the completed genome sequence', 'Montgomery, D L', 'Merck & Co, Virus and Cell Biology, West Point, PA 19486, USA. donna_montgomery@merck.com', 'United States', 9, 'Animals|BCG Vaccine|Computational Biology|Drug Design|Genome, Bacterial|Humans|Mice|Mycobacterium tuberculosis|Tuberculosis|Vaccines, DNA|Vaccines, Synthetic', 'Journal Article', '10.1093/bib/1.3.289', '11465039', 'Tuberculosis continues to be a major health problem, with more adults dying from Mycobacterium tuberculosis than any other pathogen world-wide. With the onset of the HIV epidemic and an increase in drug-resistant M. tuberculosis strains, the need for an improved vaccine has become an international priority. The recent completion of the genome sequences for two M. tuberculosis strains provides a wealth of information that can be used to design new strategies for vaccine development. The challenge comes in making rational choices from among the 4,000 genes of the most probable candidate immunogens or virulence genes.Thus, a well-designed screen is needed to reduce the number of candidates that must be tested. Presently, the most valuable role that bioinformatics can play is to provide such a screen.'),
(68147, 2000, 'bib', 'Visualising gene expression in its metabolic context', 'Wolf, D|Gray, C P|de Saizieu, A', 'F. Hoffmann-La Roche Ltd, Basel, Switzerland. Detlef.Wolf@Roche.com|F. Hoffmann-La Roche Ltd, Basel, Switzerland. Detlef.Wolf@Roche.com|F. Hoffmann-La Roche Ltd, Basel, Switzerland. Detlef.Wolf@Roche.com', 'Switzerland|Switzerland|Switzerland', 10, 'Computational Biology|Gene Expression Profiling|Haemophilus influenzae|Oligonucleotide Array Sequence Analysis|Proteome|RNA, Bacterial|RNA, Messenger|Software|Streptococcus pneumoniae', 'Journal Article', '10.1093/bib/1.3.297', '11465040', 'Relative changes in mRNA as well as protein levels induced by sublethal doses of antibiotics on bacteria are measured and results visualised in the context of metabolic pathway diagrams. The mRNA levels present at a given time point after the addition of the antibiotic are measured using microarrays from Affymetrix. Additionally, the relative amount of each protein synthesised during 3 minute intervals sampled at the given times is measured using radio-labelling followed by two-dimensional polyacrylamide gel electrophoresis and the subsequent analysis of the images produced by exposure to a phosphorimager. Metabolic pathway diagrams are both constructed in-house and imported from KEGG (Kyoto Encyclopedia of Genes and Genomes). Both protein and mRNA expression data can be displayed in the pathway diagrams such that the colour of the vectors or enzyme identifiers indicate the relative change in expression level and reproducibility.'),
(68148, 2000, 'bib', 'Exploring protein domain structure', 'Ison, J C', 'Bioinformatics Applications Group, UK MRC Human Genome Mapping Project Resource Centre, Hinxton, Cambridge, UK. jison@hgmp.mrc.ac.uk', 'United Kingdom', 2, 'Computational Biology|Databases, Factual|Humans|Internet|Protein Structure, Tertiary|Proteins|Sequence Alignment|Software', 'Evaluation Studies', '10.1093/bib/1.3.305', '11465041', 'The protein databank contains coordinates of over 10,000 protein structures, which constitute more than 25,000 structural domains in total. The investigation of protein structural, functional and evolutionary relationships is fundamental to many important fields in bioinformatics research, and will be crucial in determining the function of the human and other genomes. This review describes the SCOP and CATH databases of protein structure classification, which define, classify and annotate each domain in the protein databank. The hierarchical structure, use and annotation of the databases are explained. Other tools for exploring protein structure relationships are also described.'),
(68149, 2000, 'bib', 'Paradigm shifts in the approaches for gene annotation', 'Thanaraj, T A|Robinson, A|Muilu, J|Riethoven, J J', 'Bioinformatics Applications Group, UK MRC Human Genome Mapping Project Resource Centre, Hinxton, Cambridge, UK. jison@hgmp.mrc.ac.uk|Bioinformatics Applications Group, UK MRC Human Genome Mapping Project Resource Centre, Hinxton, Cambridge, UK. jison@hgmp.mrc.ac.uk|Bioinformatics Applications Group, UK MRC Human Genome Mapping Project Resource Centre, Hinxton, Cambridge, UK. jison@hgmp.mrc.ac.uk|Bioinformatics Applications Group, UK MRC Human Genome Mapping Project Resource Centre, Hinxton, Cambridge, UK. jison@hgmp.mrc.ac.uk', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom', 0, 'Chromosomes|Computational Biology|Genetic Techniques|Genome', 'Editorial', '10.1093/bib/1.4.324', '11465050', '-'),
(68150, 2000, 'bib', 'Discriminant analysis and its application in DNA sequence motif recognition', 'Zhang, M Q', 'Cold Spring Harbor Laboratory, Watson School of Biological Sciences, NY 11724, USA. Mzhang@cshl.org', 'United States', 16, 'Algorithms|Bayes Theorem|Computational Biology|Discriminant Analysis|Exons|Expressed Sequence Tags|Genome, Human|Humans|RNA Splice Sites|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bib/1.4.331', '11465051', 'Identification of functional motifs in a DNA sequence is fundamentally a statistical pattern recognition problem. Discriminant analysis is widely used for solving such problems. This paper will review two basic parametric methods: LDA (linear discriminant analysis) and QDA (quadratic discriminant analysis). Their usage in recognition of splice sites and exons in the human genome will be demonstrated.'),
(68151, 2000, 'bib', 'Prediction of exact boundaries of exons', 'Thanaraj, T A|Robinson, A J', 'European Bioinformatics Institute, Hinxton, UK. thanaraj@ebi.ac.uk|European Bioinformatics Institute, Hinxton, UK. thanaraj@ebi.ac.uk', 'United Kingdom|United Kingdom', 19, 'Algorithms|Computational Biology|Databases, Factual|Decision Trees|Exons|Humans|Models, Genetic|RNA Splice Sites|Software', 'Journal Article', '10.1093/bib/1.4.343', '11465052', 'It is known that while the programs used to predict genes are good at determining coding nucleotides, there are considerable inaccuracies in the determination of the gene structural elements. Among them, the most notable is that of the exact boundaries of exons. In order to assess this, we had earlier reviewed various programs that predict potential splice sites and exons. The results led to the following two observations: (i) a high proportion of false positive splice sites from computational predictions occur in the vicinity of real splice sites, and (ii) current algorithms are misled to predict wrong splice sites more often when the coding potential ends within +/-25 nucleotides from real sites than when it ends at farther positions. In this report, we review decision tree models for human splice sites and the resultant software tool, namely SpliceProximalCheck, that discriminates suchproximal false positives from real splice sites. Further presented is an integrated system (MZEF-SPC) with Splice ProximalCheck (SPC) as a front-end tool operating on the results of Michael Zhangs exon finder program. Examination of the output of the integrated program on an illustrative gene set revealed that as much as 61 of 93 MZEF-predicted false positive exons could be eliminated by SPC for a loss of only 3 out of 33 MZEF-predicted true positive exons.'),
(68152, 2000, 'bib', 'Comparative analysis of regulatory patterns in bacterial genomes', 'Gelfand, M S|Novichkov, P S|Novichkova, E S|Mironov, A A', 'State Scientific Center, GosNIIGenetika, Moscow, Russia. misha@imb.im.ac.ru|State Scientific Center, GosNIIGenetika, Moscow, Russia. misha@imb.im.ac.ru|State Scientific Center, GosNIIGenetika, Moscow, Russia. misha@imb.im.ac.ru|State Scientific Center, GosNIIGenetika, Moscow, Russia. misha@imb.im.ac.ru', '-|-|-|-', 54, 'Algorithms|Base Sequence|Computational Biology|Conserved Sequence|DNA, Bacterial|Escherichia coli|Genes, Bacterial|Genes, Regulator|Genome, Bacterial|Molecular Sequence Data|Regulon|Sequence Homology, Nucleic Acid', 'Comparative Study', '10.1093/bib/1.4.357', '11465053', 'Recognition of transcription regulatory sites in bacterial genomes is a notoriously difficult problem. There are no algorithms capable of making reliable predictions even for well-studied sites such as the CRP (cyclic AMP receptor protein) box. However, availability of complete bacterial genomes makes it possible to make reliable predictions with bad rules. This comparative approach is based on the assumption that sets of co-regulated genes are conserved in related bacteria. Thus true sites occur upstream of orthologous genes, whereas false candidates are scattered at random. This means not only that knowledge about regulation in well-studied genomes can be transferred to newly sequenced ones, but also that new members of regulons can be found. This paper reviews several recent studies. In particular, a detailed analysis of catabolite repression in gamma-purple bacteria is presented.'),
(68153, 2000, 'bib', 'Identification and functional modelling of DNA sequence elements of transcription', 'Werner, T', 'Institute of Experimental Genetics, GSF-National Research Center for Environment and Health, Neuherberg, Germany. werner@gsf.de', 'Germany', 15, 'Actins|Animals|Binding Sites|Chromosomes, Human|Computational Biology|DNA|Genome, Human|Humans|Models, Genetic|Muscles|Promoter Regions, Genetic|Transcription, Genetic', 'Journal Article', '10.1093/bib/1.4.372', '11465054', 'Identification of transcriptional elements in large sequences is a very difficult task, as individual transcription elements (eg transcription factor binding sites,TF-sites) are not clearly correlated with regions exerting transcription control. However, elucidation of the molecular organisation of genomic regions responsible for the control of gene expression is an essential part of the efforts to annotate the genomic sequences, especially within the Human Genome Project. The task for bioinformatics in this context is twofold. The first step required is the approximate localisation of regulatory sequences in large anonymous DNA sequences. Once those regions are located, the second task is the identification of individual transcriptional control elements and correlation of a subset of such elements with transcriptional functions. Part of this second task can be achieved by constructing organisational models of regulatory regions like promoters which can reveal elements important for a gene class or the coexpression of a set of genes. Comparative genomics in non-coding regions (eg phylogenetic footprinting) is a very promising approach that allows identification of potential new regulatory elements which may be used in modelling approaches.'),
(68154, 2000, 'bib', 'Genome sequence comparisons: hurdles in the fast lane to functional genomics', 'Wiehe, T|Guigó, R|Miller, W', 'Max-Planck-Institute for Chemical Ecology, Dept of Genetics and Evolution, Jena, Germany. twiehe@ice.mpg.de|Max-Planck-Institute for Chemical Ecology, Dept of Genetics and Evolution, Jena, Germany. twiehe@ice.mpg.de|Max-Planck-Institute for Chemical Ecology, Dept of Genetics and Evolution, Jena, Germany. twiehe@ice.mpg.de', 'Germany|Germany|Germany', 15, 'Animals|Computational Biology|Conserved Sequence|Evolution, Molecular|Genome|Genome, Human|Genomics|Humans|Interleukin-13|Interleukin-4|Mice|Sequence Alignment|Software', 'Comparative Study', '10.1093/bib/1.4.381', '11465055', 'An important computational technique for extracting the wealth of information hidden in human genomic sequence data is to compare the sequence with that from the corresponding region of the mouse genome, looking for segments that are conserved over evolutionary time. Moreover, the approach generalises to comparison of sequences from any two related species. The underlying rationale (which is abundantly confirmed by observation) is that a random mutation in a functional region is usually deleterious to the organism, and hence unlikely to become fixed in the population, whereas mutations in a non-functional region are free to accumulate over time. The potential value of this approach is so attractive that the public and private projects to sequence the human genome are now turning to sequencing the mouse, and you will soon be able to compare the human and mouse sequences of your favourite genomic region. We are currently witnessing an explosion of computer tools for comparative analysis of two genomic sequences. Here the capabilities of two new network servers for comparing genomic sequences from any pair of closely related species are sketched. The Syntenic Gene Prediction Program SGP-I utilises sequence comparisons to enhance the ability to locate protein coding segments in genomic data. PipMaker attempts to determine all conserved genomic regions, regardless of their function.'),
(68155, 2000, 'bib', 'Arabidopsis genome analysis as exemplified by analysis of chromosome 4', 'Mayer, K F|Lemcke, K|Schüller, C M|Rudd, S|Zaccaria, P', 'GSF Forschungszentrum f. Umwelt und Gesundheit, Max Planck Institut für Biochemie, Martinsried, Germany. mayer@mips.biochem.mpg.de|GSF Forschungszentrum f. Umwelt und Gesundheit, Max Planck Institut für Biochemie, Martinsried, Germany. mayer@mips.biochem.mpg.de|GSF Forschungszentrum f. Umwelt und Gesundheit, Max Planck Institut für Biochemie, Martinsried, Germany. mayer@mips.biochem.mpg.de|GSF Forschungszentrum f. Umwelt und Gesundheit, Max Planck Institut für Biochemie, Martinsried, Germany. mayer@mips.biochem.mpg.de|GSF Forschungszentrum f. Umwelt und Gesundheit, Max Planck Institut für Biochemie, Martinsried, Germany. mayer@mips.biochem.mpg.de', 'Germany|Germany|Germany|Germany|Germany', 3, 'Arabidopsis|Chromosomes|Computational Biology|Databases, Factual|Gene Duplication|Genetic Techniques|Genome, Plant|Humans|Models, Genetic|Sequence Analysis, DNA|Software', 'Comparative Study', '10.1093/bib/1.4.389', '11465056', 'During the last decade the small cruciferous plant Arabidopsis thaliana has become a model organism for flowering plants. Sequencing and analysis of the Arabidopsis genome is nearing completion. Beside an overview on methods and strategies for Arabidopsis genome analysis, a summary of the results from the first analysis is presented. This includes an overview on chromosomal organisation and topological features as well as a first comparison with other genomes.'),
(68156, 2000, 'bib', 'Ontology-based knowledge representation for bioinformatics', 'Stevens, R|Goble, C A|Bechhofer, S', 'Department of Computer Science, University of Manchester, UK. robert.stevens@cs.man.ac.uk|Department of Computer Science, University of Manchester, UK. robert.stevens@cs.man.ac.uk|Department of Computer Science, University of Manchester, UK. robert.stevens@cs.man.ac.uk', 'United Kingdom|United Kingdom|United Kingdom', 228, 'Artificial Intelligence|Classification|Computational Biology|Databases, Factual|Developmental Biology|Molecular Biology|Software', 'Journal Article', '10.1093/bib/1.4.398', '11465057', 'Much of biology works by applying prior knowledge (what is known) to an unknown entity, rather than the application of a set of axioms that will elicit knowledge. In addition, the complex biological data stored in bioinformatics databases often require the addition of knowledge to specify and constrain the values held in that database. One way of capturing knowledge within bioinformatics applications and databases is the use of ontologies. An ontology is the concrete form of a conceptualisation of a communitys knowledge of a domain. This paper aims to introduce the reader to the use of ontologies within bioinformatics. A description of the type of knowledge held in an ontology will be given.The paper will be illustrated throughout with examples taken from bioinformatics and molecular biology, and a survey of current biological ontologies will be presented. From this it will be seen that the use to which the ontology is put largely determines the content of the ontology. Finally, the paper will describe the process of building an ontology, introducing the reader to the techniques and methods currently in use and the open research questions in ontology development.'),
(68157, 2000, 'bib', 'HOBACGEN: homologous bacterial genes database', 'Larimer, F', 'Computational Biosciences, Oak Ridge National Laboratory, USA. larimerfwl@oml.gov', 'United States', 1, 'Computational Biology|Databases, Factual|Genes, Bacterial|Genomics|Internet|Sequence Alignment|Software', 'Comparative Study', '10.1093/bib/1.4.415', '11465058', '-'),
(68158, 2001, 'bib', 'Issues in predicting protein function from sequence', 'Ponting, C P', 'Department of Human Anatomy and Genetics, University of Oxford, UK. Chris.Ponting@anat.ox.ac.uk', 'United Kingdom', 43, 'Amino Acids|Animals|Computational Biology|Conserved Sequence|Databases, Factual|Evolution, Molecular|Gene Transfer, Horizontal|Humans|Protein Structure, Tertiary|Proteins|Sequence Alignment|Sequence Analysis, Protein', 'Journal Article', '10.1093/bib/2.1.19', '11465059', 'Identifying homologues, defined as genes that arose from a common evolutionary ancestor, is often a relatively straightforward task, thanks to recent advances made in estimating the statistical significance of sequence similarities found from database searches. The extent by which homologues possess similarities in function, however, is less amenable to statistical analysis. Consequently, predicting function by homology is a qualitative, rather than quantitative, process and requires particular care to be taken. This review focuses on the various approaches that have been developed to predict function from the scale of the atom to that of the organism. Similarities in homologues functions differ considerably at each of these different scales and also vary for different domain families. It is argued that due attention should be paid to all available clues to function, including orthologue identification, conservation of particular residue types, and the co-occurrence of domains in proteins. Pitfalls in database searching methods arising from amino acid compositional bias and database size effects are also discussed.'),
(68159, 2001, 'bib', 'Links between kinetic data and sequences in the alpha/beta-hydrolases fold database', 'Chatonnet, A|Cousin, X|Robinson, A', 'Département de Physiologie Animale, Institut National de la Recherche Agronomique, Montpellier, France. chatonne@ensam.inra.fr|Département de Physiologie Animale, Institut National de la Recherche Agronomique, Montpellier, France. chatonne@ensam.inra.fr|Département de Physiologie Animale, Institut National de la Recherche Agronomique, Montpellier, France. chatonne@ensam.inra.fr', 'France|France|France', 6, 'Animals|Computational Biology|Databases, Factual|Humans|Hydrolases|Kinetics|Mutation|Protein Folding|Sequence Analysis, Protein|Substrate Specificity', 'Journal Article', '10.1093/bib/2.1.30', '11465060', 'While the number of sequenced genes is increasing dramatically, the number of different protein structural families is expected to be more limited. Changes in enzymatic activity or protein interactions can dramatically modify the role of homologous proteins in different organisms or mutants. However, experimental data associated with sequences or mutations stored in databases are often limited to a short description of the enzymatic pathway, molecular interaction or phenotype associated with the changes in amino acid sequence. In the alpha/beta-hydrolases fold database ESTHER, we are experimenting with links between experimental kinetic data and sequences, mutations and protein structures. This effort will lead to the integration of pharmacological data with genome-wide databases.'),
(68160, 2001, 'bib', 'Protein function from the perspective of molecular interactions and genetic networks', 'Jacq, B', 'Laboratoire de Génétique et Physiologie du Développement, IBDM, Marseille, France.', 'France', 4, 'Animals|Computational Biology|DNA|Fungal Proteins|Genome|Humans|Proteins', 'Comparative Study', '10.1093/bib/2.1.38', '11465061', 'Protein function is a complex notion, which is now receiving renewed attention from a bioinformatics and genomics perspective. After a general discussion of the principles of experimental methods employed to decipher gene/protein function, the contributions made by new, high-throughput methods in terms of function discovery are discussed. Recent work on functional ontologies and the necessity to describe function within the context of hierarchical levels of complexity are presented. The concepts of molecular interactions and genetic networks are then discussed, leading to a useful new framework with which to describe protein function using new tools such as 2D interaction maps. Finally, it is proposed that interaction data could be used to develop new methods for the functional classification of proteins. An example of functional comparisons on a real data set of yeast chromosomal proteins is presented.'),
(68161, 2001, 'bib', 'Defining the function of a gene', 'Davidson, D|Bishop, M', 'Laboratoire de Génétique et Physiologie du Développement, IBDM, Marseille, France.|Laboratoire de Génétique et Physiologie du Développement, IBDM, Marseille, France.', 'France|France', 0, 'Animals|Computational Biology|Databases, Factual|Genes|Humans|Phenotype|Proteins', 'Comment', '10.1093/bib/2.1.4', '11465062', '-'),
(68162, 2001, 'bib', 'Making sense of score statistics for sequence alignments', 'Pagni, M|Jongeneel, C V', 'Swiss Institute of Bioinformatics.|Swiss Institute of Bioinformatics.', '-|-', 27, 'Amino Acid Sequence|Animals|Computational Biology|Databases, Factual|Humans|Markov Chains|Models, Statistical|Molecular Sequence Data|Proteins|Sequence Alignment|Sequence Homology, Amino Acid', 'Comparative Study', '10.1093/bib/2.1.51', '11465063', 'The search for similarity between two biological sequences lies at the core of many applications in bioinformatics. This paper aims to highlight a few of the principles that should be kept in mind when evaluating the statistical significance of alignments between sequences. The extreme value distribution is first introduced, which in most cases describes the distribution of alignment scores between a query and a database. The effects of the similarity matrix and gap penalty values on the score distribution are then examined, and it is shown that the alignment statistics can undergo an abrupt phase transition. A few types of random sequence databases used in the estimation of statistical significance are presented, and the statistics employed by the BLAST, FASTA and PRSS programs are compared. Finally the different strategies used to assess the statistical significance of the matches produced by profiles and hidden Markov models are presented.'),
(68163, 2001, 'bib', 'Phenotypic data in FlyBase', 'Drysdale, R', 'FlyBase, Department of Genetics, University of Cambridge, UK. r.drysdale@gen.cam.ac.uk', 'United Kingdom', 15, 'Alleles|Animals|Computational Biology|Databases, Factual|Drosophila|Genes, Insect|Genetic Engineering|Genome|Genotype|Phenotype', 'Journal Article', '10.1093/bib/2.1.68', '11465064', 'Phenotypic analysis combined with molecular genetics is a powerful tool for mapping gene function onto the genome. Phenotypic data are, by their nature, descriptive, and as varied as the range of mutant phenotypes that can be presented by the organism under study. This paper discusses the mechanisms FlyBase has implemented to systematise published phenotypic data about Drosophila, and provides an introduction to the query tools available for the mining of the data. Though FlyBase is specific to Drosophila, the issues faced in devising protocols for capturing, storing and reporting data are the same issues faced by any database with an interest in using phenotypic data to maximise the potential of genomic analysis.'),
(68164, 2001, 'bib', 'From molecular activities and processes to biological function', 'van Helden, J|Naim, A|Lemer, C|Mancuso, R|Eldridge, M|Wodak, S J', 'Free University of Brussels, Belgium.|Free University of Brussels, Belgium.|Free University of Brussels, Belgium.|Free University of Brussels, Belgium.|Free University of Brussels, Belgium.|Free University of Brussels, Belgium.', 'Belgium|Belgium|Belgium|Belgium|Belgium|Belgium', 26, 'Animals|Classification|Computational Biology|Databases, Factual|Humans|Metabolism|Models, Biological|Molecular Biology', 'Journal Article', '10.1093/bib/2.1.81', '11465065', 'This paper describes how biological function can be represented in terms of molecular activities and processes. It presents several key features of a data model that is based on a conceptual description of the network of interactions between molecular entities within the cell and between cells. This model is implemented in the aMAZE database that presently deals with information on metabolic pathways, gene regulation, sub- or supracellular locations, and transport. It is shown that this model constitutes a useful generalisation of data representations currently implemented in metabolic pathway databases, and that it can furthermore include multiple schemes for categorising and classifying molecular entities, activities, processes and localisations. In particular, we highlight the flexibility offered by our system in representing multiple molecular activities and their control, in viewing biological function at different levels of resolution and in updating this view as our knowledge evolves.'),
(68165, 2001, 'bib', 'Functional information in SWISS-PROT: the basis for large-scale characterisation of protein sequences', 'Apweiler, R', 'EMBL Outstation, European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, UK. apweiler@ebi.ac.uk', 'United Kingdom', 30, 'Animals|Computational Biology|Databases, Factual|Humans|Proteins|Sequence Analysis, Protein', 'Journal Article', '10.1093/bib/2.1.9', '11465066', 'With the rapid growth of sequence databases, there is an increasing need for reliable functional characterisation and annotation of newly predicted proteins. To cope with such large data volumes, faster and more effective means of protein sequence characterisation and annotation are required. One promising approach is automatic large-scale functional characterisation and annotation, which is generated with limited human interaction. However, such an approach is heavily dependent on reliable data sources. The SWISS-PROT protein sequence database plays an essential role here owing to its high level of functional information.'),
(68166, 2001, 'bib', 'Gene function Part 2', 'Davidson, D', 'EMBL Outstation, European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, UK. apweiler@ebi.ac.uk', 'United Kingdom', 0, 'Alleles|Animals|Computational Biology|Databases, Factual|Genes|Humans|Mice', 'Comment', '10.1093/bib/2.2.108', '11465729', '-'),
(68167, 2001, 'bib', 'Protein structure prediction in genomics', 'Jones, D T', 'Department of Biological Sciences, Brunel University, Uxbridge, UK. David.Jones@brunel.ac.uk', 'United Kingdom', 1, 'Animals|Computational Biology|Genome, Human|Genomics|Humans|Internet|Models, Molecular|Protein Folding|Proteins', 'Comparative Study', '10.1093/bib/2.2.111', '11465730', 'As the number of completely sequenced genomes rapidly increases, including now the complete Human Genome sequence, the post-genomic problems of genome-scale protein structure determination and the issue of gene function identification become ever more pressing. In fact, these problems can be seen as interrelated in that experimentally determining or predicting or the structure of proteins encoded by genes of interest is one possible means to glean subtle hints as to the functions of these genes. The applicability of this approach to gene characterisation is reviewed, along with a brief survey of the reliability of large-scale protein structure prediction methods and the prospects for the development of new prediction methods.'),
(68168, 2001, 'bib', 'Analysis and comparison of metabolic pathway databases', 'Wittig, U|De Beuckelaer, A', 'European Media Laboratory, Heidelberg, Germany. ulrike.wittig@eml.villa-bosch.de|European Media Laboratory, Heidelberg, Germany. ulrike.wittig@eml.villa-bosch.de', 'Germany|Germany', 28, 'Animals|Computational Biology|Databases, Factual|Genome|Humans|Internet|Metabolism|User-Computer Interface', 'Comparative Study', '10.1093/bib/2.2.126', '11465731', 'Enormous amounts of data result from genome sequencing projects and new experimental methods. Within this tremendous amount of genomic data 30-40 per cent of the genes being identified in an organism remain unknown in terms of their biological function. As a consequence of this lack of information the overall schema of all the biological functions occurring in a specific organism cannot be properly represented. To understand the functional properties of the genomic data more experimental data must be collected. A pathway database is an effort to handle the current knowledge of biochemical pathways and in addition can be used for interpretation of sequence data. Some of the existing pathway databases can be interpreted as detailed functional annotations of genomes because they are tightly integrated with genomic information. However, experimental data are often lacking in these databases. This paper summarises a list of pathway databases and some of their corresponding biological databases, and also focuses on information about the content and the structure of these databases, the organisation of the data and the reliability of stored information from a biological point of view. Moreover, information about the representation of the pathway data and tools to work with the data are given. Advantages and disadvantages of the analysed databases are pointed out, and an overview to biological scientists on how to use these pathway databases is given.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(68169, 2001, 'bib', 'A comparison of microarray databases', 'Gardiner-Garden, M|Littlejohn, T G', 'Entigen Pty Ltd, Eveleigh NSW, Australia. steve.taylor@entigen.com.au|Entigen Pty Ltd, Eveleigh NSW, Australia. steve.taylor@entigen.com.au', 'Australia|Australia', 16, 'Animals|Computational Biology|Database Management Systems|Databases, Factual|Gene Expression Profiling|Genomics|Humans|Internet|Oligonucleotide Array Sequence Analysis', 'Comparative Study', '10.1093/bib/2.2.143', '11465732', 'Microarray technology has become one of the most important functional genomics technologies. A proliferation of microarray databases has resulted. It can be difficult for researchers exploring this technology to know which bioinformatics systems best meet their requirements. In order to obtain a better understanding of the available systems, a survey and comparative analysis of microarray databases was undertaken. The survey included databases that are currently available, as well as databases that should become available in early 2001. Databases fall into three categories: (i) those that can be installed locally, (ii) those available for public data submission and (iii) those available for public query. Developers of microarray gene-expression databases were asked questions regarding the scope and availability of their database, its system requirements, its future compliance with MGED (Microarray Gene Expression Database) standards, and its associated analytical tools. Participants included AMAD (Stanford/Berkeley/UCSF), ArrayExpress (EBI), ChipDB (MIT/Whitehead), GeneX (NCGR), GeNet (Silicon Genetics), GeneDirector (BioDiscovery), GEO (NCBI), GXD (Jackson Laboratory), mAdb (NCI), maxdSQL (University of Manchester), NOMAD (UCSF), RAD (University of Pennsylvania) and SMD (Stanford University). Other database developers were contacted but data was not available at the time of manuscript preparation. Each database fulfils a different role, reflecting the widely varying needs of microarray users.'),
(68170, 2001, 'bib', 'The Edinburgh Mouse Atlas: using the CD', 'Baldock, R|Bard, J|Brune, R|Hill, B|Kaufman, M|Opstad, K|Smith, D|Stark, M|Waterhouse, A|Yang, Y|Davidson, D', 'MRC Human Genetics Unit, Western General Hospital, Edinburgh, UK. richard@hgu.mrc.ac.uk|MRC Human Genetics Unit, Western General Hospital, Edinburgh, UK. richard@hgu.mrc.ac.uk|MRC Human Genetics Unit, Western General Hospital, Edinburgh, UK. richard@hgu.mrc.ac.uk|MRC Human Genetics Unit, Western General Hospital, Edinburgh, UK. richard@hgu.mrc.ac.uk|MRC Human Genetics Unit, Western General Hospital, Edinburgh, UK. richard@hgu.mrc.ac.uk|MRC Human Genetics Unit, Western General Hospital, Edinburgh, UK. richard@hgu.mrc.ac.uk|MRC Human Genetics Unit, Western General Hospital, Edinburgh, UK. richard@hgu.mrc.ac.uk|MRC Human Genetics Unit, Western General Hospital, Edinburgh, UK. richard@hgu.mrc.ac.uk|MRC Human Genetics Unit, Western General Hospital, Edinburgh, UK. richard@hgu.mrc.ac.uk|MRC Human Genetics Unit, Western General Hospital, Edinburgh, UK. richard@hgu.mrc.ac.uk|MRC Human Genetics Unit, Western General Hospital, Edinburgh, UK. richard@hgu.mrc.ac.uk', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom', 11, 'Animals|Computational Biology|Computer Graphics|Databases, Factual|Genome|Image Processing, Computer-Assisted|Mice|Models, Anatomic|Software', 'Journal Article', '10.1093/bib/2.2.159', '11465733', 'This paper provides a simple introduction to the reconstructions and data-handling tools stored on the Edinburgh Mouse Atlas CD, together with some of the ways in which the viewers and software can be used to understand mouse development and analyse data. The key aspect of the Mouse Atlas is that the underlying models are a complete representation of the histology, which has not been constrained to a particular interpretation. This means, for example, that the current anatomy domains can be further subdivided as required to any resolution up to the resolution of the models (2-7 microm). In the CD of the early embryos described here, virtually all tissues that can be usefully distinguished either by the histology or morphologically have been delineated.'),
(68171, 2001, 'bib', 'Large-scale production of mouse phenotypes: the search for animal models for inherited diseases in humans', 'Hrabé de Angelis, M|Strivens, M', 'Institute of Experimental Genetics at the GSF Research Centre for Environment and Health, Munich, Germany.|Institute of Experimental Genetics at the GSF Research Centre for Environment and Health, Munich, Germany.', 'Germany|Germany', 11, 'Animals|Computational Biology|Data Interpretation, Statistical|Databases, Factual|Disease Models, Animal|Ethylnitrosourea|Female|Genetic Diseases, Inborn|Humans|Male|Mice|Mutagenesis|Mutagens|Phenotype|Semen Preservation', 'Journal Article', '10.1093/bib/2.2.170', '11465734', 'This paper is aimed principally at bioinformaticians and biologists as an introduction to recent advances in mouse mutagenesis, concentrating on genome-wide screens utilising the powerful mutagen N-ethyl-N-nitroso-urea (ENU). It contains a brief background to the underlying genetics as well as details of the practical aspects of organisation and data capture for such projects.'),
(68172, 2001, 'bib', 'On the parallelisation of bioinformatics applications', 'Trelles, O', 'Computer Architecture Department, University of Malaga, Spain. ots@ac.uma.es', 'Spain', 28, 'Algorithms|Animals|Computational Biology|Computer Systems|Databases, Factual|Humans|Software', 'Journal Article', '10.1093/bib/2.2.181', '11465735', 'This paper surveys the computational strategies followed to parallelise the most used software in the bioinformatics arena. The studied algorithms are computationally expensive and their computational patterns range from regular, such as database-searching applications, to very irregularly structured patterns (phylogenetic trees). Fine- and coarse-grained parallel strategies are discussed for these very diverse sets of applications. This overview outlines computational issues related to parallelism, physical machine models, parallel programming approaches and scheduling strategies for a broad range of computer architectures. In particular, it deals with shared, distributed and shared/distributed memory architectures.'),
(68173, 2001, 'bib', 'Swiss-PDB Viewer (Deep View)', 'Kaplan, W|Littlejohn, T G', 'Entigen Pty Ltd, Everleigh NSW, Australia.|Entigen Pty Ltd, Everleigh NSW, Australia.', 'Australia|Australia', 252, 'Amino Acid Sequence|Computer Graphics|Computer Simulation|Databases, Factual|Glutathione Transferase|Models, Molecular|Molecular Sequence Data|Proteins|Software|User-Computer Interface', 'Journal Article', '10.1093/bib/2.2.195', '11465736', '-'),
(68174, 2000, 'bib', 'Database searching with DNA and protein sequences: an introduction', 'Sansom, C', 'Department of Crystallography, Birkbeck College, London, WC1E 7HX UK. c.sansom@mail.cryst.bbk.ac.uk', 'United Kingdom', 8, 'Algorithms|Amino Acid Sequence|Base Sequence|Computational Biology|DNA|Databases as Topic|Europe|Genome|Information Storage and Retrieval|Japan|Proteins|Software', 'Journal Article', '10.1093/bib/1.1.22', '11466971', 'This review of sequence database searching aims to set out current practice in the area, in order to give practical guidelines to the experimental biologist. It describes the basic principles behind the programs and enumerates the range of databases available in the public domain. Of these, the most important are the equivalent DNA databases European Molecular Biology Laboratory (EMBL), GenBank and DNA Databank of Japan (DDBJ), and the protein databases Swiss-Prot and TrEMBL. The commonly used BLAST and FASTA algorithms are described in detail and alternative approaches mentioned briefly. Scoring matrices used to compare amino acid types during protein database searches are compared, with an emphasis on the PAM and BLOSUM series of observed substitution matrices.'),
(68175, 2000, 'bib', 'Computer prediction of sites associated with various elements of the nuclear matrix', 'Rogozin, I B|Glazko, G V|Glazkov, M V', 'Institute of Cytology and Genetics, pr. Lavrentyeva 10, Novosibirsk 630090, Russia.|Institute of Cytology and Genetics, pr. Lavrentyeva 10, Novosibirsk 630090, Russia.|Institute of Cytology and Genetics, pr. Lavrentyeva 10, Novosibirsk 630090, Russia.', '-|-|-', 11, 'Animals|Computational Biology|DNA|Genes|Globins|Humans|Mammals|Nuclear Matrix|Proteins|Regulatory Sequences, Nucleic Acid|Repetitive Sequences, Nucleic Acid|Reproducibility of Results|Sequence Analysis, DNA|Software', 'Comparative Study', '10.1093/bib/1.1.33', '11466972', 'Attachment regions of the eukaryotic chromosomal DNA to the nuclear scaffold/matrix (S/MARs) participate in various important cellular processes. However, no obvious characteristics common for these nucleotide sequences have been revealed, except that S/MARs are non-coding sites containing putative regulatory elements and binding sites of DNA-topoisomerase II. Heterogeneity among S/MARs can be caused by a variety of biological factors. In this paper, the accuracy of two S/MARs prediction programs, MAR-Finder (Singh, Kramer and Krawetz, 1997) and ChrClass (Glazkov, Rogozin and Glazko, 1998) are compared and it is concluded that both programs can be recommended for analysis of eukaryotic genomes. However, results of their prediction should be interpreted with caution since estimation of prediction accuracy of both programs needs further analysis. Problems of S/MARs prediction are illustrated on several examples of human protein-coding genes, repeated elements and the beta-globin locus from different mammalian species. Results of our analysis suggest that the proportion of missed S/MARs is lower for ChrClass, whereas the proportion of wrong S/MARs is lower for MAR-Finder (a default set of parameters).'),
(68176, 2000, 'bib', 'The role of pattern databases in sequence analysis', 'Attwood, T K', 'School of Biological Sciences, University of Manchester, Oxford Road, Manchester M13 9PT, UK.', 'United Kingdom', 22, 'Amino Acid Motifs|Amino Acid Sequence|Animals|Computational Biology|Databases, Factual|Evolution, Molecular|Genome|Humans|Internet|Markov Chains|Pattern Recognition, Automated|Proteins|Sequence Alignment|Software', 'Journal Article', '10.1093/bib/1.1.45', '11466973', 'In the wake of the numerous now-fruitful genome projects, we are entering an era rich in biological data. The field of bioinformatics is poised to exploit this information in increasingly powerful ways, but the abundance and growing complexity both of the data and of the tools and resources required to analyse them are threatening to overwhelm us. Databases and their search tools are now an essential part of the research environment. However, the rate of sequence generation and the haphazard proliferation of databases have made it difficult to keep pace with developments. In an age of information overload, researchers want rapid, easy-to-use, reliable tools for functional characterisation of newly determined sequences. But what are those tools? How do we access them? Which should we use? This review focuses on a particular type of database that is increasingly used in the task of routine sequence analysis--the so-called pattern database. The paper aims to provide an overview of the current status of pattern databases in common use, outlining the methods behind them and giving pointers on their diagnostic strengths and weaknesses.'),
(68177, 2000, 'bib', 'Mouse-human comparative map resources on the Web', 'Gatewood, B K|Cottingham, R W', 'Celltech Chiroscience R&D Inc, 1725 200th Street SE, Suite 200, Bothwell 98021, USA. BethGatewood@Chiroscience.com|Celltech Chiroscience R&D Inc, 1725 200th Street SE, Suite 200, Bothwell 98021, USA. BethGatewood@Chiroscience.com', 'United States|United States', 4, 'Animals|Computational Biology|DNA|Databases, Factual|Genetic Linkage|Genome|Genome, Human|Humans|Internet|Mice|Sequence Homology, Nucleic Acid', 'Comparative Study', '10.1093/bib/1.1.60', '11466974', 'Comparative maps have been a valuable resource for extrapolating biological information among organisms. The relationship between mouse and human maps provides a framework for integrating information from each species and thereby increasing the utility of all available data such as gene location, structure and function. This review describes the various public resources, both databases and web sites, containing genome-wide mouse-human comparative map information available through the World-Wide Web. We will focus on the use and applicability of these resources in their current form and consider future potential directions.'),
(68178, 2000, 'bib', 'Searching the expressed sequence tag (EST) databases: panning for genes', 'Jongeneel, C V', 'Office of Information Technology, Ludwig Institute for Cancer Research and Swiss Institute of Bioinformatics, chemin des Boveresses 155, CH-1066 Epalinges, Switzerland. Victor.Jongeneel@licr.org', 'Switzerland', 36, 'Algorithms|Animals|Computational Biology|Contig Mapping|DNA, Complementary|Databases, Factual|Expressed Sequence Tags|Frameshift Mutation|Genes|Genome|Humans|Internet|RNA, Messenger|Software|Transcription, Genetic', 'Journal Article', '10.1093/bib/1.1.76', '11466975', 'The genomes of living organisms contain many elements, including genes coding for proteins. The portions of the genes expressed as mature mRNA, collectively known as the transcriptome, represent only a small part of the genome. The expressed sequence tag (EST) databases contain an increasingly large part of the transcriptome of many species. For this reason, these databases are probably the most abundant source of new coding sequences available today. However, the raw data deposited in the EST databases are to a large extent unorganised, unannotated, redundant and of relatively low quality. This paper reviews some of the characteristics of the EST data, and the methods that can be used to find novel protein sequences within them. It also documents a collection of databases, software and web sites that can be useful to biologists interested in mining the EST databases over the Internet, or in establishing a local environment for such analyses.'),
(68179, 2000, 'bib', 'Wrapping and interoperating bioinformatics resources using CORBA', 'Stevens, R|Miller, C', 'Bioinformatics and Information Management Groups, University of Manchester, Department of Computer Science, Oxford Road, Manchester M13 9PI. robert.stevens@cs.man.ac.uk|Bioinformatics and Information Management Groups, University of Manchester, Department of Computer Science, Oxford Road, Manchester M13 9PI. robert.stevens@cs.man.ac.uk', '-|-', 12, 'Amino Acid Sequence|Base Sequence|Computational Biology|DNA|Information Storage and Retrieval|Proteins|Sequence Analysis|Software|User-Computer Interface', 'Journal Article', '10.1093/bib/1.1.9', '11466976', 'Bioinformaticians seeking to provide services to working biologists are faced with the twin problems of distribution and diversity of resources. Bioinformatics databases are distributed around the world and exist in many kinds of storage forms, platforms and access paradigms. To provide adequate services to biologists, these distributed and diverse resources have to interoperate seamlessly within single applications. The Common Object Request Broker Architecture (CORBA) offers one technical solution to these problems. The key component of CORBA is its use of object orientation as an intermediate form to translate between different representations. This paper concentrates on an explanation of object orientation and how it can be used to overcome the problems of distribution and diversity by describing the interfaces between objects.'),
(68180, 2000, 'bib', 'Mining of assembled expressed sequence tag (EST) data for protein families: application to the G protein-coupled receptor superfamily', 'Conklin, D|Yee, D P|Millar, R|Engelbrecht, J|Vissing, H', 'MRC Reproductive Biology Unit, Edinburgh.|MRC Reproductive Biology Unit, Edinburgh.|MRC Reproductive Biology Unit, Edinburgh.|MRC Reproductive Biology Unit, Edinburgh.|MRC Reproductive Biology Unit, Edinburgh.', '-|-|-|-|-', 5, 'Amino Acid Sequence|Animals|Databases, Factual|Expressed Sequence Tags|GTP-Binding Proteins|Humans|Molecular Sequence Data|Proteins|Receptors, Cell Surface|Sensitivity and Specificity', 'Journal Article', '10.1093/bib/1.1.93', '11466977', 'The availability of large expressed sequence tag (EST) databases has led to a revolution in the way new genes are identified. Mining of these databases using known protein sequences as queries is a powerful technique for discovering orthologous and paralogous genes. The scientist is often confronted, however, by an enormous amount of search output owing to the inherent redundancy of EST data. In addition, high search sensitivity often cannot be achieved using only a single member of a protein superfamily as a query. In this paper a technique for addressing both of these issues is described. Assembled EST databases are queried with every member of a protein superfamily, the results are integrated and false positives are pruned from the set. The result is a set of assemblies enriched in members of the protein superfamily under consideration. The technique is applied to the G protein-coupled receptor (GPCR) superfamily in the construction of a GPCR Resource. A novel full-length human GPCR identified from the GPCR Resource is presented, illustrating the utility of the method.'),
(68181, 2001, 'bib', 'Simultaneous modelling of metabolic, genetic and product-interaction networks', 'Juty, N S|Spence, H D|Hotz, H R|Tang, H|Goryanin, I|Hodgman, T C', 'Medicines Research Centre, GlaxoSmithKline Pharmaceuticals, UK. ncj10219@gsk.com|Medicines Research Centre, GlaxoSmithKline Pharmaceuticals, UK. ncj10219@gsk.com|Medicines Research Centre, GlaxoSmithKline Pharmaceuticals, UK. ncj10219@gsk.com|Medicines Research Centre, GlaxoSmithKline Pharmaceuticals, UK. ncj10219@gsk.com|Medicines Research Centre, GlaxoSmithKline Pharmaceuticals, UK. ncj10219@gsk.com|Medicines Research Centre, GlaxoSmithKline Pharmaceuticals, UK. ncj10219@gsk.com', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom', 5, 'Computational Biology|Escherichia coli|Genome, Bacterial|Humans|Metabolism|Models, Biological|Programming Languages|Protein Interaction Mapping|Software|Terminology as Topic|United States', 'Journal Article', '10.1093/bib/2.3.223', '11589583', 'The creation of cell models from annotated genome information, as well as additional data from other databases, requires both a format and medium for its distribution. Standards are described for the representation of the data in the form of Document Type Definitions (DTDs) for XML files. Separate DTDs are detailed for genetic, metabolic and gene product-interaction networks, which can be used to hold information on individual subsystems, or which may be combined to create a whole cell DTD. In the execution of this work, a fifth DTD was also created for a metabolite thesaurus, which allows incorporation of metabolite synonyms and generic nomenclature data into the models. A gene-regulation classification scheme was also created, to facilitate incorporation of gene regulatory information in an efficient manner. The work is described with particular reference to the metabolic network of Escherichia coli, which contains 808 individual enzymes. The assignment of confidence levels to these data, through the use of Gene Ontology evidence codes, is highlighted. In silico investigations may now be performed using the mathematical simulation workbench, DBsolve, which incorporates the facility to introduce data directly from XML.'),
(68182, 2001, 'bib', 'Engineering virtual cardiac tissue', 'Boyett, M R|Holden, A V|Zhang, H', 'School of Biomedical Sciences, Leeds University.|School of Biomedical Sciences, Leeds University.|School of Biomedical Sciences, Leeds University.', '-|-|-', 3, 'Action Potentials|Computational Biology|Heart|Humans|Ion Channels|Kinetics|Models, Anatomic|Muscle Proteins|Tissue Engineering|United States', 'Journal Article', '10.1093/bib/2.3.233', '11589584', 'The kinetics of proteins involved in ion transfer, sequestration and binding in cardiac cells can be modelled to construct a model of the electrical activity of isolated cardiac cells as a system of ordinary differential equations. These cell models may be incorporated into tissue models, which, when combined with histology and anatomy, form virtual tissues. The effects of changes in specific protein expression, or changes in protein kinetics, produced by mutations or pharmacological agents, can be simulated using these tissue models and used to account for the whole organ effects of changes in specific ion-transport protein activity.'),
(68183, 2001, 'bib', 'An approach to modelling in immunology', 'Yates, A|Chan, C C|Callard, R E|George, A J|Stark, J', 'CoMPLEX, University College London, UK.|CoMPLEX, University College London, UK.|CoMPLEX, University College London, UK.|CoMPLEX, University College London, UK.|CoMPLEX, University College London, UK.', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom', 29, 'Animals|Cell Differentiation|Computational Biology|Computer Simulation|Humans|Immune System|Immunity, Cellular|Immunologic Memory|Models, Immunological|Receptor Cross-Talk|Receptors, Antigen, T-Cell|Th1 Cells|Th2 Cells', 'Journal Article', '10.1093/bib/2.3.245', '11589585', 'Like most other fields in biology, immunology has been revolutionised by the techniques of molecular biology and the resulting explosion in available experimental data. It is argued that efforts to integrate the data to gain insight into how various subsystems in the immune system interact and function require mathematical modelling and computer simulation in close collaboration with experimentalists. This paper illustrates some of the techniques available for modelling immune systems, and highlights the issues that should be borne in mind by anyone starting down the modelling path.'),
(68184, 2001, 'bib', 'Systems biology: the reincarnation of systems theory applied in biology?', 'Wolkenhauer, O', 'Department of Biomolecular Sciences, Control Systems Centre, UMIST, Manchester, UK. o.wolkenhauer@umist.ac.uk', 'United Kingdom', 131, 'Causality|Computational Biology|Gene Expression Regulation|Metabolism|Models, Genetic|Systems Theory', 'Journal Article', '10.1093/bib/2.3.258', '11589586', 'With the availability of quantitative data on the transcriptome and proteome level, there is an increasing interest in formal mathematical models of gene expression and regulation. International conferences, research institutes and research groups concerned with systems biology have appeared in recent years and systems theory, the study of organisation and behaviour per se, is indeed a natural conceptual framework for such a task. This is, however, not the first time that systems theory has been applied in modelling cellular processes. Notably in the 1960s systems theory and biology enjoyed considerable interest among eminent scientists, mathematicians and engineers. Why did these early attempts vanish from research agendas? Here we shall review the domain of systems theory, its application to biology and the lessons that can be learned from the work of Robert Rosen. Rosen emerged from the early developments in the 1960s as a main critic but also developed a new alternative perspective to living systems, a concept that deserves a fresh look in the post-genome era of bioinformatics.'),
(68185, 2001, 'bib', 'Computational models of cells and tissues: machines, agents and fungal infection', 'Holcombe, M', 'University of Sheffield, Department of Computer Science, UK. m.holcombe@dcs.shef.ac.uk', 'United Kingdom', 8, 'Computational Biology|Computer Simulation|Magnaporthe|Models, Biological|Mycology|Oryza|Plant Diseases', 'Journal Article', '10.1093/bib/2.3.271', '11589587', 'Computational models have been of interest in biology for many years and have represented a particular approach to trying to understand biological processes and phenomena from a systems point of view. Much of the early work was rather abstract and high level and probably seemed to many to be of more philosophical than practical value. There have, however, been some advances in the development of more realistic models and the current state of computer science research provides us with new opportunities through both the emergence of models that can model seriously complex systems and also the support that modern software can give to the modelling process. This paper describes a few of the early simple models and then goes on to look at some new ideas in the area with a particular application drawn from the world of mycology. Some general principles relating to how new and emerging computational techniques can help to represent and understand extremely complex models conclude the paper.'),
(68186, 2001, 'bib', 'Can correlated mutations in protein domain families be used for protein design?', 'Nagl, S B', 'Department of Biochemistry, University College London, UK. nagl@biochem.ucl.ac.uk', 'United Kingdom', 4, 'Computational Biology|Drug Resistance, Microbial|Evolution, Molecular|HIV-1|Humans|Models, Molecular|Mutation|Protein Engineering|Protein Structure, Tertiary', 'Journal Article', '10.1093/bib/2.3.279', '11589588', 'Evidence from diverse studies, such as protein design experiments and analysis of the emergence of drug resistance in human immunodeficiency virus 1 (HIV-1), indicates that protein function can be diminished or altered by mutations at positions distant from the classic functional site. Furthermore, results from correlation analysis of the ligand-binding domain of nuclear receptors suggest that mutation events at positions distributed throughout a protein domain may be involved in functional diversification during the evolution of homologous domain families. This review explores potential applications for a protein design procedure based on correlated substitutions.'),
(68187, 2001, 'bib', 'Ontologies of developmental anatomy: their current and future roles', 'Bard, J|Winter, R', 'Department of Biomedical Sciences, University of Edinburgh, UK. j.bard@ed.ac.uk|Department of Biomedical Sciences, University of Edinburgh, UK. j.bard@ed.ac.uk', 'United Kingdom|United Kingdom', 11, 'Anatomy|Animals|Computational Biology|Congenital Abnormalities|Databases, Factual|Developmental Biology|Humans|Internet|Mice|Models, Genetic|Mutation', 'Journal Article', '10.1093/bib/2.3.289', '11589589', 'A central problem in current biology is elucidating the molecular networks that drive developmental change and physiological function. Such knowledge is needed partly to understand these networks, partly to be able to manipulate them, and partly to understand and help treat those human congenital abnormalities that arise as a result of mutation. Thus far, bioinformatics technology has been of limited use in this enterprise, mainly because its core focus has been on sequence technology and data archiving. For bioinformatics to be of use in this next tier of investigations, genetic and protein data need to be both archived and searchable by tissue since this is the level at which these networks operate. The resulting databases in turn require ontologies of developmental anatomy that can provide the formal infrastructure for handling gene expression, microarray and other tissue-based data. Here, the progress in making such ontologies, particularly for the developing mouse, is reported and the uses to which they are and will be put, together with the resources and tools currently available for investigating molecular networks and the genetic basis of congenital abnormalities, are considered.'),
(68188, 2001, 'bib', 'Software review Gepasi 30', 'Baigent, S', 'Centre for Nonlinear Dynamics and its Applications, London, UK. s.baigent@ucl.ac.uk', 'United Kingdom', 4, 'Biochemical Phenomena|Computational Biology|Computer Simulation|Humans|Software', 'Journal Article', '10.1093/bib/2.3.300', '11589590', '-'),
(68189, 2001, 'bib', 'The future of publishing microarray data', 'Spellman, P T', 'Centre for Nonlinear Dynamics and its Applications, London, UK. s.baigent@ucl.ac.uk', 'United Kingdom', 1, 'Databases, Nucleic Acid|Forecasting|Oligonucleotide Array Sequence Analysis|Publishing', 'Editorial', '10.1093/bib/2.4.316', '11808743', '-'),
(68190, 2001, 'bib', 'Open Source Software meets gene expression', 'Stewart, J E|Mangalam, H|Zhou, J', 'Open Informatics, Albuquerque, NM 87108, USA. jason@openinformatics.com|Open Informatics, Albuquerque, NM 87108, USA. jason@openinformatics.com|Open Informatics, Albuquerque, NM 87108, USA. jason@openinformatics.com', 'United States|United States|United States', 0, 'Access to Information|Algorithms|Databases as Topic|Financing, Government|Gene Expression|Software|Software Design', 'Journal Article', '10.1093/bib/2.4.319', '11808744', 'Use of the Open Source Software (OSS) development model has been crucial in a number of recent technological areas, including operating systems, applications and bioinformatics. The rationale for why OSS is often a better development model than proprietary development and some of the results of this model in the field of Gene Expression are reviewed. The paper concludes with a discussion of why funding agencies should endorse OSS and require funded software projects to be released Open Source.'),
(68191, 2001, 'bib', 'Microarray probe selection strategies', 'Tomiuk, S|Hofmann, K', 'Bioinformatics Group, MEMOREC Stoffel GmbH, Köln, Germany. Stefan.Tomiuk@memorec.com|Bioinformatics Group, MEMOREC Stoffel GmbH, Köln, Germany. Stefan.Tomiuk@memorec.com', 'Germany|Germany', 44, 'Alternative Splicing|DNA Probes|Expressed Sequence Tags|Gene Expression Profiling|Oligonucleotide Array Sequence Analysis|Poly A|Promoter Regions, Genetic', 'Journal Article', '10.1093/bib/2.4.329', '11808745', 'During recent years, DNA microarrays have become the method of choice to monitor the expression level of a large number of genes. Depending on the focus of the study and the method of microarray fabrication, a number of different strategies for probe selection may be most appropriate. One consideration concerns the length of the probe, ranging from some 25 residues used for oligonucleotide arrays to complete cDNAs. Unless resources are truly unlimited, an important decision to be made is the amount of effort to be put into the selection of genes and gene fragments. While high-throughput cDNA arraying projects usually will select from a collection of existing cDNA clones, smaller projects focusing on a number of selected genes can afford to selectively amplify fragments optimised for that purpose. This paper discusses the full scope of probe selection strategies, highlighting the problems that may be encountered in the various systems.'),
(68192, 2001, 'bib', 'Analysis of cDNA microarray images', 'Yang, Y H|Buckley, M J|Speed, T P', 'University of California, Berkeley, USA.|University of California, Berkeley, USA.|University of California, Berkeley, USA.', 'United States|United States|United States', 31, 'Data Interpretation, Statistical|Fluorescent Dyes|Image Processing, Computer-Assisted|Oligonucleotide Array Sequence Analysis', 'Journal Article', '10.1093/bib/2.4.341', '11808746', 'Microarrays are part of a new class of biotechnologies that allow the monitoring of expression levels for thousands of genes simultaneously. Image analysis is an important aspect of microarray experiments, one that can have a potentially large impact on subsequent analyses, such as clustering or the identification of differentially expressed genes. This paper reviews a number of existing image analysis methods used on cDNA microarray data. In particular, it describes and discusses the different segmentation and background adjustment methods. It was found that in some cases background adjustment can substantially reduce the precision--that is, increase the variability of low-intensity spot values. In contrast, the choice of segmentation procedure seems to have a smaller impact.'),
(68193, 2001, 'bib', 'Analysis of large-scale gene expression data', 'Sherlock, G', 'Department of Genetics, Stanford University Medical Center, CA 94306-5120, USA. sherlock@genome.stanford.edu', 'United States', 17, 'Algorithms|Data Interpretation, Statistical|Forecasting|Gene Expression Profiling|Oligonucleotide Array Sequence Analysis|Principal Component Analysis', 'Journal Article', '10.1093/bib/2.4.350', '11808747', 'DNA microarray technology has resulted in the generation of large complex data sets, such that the bottleneck in biological investigation has shifted from data generation, to data analysis. This review discusses some of the algorithms and tools for the analysis and organisation of microarray expression data, including clustering methods, partitioning methods, and methods for correlating expression data to other biological data.'),
(68194, 2001, 'bib', 'Exploiting big biology: integrating large-scale biological data for function inference', 'Marcotte, E|Date, S', 'Department of Chemistry and Biochemistry and Institute for Cellular and Molecular Biology, University of Texas at Austin, 78712, USA. marcotte@icmb.utexas.edu|Department of Chemistry and Biochemistry and Institute for Cellular and Molecular Biology, University of Texas at Austin, 78712, USA. marcotte@icmb.utexas.edu', 'United States|United States', 23, 'Data Collection|Data Interpretation, Statistical|Databases, Nucleic Acid|Databases, Protein|Genomics|Information Storage and Retrieval|Sequence Analysis, DNA', 'Journal Article', '10.1093/bib/2.4.363', '11808748', 'The amount of data produced by molecular biologists is growing at an exponential rate. Some of the fastest growing sets of data are measurements of gene expression, comparable in quantity only to gene sequences and the vast biological literature. Both gene expression data and sequence data offer hints as to the functions of thousands of newly discovered genes, but neither give complete answers. Therefore, much effort is being focused on integrating these large data sets and combining them with all available functional data to draw inferences about the functions of uncharacterised genes. This review discusses the most pertinent functional data for genome-wide functional inference and describes several methods by which these disparate data types are being integrated.'),
(68195, 2001, 'bib', 'Allelic association and disease mapping', 'Ennis, S|Maniatis, N|Collins, A', 'Genetic Epidemiology and Bioinformatics Research Group, Southampton University, UK.|Genetic Epidemiology and Bioinformatics Research Group, Southampton University, UK.|Genetic Epidemiology and Bioinformatics Research Group, Southampton University, UK.', 'United Kingdom|United Kingdom|United Kingdom', 2, 'Alleles|Case-Control Studies|Chromosome Mapping|Data Interpretation, Statistical|Genetics, Medical|Genotype|Haplotypes|Humans|Linkage Disequilibrium', 'Journal Article', '10.1093/bib/2.4.375', '11808749', 'The application of allelic association to map genes for complex traits, particularly using high-density maps of single nucleotide polymorphisms in candidate regions, is an area of very active research. Here we present some aspects of the methodology and applications to both major gene mapping, which illustrates the effectiveness of the method, and oligogenes, where methods are still in flux and for which there have been relatively few successes to date. Several important considerations emerge, including the selection of the optimal metric for measuring association and the importance of modelling the decline in association with distance given the variability in association in a candidate region. The Malecot model of association with distance is shown to have a resolution of greater than 50 kilobases but the available evidence suggests that considerably higher resolution might be achieved with dense single nucleotide polymorphism (SNP) maps.'),
(68196, 2002, 'bib', 'The computational challenges of applying comparative-based computational methods to whole genomes', 'Dubchak, Inna|Pachter, Lior', 'Lawrence Berkeley National Laboratory, National Energy Resesarch Scientific Computing Center, CA 94720, USA. ildubchak@lbl.gov|Lawrence Berkeley National Laboratory, National Energy Resesarch Scientific Computing Center, CA 94720, USA. ildubchak@lbl.gov', 'United States|United States', 1, 'Computational Biology|Genome|Regulatory Sequences, Nucleic Acid', 'Comparative Study', '10.1093/bib/3.1.18', '12002220', 'The explosion in genomic sequence available in public databases has resulted in an unprecedented opportunity for computational whole genome analyses. A number of promising comparative-based approaches have been developed for gene finding, regulatory element discovery and other purposes, and it is clear that these tools will play a fundamental role in analysing the enormous amount of new data that is currently being generated. The synthesis of computationally intensive comparative computational approaches with the requirement for whole genome analysis represents both an unprecedented challenge and opportunity for computational scientists. We focus on a few of these challenges, using by way of example the problems of alignment, gene finding and regulatory element discovery, and discuss the issues that have arisen in attempts to solve these problems in the context of whole genome analysis pipelines.'),
(68197, 2002, 'bib', 'Algorithmic strategies for the single nucleotide polymorphism haplotype assembly problem', 'Lippert, Ross|Schwartz, Russell|Lancia, Giuseppe|Istrail, Sorin', 'Informatics Research department, Celera Genomics, Rockville, MD 20850, USA. Ross.Lippert@celera.com|Informatics Research department, Celera Genomics, Rockville, MD 20850, USA. Ross.Lippert@celera.com|Informatics Research department, Celera Genomics, Rockville, MD 20850, USA. Ross.Lippert@celera.com|Informatics Research department, Celera Genomics, Rockville, MD 20850, USA. Ross.Lippert@celera.com', 'United States|United States|United States|United States', 32, 'Algorithms|Base Sequence|DNA|Haplotypes|Models, Theoretical|Polymorphism, Single-Stranded Conformational', 'Journal Article', '10.1093/bib/3.1.23', '12002221', 'With the consensus human genome sequenced and many other sequencing projects at varying stages of completion, greater attention is being paid to the genetic differences among individuals and the abilities of those differences to predict phenotypes. A significant obstacle to such work is the difficulty and expense of determining haplotypes--sets of variants genetically linked because of their proximity on the genome--for large numbers of individuals for use in association studies. This paper presents some algorithmic considerations in a new approach for haplotype determination: inferring haplotypes from localised polymorphism data gathered from short genome fragments. Formalised models of the biological system under consideration are examined, given a variety of assumptions about the goal of the problem and the character of optimal solutions. Some theoretical results and algorithms for handling haplotype assembly given the different models are then sketched. The primary conclusion is that some important simplified variants of the problem yield tractable problems while more general variants tend to be intractable in the worst case.'),
(68198, 2002, 'bib', 'Functional annotation of proteomic sequences based on consensus of sequence and structural analysis', 'Kitson, David H|Badretdinov, Azat|Zhu, Zhan-yang|Velikanov, Mikhail|Edwards, David J|Olszewski, Krzysztof|Szalma, Sándor|Yan, Lisa', 'Life Sciences Marketing Group of Accelrys Inc, Cambridge, UK. dkitson@accelrys.com|Life Sciences Marketing Group of Accelrys Inc, Cambridge, UK. dkitson@accelrys.com|Life Sciences Marketing Group of Accelrys Inc, Cambridge, UK. dkitson@accelrys.com|Life Sciences Marketing Group of Accelrys Inc, Cambridge, UK. dkitson@accelrys.com|Life Sciences Marketing Group of Accelrys Inc, Cambridge, UK. dkitson@accelrys.com|Life Sciences Marketing Group of Accelrys Inc, Cambridge, UK. dkitson@accelrys.com|Life Sciences Marketing Group of Accelrys Inc, Cambridge, UK. dkitson@accelrys.com|Life Sciences Marketing Group of Accelrys Inc, Cambridge, UK. dkitson@accelrys.com', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom', 6, 'Consensus Sequence|Databases, Genetic|Proteome|Sequence Analysis, Protein|Structure-Activity Relationship', 'Journal Article', '10.1093/bib/3.1.32', '12002222', 'To maximise the assignment of function of the proteins encoded by a genome and to aid the search for novel drug targets, there is an emerging need for sensitive methods of predicting protein function on a genome-wide basis. GeneAtlas is an automated, high-throughput pipeline for the prediction of protein structure and function using sequence similarity detection, homology modelling and fold recognition methods. GeneAtlas is described in detail here. To test GeneAtlas, a virtual genome was used, a subset of PDB structures from the SCOP database, in which the functional relationships are known. GeneAtlas detects additional relationships by building 3D models in comparison with the sequence searching method PSI-BLAST. Functionally related proteins with sequence identity below the twilight zone can be recognised correctly.'),
(68199, 2002, 'bib', 'Using fold recognition to search for useful proteins: Bayesian approach to fold recognition', 'Bienkowska, Jadwiga R', 'Biomolecular Engineering Research Center, Boston University, MA 02215, USA. jadwiga@darwin.bu.edu', 'United States', 0, 'Amino Acid Sequence|Bayes Theorem|Molecular Sequence Data|Protein Folding|Proteins|Sequence Homology, Amino Acid', 'Comparative Study', '10.1093/bib/3.1.45', '12002223', 'The wealth of protein sequence and structure data is greater than ever, thanks to the ongoing Genomics and Structural Genomics projects. The information available through such efforts needs to be analysed by new methods that combine both databases. One important result of genomic sequence analysis is the inference of functional homology among proteins. Until recently sequence similarity comparison was the only method for homologue inference. The new fold recognition approach reviewed in this paper enhances sequence comparison methods by including structural information in the process of protein comparison. This additional information often allows for the detection of similarities that cannot be found by methods that only use sequence information.'),
(68200, 2002, 'bib', 'Application of in silico positional cloning and bioinformatic mutation analysis to the study of eye diseases', 'Lichanska, Agnieszka M|Simpson, David A C', 'Department of Ophthalmology, Queens University Belfast, Institute of Clinical Science, UK. A.Lichanska@qub.ac.uk|Department of Ophthalmology, Queens University Belfast, Institute of Clinical Science, UK. A.Lichanska@qub.ac.uk', 'United Kingdom|United Kingdom', 0, 'Chromosome Mapping|Cloning, Molecular|Computational Biology|DNA Mutational Analysis|Eye Diseases|Humans|Internet|Mutation', 'Journal Article', '10.1093/bib/3.1.59', '12002224', 'A vast amount of DNA and protein sequence is now available and a plethora of programs have been developed to analyse the data. The bewildering variety of analyses that can be performed via the World-Wide Web can deter researchers from applying bioinformatics to augment their traditional genetic research. Focusing on the inherited eye diseases, this paper provides a guide to the appropriate software required for identification of candidate genes through to the detection and analysis of mutations.'),
(68201, 2002, 'bib', 'Large-scale analysis of gene expression profiles', 'Wu, Thomas D', 'Department of Bioinformatics, Genetech Inc, South San Francisco, CA 94080, USA. twu@gene.com', 'United States', 1, 'Cluster Analysis|Gene Expression Profiling|Oligonucleotide Array Sequence Analysis', 'Journal Article', '10.1093/bib/3.1.7', '12002225', 'The accumulation of DNA microarray data has now made it possible to use gene expression profiles to analyse expression data. A gene expression profile contains the expression data for a given gene over various samples, and can be contrasted with an expression signature, which contains the expression data for a single sample. Gene expression profiles are most revealing when samples are grouped appropriately, either by standard clinical or pathological categories or by categories discovered through cluster analysis techniques. Expression profiles can exist at various levels of abstraction, yielding information across various tissues or across diseases within a particular tissue. Hypothesis tests may be applied to expression profiles on a large scale to identify candidate genes of interest.'),
(68202, 2002, 'bib', 'The role of quantitative structure--activity relationships (QSAR) in biomolecular discovery', 'Winkler, David A', 'CSIRO Molecular Science, Clayton South MDC, Australia. Dave.winkler@molsci.csiro.au', 'Australia', 17, 'Models, Molecular|Molecular Structure|Quantitative Structure-Activity Relationship|Reproducibility of Results', 'Journal Article', '10.1093/bib/3.1.73', '12002226', 'Empirical methods for building predictive models of the relationships between molecular structure and useful properties are becoming increasingly important. This has arisen because drug discovery and development have become more complex. A large amount of biological target information is becoming available through molecular biology. Automation of chemical synthesis and pharmacological screening has also provided a vast amount of experimental data. Tools for designing libraries and extracting information from molecular databases and high-throughput screening experiments robustly and quickly enable leads to be discovered more effectively. As drug leads progress down the development pipeline, the ability to predict physicochemical, pharmacokinetic and toxicological properties of these leads is becoming increasingly important in reducing the number of expensive, late development failures. Quantitative structure-activity relationship (QSAR) methods have much to offer in these areas. However, QSAR analysis has many traps for unwary practitioners. This review introduces the concepts behind QSAR, points out problems that may be encountered, suggests ways of avoiding the pitfalls and introduces several exciting, new QSAR methods discovered during the last decade.'),
(68203, 2002, 'bib', 'EMBOSS opens up sequence analysis European Molecular Biology Open Software Suite', 'Olson, Sue A', 'Center for Genomics and Bioinformatics, Indiana University, Bloomington 47405, USA.', 'India', 120, 'Sequence Analysis|Software', 'Journal Article', '10.1093/bib/3.1.87', '12002227', '-'),
(68204, 2002, 'bib', 'Short EMBOSS User Guide European Molecular Biology Open Software Suite', 'Mullan, Lisa J|Bleasby, Alan J', 'MRC UK HGMP Resource Centre, Hinxton, Cambridge, UK. lmullan@hgmp.mrc.ac.uk|MRC UK HGMP Resource Centre, Hinxton, Cambridge, UK. lmullan@hgmp.mrc.ac.uk', 'United Kingdom|United Kingdom', 9, 'Computer User Training|Molecular Biology|Software|User-Computer Interface', 'Journal Article', '10.1093/bib/3.1.92', '12002228', '-'),
(68205, 2002, 'bib', 'Analytic approaches to twin data using structural equation models', 'Rijsdijk, Frühling V|Sham, Pak C', 'SGDP Centre, Institute of Psychiatry, Kings College London, UK. f.rijsdijk@iop.kcl.ac.uk|SGDP Centre, Institute of Psychiatry, Kings College London, UK. f.rijsdijk@iop.kcl.ac.uk', 'United Kingdom|United Kingdom', 196, 'Chi-Square Distribution|Data Interpretation, Statistical|Humans|Likelihood Functions|Models, Genetic|Multivariate Analysis|Software|Twin Studies as Topic', 'Journal Article', '10.1093/bib/3.2.119', '12139432', 'The classical twin study is the most popular design in behavioural genetics. It has strong roots in biometrical genetic theory, which allows predictions to be made about the correlations between observed traits of identical and fraternal twins in terms of underlying genetic and environmental components. One can infer the relative importance of these latent factors (model parameters) by structural equation modelling (SEM) of observed covariances of both twin types. SEM programs estimate model parameters by minimising a goodness-of-fit function between observed and predicted covariance matrices, usually by the maximum-likelihood criterion. Likelihood ratio statistics also allow the comparison of fit of different competing models. The program Mx, specifically developed to model genetically sensitive data, is now widely used in twin analyses. The flexibility of Mx allows the modelling of multivariate data to examine the genetic and environmental relations between two or more phenotypes and the modelling to categorical traits under liability-threshold models.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(68206, 2002, 'bib', 'Emerging bioinformatics for the metabolome', 'Mendes, Pedro', 'Virginia Bioinformatics Institute, and State University, Blacksburg 24061, USA. mendes@vt.edu', 'United States', 9, 'Animals|Computational Biology|Data Interpretation, Statistical|Databases, Factual|Gas Chromatography-Mass Spectrometry|Humans|Metabolism', 'Journal Article', '10.1093/bib/3.2.134', '12139433', 'Metabolic profiling applied to functional genomics (metabolomics) is in an early stage of development. Here, the technologies used for metabolite profiling are briefly covered, illustrated by a few pioneering studies. Issues related to bioinformatics, namely data analysis, visualisation and archival, are the main focus of this review. Arguably there is already a need for databases containing metabolite profiles specific for a single organism, and a generic repository containing all metabolite profiling results, regardless of species. Data analyses and visualisations that combine the biological context with chemistry details are suggested as being the most promising.'),
(68207, 2002, 'bib', 'Genetic association studies: design, analysis and interpretation', 'Lewis, Cathryn M', 'Division of Medical and Molecular Genetics, Guys Hospital, London, UK. cathryn.lewis@kcl.ac.uk', 'United Kingdom', 93, 'Case-Control Studies|Data Interpretation, Statistical|Gene Frequency|Genetic Techniques|Haplotypes|Linkage Disequilibrium|Microsatellite Repeats|Odds Ratio|Polymorphism, Single Nucleotide|Research Design', 'Journal Article', '10.1093/bib/3.2.146', '12139434', 'This paper provides a review of the design and analysis of genetic association studies. In case control studies, the different contingency tables and their relationships to the underlying genetic model are defined. Population stratification is discussed, with suggested methods to identify and correct for the effect. The transmission disequilibrium test is provided as an alternative family-based test, which is robust to population stratification. The relative benefits of each analysis are summarised.'),
(68208, 2002, 'bib', 'Information extraction in molecular biology', 'Blaschke, Christian|Hirschman, Lynette|Valencia, Alfonso', 'Protein Design Group, National Center for Biotechnology, CNB-CSIC, Cantoblanco, Madrid, Spain.|Protein Design Group, National Center for Biotechnology, CNB-CSIC, Cantoblanco, Madrid, Spain.|Protein Design Group, National Center for Biotechnology, CNB-CSIC, Cantoblanco, Madrid, Spain.', 'Spain|Spain|Spain', 80, 'Information Management|Information Storage and Retrieval|MEDLINE|Molecular Biology|PubMed', 'Journal Article', '10.1093/bib/3.2.154', '12139435', 'Information extraction has become a very active field in bioinformatics recently and a number of interesting papers have been published. Most of the efforts have been concentrated on a few specific problems, such as the detection of protein-protein interactions and the analysis of DNA expression arrays, although it is obvious that there are many other interesting areas of potential application (document retrieval, protein functional description, and detection of disease-related genes to name a few). Paradoxically, these exciting developments have not yet crystallised into general agreement on a set of standard evaluation criteria, such as the ones developed in fields such as protein structure prediction, which makes it very difficult to compare performance across these different systems. In this review we introduce the general field of information extraction, we outline the status of the applications in molecular biology, and we then discuss some ideas about possible standards for evaluation that are needed for the future development of the field.'),
(68209, 2002, 'bib', 'Conceptual data modelling for bioinformatics', 'Bornberg-Bauer, Erich|Paton, Norman W', 'School of Biological Sciences, University of Manchester, UK. ebb@bioinf.man.ac.uk|School of Biological Sciences, University of Manchester, UK. ebb@bioinf.man.ac.uk', 'United Kingdom|United Kingdom', 29, 'Computational Biology|Databases, Protein|Models, Biological|Sequence Analysis, DNA', 'Journal Article', '10.1093/bib/3.2.166', '12139436', 'Current research in the biosciences depends heavily on the effective exploitation of huge amounts of data. These are in disparate formats, remotely dispersed, and based on the different vocabularies of various disciplines. Furthermore, data are often stored or distributed using formats that leave implicit many important features relating to the structure and semantics of the data. Conceptual data modelling involves the development of implementation-independent models that capture and make explicit the principal structural properties of data. Entities such as a biopolymer or a reaction, and their relations, eg catalyses, can be formalised using a conceptual data model. Conceptual models are implementation-independent and can be transformed in systematic ways for implementation using different platforms, eg traditional database management systems. This paper describes the basics of the most widely used conceptual modelling notations, the ER (entity-relationship) model and the class diagrams of the UML (unified modelling language), and illustrates their use through several examples from bioinformatics. In particular, models are presented for protein structures and motifs, and for genomic sequences.'),
(68210, 2002, 'bib', 'Exact mapping of prokaryotic gene starts', 'Baytaluk, Mikhail V|Gelfand, Mikhail S|Mironov, Andrey A', 'Integrated Genomics, Moscow, Russia.|Integrated Genomics, Moscow, Russia.|Integrated Genomics, Moscow, Russia.', '-|-|-', 3, 'Algorithms|Chromosome Mapping|Computational Biology|Genes, Bacterial|Sequence Alignment|Sequence Analysis, DNA|Transcription Initiation Site', 'Journal Article', '10.1093/bib/3.2.181', '12139437', 'It is known that while the programs used to find genes in prokaryotic genomes reliably map protein-coding regions, they often fail in the exact determination of gene starts. This problem is further aggravated by sequencing errors, most notably insertions and deletions leading to frame-shifts. Therefore, the exact mapping of gene starts and identification of frame-shifts are important problems of the computer-assisted functional analysis of newly sequenced genomes. Here we review methods of gene recognition and describe a new algorithm for correction of gene starts and identification of frame-shifts in prokaryotic genomes. The algorithm is based on the comparison of nucleotide and protein sequences of homologous genes from related organisms, using the assumption that the rate of evolutionary changes in protein-coding regions is lower than that in non-coding regions. A dynamic programming algorithm is used to align protein sequences obtained by formal translation of genomic nucleotide sequences. The possibility of frame-shifts is taken into account. The algorithm was tested on several groups of related organisms: gamma-proteobacteria, the Bacillus/Clostridium group, and three Pyrococcus genomes. The testing demonstrated that, dependent or a genome, 1-10 per cent of genes have incorrect starts or contain frame-shifts. The algorithm is implemented in the program package Orthologator-GeneCorrector.'),
(68211, 2002, 'bib', 'Computer programs for eukaryotic gene prediction', 'Makarov, Vladimir', 'Bionomix, Inc, Pasadena, CA 91106, USA. vladimir_makarov@hotmail.com', 'United States', 17, 'Animals|Computational Biology|Eukaryotic Cells|Genes|Humans|Software', 'Journal Article', '10.1093/bib/3.2.195', '12139438', 'Seven popular programs for gene prediction in eukaryotic organisms are described and evaluated on the basis of availability for in-house and on-line use and prediction accuracy. This report outlines generally applicable approaches to computational gene prediction and known limitations in this field.'),
(68212, 2002, 'bib', 'BLAST and go?', 'Mullan, Lisa J|Williams, Gary W', 'MRC UK Human Genome Mapping Project Resource Centre, Hinxton, Cambridge, UK. lmullan@hgmp.mrc.ac.uk|MRC UK Human Genome Mapping Project Resource Centre, Hinxton, Cambridge, UK. lmullan@hgmp.mrc.ac.uk', 'United Kingdom|United Kingdom', 0, 'Algorithms|Computational Biology|Sequence Alignment|Software', 'Journal Article', '10.1093/bib/3.2.200', '12139439', '-'),
(68213, 2002, 'bib', 'InterPro: an integrated documentation resource for protein families, domains and functional sites', 'Mulder, Nicola J|Apweiler, Rolf|Attwood, Terri K|Bairoch, Amos|Bateman, Alex|Binns, David|Biswas, Margaret|Bradley, Paul|Bork, Peer|Bucher, Phillip|Copley, Richard|Courcelle, Emmanuel|Durbin, Richard|Falquet, Laurent|Fleischmann, Wolfgang|Gouzy, Jerome|Griffith-Jones, Sam|Haft, Daniel|Hermjakob, Henning|Hulo, Nicolas|Kahn, Daniel|Kanapin, Alexander|Krestyaninova, Maria|Lopez, Rodrigo|Letunic, Ivica|Orchard, Sandra|Pagni, Marco|Peyruc, David|Ponting, Chris P|Servant, Florence|Sigrist, Christian J A|InterPro Consortium', 'EMBL Outstation, European Bioinformatics Institute, Hinxton, Cambridge, UK. mulder@ebi.ac.uk|EMBL Outstation, European Bioinformatics Institute, Hinxton, Cambridge, UK. mulder@ebi.ac.uk|EMBL Outstation, European Bioinformatics Institute, Hinxton, Cambridge, UK. mulder@ebi.ac.uk|EMBL Outstation, European Bioinformatics Institute, Hinxton, Cambridge, UK. mulder@ebi.ac.uk|EMBL Outstation, European Bioinformatics Institute, Hinxton, Cambridge, UK. mulder@ebi.ac.uk|EMBL Outstation, European Bioinformatics Institute, Hinxton, Cambridge, UK. mulder@ebi.ac.uk|EMBL Outstation, European Bioinformatics Institute, Hinxton, Cambridge, UK. mulder@ebi.ac.uk|EMBL Outstation, European Bioinformatics Institute, Hinxton, Cambridge, UK. mulder@ebi.ac.uk|EMBL Outstation, European Bioinformatics Institute, Hinxton, Cambridge, UK. mulder@ebi.ac.uk|EMBL Outstation, European Bioinformatics Institute, Hinxton, Cambridge, UK. mulder@ebi.ac.uk|EMBL Outstation, European Bioinformatics Institute, Hinxton, Cambridge, UK. mulder@ebi.ac.uk|EMBL Outstation, European Bioinformatics Institute, Hinxton, Cambridge, UK. mulder@ebi.ac.uk|EMBL Outstation, European Bioinformatics Institute, Hinxton, Cambridge, UK. mulder@ebi.ac.uk|EMBL Outstation, European Bioinformatics Institute, Hinxton, Cambridge, UK. mulder@ebi.ac.uk|EMBL Outstation, European Bioinformatics Institute, Hinxton, Cambridge, UK. mulder@ebi.ac.uk|EMBL Outstation, European Bioinformatics Institute, Hinxton, Cambridge, UK. mulder@ebi.ac.uk|EMBL Outstation, European Bioinformatics Institute, Hinxton, Cambridge, UK. mulder@ebi.ac.uk|EMBL Outstation, European Bioinformatics Institute, Hinxton, Cambridge, UK. mulder@ebi.ac.uk|EMBL Outstation, European Bioinformatics Institute, Hinxton, Cambridge, UK. mulder@ebi.ac.uk|EMBL Outstation, European Bioinformatics Institute, Hinxton, Cambridge, UK. mulder@ebi.ac.uk|EMBL Outstation, European Bioinformatics Institute, Hinxton, Cambridge, UK. mulder@ebi.ac.uk|EMBL Outstation, European Bioinformatics Institute, Hinxton, Cambridge, UK. mulder@ebi.ac.uk|EMBL Outstation, European Bioinformatics Institute, Hinxton, Cambridge, UK. mulder@ebi.ac.uk|EMBL Outstation, European Bioinformatics Institute, Hinxton, Cambridge, UK. mulder@ebi.ac.uk|EMBL Outstation, European Bioinformatics Institute, Hinxton, Cambridge, UK. mulder@ebi.ac.uk|EMBL Outstation, European Bioinformatics Institute, Hinxton, Cambridge, UK. mulder@ebi.ac.uk|EMBL Outstation, European Bioinformatics Institute, Hinxton, Cambridge, UK. mulder@ebi.ac.uk|EMBL Outstation, European Bioinformatics Institute, Hinxton, Cambridge, UK. mulder@ebi.ac.uk|EMBL Outstation, European Bioinformatics Institute, Hinxton, Cambridge, UK. mulder@ebi.ac.uk|EMBL Outstation, European Bioinformatics Institute, Hinxton, Cambridge, UK. mulder@ebi.ac.uk|EMBL Outstation, European Bioinformatics Institute, Hinxton, Cambridge, UK. mulder@ebi.ac.uk|EMBL Outstation, European Bioinformatics Institute, Hinxton, Cambridge, UK. mulder@ebi.ac.uk', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|', 116, 'Algorithms|Computational Biology|Databases, Protein|Humans|Information Services|Internet|Proteins|Software', 'Journal Article', '10.1093/bib/3.3.225', '12230031', 'The exponential increase in the submission of nucleotide sequences to the nucleotide sequence database by genome sequencing centres has resulted in a need for rapid, automatic methods for classification of the resulting protein sequences. There are several signature and sequence cluster-based methods for protein classification, each resource having distinct areas of optimum application owing to the differences in the underlying analysis methods. In recognition of this, InterPro was developed as an integrated documentation resource for protein families, domains and functional sites, to rationalise the complementary efforts of the individual protein signature database projects. The member databases - PRINTS, PROSITE, Pfam, ProDom, SMART and TIGRFAMs - form the InterPro core. Related signatures from each member database are unified into single InterPro entries. Each InterPro entry includes a unique accession number, functional descriptions and literature references, and links are made back to the relevant member database(s). Release 4.0 of InterPro (November 2001) contains 4,691 entries, representing 3,532 families, 1,068 domains, 74 repeats and 15 sites of post-translational modification (PTMs) encoded by different regular expressions, profiles, fingerprints and hidden Markov models (HMMs). Each InterPro entry lists all the matches against SWISS-PROT and TrEMBL (2,141,621 InterPro hits from 586,124 SWISS-PROT and TrEMBL protein sequences). The database is freely accessible for text- and sequence-based searches.'),
(68214, 2002, 'bib', 'HMM-based databases in InterPro', 'Bateman, Alex|Haft, Daniel H', 'Pfam Group, The Wellcome Trust Sanger Institute, Hinxton, UK.|Pfam Group, The Wellcome Trust Sanger Institute, Hinxton, UK.', 'United Kingdom|United Kingdom', 10, 'Computational Biology|Databases, Protein|Proteins|Sequence Alignment|Sequence Analysis, Protein|Software', 'Journal Article', '10.1093/bib/3.3.236', '12230032', 'Protein family databases are an important resource for protein annotation and understanding protein evolution and function. In recent years hidden Markov models (HMMs) have become one of the key technologies used for detection of members of these families. This paper reviews the Pfam, TIGRFAMs and SMART databases that use the profile-HMMs provided by the HMMER package.'),
(68215, 2002, 'bib', 'ProDom: automated clustering of homologous domains', 'Servant, Florence|Bru, Catherine|Carrxc3xa8re, Sxc3xa9bastien|Courcelle, Emmanuel|Gouzy, Jxc3xa9rxc4xb5me|Peyruc, David|Kahn, Daniel', 'SWISSPROT Group, EBI.|SWISSPROT Group, EBI.|SWISSPROT Group, EBI.|SWISSPROT Group, EBI.|SWISSPROT Group, EBI.|SWISSPROT Group, EBI.|SWISSPROT Group, EBI.', '-|-|-|-|-|-|-', 109, 'Databases, Protein|Genome|Information Services|Internet|Models, Molecular|Phylogeny|Protein Structure, Tertiary|Proteins|Sequence Alignment|Sequence Analysis, Protein|User-Computer Interface', 'Journal Article', '10.1093/bib/3.3.246', '12230033', 'The ProDom database is a comprehensive set of protein domain families automatically generated from the SWISS-PROT and TrEMBL sequence databases. An associated database, ProDom-CG, has been derived as a restriction of ProDom to completely sequenced genomes. The ProDom construction method is based on iterative PSI-BLAST searches and multiple alignments are generated for each domain family. The ProDom web server provides the user with a set of tools to visualise multiple alignments, phylogenetic trees and domain architectures of proteins, as well as a BLAST-based server to analyse new sequences for homologous domains. The comprehensive nature of ProDom makes it particularly useful to help sustain the growth of InterPro.'),
(68216, 2002, 'bib', 'The PRINTS database: a resource for identification of protein families', 'Attwood, Terri K', 'School of Biological Sciences, Department of Computer Science, The University of Manchester, UK. attwood@bioinf.man.ac.uk', 'United Kingdom', 64, 'Amino Acid Motifs|Amino Acid Sequence|Animals|Computational Biology|Database Management Systems|Databases, Protein|Evolution, Molecular|Humans|Internet|Peptide Mapping|Prions|Proteins|Receptor, Muscarinic M1|Receptors, Muscarinic|Rhodopsin|Sequence Alignment', 'Journal Article', '10.1093/bib/3.3.252', '12230034', 'The PRINTS database houses a collection of protein fingerprints, which may be used to assign family and functional attributes to uncharacterised sequences, such as those currently emanating from the various genome-sequencing projects. The April 2002 release includes 1,700 family fingerprints, encoding approximately 10,500 motifs, covering a range of globular and membrane proteins, modular polypeptides and so on. Fingerprints are groups of conserved motifs that, taken together, provide diagnostic protein family signatures. They derive much of their potency from the biological context afforded by matching motif neighbours, this makes them at once more flexible and powerful than single-motif approaches. The technique further departs from other pattern-matching methods by readily allowing the creation of fingerprints at superfamily-, family- and subfamily-specific levels, thereby allowing more fine-grained diagnoses. Here, we provide an overview of the method of protein fingerprinting and how the results of fingerprint analyses are used to build PRINTS and its relational cousin, PRINTS-S.'),
(68217, 2002, 'bib', 'PROSITE: a documented database using patterns and profiles as motif descriptors', 'Sigrist, Christian J A|Cerutti, Lorenzo|Hulo, Nicolas|Gattiker, Alexandre|Falquet, Laurent|Pagni, Marco|Bairoch, Amos|Bucher, Philipp', 'Swiss Institute of Bioinformatics CMU, University of Geneva. christian.sigrist@isb-sib.ch|Swiss Institute of Bioinformatics CMU, University of Geneva. christian.sigrist@isb-sib.ch|Swiss Institute of Bioinformatics CMU, University of Geneva. christian.sigrist@isb-sib.ch|Swiss Institute of Bioinformatics CMU, University of Geneva. christian.sigrist@isb-sib.ch|Swiss Institute of Bioinformatics CMU, University of Geneva. christian.sigrist@isb-sib.ch|Swiss Institute of Bioinformatics CMU, University of Geneva. christian.sigrist@isb-sib.ch|Swiss Institute of Bioinformatics CMU, University of Geneva. christian.sigrist@isb-sib.ch|Swiss Institute of Bioinformatics CMU, University of Geneva. christian.sigrist@isb-sib.ch', '-|-|-|-|-|-|-|-', 208, 'Amino Acid Motifs|Amino Acid Sequence|Computational Biology|Databases, Protein|Internet|Models, Molecular|Protein Structure, Tertiary|Proteins|Sequence Alignment|Software', 'Journal Article', '10.1093/bib/3.3.265', '12230035', 'Among the various databases dedicated to the identification of protein families and domains, PROSITE is the first one created and has continuously evolved since. PROSITE currently consists of a large collection of biologically meaningful motifs that are described as patterns or profiles, and linked to documentation briefly describing the protein family or domain they are designed to detect. The close relationship of PROSITE with the SWISS-PROT protein database allows the evaluation of the sensitivity and specificity of the PROSITE motifs and their periodic reviewing. In return, PROSITE is used to help annotate SWISS-PROT entries. The main characteristics and the techniques of family and domain identification used by PROSITE are reviewed in this paper.'),
(68218, 2002, 'bib', 'High-quality protein knowledge resource: SWISS-PROT and TrEMBL', 'ODonovan, Claire|Martin, Maria Jesus|Gattiker, Alexandre|Gasteiger, Elisabeth|Bairoch, Amos|Apweiler, Rolf', 'EMBL Outstation, European Bioinformatics Institute, Hinxton, Cambridge, UK. odonovan@ebi.ac.uk|EMBL Outstation, European Bioinformatics Institute, Hinxton, Cambridge, UK. odonovan@ebi.ac.uk|EMBL Outstation, European Bioinformatics Institute, Hinxton, Cambridge, UK. odonovan@ebi.ac.uk|EMBL Outstation, European Bioinformatics Institute, Hinxton, Cambridge, UK. odonovan@ebi.ac.uk|EMBL Outstation, European Bioinformatics Institute, Hinxton, Cambridge, UK. odonovan@ebi.ac.uk|EMBL Outstation, European Bioinformatics Institute, Hinxton, Cambridge, UK. odonovan@ebi.ac.uk', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom', 65, 'Amino Acid Sequence|Computational Biology|Databases, Nucleic Acid|Databases, Protein|Haemophilus influenzae|Humans|Protein Conformation|Proteins|Proteome|Proteomics', 'Journal Article', '10.1093/bib/3.3.275', '12230036', 'SWISS-PROT is a curated protein sequence database which strives to provide a high level of annotation (such as the description of the function of a protein, its domain structure, post-translational modifications, variants, etc.), a minimal level of redundancy and a high level of integration with other databases. Together with its automatically annotated supplement TrEMBL, it provides a comprehensive and high-quality view of the current state of knowledge about proteins. Ongoing developments include the further improvement of functional and automatic annotation in the databases including evidence attribution with particular emphasis on the human, archaeal and bacterial proteomes and the provision of additional resources such as the International Protein Index (IPI) and XML format of SWISS-PROT and TrEMBL to the user community.'),
(68219, 2002, 'bib', 'Applications of InterPro in protein annotation and genome analysis', 'Biswas, Margaret|ORourke, John F|Camon, Evelyn|Fraser, Gill|Kanapin, Alexander|Karavidopoulou, Youla|Kersey, Paul|Kriventseva, Evgenia|Mittard, Virginie|Mulder, Nicola|Phan, Isabelle|Servant, Florence|Apweiler, Rolf', 'EBI, Hinxton, Cambridge, UK.|EBI, Hinxton, Cambridge, UK.|EBI, Hinxton, Cambridge, UK.|EBI, Hinxton, Cambridge, UK.|EBI, Hinxton, Cambridge, UK.|EBI, Hinxton, Cambridge, UK.|EBI, Hinxton, Cambridge, UK.|EBI, Hinxton, Cambridge, UK.|EBI, Hinxton, Cambridge, UK.|EBI, Hinxton, Cambridge, UK.|EBI, Hinxton, Cambridge, UK.|EBI, Hinxton, Cambridge, UK.|EBI, Hinxton, Cambridge, UK.', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom', 41, 'Amino Acid Sequence|Computational Biology|Databases, Protein|Genome|Genome, Human|Humans|Internet|Protein Conformation|Proteins|Proteome|Sequence Analysis, Protein|Software', 'Journal Article', '10.1093/bib/3.3.285', '12230037', 'The applications of InterPro span a range of biologically important areas that includes automatic annotation of protein sequences and genome analysis. In automatic annotation of protein sequences InterPro has been utilised to provide reliable characterisation of sequences, identifying them as candidates for functional annotation. Rules based on the InterPro characterisation are stored and operated through a database called RuleBase. RuleBase is used as the main tool in the sequence database group at the EBI to apply automatic annotation to unknown sequences. The annotated sequences are stored and distributed in the TrEMBL protein sequence database. InterPro also provides a means to carry out statistical and comparative analyses of whole genomes. In the Proteome Analysis Database, InterPro analyses have been combined with other analyses based on CluSTr, the Gene Ontology (GO) and structural information on the proteins.'),
(68220, 2002, 'bib', 'The Bio* toolkits--a brief overview', 'Mangalam, Harry', 'tacg Informatics, Irvine, CA 92612, USA. hjm@tacgi.com', 'United States', 49, 'Computational Biology|Computer Systems|Humans|Internet|Programming Languages|Software|User-Computer Interface', 'Journal Article', '10.1093/bib/3.3.296', '12230038', 'Bioinformatics research is often difficult to do with commercial software. The Open Source BioPerl, BioPython and Biojava projects provide toolkits with multiple functionality that make it easier to create customised pipelines or analysis. This review briefly compares the quirks of the underlying languages and the functionality, documentation, utility and relative advantages of the Bio counterparts, particularly from the point of view of the beginning biologist programmer.'),
(68221, 2002, 'bib', 'Multiple sequence alignment--the gateway to further analysis', 'Mullan, Lisa J', 'MRC UK Human Genome Mapping Project Resource Centre, Hinxton, Cambridge, UK. lmullan@hgmp.mrc.ac.uk', 'United Kingdom', 9, 'Algorithms|Amino Acid Sequence|Databases, Protein|Internet|Phylogeny|Proteins|Sequence Alignment|Sequence Analysis, Protein', 'Journal Article', '10.1093/bib/3.3.303', '12230039', '-'),
(68222, 2002, 'bib', 'Bioinformatics and economics', 'Parsons, Jeremy', 'MRC UK Human Genome Mapping Project Resource Centre, Hinxton, Cambridge, UK. lmullan@hgmp.mrc.ac.uk', 'United Kingdom', 0, 'Computational Biology|Databases, Factual|Drug Industry|Economics|Humans|Nobel Prize', 'Editorial', '10.1093/bib/3.4.328', '12511061', '-'),
(68223, 2002, 'bib', 'BioMOBY: an open source biological web services proposal', 'Wilkinson, Mark D|Links, Matthew', 'BioMOBY Project, Plant Biotechnology Institute, National Research Council Canada, Saskatoon, Saskatchewan. mwilkinson@gene.pbi.nrc.ca|BioMOBY Project, Plant Biotechnology Institute, National Research Council Canada, Saskatoon, Saskatchewan. mwilkinson@gene.pbi.nrc.ca', 'Canada|Canada', 239, 'Computer Communication Networks|Database Management Systems|Databases, Nucleic Acid|Humans|Information Storage and Retrieval|International Cooperation|Internet|User-Computer Interface', 'Journal Article', '10.1093/bib/3.4.331', '12511062', 'BioMOBY is an Open Source research project which aims to generate an architecture for the discovery and distribution of biological data through web services, data and services are decentralised, but the availability of these resources, and the instructions for interacting with them, are registered in a central location called MOBY Central. BioMOBY adds to the web services paradigm, as exemplified by Universal Data Discovery and Integration (UDDI), by having an object-driven registry query system with object and service ontologies. This allows users to traverse expansive and disparate data sets where each possible next step is presented based on the data object currently in-hand. Moreover, a path from the current data object to a desired final data object could be automatically discovered using the registry. Native BioMOBY objects are lightweight XML, and make up both the query and the response of a simple object access protocol (SOAP) transaction.'),
(68224, 2002, 'bib', 'Phase4: automatic evaluation of database search methods', 'Rehmsmeier, Marc', 'International NRW Graduate School in Bioinformatics and Genome Research, Bielefeld University, Germany. marc@techfak.uni-bielefeld.de', 'Germany', 5, 'Data Interpretation, Statistical|Database Management Systems|Databases as Topic|Databases, Factual|Evaluation Studies as Topic|Humans|Information Storage and Retrieval|Programming Languages|ROC Curve|Reproducibility of Results|Software|User-Computer Interface', 'Journal Article', '10.1093/bib/3.4.342', '12511063', 'It has become standard to evaluate newly devised database search methods in terms of sensitivity and selectivity and to compare them with existing methods. This involves the construction of a suitable evaluation scenario, the execution of the methods, the assessment of their performances, and the presentation of the results. Each of these four phases and their smooth connection usually imposes formidable work. To relieve the evaluator of this burden, a system has been designed with which evaluations can be effected rapidly. It is implemented in the programming language Python whose object-oriented features are used to offer a great flexibility in changing the evaluation design. A graphical user interface is provided which offers the usual amenities such as radio- and checkbuttons or file browsing facilities.'),
(68225, 2002, 'bib', 'Bioinformatics training in the USA', 'Zatz, Marion M', 'NIGMS, Bethesda, MD 201892-6200, USA. ZATZM@nigms.nih.gov', 'United States', 15, 'Academic Dissertations as Topic|Computational Biology|Curriculum|Government Agencies|Humans|National Institutes of Health (U.S.)|Training Support|United States|Universities', 'Journal Article', '10.1093/bib/3.4.353', '12511064', 'This paper provides an overview of the history and funding of bioinformatics training in the USA, and summarises some of the challenges and key features associated with bioinformatics training programmes at PhD level. The paper includes compilations of current PhD bioinformatics training programmes and sources of funding.'),
(68226, 2002, 'bib', 'Predicting protein subcellular localisation from amino acid sequence information', 'Emanuelsson, Olof', 'Stockholm Bioinformatics Center, Stockholm University, Stockholm, Sweden. olof@sbc.su.se', 'Sweden', 61, 'Amino Acid Sequence|Cell Membrane|Eukaryotic Cells|Protein Sorting Signals|Protein Transport|Proteins|Software', 'Journal Article', '10.1093/bib/3.4.361', '12511065', 'Predicting the subcellular localisation of proteins is an important part of the elucidation of their functions and interactions. Here, the amino acid sequence motifs that direct proteins to their proper subcellular compartment are surveyed, different methods for localisation prediction are discussed, and some benchmarks for the more commonly used predictors are presented.'),
(68227, 2002, 'bib', 'Global computing for bioinformatics', 'Loewe, Laurence', 'Microbial Ecology Group, Department of Biosciences, Technische Universität München, Weihenstephaner Berg 3, 85354 Freising, Germany. Laurence.Loewe@evolutionary-research.net', 'Germany', 9, 'Computational Biology|Computer Communication Networks|Databases, Factual|Humans|Information Storage and Retrieval|Software|Systems Integration|User-Computer Interface', 'Journal Article', '10.1093/bib/3.4.377', '12511066', 'Global computing, the collaboration of idle PCs via the Internet in a SETI@home style, emerges as a new way of massive parallel multiprocessing with potentially enormous CPU power. Its relations to the broader, fast-moving field of Grid computing are discussed without attempting a review of the latter. This review (i) includes a short table of milestones in global computing history, (ii) lists opportunities global computing offers for bioinformatics, (iii) describes the structure of problems well suited for such an approach, (iv) analyses the anatomy of successful projects and (v) points to existing software frameworks. Finally, an evaluation of the various costs shows that global computing indeed has merit, if the problem to be solved is already coded appropriately and a suitable global computing framework can be found. Then, either significant amounts of computing power can be recruited from the general public, or--if employed in an enterprise-wide Intranet for security reasons--idle desktop PCs can substitute for an expensive dedicated cluster.'),
(68228, 2002, 'bib', 'Technologies for integrating biological data', 'Wong, Limsoon', 'Institute for Infocomm Research, Singapore. limsoon@izr.a-star.edu.sg', 'Singapore', 33, 'Database Management Systems|Databases, Factual|Humans|Information Storage and Retrieval|Software|User-Computer Interface', 'Journal Article', '10.1093/bib/3.4.389', '12511067', 'The process of building a new database relevant to some field of study in biomedicine involves transforming, integrating and cleansing multiple data sources, as well as adding new material and annotations. This paper reviews some of the requirements of a general solution to this data integration problem. Several representative technologies and approaches to data integration in biomedicine are surveyed. Then some interesting features that separate the more general data integration technologies from the more specialised ones are highlighted.'),
(68229, 2002, 'bib', 'Pise: software for building bioinformatics webs', 'Gilbert, Don', 'Biology Department, and Center for Genomics and Bioinformatics, Indiana University, Bloomington 47405, USA. gilbertd@bio.indiana.edu', 'India', 2, 'Computational Biology|Computer Communication Networks|Humans|Programming Languages|Software|Software Design|Systems Integration|User-Computer Interface', 'Journal Article', '10.1093/bib/3.4.405', '12511068', 'Pise is interface construction software for bioinformatics applications that run by command-line operations. It creates common, easy-to-use interfaces to these applications for the Web, or other uses. It is adaptable to new bioinformatics tools, and offers program chaining, Unix system batch and other controls, making it an attractive method for building and using your own bioinformatics web services.'),
(68230, 2002, 'bib', 'Protein 3D structural data--where it is, and why we need it', 'Mullan, Lisa J', 'MRC UK Human Genome Mapping Project Resource Centre, Hinxton, Cambridge, UK. lmullan@hgmp.mrc.ac.uk', 'United Kingdom', 0, 'Databases, Protein|Genome|Humans|Protein Conformation|Proteins|Software', 'Journal Article', '10.1093/bib/3.4.410', '12511069', '-'),
(68231, 2003, 'bib', 'Identity and interoperability in bioinformatics', 'Clark, Tim', 'MRC UK Human Genome Mapping Project Resource Centre, Hinxton, Cambridge, UK. lmullan@hgmp.mrc.ac.uk', 'United Kingdom', 4, 'Computational Biology|Human Genome Project|Humans|Internet', 'Editorial', '10.1093/bib/4.1.4', '12715829', '-'),
(68232, 2003, 'bib', 'A review of bioinformatics education in the UK', 'Counsell, Damian', 'Medical Research Councils Human Genome Mapping Project Resource Centre, Cambridge, UK. d.counsell@hgmp.mrc.ac.uk', 'United Kingdom', 36, 'Computational Biology|Research|United Kingdom', 'Journal Article', '10.1093/bib/4.1.7', '12715830', 'If the completion of the first draft of the human genome represents the coming of age of bioinformatics, then the emergence of bioinformatics as a university degree subject represents its establishment. In this paper bioinformatics as a subject for formal study is discussed, rather than as a subject for research, and a selection of the taught, mainly graduate, courses currently available in the UK are reviewed. Throughout, the author tries to draw parallels between the integration of bioinformatics into biomedical research and teaching today, and that of molecular biology, two decades ago. Others have made this analogy between these two relatively young disciplines. Although research sources are referenced, the author makes no pretence of objectivity. This article contains his opinions, and those of a number of current bioinformatics course organisers whose comments on the subject were solicited in advance specifically for this paper. The course organisers kindly advised how they planned their curricula, and described the special strengths of their programmes. Comments from present and former students of several bioinformatics degree programmes were also solicited. Except where individuals are directly quoted, any opinions expressed herein should be considered the authors. Compared with its sister piece [Marion Zatz, in previous issue of Briefings in Bioinformatics pp. 353], this paper is less about funding policy--which, in the UK, has lately (if belatedly) been more generous towards bioinformatics teaching--than it is about practice and content, the requirements of the bioinformatics research communities, the corresponding emphases of bioinformatics courses, and the general market for holders of bioinformatics degrees. Individual courses are cited throughout as examples, but the final section contains a full annotated listing with URL addresses. Based on the authors own experience of practising and teaching bioinformatics, he describes the skills he believes will be most useful to bioinformaticians in the near future and suggests ways to prepare students of bioinformatics for a fall in demand for those abilities.'),
(68233, 2003, 'bib', 'The state of the art of mammalian promoter recognition', 'Werner, Thomas', 'Genomatix Software GmbH. Landsberger Strasse 6 D-80339. München, Germany. Werner@genomatix.de', 'Germany', 41, 'Animals|DNA, Complementary|Promoter Regions, Genetic', 'Journal Article', '10.1093/bib/4.1.22', '12715831', 'The draft sequences of whole genomes are being published at an ever-increasing pace, thus providing access to the human genomic sequence and, more recently, the mouse sequence. Genomes of the invertebrates are also becoming available. Now that the genomic DNA of mammalian species is available, an old problem can be tackled with renewed vigour mammalian promoter prediction. Gene promoters have proved elusive for more than a decade, despite their pivotal role in gene regulation. Recently, however, several new developments have made it possible to make meaningful large-scale predictions. This paper reviews the methods used for the prediction of mammalian, mostly human, promoters.'),
(68234, 2003, 'bib', 'Clustering-based approaches to discovering and visualising microarray data patterns', 'Azuaje, Francisco', 'School of Computing and Mathematics, University of Ulster at Jordanstown, Newtownabbey, UK. fj.azuaje@ieee.org', 'Jordan', 23, 'Multigene Family|Oligonucleotide Array Sequence Analysis', 'Journal Article', '10.1093/bib/4.1.31', '12715832', 'This article focuses on clustering techniques for the analysis of microarray data and discusses contributions and applications for the implementation of intelligent diagnostic systems and therapy design studies. Approaches to validating and visualising expression clustering results and software and other relevant resources to support clustering-based analyses are reviewed. Finally, this paper addresses current limitations and problems that need to be investigated for the development of an advanced generation of pattern discovery tools.'),
(68235, 2003, 'bib', 'Compositional features of eukaryotic genomes for checking predicted genes', 'Cruveiller, Stéphane|Jabbari, Kamel|Clay, Oliver|Bemardi, Giorgio', 'Genoscope, French National Sequencing Center, Evry.|Genoscope, French National Sequencing Center, Evry.|Genoscope, French National Sequencing Center, Evry.|Genoscope, French National Sequencing Center, Evry.', '-|-|-|-', 15, 'Animals|DNA|Genome|Humans|Reproducibility of Results', 'Journal Article', '10.1093/bib/4.1.43', '12715833', 'Gene prediction relies on the identification of characteristic features of coding sequences that distinguish them from non-coding DNA. The recent large-scale sequencing of entire genomes from higher eukaryotes, in conjunction with currently used gene prediction algorithms, has provided an abundance of putative genes that can now be analysed for their compositional properties. Strong, systematic differences still exist, in several species, between the compositional properties of sets of ex novo predicted genes and genes that have been experimentally detected and/or verified. This is particularly evident in the estimated gene set (>45,000 genes) of the recently sequenced rice genome, where roughly half the predicted genes are compositionally unusual and have no known orthologues in the dicot Arabidopsis. In a few cases such differences might suggest a bias in experimental gene-finding protocols, but the quasi-random nature of the compositionally aberrant predicted genes is a strong indication that many, if not most, of them are false positives. It therefore appears that some important features of coding regions have not yet been taken into account in existing gene prediction programs. Statistical base compositional properties of curated gene data sets from vertebrates, which we briefly review here, should therefore provide a useful benchmark for fine-tuning probabilistic gene models and model parameters that are currently in use.'),
(68236, 2003, 'bib', 'Bioinformatics for venom and toxin sciences', 'Tan, Paul T J|Khan, Asif M|Brusic, Vladimir', 'National University of Singapore.|National University of Singapore.|National University of Singapore.', 'Singapore|Singapore|Singapore', 34, 'Animals|Computational Biology|Toxins, Biological|Venoms', 'Journal Article', '10.1093/bib/4.1.53', '12715834', 'Venomous animals produce a myriad of important pharmacological components. The individual components, or venoms (toxins), are used in ion channel and receptor studies, drug discovery, and formulation of insecticides. The toxin data are scattered across public databases which provide sequence and structural descriptions, but very limited functional annotation. The exponential growth of newly identified toxin data has created a need for better data management. Venominformatics is a systematic bioinformatics approach in which classified, consolidated and cleaned venom data are stored into repositories and integrated with advanced bioinformatics tools for the analysis of structure and function of toxins. Venominformatics complements experimental studies and helps reduce the number of essential experiments.'),
(68237, 2003, 'bib', 'Current approaches to whole genome phylogenetic analysis', 'Sawa, George|Dicks, Jo|Roberts, Ian N', 'Bioinformatics Research Group at the John Innes Centre, Colney, Norfolk, UK. george.savva@bbsrc.ac.uk|Bioinformatics Research Group at the John Innes Centre, Colney, Norfolk, UK. george.savva@bbsrc.ac.uk|Bioinformatics Research Group at the John Innes Centre, Colney, Norfolk, UK. george.savva@bbsrc.ac.uk', 'United Kingdom|United Kingdom|United Kingdom', 17, 'Evolution, Molecular|Genome|Likelihood Functions|Phylogeny', 'Journal Article', '10.1093/bib/4.1.63', '12715835', 'It has long been known that evolutionary trees (phylogenies) can be estimated by comparing the DNA or protein sequences of homologous genes across different organisms. More recently, attempts have been made to estimate phylogenies by comparing entire genomes. These attempts have focused largely on comparisons of gene content and gene order. Many different methods have been proposed for making these comparisons. These include primarily maximum parsimony and distance methods, although more recently maximum likelihood and Bayesian methods are being developed. This paper discusses each of these approaches in turn, including their merits and limitations, and any software which is available to make use of them.'),
(68238, 2003, 'bib', 'Biological sequence databases', 'Mullan, Lisa J', 'MRC UK Human Genome Mapping Project Resource Centre, Hinxton, Cambridge, UK. lmullan@hgmp.mrc.ac.uk', 'United Kingdom', 0, 'Databases, Genetic', 'Journal Article', '10.1093/bib/4.1.75', '12715836', '-'),
(68239, 2003, 'bib', 'A review of new databases and associated web tools', 'Gonzalez, Daniel S', 'MRC UK Human Genome Mapping Project Resource Centre, Hinxton, Cambridge, UK. lmullan@hgmp.mrc.ac.uk', 'United Kingdom', 0, 'Database Management Systems|Internet', 'News', '10.1093/bib/4.1.91', '12715837', '-'),
(68240, 2003, 'bib', 'Genomics [editorial]', 'Bishop, Martin', 'MRC UK Human Genome Mapping Project Resource Centre, Hinxton, Cambridge, UK. lmullan@hgmp.mrc.ac.uk', 'United Kingdom', 0, 'Aged|Computational Biology|Databases, Genetic|Genomics|Humans|Middle Aged', 'Journal Article', '10.1093/bib/4.2.104', '12846392', '-'),
(68241, 2003, 'bib', 'An applications-focused review of comparative genomics tools: capabilities, limitations and future challenges', 'Chain, Patrick|Kurtz, Stefan|Ohlebusch, Enno|Slezak, Tom', 'Biodefense Division, Comparative Analysis Group, Biology and Biotechnology Research Program, Lawrence Livermore National Laboratory, Livermore, California 94550, USA.|Biodefense Division, Comparative Analysis Group, Biology and Biotechnology Research Program, Lawrence Livermore National Laboratory, Livermore, California 94550, USA.|Biodefense Division, Comparative Analysis Group, Biology and Biotechnology Research Program, Lawrence Livermore National Laboratory, Livermore, California 94550, USA.|Biodefense Division, Comparative Analysis Group, Biology and Biotechnology Research Program, Lawrence Livermore National Laboratory, Livermore, California 94550, USA.', 'United States|United States|United States|United States', 50, 'Algorithms|Animals|Bacteria|Genome|Genomics|Humans|Sequence Alignment|Sequence Analysis, DNA|Software|Viruses', 'Journal Article', '10.1093/bib/4.2.105', '12846393', 'A team at the Lawrence Livermore National Laboratory (LLNL) was given the task of using computational tools to speed up the development of DNA diagnostics for pathogen detection. This work will be described in another paper in this issue (see pages 133-149). To achieve this goal it was necessary to understand the merits and limitations of the various available comparative genomics tools. A review of some recent tools for multisequence/genome alignment and substring comparison is presented, within the general framework of applicability to a large-scale application. We note that genome alignments are important for many things, only one of which is pathogen detection. Understanding gene function, gene regulation, gene networks, phylogenetic studies and other aspects of evolution all depend on accurate nucleic acid and protein sequence alignment. Selecting appropriate tools can make a large difference in the quality of results obtained and the effort required.'),
(68242, 2003, 'bib', 'Viewing and annotating sequence data with Artemis', 'Berriman, Matt|Rutherford, Kim', 'The Wellcome Trust Sanger Institute, Genome Campus, Hinxton CB10 1SA, UK. mb4@sanger.ac.uk|The Wellcome Trust Sanger Institute, Genome Campus, Hinxton CB10 1SA, UK. mb4@sanger.ac.uk', 'United Kingdom|United Kingdom', 114, 'Animals|Computational Biology|Databases, Nucleic Acid|Genes, Fungal|Genes, Protozoan|Genome|Proteome|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bib/4.2.124', '12846394', 'Artemis is a widely used software tool for annotating and viewing sequence data. No database is required to use Artemis. Instead, individual sequence data files can be analysed with little or no formatting, making it particularly suited to the study of small genomes and chromosomes, and straightforward for a novice user to get started. Since its release in 1999, Artemis has been used to annotate a diverse collection of prokaryotic and eukaryotic genomes, ranging from Streptomyces coelicolor to, more recently, a large proportion of the Plasmodium falciparum genome. Artemis allows annotated genomes to be easily browsed and makes it simple to add useful biological information to raw sequence data. This paper gives an overview of some of the features of Artemis and includes how it facilitates manual gene prediction and can provide an overview of entire chromosomes or small compact genomes--useful for uncovering unusual features such as pathogenicity islands.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(68243, 2003, 'bib', 'Comparative genomics tools applied to bioterrorism defence', 'Slezak, Tom|Kuczmarski, Tom|Ott, Linda|Torres, Clinton|Medeiros, Dan|Smith, Jason|Truitt, Brian|Mulakken, Nisha|Lam, Marisa|Vitalis, Elizabeth|Zemla, Adam|Zhou, Carol Ecale|Gardner, Shea', 'Lawrence Livermore National Laboratory, Livermore, CA 94550, USA. Slezak@LLNL.GOV|Lawrence Livermore National Laboratory, Livermore, CA 94550, USA. Slezak@LLNL.GOV|Lawrence Livermore National Laboratory, Livermore, CA 94550, USA. Slezak@LLNL.GOV|Lawrence Livermore National Laboratory, Livermore, CA 94550, USA. Slezak@LLNL.GOV|Lawrence Livermore National Laboratory, Livermore, CA 94550, USA. Slezak@LLNL.GOV|Lawrence Livermore National Laboratory, Livermore, CA 94550, USA. Slezak@LLNL.GOV|Lawrence Livermore National Laboratory, Livermore, CA 94550, USA. Slezak@LLNL.GOV|Lawrence Livermore National Laboratory, Livermore, CA 94550, USA. Slezak@LLNL.GOV|Lawrence Livermore National Laboratory, Livermore, CA 94550, USA. Slezak@LLNL.GOV|Lawrence Livermore National Laboratory, Livermore, CA 94550, USA. Slezak@LLNL.GOV|Lawrence Livermore National Laboratory, Livermore, CA 94550, USA. Slezak@LLNL.GOV|Lawrence Livermore National Laboratory, Livermore, CA 94550, USA. Slezak@LLNL.GOV|Lawrence Livermore National Laboratory, Livermore, CA 94550, USA. Slezak@LLNL.GOV', 'United States|United States|United States|United States|United States|United States|United States|United States|United States|United States|United States|United States|United States', 55, 'Amino Acid Sequence|Animals|Bacterial Proteins|Base Sequence|Bioterrorism|Genes, Bacterial|Genes, Viral|Genome|Genomics|Humans|Models, Molecular|Protein Structure, Tertiary|Sequence Alignment|Software|United States|Viral Proteins', 'Journal Article', '10.1093/bib/4.2.133', '12846395', 'Rapid advances in the genomic sequencing of bacteria and viruses over the past few years have made it possible to consider sequencing the genomes of all pathogens that affect humans and the crops and livestock upon which our lives depend. Recent events make it imperative that full genome sequencing be accomplished as soon as possible for pathogens that could be used as weapons of mass destruction or disruption. This sequence information must be exploited to provide rapid and accurate diagnostics to identify pathogens and distinguish them from harmless near-neighbours and hoaxes. The Chem-Bio Non-Proliferation (CBNP) programme of the US Department of Energy (DOE) began a large-scale effort of pathogen detection in early 2000 when it was announced that the DOE would be providing bio-security at the 2002 Winter Olympic Games in Salt Lake City, Utah. Our team at the Lawrence Livermore National Lab (LLNL) was given the task of developing reliable and validated assays for a number of the most likely bioterrorist agents. The short timeline led us to devise a novel system that utilised whole-genome comparison methods to rapidly focus on parts of the pathogen genomes that had a high probability of being unique. Assays developed with this approach have been validated by the Centers for Disease Control (CDC). They were used at the 2002 Winter Olympics, have entered the public health system, and have been in continual use for non-publicised aspects of homeland defence since autumn 2001. Assays have been developed for all major threat list agents for which adequate genomic sequence is available, as well as for other pathogens requested by various government agencies. Collaborations with comparative genomics algorithm developers have enabled our LLNL team to make major advances in pathogen detection, since many of the existing tools simply did not scale well enough to be of practical use for this application. It is hoped that a discussion of a real-life practical application of comparative genomics algorithms may help spur algorithm developers to tackle some of the many remaining problems that need to be addressed. Solutions to these problems will advance a wide range of biological disciplines, only one of which is pathogen detection. For example, exploration in evolution and phylogenetics, annotating gene coding regions, predicting and understanding gene function and regulation, and untangling gene networks all rely on tools for aligning multiple sequences, detecting gene rearrangements and duplications, and visualising genomic data. Two key problems currently needing improved solutions are: (1) aligning incomplete, fragmentary sequence (eg draft genome contigs or arbitrary genome regions) with both complete genomes and other fragmentary sequences, and (2) ordering, aligning and visualising non-colinear gene rearrangements and inversions in addition to the colinear alignments handled by current tools.'),
(68244, 2003, 'bib', 'Making decisions for structural genomics', 'Rodrigues, Ana|Hubbard, Roderick E', 'Structural Biology Laboratory, Department of Chemistry, University of York, Heslington, York, UK. rodrigues@ysbl.york.ac.uk|Structural Biology Laboratory, Department of Chemistry, University of York, Heslington, York, UK. rodrigues@ysbl.york.ac.uk', 'United Kingdom|United Kingdom', 14, 'Animals|Computational Biology|Computer Simulation|Databases, Genetic|Decision Making|Evolution, Molecular|Genomics|Humans|Protein Conformation|Protein Folding', 'Journal Article', '10.1093/bib/4.2.150', '12846396', 'A large number of structural genomics programmes have been established worldwide with the common aim of large-scale, high-throughput protein structure determination. Due to the considerable challenges posed by the experimental methods of structural determination (primarily X-ray crystallography and nuclear magnetic resonance spectroscopy) it is important to select and prioritise candidate molecules that will maximise the information gained from each new structure. This paper describes the scientific principles that underlie target selection and the various bioinformatics tools that may be employed in such selection procedures. Then follows a discussion of the availability of resources incorporating these methods and a description of the design and application of a purpose-built target selection resource for structural genomics.'),
(68245, 2003, 'bib', 'Dimensionality and dependence problems in statistical genomics', 'Liò, Pietro', 'EMBL-EBI European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, UK. pietrol@ebi.ac.uk', 'United Kingdom', 2, 'Animals|Base Composition|Chromatin|Computational Biology|Evolution, Molecular|Gene Expression Regulation|Genome|Genomics|Humans|Likelihood Functions|Models, Genetic|Phylogeny|Transcription Initiation Site', 'Journal Article', '10.1093/bib/4.2.168', '12846397', 'Genome studies have become central to a wide range of biological areas. This paper is intended to discuss the state of art and potentiality of using statistics in genome comparison. The problem of dependence and dimensionality in genome data is addressed first. The focus is on using phylogenetic methods in genome comparison, on combining sequence and gene expression data to find transcription sites, and on multiple hypothesis testing in gene expression data. The paper concludes with speculations on the future course of statistical and bioinformatics analyses.'),
(68246, 2003, 'bib', 'Entrez: making use of its power', 'Geer, Renata C|Sayers, Eric W', 'National Center for Biotechnology Information, National Library of Medicine, Bethesda, MD 20894, USA. renata@ncbi.nlm.nih.gov|National Center for Biotechnology Information, National Library of Medicine, Bethesda, MD 20894, USA. renata@ncbi.nlm.nih.gov', 'United States|United States', 11, 'Adaptor Proteins, Signal Transducing|Algorithms|Carrier Proteins|Database Management Systems|Databases as Topic|Humans|Information Storage and Retrieval|Internet|MutL Protein Homolog 1|Mutation|National Library of Medicine (U.S.)|Neoplasm Proteins|Nuclear Proteins|Protein Structure, Tertiary|Software|United States', 'Journal Article', '10.1093/bib/4.2.179', '12846398', '-'),
(68247, 2003, 'bib', 'Protein family alignment annotation', 'Gilbert, Don', 'Biology Department, Center for Genomics and Bioinformatics, Indiana University, Bloomington 47405, USA. gilbertd@bio.indiana.edu', 'India', 0, 'Amino Acid Sequence|Databases, Protein|Molecular Sequence Data|Phylogeny|Protein Structure, Tertiary|Proteins|Sequence Alignment|Sequence Analysis, Protein|Software', 'Journal Article', '10.1093/bib/4.2.192', '12846399', 'For bioscientists studying protein structure and function, the Protein Family Alignment Annotation Tool (Pfaat) is a useful and simple program for annotating collections of proteins. This open-source software includes methods for viewing and aligning protein families, and for annotating sequence structure and residues with known functions. It offers new options to aid the study of proteins, and an extensible annotation tool for bioinformatics developers.'),
(68248, 2003, 'bib', 'e-Science', 'Bishop, Martin', 'Biology Department, Center for Genomics and Bioinformatics, Indiana University, Bloomington 47405, USA. gilbertd@bio.indiana.edu', 'India', 1, 'Humans|International Cooperation|Internet|Research|Science', 'Editorial', '10.1093/bib/4.3.208', '14582515', '-'),
(68249, 2003, 'bib', 'Computational analysis of mutation spectra', 'Rogozin, Igor B|Babenko, Vladimir N|Milanesi, Luciano|Pavlov, Youri I', 'National Center for Biotechnology Information NLM/NIH, Bethesda, MD 20894, USA. rogozin@ncbi.nlm.nih.gov|National Center for Biotechnology Information NLM/NIH, Bethesda, MD 20894, USA. rogozin@ncbi.nlm.nih.gov|National Center for Biotechnology Information NLM/NIH, Bethesda, MD 20894, USA. rogozin@ncbi.nlm.nih.gov|National Center for Biotechnology Information NLM/NIH, Bethesda, MD 20894, USA. rogozin@ncbi.nlm.nih.gov', 'United States|United States|United States|United States', 6, 'Amino Acid Motifs|Animals|Base Composition|Base Sequence|Computational Biology|DNA|DNA Mutational Analysis|DNA Repair|Databases, Nucleic Acid|Genes, Immunoglobulin|Humans|Molecular Sequence Data|Mutagens|Nucleic Acid Conformation|Software|Statistics as Topic', 'Journal Article', '10.1093/bib/4.3.210', '14582516', 'Mutation frequencies vary along a nucleotide sequence, and nucleotide positions with an exceptionally high mutation frequency are called hotspots. Mutation hotspots in DNA often reflect intrinsic properties of the mutation process, such as the specificity with which mutagens interact with nucleic acids and the sequence-specificity of DNA repair/replication enzymes. They might also reflect structural and functional features of target protein or RNA sequences in which they occur. The determinants of mutation frequency and specificity are complex and there are many analytical methods for their study. This paper discusses computational approaches to analysing mutation spectra (distribution of mutations along the target genes) that include many detectable (mutable) positions. The following methods are reviewed: mutation hotspot prediction, pairwise and multiple comparisons of mutation spectra, derivation of a consensus sequence, and analysis of correlation between nucleotide sequence features and mutation spectra. Spectra of spontaneous and induced mutations are used for illustration of the complexities and pitfalls of such analyses. In general, the DNA sequence context of mutation hotspots is a fingerprint of interactions between DNA and DNA repair/replication/modification enzymes, and the analysis of hotspot context provides evidence of such interactions.'),
(68250, 2003, 'bib', 'Inferring gene networks from time series microarray data using dynamic Bayesian networks', 'Kim, Sun Yong|Imoto, Seiya|Miyano, Satoru', 'Laboratory of DNA analysis, Human Genome Centre, Institute of Medical Science, University of Tokyo, Japan. sunk@ims.u-tokyo.ac.jp|Laboratory of DNA analysis, Human Genome Centre, Institute of Medical Science, University of Tokyo, Japan. sunk@ims.u-tokyo.ac.jp|Laboratory of DNA analysis, Human Genome Centre, Institute of Medical Science, University of Tokyo, Japan. sunk@ims.u-tokyo.ac.jp', 'Japan|Japan|Japan', 219, 'Algorithms|Bayes Theorem|Computational Biology|Computer Simulation|Gene Expression Profiling|Gene Expression Regulation, Fungal|Mathematics|Models, Genetic|Oligonucleotide Array Sequence Analysis|Saccharomyces cerevisiae|Time Factors', 'Journal Article', '10.1093/bib/4.3.228', '14582517', 'Dynamic Bayesian networks (DBNs) are considered as a promising model for inferring gene networks from time series microarray data. DBNs have overtaken Bayesian networks (BNs) as DBNs can construct cyclic regulations using time delay information. In this paper, a general framework for DBN modelling is outlined. Both discrete and continuous DBN models are constructed systematically and criteria for learning network structures are introduced from a Bayesian statistical viewpoint. This paper reviews the applications of DBNs over the past years. Real data applications for Saccharomyces cerevisiae time series gene expression data are also shown.'),
(68251, 2003, 'bib', 'Modelling protein functional domains in signal transduction using Maude', 'Sriram, M G', 'National Space Biomedical Research Institute, Baylor College of Medicine/NASA Johnson Space Center, Houston, TX 77058-3607, USA. msriram@bcm.tmc.edu', 'United States', 3, 'Algorithms|Antigens, CD|Apoptosis|Computational Biology|Computer Simulation|Models, Biological|Programming Languages|Protein Structure, Tertiary|Proteins|Receptors, Tumor Necrosis Factor|Receptors, Tumor Necrosis Factor, Type I|Signal Transduction|TNF Receptor-Associated Factor 1', 'Journal Article', '10.1093/bib/4.3.236', '14582518', 'Modelling of protein-protein interactions in signal transduction is receiving increased attention in computational biology. This paper describes recent research in the application of Maude, a symbolic language founded on rewriting logic, to the modelling of functional domains within signalling proteins. Protein functional domains (PFDs) are a critical focus of modern signal transduction research. In general, Maude models can simulate biological signalling networks and produce specific testable hypotheses at various levels of abstraction. Developing symbolic models of signalling proteins containing functional domains is important because of the potential to generate analyses of complex signalling networks based on structure-function relationships.'),
(68252, 2003, 'bib', 'An overview of data models for the analysis of biochemical pathways', 'Deville, Yves|Gilbert, David|van Helden, Jacques|Wodak, Shoshana J', 'Computing Science and Engineering Department, Université Catholique de Louvain, Louvain-la-Neuve, Belgium. deville@info.ucl.ac.be|Computing Science and Engineering Department, Université Catholique de Louvain, Louvain-la-Neuve, Belgium. deville@info.ucl.ac.be|Computing Science and Engineering Department, Université Catholique de Louvain, Louvain-la-Neuve, Belgium. deville@info.ucl.ac.be|Computing Science and Engineering Department, Université Catholique de Louvain, Louvain-la-Neuve, Belgium. deville@info.ucl.ac.be', 'Belgium|Belgium|Belgium|Belgium', 11, 'Cell Physiological Phenomena|Computational Biology|Computer Simulation|Models, Biological', 'Journal Article', '10.1093/bib/4.3.246', '14582519', 'Biochemical pathways such as metabolic, regulatory or signal transduction pathways can be viewed as interconnected processes forming an intricate network of functional and physical interactions between molecular species in the cell. The amount of information available on such pathways for different organisms is increasing very rapidly. This is offering the possibility of performing various analyses on the structure of the full network of pathways for one organism as well as across different organisms, and has therefore generated interest in developing databases for storing and managing this information. Analysing these networks remains far from straightforward owing to the nature of the databases, which are often heterogeneous, incomplete or inconsistent. Pathway analysis is hence a challenging problem in systems biology and in bioinformatics. Various forms of data models have been devised for the analysis of biochemical pathways. This paper presents an overview of the types of models used for this purpose, concentrating on those concerned with the structural aspects of biochemical networks. In particular, the different types of data models found in the literature are classified using a unified framework. In addition, how these models have been used in the analysis of biochemical networks is described. This enables us to underline the strengths and weaknesses of the different approaches, as well as to highlight relevant future research directions.'),
(68253, 2003, 'bib', 'Computational knowledge integration in biopharmaceutical research', 'Ficenec, David|Osborne, Mark|Pradines, Joel|Richards, Dan|Felciano, Ramon|Cho, Raymond J|Chen, Richard O|Liefeld, Ted|Owen, James|Ruttenberg, Alan|Reich, Christian|Horvath, Joseph|Clark, Tim', 'Washington University Genome Center, USA.|Washington University Genome Center, USA.|Washington University Genome Center, USA.|Washington University Genome Center, USA.|Washington University Genome Center, USA.|Washington University Genome Center, USA.|Washington University Genome Center, USA.|Washington University Genome Center, USA.|Washington University Genome Center, USA.|Washington University Genome Center, USA.|Washington University Genome Center, USA.|Washington University Genome Center, USA.|Washington University Genome Center, USA.', 'United States|United States|United States|United States|United States|United States|United States|United States|United States|United States|United States|United States|United States', 30, 'Algorithms|Computational Biology|Database Management Systems|Databases, Factual|Drug Industry|Gene Expression Profiling|Humans|Information Management|Information Storage and Retrieval|Knowledge|Neoplasms|Research Design|Software|Systems Integration|Vocabulary, Controlled', 'Journal Article', '10.1093/bib/4.3.260', '14582520', 'An initiative to increase biopharmaceutical research productivity by capturing, sharing and computationally integrating proprietary scientific discoveries with public knowledge is described. This initiative involves both organisational process change and multiple interoperating software systems. The software components rely on mutually supporting integration techniques. These include a richly structured ontology, statistical analysis of experimental data against stored conclusions, natural language processing of public literature, secure document repositories with lightweight metadata, web services integration, enterprise web portals and relational databases. This approach has already begun to increase scientific productivity in our enterprise by creating an organisational memory (OM) of internal research findings, accessible on the web. Through bringing together these components it has also been possible to construct a very large and expanding repository of biological pathway information linked to this repository of findings which is extremely useful in analysis of DNA microarray data. This repository, in turn, enables our research paradigm to be shifted towards more comprehensive systems-based understandings of drug action.'),
(68254, 2003, 'bib', 'Hydropathy--a window on the evasion of water', 'Mullan, Lisa J', 'MRC Rosalind Franklin Centre for Genome Research, Hinxton, Cambridge. lmullan@hgmp.mrc.ac.uk', '-', 0, 'Computational Biology|Internet|Protein Conformation|Protein Folding|Proteins|Solubility|Thermodynamics|Water', 'Journal Article', '10.1093/bib/4.3.279', '14582521', '-'),
(68255, 2003, 'bib', 'Shopping in the genome market with EnsMart', 'Gilbert, Don', 'Biology Department and Center for Genomics and Bioinformatics, Indiana University, Bloomington 47405, USA. gilbertd@bio.indiana.edu', 'India', 4, 'Animals|Databases, Nucleic Acid|Genome|Genomics|Humans|Information Storage and Retrieval|Software', 'Journal Article', '10.1093/bib/4.3.292', '14582522', 'Life scientists who work with the supermarket of genome data will find the EnsMart database and software package offers a valuable door to a wealth of genes and genome features. Not only available to lab biologists on the web, this popular multi-organism genome database can be installed and used on your own Unix computer with relative ease. It offers a flexible, fast and practical data-mining framework for computer-savvy biologists and bioinformaticians.'),
(68256, 2003, 'bib', 'Omics research and bioinformatics--joined-up thinking or anarchy?', 'Bishop, Martin', 'Biology Department and Center for Genomics and Bioinformatics, Indiana University, Bloomington 47405, USA. gilbertd@bio.indiana.edu', 'India', 1, 'Computational Biology|Genomics|Proteomics', 'Editorial', '10.1093/bib/4.4.313', '14725343', '-'),
(68257, 2003, 'bib', 'In silico tools for signal transduction research', 'Lindvall, Jessica M|Blomberg, K Emelie M|Smith, C I Edvard', 'Clinical Research Center, Karolinska Institutet, NOVUM, SE-141 86 Huddinge, Sweden. jessica.lindvall@crc.ki.se|Clinical Research Center, Karolinska Institutet, NOVUM, SE-141 86 Huddinge, Sweden. jessica.lindvall@crc.ki.se|Clinical Research Center, Karolinska Institutet, NOVUM, SE-141 86 Huddinge, Sweden. jessica.lindvall@crc.ki.se', 'Sweden|Sweden|Sweden', 1, 'Computational Biology|Forecasting|Information Systems|Internet|Signal Transduction', 'Journal Article', '10.1093/bib/4.4.315', '14725344', 'Signal transduction is a fundamental process that takes place in all living organisms and understanding how this event occurs at the cellular level is of vital importance to virtually all fields of biomedicine. There are several major steps involved in deciphering the signalling pathways: (a) Which molecules are involved in signalling? (b) Who talks to whom?, ie making sense of the molecular interactions in a context-dependent way. (c) Where are the signalling events taking place?, eg when a resting cell becomes activated. The challenge lies in reconstructing signalling modules and networks evoked in a particular response to a single input as well as correlating the signalling response to different cellular inputs. There is also the need for interpretation of cross-talk between signalling modules in response to single and multiple inputs. To follow up these questions there are many good databases that provide an information system on regulatory networks. This review aims to find some of the bioinformatics tools and websites available to conduct signal transduction research and to discuss the representation of databases available for the processes of signalling. The databases considered here can provide a well-structured overview on the subject and a basis for advanced bioinformatics analysis to interpret the function of genomic sequences or to analyse signalling networks within a cell. However, the knowledge of most signalling pathways is incomplete and for this reason the existing databases will provide insight, but very rarely a more complete picture.'),
(68258, 2003, 'bib', 'Signal transduction-related bioinformatics services', 'Vihinen, Mauno', 'Institute of Medical Technology, University of Tampere, Finland. mauno.vihinen@uta.fi', 'Finland', 0, 'Computational Biology|Phosphoprotein Phosphatases|Phosphorylation|Protein Kinases|Protein Structure, Tertiary|Signal Transduction', 'Journal Article', '10.1093/bib/4.4.325', '14725345', 'Signal transduction pathways are crucial for the regulation of a very wide variety of cellular functions ranging, for example, from translation to intercellular communication, and from metabolism to apoptosis. Protein kinases and phosphatases, together with their binding partners, are key players in these cascades. They also form a substantial part of the genes in genomes and proteins in proteomes in all animals. Signalling can be studied in many different levels and ways. This has resulted in large body of publications and Internet services. This paper describes open-access databases and software aiming at presenting the kind of data available and how to perform bioinformatics analyses.'),
(68259, 2003, 'bib', 'Public services from the European Bioinformatics Institute', 'Lopez, Rodrigo|Duggan, Karyn|Harte, Nicola|Kibria, Asif', 'European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK. rls@ebi.ac.uk|European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK. rls@ebi.ac.uk|European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK. rls@ebi.ac.uk|European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK. rls@ebi.ac.uk', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom', 4, 'Computational Biology|Databases, Nucleic Acid|Databases, Protein|Europe|Internet|Sequence Homology', 'Journal Article', '10.1093/bib/4.4.332', '14725346', 'The European Bioinformatics Institute (EBI) provides numerous free-of-charge, publicly available bioinformatics services that can be divided into the following categories: ftp downloads, data submissions processing and biological database production, access to query, analysis and retrieval systems and tools, user support, training and education and industry support through EBIs SME program. These services are all available at the website. It is imperative that EBIs data as well as the tools to analyse it efficiently are made available in a free and unambiguous way to the scientific community. An important part of the EBIs mission is to make this happen in a fast, reliable and efficient manner. This paper serves as a brief introduction to each of these services.'),
(68260, 2003, 'bib', 'In silico prediction of the structure of membrane proteins: is it feasible?', 'Casadio, Rita|Fariselli, Piero|Martelli, Pier Luigi', 'Laboratory of Biocomputing, CIRB/Department of Biology, University of Bologna, Via Irnerio 42, 40126 Bologna, Italy. casadio@alma.unibo.it|Laboratory of Biocomputing, CIRB/Department of Biology, University of Bologna, Via Irnerio 42, 40126 Bologna, Italy. casadio@alma.unibo.it|Laboratory of Biocomputing, CIRB/Department of Biology, University of Bologna, Via Irnerio 42, 40126 Bologna, Italy. casadio@alma.unibo.it', 'Italy|Italy|Italy', 18, 'Computational Biology|Membrane Proteins|Models, Molecular|Protein Structure, Tertiary|Sequence Analysis, Protein', 'Journal Article', '10.1093/bib/4.4.341', '14725347', 'In the omic era, hundreds of genomes are available for protein sequence analysis, and some 30 per cent of all sequences are of membrane proteins. Unlike globular proteins, a 3D model for membrane proteins can hardly be computed starting from the sequence. Why is this so? What can we really compute and with what reliability? These and other matters are outlined.'),
(68261, 2003, 'bib', 'GeneAnnot: interfacing GeneCards with high-throughput gene expression compendia', 'Chalifa-Caspi, Vered|Shmueli, Orit|Benjamin-Rodrig, Hila|Rosen, Naomi|Shmoish, Michael|Yanai, Itai|Ophir, Ron|Kats, Pavel|Safran, Marilyn|Lancet, Doron', 'Bioinformatics Support Unit, Life Sciences Dept., Ben-Gurion University of the Negev, Beer-Sheva, Israel. veredcc@bgumail.bgu.ac.il|Bioinformatics Support Unit, Life Sciences Dept., Ben-Gurion University of the Negev, Beer-Sheva, Israel. veredcc@bgumail.bgu.ac.il|Bioinformatics Support Unit, Life Sciences Dept., Ben-Gurion University of the Negev, Beer-Sheva, Israel. veredcc@bgumail.bgu.ac.il|Bioinformatics Support Unit, Life Sciences Dept., Ben-Gurion University of the Negev, Beer-Sheva, Israel. veredcc@bgumail.bgu.ac.il|Bioinformatics Support Unit, Life Sciences Dept., Ben-Gurion University of the Negev, Beer-Sheva, Israel. veredcc@bgumail.bgu.ac.il|Bioinformatics Support Unit, Life Sciences Dept., Ben-Gurion University of the Negev, Beer-Sheva, Israel. veredcc@bgumail.bgu.ac.il|Bioinformatics Support Unit, Life Sciences Dept., Ben-Gurion University of the Negev, Beer-Sheva, Israel. veredcc@bgumail.bgu.ac.il|Bioinformatics Support Unit, Life Sciences Dept., Ben-Gurion University of the Negev, Beer-Sheva, Israel. veredcc@bgumail.bgu.ac.il|Bioinformatics Support Unit, Life Sciences Dept., Ben-Gurion University of the Negev, Beer-Sheva, Israel. veredcc@bgumail.bgu.ac.il|Bioinformatics Support Unit, Life Sciences Dept., Ben-Gurion University of the Negev, Beer-Sheva, Israel. veredcc@bgumail.bgu.ac.il', 'Israel|Israel|Israel|Israel|Israel|Israel|Israel|Israel|Israel|Israel', 11, 'Data Interpretation, Statistical|Oligonucleotide Array Sequence Analysis|Software', 'Journal Article', '10.1093/bib/4.4.349', '14725348', 'The interpretation of microarray expression results often includes extensive efforts to identify and annotate the gene representatives immobilised on the arrays. In this paper we describe the usage of our automatic GeneAnnot system, which links between Affymetrix arrays and the rich human gene annotations available in GeneCards. We explain GeneCards search options and results display, elaborate on the presentation of expression information in GeneCards, including both our whole-genome GeneNote project and external expression resources, describe the various parameters and displays used by GeneAnnot to assess the annotation quality and probeset specificity, and show how to search GeneAnnot and GeneNote websites directly.'),
(68262, 2003, 'bib', 'Designing microarray oligonucleotide probes', 'Russell, Roslin', 'MRC UK Human Genome Mapping Project Resource Centre, Hinxton, Cambridge, UK. rrussell@hgmp.mrc.ac.uk', 'United Kingdom', 2, 'DNA Probes|Oligonucleotide Array Sequence Analysis|Software', 'Journal Article', '10.1093/bib/4.4.361', '14725349', '-'),
(68263, 2003, 'bib', 'Federating data with Information Integrator', 'Arenson, Andrew D', 'INGEN IT Core, Research and Academic Computing Division, University Information Technology Services, Indiana University, Indianapolis 46202, USA. aarenson@iupui.edu', 'India', 0, 'Databases as Topic|Internet|Software', 'Journal Article', '10.1093/bib/4.4.375', '14725350', 'Information Integrator is an extension to IBMs relational database DB2, which uses data federation to provide benefits to molecular biology researchers through two unique capabilities: increased flexibility in combining data from disparate sources, and SQL access to non-SQL data, easing the task of automating data analysis.'),
(68264, 2004, 'bib', 'Building successful biological databases', 'Altman, Russ B', 'INGEN IT Core, Research and Academic Computing Division, University Information Technology Services, Indiana University, Indianapolis 46202, USA. aarenson@iupui.edu', 'India', 19, 'Databases, Factual|Information Storage and Retrieval|Publishing', 'Editorial', '10.1093/bib/5.1.4', '15153301', '-'),
(68265, 2004, 'bib', 'Saccharomyces genome database: underlying principles and organisation', 'Dwight, Selina S|Balakrishnan, Rama|Christie, Karen R|Costanzo, Maria C|Dolinski, Kara|Engel, Stacia R|Feierbach, Becket|Fisk, Dianna G|Hirschman, Jodi|Hong, Eurie L|Issel-Tarver, Laurie|Nash, Robert S|Sethuraman, Anand|Starr, Barry|Theesfeld, Chandra L|Andrada, Rey|Binkley, Gail|Dong, Qing|Lane, Christopher|Schroeder, Mark|Weng, Shuai|Botstein, David|Cherry, J Michael', 'Department of Genetics, School of Medicine, Standford University, Standford, CA 94305-5120, USA.|Department of Genetics, School of Medicine, Standford University, Standford, CA 94305-5120, USA.|Department of Genetics, School of Medicine, Standford University, Standford, CA 94305-5120, USA.|Department of Genetics, School of Medicine, Standford University, Standford, CA 94305-5120, USA.|Department of Genetics, School of Medicine, Standford University, Standford, CA 94305-5120, USA.|Department of Genetics, School of Medicine, Standford University, Standford, CA 94305-5120, USA.|Department of Genetics, School of Medicine, Standford University, Standford, CA 94305-5120, USA.|Department of Genetics, School of Medicine, Standford University, Standford, CA 94305-5120, USA.|Department of Genetics, School of Medicine, Standford University, Standford, CA 94305-5120, USA.|Department of Genetics, School of Medicine, Standford University, Standford, CA 94305-5120, USA.|Department of Genetics, School of Medicine, Standford University, Standford, CA 94305-5120, USA.|Department of Genetics, School of Medicine, Standford University, Standford, CA 94305-5120, USA.|Department of Genetics, School of Medicine, Standford University, Standford, CA 94305-5120, USA.|Department of Genetics, School of Medicine, Standford University, Standford, CA 94305-5120, USA.|Department of Genetics, School of Medicine, Standford University, Standford, CA 94305-5120, USA.|Department of Genetics, School of Medicine, Standford University, Standford, CA 94305-5120, USA.|Department of Genetics, School of Medicine, Standford University, Standford, CA 94305-5120, USA.|Department of Genetics, School of Medicine, Standford University, Standford, CA 94305-5120, USA.|Department of Genetics, School of Medicine, Standford University, Standford, CA 94305-5120, USA.|Department of Genetics, School of Medicine, Standford University, Standford, CA 94305-5120, USA.|Department of Genetics, School of Medicine, Standford University, Standford, CA 94305-5120, USA.|Department of Genetics, School of Medicine, Standford University, Standford, CA 94305-5120, USA.|Department of Genetics, School of Medicine, Standford University, Standford, CA 94305-5120, USA.', 'United States|United States|United States|United States|United States|United States|United States|United States|United States|United States|United States|United States|United States|United States|United States|United States|United States|United States|Uni', 40, 'Databases, Nucleic Acid|Genome, Fungal|Genomics|Information Dissemination|Information Storage and Retrieval|Internet|Saccharomyces cerevisiae', 'Journal Article', '10.1093/bib/5.1.9', '15153302', 'A scientific database can be a powerful tool for biologists in an era where large-scale genomic analysis, combined with smaller-scale scientific results, provides new insights into the roles of genes and their products in the cell. However, the collection and assimilation of data is, in itself, not enough to make a database useful. The data must be incorporated into the database and presented to the user in an intuitive and biologically significant manner. Most importantly, this presentation must be driven by the users point of view, that is, from a biological perspective. The success of a scientific database can therefore be measured by the response of its users - statistically, by usage numbers and, in a less quantifiable way, by its relationship with the community it serves and its ability to serve as a model for similar projects. Since its inception ten years ago, the Saccharomyces Genome Database (SGD) has seen a dramatic increase in its usage, has developed and maintained a positive working relationship with the yeast research community, and has served as a template for at least one other database. The success of SGD, as measured by these criteria, is due in large part to philosophies that have guided its mission and organisation since it was established in 1993. This paper aims to detail these philosophies and how they shape the organisation and presentation of the database.'),
(68266, 2004, 'bib', 'The Protein Data Bank and lessons in data management', 'Bourne, Philip E|Westbrook, John|Berman, Helen M', 'Department of Pharmacology and San Diego Supercomputer Center, University of California San Diego, 9500 Gilman Drive, La Jolla, CA 92093-0537, USA. bourne@sdsc.edu|Department of Pharmacology and San Diego Supercomputer Center, University of California San Diego, 9500 Gilman Drive, La Jolla, CA 92093-0537, USA. bourne@sdsc.edu|Department of Pharmacology and San Diego Supercomputer Center, University of California San Diego, 9500 Gilman Drive, La Jolla, CA 92093-0537, USA. bourne@sdsc.edu', 'United States|United States|United States', 14, 'Database Management Systems|Databases, Protein|History, 20th Century|History, 21st Century|Humans|Information Storage and Retrieval|Internet', 'Historical Article', '10.1093/bib/5.1.23', '15153303', 'The Protein Data Bank (PDB) is a widely used biological database of macromolecular structures with a long history. This history is treated as lessons learned and is used to highlight what are believed to be the best practices important to developers of biological databases today. While the focus is on data quality, data representation and the information technology to support these data, the non-data and technology issues cannot be ignored. The role of the human factor in the form of users, collaborators, scientific society and ad hoc committees is also included.'),
(68267, 2004, 'bib', 'Biological database design and implementation', 'Birney, Ewan|Clamp, Michele', 'EBI, Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK. birney@ebi.ac.uk|EBI, Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK. birney@ebi.ac.uk', 'United Kingdom|United Kingdom', 4, 'Database Management Systems|Databases, Factual|Information Storage and Retrieval|Software Design|User-Computer Interface', 'Journal Article', '10.1093/bib/5.1.31', '15153304', 'We present our experience of building biological databases. Such databases have most aspects in common with other complex databases in other fields. We do not believe that biological data are that different from complex data in other fields. Our experience has led us to emphasise simplicity and conservative technology choices when building these databases. This is a short paper of advice that we hope is useful to people designing their own biological database.'),
(68268, 2004, 'bib', 'Swiss-Prot: juggling between evolution and stability', 'Bairoch, Amos|Boeckmann, Brigitte|Ferro, Serenella|Gasteiger, Elisabeth', 'Swiss Institute of Bioinformatics, Centre Medical Universitaire, 1 Rue Michel Servet, 1211 Geneva 4, Switzerland. swiss-prot@expasy.org|Swiss Institute of Bioinformatics, Centre Medical Universitaire, 1 Rue Michel Servet, 1211 Geneva 4, Switzerland. swiss-prot@expasy.org|Swiss Institute of Bioinformatics, Centre Medical Universitaire, 1 Rue Michel Servet, 1211 Geneva 4, Switzerland. swiss-prot@expasy.org|Swiss Institute of Bioinformatics, Centre Medical Universitaire, 1 Rue Michel Servet, 1211 Geneva 4, Switzerland. swiss-prot@expasy.org', 'Switzerland|Switzerland|Switzerland|Switzerland', 284, 'Amino Acid Sequence|Animals|Databases, Protein|History, 20th Century|History, 21st Century|Humans|Information Storage and Retrieval|Internet|Proteins|Software Design|User-Computer Interface', 'Historical Article', '10.1093/bib/5.1.39', '15153305', 'We describe some of the aspects of Swiss-Prot that make it unique, explain what are the developments we believe to be necessary for the database to continue to play its role as a focal point of protein knowledge, and provide advice pertinent to the development of high-quality knowledge resources on one aspect or the other of the life sciences.'),
(68269, 2004, 'bib', 'Globally distributed object identification for biological knowledgebases', 'Clark, Tim|Martin, Sean|Liefeld, Ted', 'tim.clark@acm.org|tim.clark@acm.org|tim.clark@acm.org', '-|-|-', 114, 'Animals|Computational Biology|Databases, Factual|Internet|Software Design', 'Journal Article', '10.1093/bib/5.1.59', '15153306', 'The World-Wide Web provides a globally distributed communication framework that is essential for almost all scientific collaboration, including bioinformatics. However, several limits and inadequacies have become apparent, one of which is the inability to programmatically identify locally named objects that may be widely distributed over the network. This shortcoming limits our ability to integrate multiple knowledgebases, each of which gives partial information of a shared domain, as is commonly seen in bioinformatics. The Life Science Identifier (LSID) and LSID Resolution System (LSRS) provide simple and elegant solutions to this problem, based on the extension of existing internet technologies. LSID and LSRS are consistent with next-generation semantic web and semantic grid approaches. This article describes the syntax, operations, infrastructure compatibility considerations, use cases and potential future applications of LSID and LSRS. We see the adoption of these methods as important steps toward simpler, more elegant and more reliable integration of the worlds biological knowledgebases, and as facilitating stronger global collaboration in biology.'),
(68270, 2004, 'bib', 'Tutorial section: domains and motifs - proteins in bite-sized chunks', 'Mullan, Lisa', 'MRC Rosalind Franklin Centre for Genomics Research (formerly HGMP-RC), Genome Campus, Hinxton, Cambridge CB10 1SB, UK. lmullan@hrfcgr.mrc.ac.uk', 'United Kingdom', 0, 'Amino Acid Motifs|Databases, Factual|Internet|Protein Conformation|Protein Structure, Tertiary|Proteins|Software', 'Journal Article', '10.1093/bib/5.1.71', '15153307', 'Possibly the ultimate goal of bioinformatics is to be able to predict protein tertiary structure and chemical functionality from the initial amino acid sequence. Despite the best efforts of many researchers over the past two decades, a reliable modelling method has yet to be found and the folding problem continues to be a hurdle for scientists.'),
(68271, 2004, 'bib', 'Analysis for free: comparing programs for sequence analysis', 'Tippmann, Helge-Friedrich', 'Plant Research Department, Riso National Laboratory, DK-4000 Roskilde, Denmark. helge.tippmann@tu-berlin.de', 'Denmark', 112, 'Base Sequence|Sequence Alignment|Sequence Analysis|Software|User-Computer Interface', 'Journal Article', '10.1093/bib/5.1.82', '15153308', 'Programs to import, manage and align sequences and to analyse the properties of DNA, RNA and proteins are essential for every biological laboratory. This review describes two different freeware (BioEdit and pDRAW for MS Windows) and a commercial program (Sequencher for MS Windows and Apple MacOS). Bioedit and Sequencher offer functions such as sequence alignment and editing plus reading of sequence trace files. pDRAW is a very comfortable visualisation tool with a variety of analysis functions. While Sequencher impresses with a very user-friendly interface and easy-to-use tools, BioEdit offers the largest and most customisable variety of tools. The strength of pDRAW is drawing and analysis of single sequences for priming and restriction sites and virtual cloning. It has a database function for user-specific oligonucleotides and restriction enzymes.'),
(68272, 2004, 'bib', 'Editorial: The informatics of post-translational modification and its implications for systems biology', 'Hodgman, Charlie', 'Plant Research Department, Riso National Laboratory, DK-4000 Roskilde, Denmark. helge.tippmann@tu-berlin.de', 'Denmark', 0, 'Computational Biology|Molecular Sequence Data|Oligosaccharides|Protein Processing, Post-Translational|Systems Biology', 'Editorial', '10.1093/bib/5.2.104', '15260891', '-'),
(68273, 2004, 'bib', 'From masking repeats to identifying functional repeats in the mouse transcriptome', 'Schönbach, Christian', 'RIKEN Genomic Sciences Center (GSC), RIKEN Yokohama Institute, Suehiro-cho, Tsurumi, Kanagawa 230-0045, Japan. schoen@postman.riken.jp', 'Japan', 4, 'Animals|Databases, Genetic|Gene Expression Profiling|Genome|Humans|MEDLINE|Mice|RNA Processing, Post-Transcriptional|Repetitive Sequences, Nucleic Acid|Software|Transcription, Genetic', 'Journal Article', '10.1093/bib/5.2.107', '15260892', 'The back-to-back release of the mouse genome and the functionally annotated RIKEN mouse full-length cDNA collection was an important milestone in mammalian genomics. Yet much of the data remain to be explored in terms of biological effects and mechanisms. For example, interspersed repeats account for 39 per cent of the mouse genome sequence and 11 per cent of representative transcripts. A considerable number of transposable repeat elements are still active and propagating in mouse compared with human. While existing repeat databases and tools assist the classification of repeats or identification of new repeats, there is little bioinformatic support towards exploring the extent and role of repeats in transcriptional variation, modulation of protein function, or gene regulatory events. Since the mouse is used as a model organism to study human genes and their disease associations, this review focuses on information extraction and collation that captures the functional context of repeats in mouse transcripts to facilitate the biological interpretation and extrapolation of findings to the human.'),
(68274, 2004, 'bib', 'Probabilistic methods of identifying genes in prokaryotic genomes: connections to the HMM theory', 'Azad, Rajeev K|Borodovsky, Mark', 'School of Biology and School of Biomedical Engineering, Georgia Institute of Technology, Atlanta, GA 30332-0230, USA. mark@amber.biology.gatech.edu|School of Biology and School of Biomedical Engineering, Georgia Institute of Technology, Atlanta, GA 30332-0230, USA. mark@amber.biology.gatech.edu', 'Georgia|Georgia', 36, 'Algorithms|Bayes Theorem|Genome, Archaeal|Genome, Bacterial|Markov Chains|Nucleotides|Proteins|Sequence Analysis, DNA', 'Journal Article', '10.1093/bib/5.2.118', '15260893', 'In this paper, we review developments in probabilistic methods of gene recognition in prokaryotic genomes with the emphasis on connections to the general theory of hidden Markov models (HMM). We show that the Bayesian method implemented in GeneMark, a frequently used gene-finding tool, can be augmented and reintroduced as a rigorous forward-backward (FB) algorithm for local posterior decoding described in the HMM theory. Another earlier developed method, prokaryotic GeneMark.hmm, uses a modification of the Viterbi algorithm for HMM with duration to identify the most likely global path through hidden functional states given the DNA sequence. GeneMark and GeneMark.hmm programs are worth using in concert for analysing prokaryotic DNA sequences that arguably do not follow any exact mathematical model. The new extension of GeneMark using the FB algorithm was implemented in the software program GeneMark.fba. Given the DNA sequence, this program determines an a posteriori probability for each nucleotide to belong to coding or non-coding region. Also, for any open reading frame (ORF), it assigns a score defined as a probabilistic measure of all paths through hidden states that traverse the ORF as a coding region. The prediction accuracy of GeneMark.fba determined in our tests was compared favourably to the accuracy of the initial (standard) GeneMark program. Comparison to the prokaryotic GeneMark.hmm has also demonstrated a certain, yet species-specific, degree of improvement in raw gene detection, ie detection of correct reading frame (and stop codon). The accuracy of exact gene prediction, which is concerned about precise prediction of gene start (which in a prokaryotic genome unambiguously defines the reading frame and stop codon, thus, the whole protein product), still remains more accurate in GeneMarkS, which uses more elaborate HMM to specifically address this task.'),
(68275, 2004, 'bib', 'Computational approaches for the analysis of gene neighbourhoods in prokaryotic genomes', 'Rogozin, Igor B|Makarova, Kira S|Wolf, Yuri I|Koonin, Eugene V', 'NCBI/NLM/NIH, 8600 Rockville Pike, Bldg. 38A, Bethesda, MD 20894, USA. rogozin@ncbi.nlm.nih.gov|NCBI/NLM/NIH, 8600 Rockville Pike, Bldg. 38A, Bethesda, MD 20894, USA. rogozin@ncbi.nlm.nih.gov|NCBI/NLM/NIH, 8600 Rockville Pike, Bldg. 38A, Bethesda, MD 20894, USA. rogozin@ncbi.nlm.nih.gov|NCBI/NLM/NIH, 8600 Rockville Pike, Bldg. 38A, Bethesda, MD 20894, USA. rogozin@ncbi.nlm.nih.gov', 'United States|United States|United States|United States', 21, 'Algorithms|Evolution, Molecular|Genome, Archaeal|Genome, Bacterial|Models, Genetic|Multigene Family|Operon|Phylogeny|Ribosomes|Sequence Analysis, DNA', 'Journal Article', '10.1093/bib/5.2.131', '15260894', 'Gene order in prokaryotes is conserved to a much lesser extent than protein sequences. Only some operons, primarily those that encode physically interacting proteins, are conserved in all or most of the bacterial and archaeal genomes. Nevertheless, even the limited conservation of operon organisation that is observed provides valuable evolutionary and functional clues through multiple genome comparisons. With the rapid growth in the number and diversity of sequenced prokaryotic genomes, functional inferences for uncharacterized genes located in the same conserved gene neighborhood with well-studied genes are becoming increasingly important. In this review, we discuss various computational approaches for identification of conserved gene strings and construction of local alignments of gene orders in prokaryotic genomes.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(68276, 2004, 'bib', 'MEGA3: Integrated software for Molecular Evolutionary Genetics Analysis and sequence alignment', 'Kumar, Sudhir|Tamura, Koichiro|Nei, Masatoshi', 'Life Sciences A-351, The Biodesign Institute, Tempe, AZ 85287-4501, USA. s.kumar@asu.edu|Life Sciences A-351, The Biodesign Institute, Tempe, AZ 85287-4501, USA. s.kumar@asu.edu|Life Sciences A-351, The Biodesign Institute, Tempe, AZ 85287-4501, USA. s.kumar@asu.edu', 'United States|United States|United States', 2098, 'Databases, Genetic|Evolution, Molecular|Internet|Phylogeny|Sequence Alignment|Sequence Analysis, DNA|Sequence Analysis, Protein|Software', 'Journal Article', '10.1093/bib/5.2.150', '15260895', 'With its theoretical basis firmly established in molecular evolutionary and population genetics, the comparative DNA and protein sequence analysis plays a central role in reconstructing the evolutionary histories of species and multigene families, estimating rates of molecular evolution, and inferring the nature and extent of selective forces shaping the evolution of genes and genomes. The scope of these investigations has now expanded greatly owing to the development of high-throughput sequencing techniques and novel statistical and computational methods. These methods require easy-to-use computer programs. One such effort has been to produce Molecular Evolutionary Genetics Analysis (MEGA) software, with its focus on facilitating the exploration and analysis of the DNA and protein sequence variation from an evolutionary perspective. Currently in its third major release, MEGA3 contains facilities for automatic and manual sequence alignment, web-based mining of databases, inference of the phylogenetic trees, estimation of evolutionary distances and testing evolutionary hypotheses. This paper provides an overview of the statistical methods, computational tools, and visual exploration modules for data input and the results obtainable in MEGA.'),
(68277, 2004, 'bib', 'Bioinformatics for glycomics: status, methods, requirements and perspectives', 'von der Lieth, Claus-Wilhelm|Bohne-Lang, Andreas|Lohmann, Klaus Karl|Frank, Martin', 'German Cancer Research Center, Central Spectroscopic Department B090, Im Neuenheimer Feld 280, D-69120 Heidelberg, Germany. w.vonderlieth@dkfz.de|German Cancer Research Center, Central Spectroscopic Department B090, Im Neuenheimer Feld 280, D-69120 Heidelberg, Germany. w.vonderlieth@dkfz.de|German Cancer Research Center, Central Spectroscopic Department B090, Im Neuenheimer Feld 280, D-69120 Heidelberg, Germany. w.vonderlieth@dkfz.de|German Cancer Research Center, Central Spectroscopic Department B090, Im Neuenheimer Feld 280, D-69120 Heidelberg, Germany. w.vonderlieth@dkfz.de', 'Germany|Germany|Germany|Germany', 76, 'Animals|Carbohydrate Conformation|Computational Biology|Databases, Factual|Glycosylation|Glycosyltransferases|Mass Spectrometry|Models, Molecular|Polysaccharides|Proteome|Software', 'Journal Article', '10.1093/bib/5.2.164', '15260896', 'The term glycomics describes the scientific attempt to identify and study all the glycan molecules - the glycome - synthesised by an organism. The aim is to create a cell-by-cell catalogue of glycosyltransferase expression and detected glycan structures. The current status of databases and bioinformatics tools, which are still in their infancy, is reviewed. The structures of glycans as secondary gene products cannot be easily predicted from the DNA sequence. Glycan sequences cannot be described by a simple linear one-letter code as each pair of monosaccharides can be linked in several ways and branched structures can be formed. Few of the bioinformatics algorithms developed for genomics/proteomics can be directly adapted for glycomics. The development of algorithms, which allow a rapid, automatic interpretation of mass spectra to identify glycan structures is currently the most active field of research. The lack of generally accepted ways to normalise glycan structures and exchange glycan formats hampers an efficient cross-linking and the automatic exchange of distributed data. The upcoming glycomics should accept that unrestricted dissemination of scientific data accelerates scientific findings and initiates a number of new initiatives to explore the data.'),
(68278, 2004, 'bib', 'Delineation of modular proteins: domain boundary prediction from sequence information', 'Kong, Lesheng|Ranganathan, Shoba', 'Biotechnology Research Institute, Macquarie University, NSW 2109, Australia.|Biotechnology Research Institute, Macquarie University, NSW 2109, Australia.', 'Australia|Australia', 24, 'Algorithms|Amino Acid Sequence|Databases, Protein|Evolution, Molecular|Models, Molecular|Protein Structure, Tertiary|Proteins', 'Journal Article', '10.1093/bib/5.2.179', '15260897', 'The delineation of domain boundaries of a given sequence in the absence of known 3D structures or detectable sequence homology to known domains benefits many areas in protein science, such as protein engineering, protein 3D structure determination and protein structure prediction. With the exponential growth of newly determined sequences, our ability to predict domain boundaries rapidly and accurately from sequence information alone is both essential and critical from the viewpoint of gene function annotation. Anyone attempting to predict domain boundaries for a single protein sequence is invariably confronted with a plethora of databases that contain boundary information available from the internet and a variety of methods for domain boundary prediction. How are these derived and how well do they work? What definition of domain do they use? We will first clarify the different definitions of protein domains, and then describe the available public databases with domain boundary information. Finally, we will review existing domain boundary prediction methods and discuss their strengths and weaknesses.'),
(68279, 2004, 'bib', 'Jemboss reloaded', 'Mullan, Lisa', 'European Bioinformatics Institute, Genome Campus, Hinxton, Cambridge CB10 1SD, UK. lisa@ebi.ac.uk', 'United Kingdom', 2, 'Computational Biology|Databases, Factual|Internet|Software|User-Computer Interface', 'Journal Article', '10.1093/bib/5.2.193', '15260898', 'Bioinformatics tools are freely available from websites all over the world. Often they are presented as web services, although there are many tools for download and use on a local machine. This tutorial section looks at Jemboss, a Java-based graphical user interface (GUI) for the EMBOSS bioinformatics suite, which combines the advantages of both web service and downloaded software.'),
(68280, 2004, 'bib', 'In silico representation and discovery of transcription factor binding sites', 'Pavesi, Giulio|Mauri, Giancarlo|Pesole, Graziano', 'University of Milan, Milano, Italy.|University of Milan, Milano, Italy.|University of Milan, Milano, Italy.', 'Italy|Italy|Italy', 23, 'Algorithms|Amino Acid Motifs|Binding Sites|Chromosome Mapping|Genes, Regulator|Oligonucleotide Array Sequence Analysis|Protein Binding|Proteins|Sequence Alignment|Sequence Analysis|Software|Transcription Factors', 'Journal Article', '10.1093/bib/5.3.217', '15383209', 'Understanding the complex mechanisms governing basic biological processes requires the characterisation of regulatory motifs modulating gene expression at transcriptional and post-transcriptional level. In particular, extent, chronology and cell-specificity of transcription are modulated by the interaction of transcription factors with their corresponding binding sites, mostly located near (or sometimes quite far away from) the transcription start site of the gene. The constantly growing amount of genomic data, complemented by other sources of information such as expression data derived from microarray experiments, has opened new opportunities to researchers in this field. Many different methods have been proposed for the identification of transcription factor binding sites in the regulatory regions of co-expressed genes: unfortunately this is a very challenging problem both from the computational and the biological viewpoint. This paper provides a survey of existing methods proposed for the problem, focusing both on the ideas underlying them and their availability to the scientific community.'),
(68281, 2004, 'bib', 'Comparative genome assembly', 'Pop, Mihai|Phillippy, Adam|Delcher, Arthur L|Salzberg, Steven L', 'The Institute for Genomic Research, 9712 Medical Center Drive, Rockville, MD 20850, USA. mpop@tigr.org|The Institute for Genomic Research, 9712 Medical Center Drive, Rockville, MD 20850, USA. mpop@tigr.org|The Institute for Genomic Research, 9712 Medical Center Drive, Rockville, MD 20850, USA. mpop@tigr.org|The Institute for Genomic Research, 9712 Medical Center Drive, Rockville, MD 20850, USA. mpop@tigr.org', 'United States|United States|United States|United States', 94, 'Algorithms|Chromosome Mapping|Consensus Sequence|DNA Mutational Analysis|Genetic Variation|Genome, Bacterial|Sequence Alignment|Sequence Analysis, DNA|Sequence Homology, Nucleic Acid|Software|Streptococcus agalactiae', 'Journal Article', '10.1093/bib/5.3.237', '15383210', 'One of the most complex and computationally intensive tasks of genome sequence analysis is genome assembly. Even today, few centres have the resources, in both software and hardware, to assemble a genome from the thousands or millions of individual sequences generated in a whole-genome shotgun sequencing project. With the rapid growth in the number of sequenced genomes has come an increase in the number of organisms for which two or more closely related species have been sequenced. This has created the possibility of building a comparative genome assembly algorithm, which can assemble a newly sequenced genome by mapping it onto a reference genome. We describe here a novel algorithm for comparative genome assembly that can accurately assemble a typical bacterial genome in less than four minutes on a standard desktop computer. The software is available as part of the open-source AMOS project.'),
(68282, 2004, 'bib', 'Integrated analysis of the genome and the transcriptome by FANTOM', 'Katayama, Shintaro|Kanamori, Mutsumi|Hayashizaki, Yoshihide', 'Laboratory for Genome Exploration Research Group, RIKEN Genomic Sciences Center, RIKEN Yokohama Institute, Suehiro-cho, Tsurumi-ku, Yokohama, Kanagawa, 230--0045, Japan. katayama@gsc.riken.jp|Laboratory for Genome Exploration Research Group, RIKEN Genomic Sciences Center, RIKEN Yokohama Institute, Suehiro-cho, Tsurumi-ku, Yokohama, Kanagawa, 230--0045, Japan. katayama@gsc.riken.jp|Laboratory for Genome Exploration Research Group, RIKEN Genomic Sciences Center, RIKEN Yokohama Institute, Suehiro-cho, Tsurumi-ku, Yokohama, Kanagawa, 230--0045, Japan. katayama@gsc.riken.jp', 'Japan|Japan|Japan', 5, 'Abstracting and Indexing as Topic|Algorithms|Animals|Chromosome Mapping|Database Management Systems|Documentation|Genome|Information Storage and Retrieval|Mice|Phylogeny|Proteome|Sequence Alignment|Sequence Analysis|Software|Systems Integration|Transcription Factors|User-Computer Interface', 'Journal Article', '10.1093/bib/5.3.249', '15383211', 'The key to reliable annotation of a mammalian genome is broad characterisation of the transcriptional output, the transcriptome. FANTOM, the functional annotation of mouse cDNA, is a large-scale analysis of both the genome and the transcriptome of the mouse. In the early days of this work, the transcripts were characterised using our sophisticated methods. After the timely release of the first draft of mouse genome sequences, interesting information was obtained by its integration with these one-by-one annotations. Moreover, each transcript included its expression profile. Here, the two integrated annotation methods used by FANTOM are reviewed: one-by-one and categorised. One-by-one annotation refers to naming carried out based on well-known transcripts or its fragments using the top-down-style pipeline developed mostly by the FANTOM project. Categorised annotation, which refers to transcript grouping, not only helps naming of unknown transcripts, but will be the most utilised method for integration of the genome and the transcriptome from now on.'),
(68283, 2004, 'bib', 'Modelling the dynamics of biosystems', 'Priami, Corrado|Quaglia, Paola', 'Department of Computer Science and Telecommunications, University of Trento, Italy. priami@dit.unitn.it|Department of Computer Science and Telecommunications, University of Trento, Italy. priami@dit.unitn.it', 'Italy|Italy', 1, 'Algorithms|Animals|Cell Physiological Phenomena|Computational Biology|Humans|Kinetics|Models, Biological|Nonlinear Dynamics|Software|Systems Biology', 'Journal Article', '10.1093/bib/5.3.259', '15383212', 'The need for a more formal handling of biological information processing with stochastic and mobile process algebras is addressed. Biology can benefit this approach, yielding a better understanding of behavioural properties of cells, and computer science can benefit this approach, obtaining new computational models inspired by nature.'),
(68284, 2004, 'bib', 'Representing bioinformatics causality', 'Mandel, Johannes|Palfreyman, Niall M|Lopez, Jesus A|Dubitzky, Werner', 'Dept. of Biotechnology & Bioinformatics, Weihenstephan University of Applied Sciences, Freising, Germany. johannes.mandel@fh-weihenstephan.de|Dept. of Biotechnology & Bioinformatics, Weihenstephan University of Applied Sciences, Freising, Germany. johannes.mandel@fh-weihenstephan.de|Dept. of Biotechnology & Bioinformatics, Weihenstephan University of Applied Sciences, Freising, Germany. johannes.mandel@fh-weihenstephan.de|Dept. of Biotechnology & Bioinformatics, Weihenstephan University of Applied Sciences, Freising, Germany. johannes.mandel@fh-weihenstephan.de', 'Germany|Germany|Germany|Germany', 19, 'Algorithms|Animals|Causality|Cell Physiological Phenomena|Computational Biology|Computer Graphics|Escherichia coli|Gene Expression Regulation|Humans|Lac Operon|Models, Biological|Signal Transduction|Software|Systems Biology|Systems Theory|User-Computer Interface|beta-Galactosidase', 'Journal Article', '10.1093/bib/5.3.270', '15383213', 'This paper reviews a variety of different graphical notations currently in active use for modelling dynamic processes in bioinformatics and biotechnology, and crystallises from these notations a set of properties essential to any proposal for a modelling language seeking to provide an adequate systemic description of biological processes.'),
(68285, 2004, 'bib', 'Bioinformatics software resources', 'Gilbert, Don', 'Biology Department, Indiana University, Bloomington, Indiana 47405, USA. gilbertd@indiana.edu', 'India', 8, 'Algorithms|Archives|Computational Biology|Databases, Factual|Internet|Software', 'Journal Article', '10.1093/bib/5.3.300', '15383216', 'This review looks at internet archives, repositories and lists for obtaining popular and useful biology and bioinformatics software. Resources include collections of free software, services for the collaborative development of new programs, software news media and catalogues of links to bioinformatics software and web tools. Problems with such resources arise from needs for continued curator effort to collect and update these, combined with less than optimal community support, funding and collaboration. Despite some problems, the available software repositories provide needed public access to many tools that are a foundation for analyses in bioscience research efforts.'),
(68286, 2004, 'bib', 'Stable funding for open source software?', 'Bleasby, Alan', 'Biology Department, Indiana University, Bloomington, Indiana 47405, USA. gilbertd@indiana.edu', 'India', 0, 'Computational Biology|European Union|Financing, Organized|Information Dissemination|Internationality|Sequence Analysis|Software', 'Editorial', '10.1093/bib/5.4.312', '15606967', '-'),
(68287, 2004, 'bib', 'Challenges and prospects in the analysis of large-scale gene expression data', 'Ihmels, Jan H|Bergmann, Sven', 'Department of Molecular Genetics, Weizmann Institute, 76100 Rehovot, Israel. jan@weizmann.ac.il|Department of Molecular Genetics, Weizmann Institute, 76100 Rehovot, Israel. jan@weizmann.ac.il', 'Israel|Israel', 4, 'Algorithms|Animals|Computer Simulation|Gene Expression Profiling|Gene Expression Regulation|Humans|Models, Biological|Oligonucleotide Array Sequence Analysis|Signal Transduction|Transcription Factors', 'Evaluation Studies', '10.1093/bib/5.4.313', '15606968', 'Large heterogeneous expression data comprising a variety of cellular conditions hold the promise of a global view of transcriptional regulation. While standard analysis methods have been successfully applied to smaller data sets, large-scale data pose specific challenges that have prompted the development of new and more sophisticated approaches. This paper focuses on one such approach (the Signature Algorithm) and discusses the central challenges in the analysis of large data sets, and how they might be overcome. Biological questions that have been addressed using the Signature Algorithm are highlighted and a summary of other important methods from the literature is provided.'),
(68288, 2004, 'bib', 'Biological applications of support vector machines', 'Yang, Zheng Rong', 'Department of Computer Science, University of Exeter, Exeter, UK. Z.R.Yang@exeter.ac.uk', 'United Kingdom', 121, 'Algorithms|Animals|Artificial Intelligence|Cluster Analysis|Computing Methodologies|Humans|Neural Networks (Computer)|Pattern Recognition, Automated|Sequence Analysis, DNA|Sequence Analysis, Protein|Systems Biology', 'Journal Article', '10.1093/bib/5.4.328', '15606969', 'One of the major tasks in bioinformatics is the classification and prediction of biological data. With the rapid increase in size of the biological databanks, it is essential to use computer programs to automate the classification process. At present, the computer programs that give the best prediction performance are support vector machines (SVMs). This is because SVMs are designed to maximise the margin to separate two classes so that the trained model generalises well on unseen data. Most other computer programs implement a classifier through the minimisation of error occurred in training, which leads to poorer generalisation. Because of this, SVMs have been widely applied to many areas of bioinformatics including protein function prediction, protease functional site recognition, transcription initiation site prediction and gene expression data classification. This paper will discuss the principles of SVMs and the applications of SVMs to the analysis of biological data, mainly protein and DNA sequences.'),
(68289, 2004, 'bib', 'Computational aspects of host-parasite phylogenies', 'Stevens, Jamie', 'Department of Biological Sciences, University of Exeter, Exeter, UK. j.r.stevens@ex.ac.uk', 'United Kingdom', 6, 'Algorithms|Animals|Biological Evolution|Computer Simulation|Host-Parasite Interactions|Humans|Models, Biological|Phylogeny|Population Dynamics|Software', 'Journal Article', '10.1093/bib/5.4.339', '15606970', 'Computational aspects of host-parasite phylogenies form part of a set of general associations between areas and organisms, hosts and parasites, and species and genes. The problem is not new and the commonalities of exploring vicariance biogeography (organisms tracking areas) and host-parasite co-speciation (parasites tracking hosts) have been recognised for some time. Methods for comparing host-parasite phylogenies are now well established and fall within two basic categories defined in terms of the way the data are interpreted in relation to the comparison of host-parasite phylogenies, so-called a posteriori, eg Brooks Parsimony Analysis (BPA), or a priori, eg reconciled trees and other model-based methods, as implemented in the program TreeMap, the relative merits of the two philosophies inherent in these two approaches remain hotly debated. This paper reviews the computational methods currently available to analyse host-parasite relationships.'),
(68290, 2004, 'bib', 'A review of bioinformatics degrees in Australia', 'Cattley, Sonia', 'Australian National Genomic Information Service, University of Sydney, Camperdown, NSW, Australia. education@angis.org.au', 'Australia', 12, 'Australia|Computational Biology|Curriculum|Education, Graduate|Education, Professional|Universities', 'Journal Article', '10.1093/bib/5.4.350', '15606971', 'Bioinformatics has been a hot topic in Australias biotechnology circles for the past five years. As with biotechnology in the 1990s, there has been a sudden increase in the number of Bioinformatics undergraduate degrees. For students in the 2005 intake there are six undergraduate Bioinformatics degrees to choose from and another five Bioinformatics streams within a Bachelor of Science degree. The courses vary from three to four years of full-time study. This report aims at dissecting each of these degrees to determine where the differences lie, to give the prospective students an idea as to which degree suits their career goals and to give an overview of the pedagogy of Australian bioinformatics education.'),
(68291, 2004, 'bib', 'Linkage disequilibrium for different scales and applications', 'Mueller, Jakob C', 'National Centre for Genetic Epidemiological Methods, Institute for Human Genetics, GSF-National Research Institute for Environment and Health, 85764 Neuherberg, Germany. jakob.mueller@gsf.de', 'Germany', 48, 'Algorithms|Animals|Chromosome Mapping|Computer Simulation|DNA Mutational Analysis|Humans|Linkage Disequilibrium|Models, Genetic|Models, Statistical|Sequence Alignment|Sequence Analysis, DNA', 'Journal Article', '10.1093/bib/5.4.355', '15606972', 'Assessing the patterns of linkage disequilibrium (LD) has become an important issue in both evolutionary biology and medical genetics since the rapid accumulation of densely spaced DNA sequence variation data in several organisms. LD deals with the correlation of genetic variation at two or more loci or sites in the genome within a given population. There are a variety of LD measures which range from traditional pairwise LD measures such as D or r2 to entropy-based multi-locus measures or haplotype-specific approaches. Understanding the evolutionary forces (in particular recombination) that generate the observed variation of LD patterns across genomic regions is addressed by model-based LD analysis. Marker type and its allelic composition also influence the observed LD pattern, microsatellites having a greater power to detect LD in population isolates than SNPs. This review aims to explain basic LD measures and their application properties.'),
(68292, 2004, 'bib', 'Genomes, genomes everywhere--but where to browse?', 'Mullan, Lisa', 'European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK. lisa@ebi.ac.uk', 'United Kingdom', 0, 'Algorithms|Animals|Chromosome Mapping|Database Management Systems|Databases, Genetic|Genome|Humans|Information Dissemination|Information Storage and Retrieval|Internet|Software|User-Computer Interface', 'Journal Article', '10.1093/bib/5.4.365', '15606973', '-'),
(68293, 2004, 'bib', 'Vector NTI, a balanced all-in-one sequence analysis suite', 'Lu, Guoqing|Moriyama, Etsuko N', 'School of Biological Sciences and Center for Biotechnology, University of Nebraska-Lincoln, Lincoln, NE 68588, USA. glu3@unl.edu|School of Biological Sciences and Center for Biotechnology, University of Nebraska-Lincoln, Lincoln, NE 68588, USA. glu3@unl.edu', 'United States|United States', 222, 'Algorithms|Artificial Intelligence|Chromosome Mapping|Database Management Systems|Databases, Genetic|Information Storage and Retrieval|Sequence Alignment|Sequence Analysis|Software|Software Validation|User-Computer Interface', 'Evaluation Studies', '10.1093/bib/5.4.378', '15606974', 'Vector NTI is a well-balanced desktop application integrated for molecular sequence analysis and biological data management. It has a centralised database and five application modules: Vector NTI, AlignX, BioAnnotator, ContigExpress and GenomBench. In this review, the features and functions available in this software are examined. These include database management, primer design, virtual cloning, alignments, sequence assembly, 3D molecular viewer and internet tools. Some problems encountered when using this software are also discussed. It is hoped that this review will introduce this software to more molecular biologists so they can make better-informed decisions when choosing computational tools to facilitate their everyday laboratory work. This tool can save time and enhance analysis but it requires some learning on the users part and there are some issues that need to be addressed by the developer.'),
(68294, 2005, 'bib', 'The many faces of sequence alignment', 'Batzoglou, Serafim', 'Department of Computer Science, Stanford University, James H. Clark Center, 318 Campus Drive, RM S-266, Stanford, CA 94305-5428, USA. serafim@cs.stanford.edu', 'United States', 22, 'Algorithms|Animals|Conserved Sequence|Humans|Models, Genetic|Models, Statistical|Pattern Recognition, Automated|Sequence Alignment|Sequence Analysis, DNA|Sequence Analysis, Protein|Sequence Homology', 'Journal Article', '10.1093/bib/6.1.6', '15826353', 'Starting with the sequencing of the mouse genome in 2002, we have entered a period where the main focus of genomics will be to compare multiple genomes in order to learn about human biology and evolution at the DNA level. Alignment methods are the main computational component of this endeavour. This short review aims to summarise the current status of research in alignments, emphasising large-scale genomic comparisons and suggesting possible directions that will be explored in the near future.'),
(68295, 2005, 'bib', 'Bioinformatics analysis of alternative splicing', 'Lee, Christopher|Wang, Qi', 'Molecular Biology Institute, Center for Genomics and Proteomics, Dept. of Chemistry & Biochemistry, University of California, Los Angeles, Los Angeles, CA 90095-1570, USA. leec@mbi.ucla.edu|Molecular Biology Institute, Center for Genomics and Proteomics, Dept. of Chemistry & Biochemistry, University of California, Los Angeles, Los Angeles, CA 90095-1570, USA. leec@mbi.ucla.edu', 'United States|United States', 26, 'Algorithms|Alternative Splicing|Animals|Chromosome Mapping|Computational Biology|Gene Expression Profiling|Gene Expression Regulation|Humans|Models, Genetic|Oligonucleotide Array Sequence Analysis|Transcription Factors', 'Journal Article', '10.1093/bib/6.1.23', '15826354', 'Over the past few years, the analysis of alternative splicing using bioinformatics has emerged as an important new field, and has significantly changed our view of genome function. One exciting front has been the analysis of microarray data to measure alternative splicing genome-wide. Pioneering studies of both human and mouse data have produced algorithms for discerning evidence of alternative splicing and clustering genes and samples by their alternative splicing patterns. Moreover, these data indicate the presence of alternative splice forms in up to 80 per cent of human genes. Comparative genomics studies in both mammals and insects have demonstrated that alternative splicing can in some cases be predicted directly from comparisons of genome sequences, based on heightened sequence conservation and exon length. Such studies have also provided new insights into the connection between alternative splicing and a variety of evolutionary processes such as Alu-based exonisation, exon creation and loss. A number of groups have used a combination of bioinformatics, comparative genomics and experimental validation to identify new motifs for splice regulatory factors, analyse the balance of factors that regulate alternative splicing, and propose a new mechanism for regulation based on the interaction of alternative splicing and nonsense-mediated decay. Bioinformatics studies of the functional impact of alternative splicing have revealed a wide range of regulatory mechanisms, from NAGNAG sites that add a single amino acid, to short peptide segments that can play surprisingly complex roles in switching protein conformation and function (as in the Piccolo C2A domain), to events that entirely remove a specific protein interaction domain or membrane anchoring domain. Common to many bioinformatics studies is a new emphasis on graph representations of alternative splicing structures, which have many advantages for analysis.'),
(68296, 2005, 'bib', 'Putting microarrays in a context: integrated analysis of diverse biological data', 'Troyanskaya, Olga G', 'Department of Computer Science and Lewis-Sigler Institute for Integrative Genomics, Princeton University, NJ 08544, USA. ogt@CS.Princeton.EDU', 'United States', 45, 'Algorithms|Animals|Biology|Chromosome Mapping|Databases, Genetic|Gene Expression Profiling|Gene Expression Regulation|Humans|Information Storage and Retrieval|Models, Biological|Oligonucleotide Array Sequence Analysis|Systems Integration|User-Computer Interface', 'Journal Article', '10.1093/bib/6.1.34', '15826355', 'In recent years, multiple types of high-throughput functional genomic data that facilitate rapid functional annotation of sequenced genomes have become available. Gene expression microarrays are the most commonly available source of such data. However, genomic data often sacrifice specificity for scale, yielding very large quantities of relatively lower-quality data than traditional experimental methods. Thus sophisticated analysis methods are necessary to make accurate functional interpretation of these large-scale data sets. This review presents an overview of recently developed methods that integrate the analysis of microarray data with sequence, interaction, localisation and literature data, and further outlines current challenges in the field. The focus of this review is on the use of such methods for gene function prediction, understanding of protein regulation and modelling of biological networks.'),
(68297, 2005, 'bib', 'Bioinformatics approaches and resources for single nucleotide polymorphism functional analysis', 'Mooney, Sean', 'Center for Computational Biology and Bioinformatics, Department of Medical and Molecular Genetics, Indiana University School of Medicine, 714 N Senate Ave, EF 250, Indianapolis, IN 46202, USA. sdmooney@iupui.edu', 'India', 50, 'Algorithms|Chromosome Mapping|Computational Biology|DNA Mutational Analysis|Genetic Variation|Polymorphism, Single Nucleotide|Sequence Alignment|Sequence Analysis, DNA|Software|User-Computer Interface', 'Journal Article', '10.1093/bib/6.1.44', '15826356', 'Since the initial sequencing of the human genome, many projects are underway to understand the effects of genetic variation between individuals. Predicting and understanding the downstream effects of genetic variation using computational methods are becoming increasingly important for single nucleotide polymorphism (SNP) selection in genetics studies and understanding the molecular basis of disease. According to the NIH, there are now more than four million validated SNPs in the human genome. The volume of known genetic variations lends itself well to an informatics approach. Bioinformaticians have become very good at functional inference methods derived from functional and structural genomics. This review will present a broad overview of the tools and resources available to collect and understand functional variation from the perspective of structure, expression, evolution and phenotype. Additionally, public resources available for SNP identification and characterisation are summarised.'),
(68298, 2005, 'bib', 'A survey of current work in biomedical text mining', 'Cohen, Aaron M|Hersh, William R', 'Department of Medical Informatics and Clinical Epidemiology, School of Medicine, Oregon Health & Science University, 3181 S.W. Sam Jackson Park Road, Portland, OR 97239-309, USA. cohenaa@ohsu.edu|Department of Medical Informatics and Clinical Epidemiology, School of Medicine, Oregon Health & Science University, 3181 S.W. Sam Jackson Park Road, Portland, OR 97239-309, USA. cohenaa@ohsu.edu', 'United States|United States', 507, 'Abstracting and Indexing as Topic|Algorithms|Biomedical Research|Database Management Systems|Databases, Bibliographic|Documentation|Information Storage and Retrieval|Natural Language Processing|Pattern Recognition, Automated|Periodicals as Topic|PubMed|Semantics|Vocabulary, Controlled', 'Journal Article', '10.1093/bib/6.1.57', '15826357', 'The volume of published biomedical research, and therefore the underlying biomedical knowledge base, is expanding at an increasing rate. Among the tools that can aid researchers in coping with this information overload are text mining and knowledge extraction. Significant progress has been made in applying text mining to named entity recognition, text classification, terminology extraction, relationship extraction and hypothesis generation. Several research groups are constructing integrated flexible text-mining systems intended for multiple uses. The major challenge of biomedical text mining over the next 5-10 years is to make these systems useful to biomedical researchers. This will require enhanced access to full text, better understanding of the feature space of biomedical literature, better methods for measuring the usefulness of systems to users, and continued cooperation with the biomedical research community to ensure that their needs are addressed.'),
(68299, 2005, 'bib', 'Current efforts in the analysis of RNAi and RNAi target genes', 'Bengert, Peter|Dandekar, Thomas', 'Department of Bioinformatics, Biocenter, Am Hubland, University of Würzburg, Germany.|Department of Bioinformatics, Biocenter, Am Hubland, University of Würzburg, Germany.', 'Germany|Germany', 12, 'Animals|Gene Expression Profiling|Gene Expression Regulation|Gene Targeting|Genetic Engineering|Humans|MicroRNAs|RNA Interference|Sequence Analysis, RNA', 'Journal Article', '10.1093/bib/6.1.72', '15826358', 'RNAi is RNA interference by short RNAs. It influences gene-expression by down-regulation of mRNAs, typically by complementarity to the 3 UTR (untranslated region) of the mRNA. microRNAs (miRNAs) are short RNAs acting as natural RNAi. miRNAs mediate down-regulation of many mRNAs from developmental genes and transcription factor genes. Natural examples for this additional level of post-transcriptional control are increasing. Suitable computer-based search strategies for new miRNA candidates include precursor folding as well as different compositional search strategies. Example programs for this are presented. New own and other data are provided for an overview on such strategies. A strategy feasible in plants for miRNA target identification is direct base pairing of miRNAs to potential mRNA target 3 UTRs. Correct identification in animals usually requires comparative genomics and conserved UTR regions pairing to conserved miRNA substructures. A number of example programs and target examples for these tasks are examined. Finally, strategies and programs for artificial gene silencing by designed RNAi are explained.'),
(68300, 2005, 'bib', 'There is no silver bullet--a guide to low-level data transforms and normalisation methods for microarray data', 'Kreil, David P|Russell, Roslin R', 'Dept of Genetics, Univ. of Cambridge, Downing Street, Cambridge CB2 3EH, UK. D.Kreil@gen.cam.ac.uk|Dept of Genetics, Univ. of Cambridge, Downing Street, Cambridge CB2 3EH, UK. D.Kreil@gen.cam.ac.uk', 'United Kingdom|United Kingdom', 8, 'Algorithms|Artifacts|Calibration|Data Interpretation, Statistical|Gene Expression Profiling|Genetic Variation|Models, Genetic|Models, Statistical|Numerical Analysis, Computer-Assisted|Oligonucleotide Array Sequence Analysis|Sequence Analysis, DNA', 'Comparative Study', '10.1093/bib/6.1.86', '15826359', 'To overcome random experimental variation, even for simple screens, data from multiple microarrays have to be combined. There are, however, systematic differences between arrays, and any bias remaining after experimental measures to ensure consistency needs to be controlled for. It is often difficult to make the right choice of data transformation and normalisation methods to achieve this end. In this tutorial paper we review the problem and a selection of solutions, explaining the basic principles behind normalisation procedures and providing guidance for their application.'),
(68301, 2005, 'bib', 'Analysis of evolution of exon-intron structure of eukaryotic genes', 'Rogozin, Igor B|Sverdlov, Alexander V|Babenko, Vladimir N|Koonin, Eugene V', 'National Center for Biotechnology Information/NLM/NIH, 8600 Rockville Pike, Bldg. 38A, Bethesda, MD 20894, USA. rogozin@ncbi.nlm.nih.gov|National Center for Biotechnology Information/NLM/NIH, 8600 Rockville Pike, Bldg. 38A, Bethesda, MD 20894, USA. rogozin@ncbi.nlm.nih.gov|National Center for Biotechnology Information/NLM/NIH, 8600 Rockville Pike, Bldg. 38A, Bethesda, MD 20894, USA. rogozin@ncbi.nlm.nih.gov|National Center for Biotechnology Information/NLM/NIH, 8600 Rockville Pike, Bldg. 38A, Bethesda, MD 20894, USA. rogozin@ncbi.nlm.nih.gov', 'United States|United States|United States|United States', 150, 'Animals|Biological Evolution|Chromosome Mapping|Conserved Sequence|DNA Mutational Analysis|Eukaryotic Cells|Evolution, Molecular|Exons|Humans|Introns|Phylogeny|Sequence Analysis, DNA|Sequence Homology, Nucleic Acid', 'Journal Article', '10.1093/bib/6.2.118', '15975222', 'The availability of multiple, complete eukaryotic genome sequences allows one to address many fundamental evolutionary questions on genome scale. One such important, long-standing problem is evolution of exon-intron structure of eukaryotic genes. Analysis of orthologous genes from completely sequenced genomes revealed numerous shared intron positions in orthologous genes from animals and plants and even between animals, plants and protists. The data on shared and lineage-specific intron positions were used as the starting point for evolutionary reconstruction with parsimony and maximum-likelihood approaches. Parsimony methods produce reconstructions with intron-rich ancestors but also infer lineage-specific, in many cases, high levels of intron loss and gain. Different probabilistic models gave opposite results, apparently depending on model parameters and assumptions, from domination of intron loss, with extremely intron-rich ancestors, to dramatic excess of gains, to the point of denying any true conservation of intron positions among deep eukaryotic lineages. Development of models with adequate, realistic parameters and assumptions seems to be crucial for obtaining more definitive estimates of intron gain and loss in different eukaryotic lineages. Many shared intron positions were detected in ancestral eukaryotic paralogues which evolved by duplication prior to the divergence of extant eukaryotic lineages. These findings indicate that numerous introns were present in eukaryotic genes already at the earliest stages of evolution of eukaryotes and are compatible with the hypothesis that the original, catastrophic intron invasion accompanied the emergence of the eukaryotic cells. Comparison of various features of old and younger introns starts shedding light on probable mechanisms of intron insertion, indicating that propagation of old introns is unlikely to be a major mechanism for origin of new ones. The existence and structure of ancestral protosplice sites were addressed by examining the context of introns inserted within codons that encode amino acids conserved in all eukaryotes and, accordingly, are not subject to selection for splicing efficiency. It was shown that introns indeed predominantly insert into or are fixed in specific protosplice sites which have the consensus sequence (A/C)AG|Gt.'),
(68302, 2005, 'bib', 'Combining microarrays and genetic analysis', 'Alberts, Rudi|Fu, Jingyuan|Swertz, Morris A|Lubbers, L Alrik|Albers, Casper J|Jansen, Ritsert C', 'Groningen Bioinformatics Centre, University of Groningen, Kerklaan 30, 9751 NN, Haren, the Netherlands.|Groningen Bioinformatics Centre, University of Groningen, Kerklaan 30, 9751 NN, Haren, the Netherlands.|Groningen Bioinformatics Centre, University of Groningen, Kerklaan 30, 9751 NN, Haren, the Netherlands.|Groningen Bioinformatics Centre, University of Groningen, Kerklaan 30, 9751 NN, Haren, the Netherlands.|Groningen Bioinformatics Centre, University of Groningen, Kerklaan 30, 9751 NN, Haren, the Netherlands.|Groningen Bioinformatics Centre, University of Groningen, Kerklaan 30, 9751 NN, Haren, the Netherlands.', 'Netherlands|Netherlands|Netherlands|Netherlands|Netherlands|Netherlands', 12, 'Chromosome Mapping|Computer Simulation|Gene Expression Profiling|Genomics|Models, Genetic|Oligonucleotide Array Sequence Analysis|Quantitative Trait Loci', 'Journal Article', '10.1093/bib/6.2.135', '15975223', 'Gene expression can be studied at a genome-wide scale with the aid of modern microarray technologies. Expression profiling of tens to hundreds of individuals in a genetic population can reveal the consequences of genetic variation. In this paper it is argued that the design and analysis of such a study is not a matter of simply applying the existing and more-or-less standard computational tools for microarrays to a new type of experimental data. It is shown how to fully exploit the power of genetics through optimal experimental design and analysis for two major microarray technologies, cDNA two-colour arrays and Affymetrix short oligonucleotide arrays.'),
(68303, 2005, 'bib', 'RNAi as a bioinformatics consumer', 'Sachidanandam, Ravi', 'Cold Spring Harbor Laboratory, NY 11724, USA. sachidan@cshl.org', 'United States', 5, 'Computational Biology|Gene Targeting|MicroRNAs|Oligonucleotide Array Sequence Analysis|RNA Interference|Transcriptional Activation', 'Journal Article', '10.1093/bib/6.2.146', '15975224', 'RNAi has shown great potential for use as a tool for biological discovery, analysis and therapeutics. The involvement of the RNAi pathway in post-transcription silencing, transcriptional silencing and epigenetic silencing as well as its use as a tool for forward genetics and therapeutics throws up several bioinformatics challenges. This paper delineates several areas of research and reviews work that has already been done, the tools that are available and the challenges that lie ahead.'),
(68304, 2005, 'bib', 'Mathematical models of cell cycle regulation', 'Fuss, Hendrik|Dubitzky, Werner|Downes, C Stephen|Kurth, Mary Jo', 'School of Biomedical Sciences, University of Ulster, Cromore Road, Coleraine, BT52 1SA, UK. fuss-h@ulster.ac.uk|School of Biomedical Sciences, University of Ulster, Cromore Road, Coleraine, BT52 1SA, UK. fuss-h@ulster.ac.uk|School of Biomedical Sciences, University of Ulster, Cromore Road, Coleraine, BT52 1SA, UK. fuss-h@ulster.ac.uk|School of Biomedical Sciences, University of Ulster, Cromore Road, Coleraine, BT52 1SA, UK. fuss-h@ulster.ac.uk', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom', 4, 'Algorithms|Animals|Cell Cycle|Cell Cycle Proteins|Computer Simulation|Feedback|Gene Expression Regulation|Humans|Models, Biological|Signal Transduction', 'Journal Article', '10.1093/bib/6.2.163', '15975225', 'The cell division cycle is a fundamental process of cell biology and a detailed understanding of its function, regulation and other underlying mechanisms is critical to many applications in biotechnology and medicine. Since a comprehensive analysis of the molecular mechanisms involved is too complex to be performed intuitively, mathematical and computational modelling techniques are essential. This paper is a review and analysis of recent approaches attempting to model cell cycle regulation by means of protein-protein interaction networks.'),
(68305, 2005, 'bib', 'Evolution of web services in bioinformatics', 'Neerincx, Pieter B T|Leunissen, Jack A M', 'Laboratory of Bioinformatics, Department of Plant Sciences, Wageningen University and Research Centre (WUR), Wageningen, the Netherlands. pieter.neerinx@wur.nl|Laboratory of Bioinformatics, Department of Plant Sciences, Wageningen University and Research Centre (WUR), Wageningen, the Netherlands. pieter.neerinx@wur.nl', 'Netherlands|Netherlands', 54, 'Computational Biology|Database Management Systems|Databases, Factual|Information Storage and Retrieval|Internet|Online Systems|Software|Systems Integration|User-Computer Interface', 'Journal Article', '10.1093/bib/6.2.178', '15975226', 'Bioinformaticians have developed large collections of tools to make sense of the rapidly growing pool of molecular biological data. Biological systems tend to be complex and in order to understand them, it is often necessary to link many data sets and use more than one tool. Therefore, bioinformaticians have experimented with several strategies to try to integrate data sets and tools. Owing to the lack of standards for data sets and the interfaces of the tools this is not a trivial task. Over the past few years building services with web-based interfaces has become a popular way of sharing the data and tools that have resulted from many bioinformatics projects. This paper discusses the interoperability problem and how web services are being used to try to solve it, resulting in the evolution of tools with web interfaces from HTML/web form-based tools not suited for automatic workflow generation to a dynamic network of XML-based web services that can easily be used to create pipelines.'),
(68306, 2005, 'bib', 'VEGA, the genome browser with a difference', 'Loveland, Jane', 'HAVANA Group, Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton CB10 1SA, UK. jel@sanger.ac.uk', 'United Kingdom', 7, 'Animals|Chromosome Mapping|Computer Graphics|Databases, Genetic|Internet|Sequence Analysis, DNA|Software|User-Computer Interface|Vertebrates', 'Journal Article', '10.1093/bib/6.2.189', '15975227', 'The Vertebrate Genome Annotation (Vega) database is a community resource for browsing manual annotation from a variety of vertebrate genomes of finished sequence (http://vega.sanger.ac.uk). Vega is different from other genome browsers as it has a standardised classification of genes which encompasses pseudogenes and non-coding transcripts. The data is manually curated, which is more accurate at identifying splice variants, pseudogenes poly(A) features, non-coding and complex gene structures and arrangements than current automated methods. The database also contains annotation from regions, not just whole genomes, and displays multiple species annotation (human, mouse, dog and zebrafish) for comparative analysis. Vega encourages community feedback that results in annotation updates and manual annotation of finished vertebrate sequence.'),
(68307, 2005, 'bib', 'Biomolecular interaction network database', 'Gilbert, Don', 'Biology Department, Indiana University, Bloomington, Indiana 47405, USA. gilbertd@indiana.edu', 'India', 50, 'Binding Sites|Database Management Systems|Databases, Protein|Information Storage and Retrieval|Protein Binding|Protein Interaction Mapping|Proteins|Software|User-Computer Interface', 'Journal Article', '10.1093/bib/6.2.194', '15975228', 'This software review looks at the utility of the Biomolecular Interaction Network Database (BIND) as a web database. BIND offers methods common to related biology databases and specialisations for its protein interaction data. Searching and browsing this database is easy and well integrated with the underlying data and the needs of scientists. Interaction networks are visualised with software that offers many useful options. The innovative ontoglyphs are used throughout to provide visual cues to protein functions, localisation and other aspects one needs to know for this data set. One can expect to get useful results that may be well integrated with ones research needs.'),
(68308, 2005, 'bib', 'Hairpins in bookstacks: information retrieval from biomedical text', 'Shatkay, Hagit', 'School of Computing, Queens University, Kingston, Ontario, K7L 3N6, Canada. shatkay@cs.queensu.ca', 'Canada', 27, 'Abstracting and Indexing as Topic|Artificial Intelligence|Biology|Books|Database Management Systems|Databases, Bibliographic|Documentation|Information Storage and Retrieval|Medicine|Natural Language Processing|Pattern Recognition, Automated|Periodicals as Topic|Terminology as Topic', 'Journal Article', '10.1093/bib/6.3.222', '16212771', 'Current advances in high-throughput biology are accompanied by a tremendous increase in the number of related publications. Much biomedical information is reported in the vast amount of literature. The ability to rapidly and effectively survey the literature is necessary for both the design and the interpretation of large-scale experiments, and for curation of structured biomedical knowledge in public databases. Given the millions of published documents, the field of information retrieval, which is concerned with the automatic identification of relevant documents from large text collections, has much to offer. This paper introduces the basics of information retrieval, discusses its applications in biomedicine, and presents traditional and non-traditional ways in which it can be used.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(68309, 2005, 'bib', 'Text mining and ontologies in biomedicine: making sense of raw text', 'Spasic, Irena|Ananiadou, Sophia|McNaught, John|Kumar, Anand', 'School of Chemistry, The University of Manchester, Sackville Street, PO Box 88, Manchester M60 1QD, UK. i.spasic@manchester.ac.uk|School of Chemistry, The University of Manchester, Sackville Street, PO Box 88, Manchester M60 1QD, UK. i.spasic@manchester.ac.uk|School of Chemistry, The University of Manchester, Sackville Street, PO Box 88, Manchester M60 1QD, UK. i.spasic@manchester.ac.uk|School of Chemistry, The University of Manchester, Sackville Street, PO Box 88, Manchester M60 1QD, UK. i.spasic@manchester.ac.uk', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom', 49, 'Abstracting and Indexing as Topic|Artificial Intelligence|Biology|Books|Database Management Systems|Databases, Bibliographic|Documentation|Information Storage and Retrieval|Medicine|Natural Language Processing|Pattern Recognition, Automated|Periodicals as Topic|Terminology as Topic', 'Journal Article', '10.1093/bib/6.3.239', '16212772', 'The volume of biomedical literature is increasing at such a rate that it is becoming difficult to locate, retrieve and manage the reported information without text mining, which aims to automatically distill information, extract facts, discover implicit links and generate hypotheses relevant to user needs. Ontologies, as conceptual models, provide the necessary framework for semantic representation of textual information. The principal link between text and an ontology is terminology, which maps terms to domain-specific concepts. This paper summarises different approaches in which ontologies have been used for text-mining applications in biomedicine.'),
(68310, 2005, 'bib', 'Information retrieval and knowledge discovery utilising a biomedical Semantic Web', 'Mukherjea, Sougata', 'IBM India Research Laboratory, Block I, Indian Institue of Technology, Hauz Khas, New Delhi - 110016, India. smukherj@in.ibm.com', 'India', 30, 'Abstracting and Indexing as Topic|Artificial Intelligence|Biology|Database Management Systems|Databases, Bibliographic|Documentation|Information Storage and Retrieval|Internet|Medicine|Natural Language Processing|Pattern Recognition, Automated|Periodicals as Topic|Semantics|Terminology as Topic|Unified Medical Language System', 'Journal Article', '10.1093/bib/6.3.252', '16212773', 'Although various ontologies and knowledge sources have been developed in recent years to facilitate biomedical research, it is difficult to assimilate information from multiple knowledge sources. To enable researchers to easily gain understanding of a biomedical concept, a biomedical Semantic Web that seamlessly integrates knowledge from biomedical ontologies, publications and patents would be very helpful. In this paper, current research efforts in representing biomedical knowledge in Semantic Web languages are surveyed. Techniques are presented for information retrieval and knowledge discovery from the Semantic Web that extend traditional keyword search and database querying techniques. Finally, some of the challenges that have to be addressed to make the vision of a biomedical Semantic Web a reality are discussed.'),
(68311, 2005, 'bib', 'Extraction of biological interaction networks from scientific literature', 'Skusa, Andre|Rüegg, Alexander|Köhler, Jacob', 'NRW International Graduate School in Bioinformatics and Genome Research, Bielefeld University, Postfach 10 01 31, D-33501 Bielefeld, Germany. askusa@cebitec.uni-bielefeld.de|NRW International Graduate School in Bioinformatics and Genome Research, Bielefeld University, Postfach 10 01 31, D-33501 Bielefeld, Germany. askusa@cebitec.uni-bielefeld.de|NRW International Graduate School in Bioinformatics and Genome Research, Bielefeld University, Postfach 10 01 31, D-33501 Bielefeld, Germany. askusa@cebitec.uni-bielefeld.de', 'Germany|Germany|Germany', 12, 'Abstracting and Indexing as Topic|Animals|Artificial Intelligence|Biology|Cell Physiological Phenomena|Database Management Systems|Databases, Bibliographic|Documentation|Gene Expression Regulation|Humans|Information Storage and Retrieval|Models, Biological|Natural Language Processing|Pattern Recognition, Automated|Periodicals as Topic|Science|Signal Transduction|Terminology as Topic', 'Journal Article', '10.1093/bib/6.3.263', '16212774', 'Biology can be regarded as a science of networks: interactions between various biological entities (eg genes, proteins, metabolites) on different levels (eg gene regulation, cell signalling) can be represented as graphs and, thus, analysis of such networks might shed new light on the function of biological systems. Such biological networks can be obtained from different sources. The extraction of networks from text is an important technique that requires the integration of several different computational disciplines. This paper summarises the most important steps in network extraction and reviews common approaches and solutions for the extraction of biological networks from scientific literature.'),
(68312, 2005, 'bib', 'Online tools to support literature-based discovery in the life sciences', 'Weeber, Marc|Kors, Jan A|Mons, Barend', 'Department of Medical Informatics, Erasmus MC - University Medical Center Rotterdam, PO Box 1738, 3000 DR Rotterdam, The Netherlands. m.weeber@erasmusmc.nl|Department of Medical Informatics, Erasmus MC - University Medical Center Rotterdam, PO Box 1738, 3000 DR Rotterdam, The Netherlands. m.weeber@erasmusmc.nl|Department of Medical Informatics, Erasmus MC - University Medical Center Rotterdam, PO Box 1738, 3000 DR Rotterdam, The Netherlands. m.weeber@erasmusmc.nl', 'Netherlands|Netherlands|Netherlands', 70, 'Abstracting and Indexing as Topic|Artificial Intelligence|Biological Science Disciplines|Database Management Systems|Databases, Bibliographic|Documentation|Information Storage and Retrieval|Internet|Natural Language Processing|Online Systems|Pattern Recognition, Automated|Periodicals as Topic|Terminology as Topic', 'Journal Article', '10.1093/bib/6.3.277', '16212775', 'In biomedical research, the amount of experimental data and published scientific information is overwhelming and ever increasing, which may inhibit rather than stimulate scientific progress. Not only are text-mining and information extraction tools needed to render the biomedical literature accessible but the results of these tools can also assist researchers in the formulation and evaluation of novel hypotheses. This requires an additional set of technological approaches that are defined here as literature-based discovery (LBD) tools. Recently, several LBD tools have been developed for this purpose and a few well-motivated, specific and directly testable hypotheses have been published, some of which have even been validated experimentally. This paper presents an overview of recent LBD research and discusses methodology, results and online tools that are available to the scientific community.'),
(68313, 2005, 'bib', 'The next generation of literature analysis: integration of genomic analysis into text mining', 'Scherf, Matthias|Epple, Anton|Werner, Thomas', 'Genomatix Software GmbH, Landsberger Strasse 6, Munich, D-80339, Germany. scherf@genomatix.de|Genomatix Software GmbH, Landsberger Strasse 6, Munich, D-80339, Germany. scherf@genomatix.de|Genomatix Software GmbH, Landsberger Strasse 6, Munich, D-80339, Germany. scherf@genomatix.de', 'Germany|Germany|Germany', 75, 'Abstracting and Indexing as Topic|Artificial Intelligence|Database Management Systems|Databases, Bibliographic|Documentation|Forecasting|Genomics|Information Storage and Retrieval|Natural Language Processing|Pattern Recognition, Automated|Periodicals as Topic|Systems Integration|Terminology as Topic', 'Journal Article', '10.1093/bib/6.3.287', '16212776', 'Text-mining systems are indispensable tools to reduce the increasing flux of information in scientific literature to topics pertinent to a particular interest in focus. Most of the scientific literature is published as unstructured free text, complicating the development of data processing tools, which rely on structured information. To overcome the problems of free text analysis, structured, hand-curated information derived from literature is integrated in text-mining systems to improve precision and recall. In this paper several text-mining approaches are reviewed and the next step in development of text-mining systems, which is based on a concept of multiple lines of evidence, is described: results from literature analysis are combined with evidence from experiments and genome analysis to improve the accuracy of results and to generate additional knowledge beyond what is known solely from literature.'),
(68314, 2005, 'bib', 'Get ready to GO! A biologists guide to the Gene Ontology', 'Lomax, Jane', 'European Bioinformatics Institute, Wellcome Trust Genome Campus, Cambridge, UK. jane@ebi.ac.uk', 'United Kingdom', 24, 'Abstracting and Indexing as Topic|Artificial Intelligence|Biology|Database Management Systems|Databases, Protein|Documentation|Information Storage and Retrieval|Natural Language Processing|Pattern Recognition, Automated|Proteins|Terminology as Topic', 'Journal Article', '10.1093/bib/6.3.298', '16212777', 'The Gene Ontology (GO) project provides a controlled vocabulary to facilitate high-quality functional gene annotation for all species. Genes in biological databases are linked to GO terms, allowing biologists to ask questions about gene function in a manner independent of species. This tutorial provides an introduction for biologists to the GO resources and covers three of the most common methods of querying GO: by individual gene, by gene function and by using a list of genes. [For the sake of brevity, the term gene is used throughout this paper to refer to genes and their products (proteins and RNAs). GO annotations are always based on the characteristics of gene products, even though it may be the gene that is cited in the annotation.].'),
(68315, 2005, 'bib', 'A Bayesian method for analysing spotted microarray data', 'Meiklejohn, Colin D|Townsend, Jeffrey P', 'Brown University, USA.|Brown University, USA.', 'United States|United States', 18, 'Algorithms|Bayes Theorem|Computer Simulation|Gene Expression Profiling|Image Interpretation, Computer-Assisted|In Situ Hybridization, Fluorescence|Microscopy, Fluorescence|Models, Genetic|Models, Statistical|Oligonucleotide Array Sequence Analysis|Software', 'Journal Article', '10.1093/bib/6.4.318', '16420731', 'In the decade since their invention, spotted microarrays have been undergoing technical advances that have increased the utility, scope and precision of their ability to measure gene expression. At the same time, more researchers are taking advantage of the fundamentally quantitative nature of these tools with refined experimental designs and sophisticated statistical analyses. These new approaches utilise the power of microarrays to estimate differences in gene expression levels, rather than just categorising genes as up- or down-regulated, and allow the comparison of expression data across multiple samples. In this review, some of the technical aspects of spotted microarrays that can affect statistical inference are highlighted, and a discussion is provided of how several methods for estimating gene expression level across multiple samples deal with these challenges. The focus is on a Bayesian analysis method, BAGEL, which is easy to implement and produces easily interpreted results.'),
(68316, 2005, 'bib', 'Unsupervised pattern recognition: an introduction to the whys and wherefores of clustering microarray data', 'Boutros, Paul C|Okey, Allan B', 'Department of Medical Biophysics, University of Toronto, Ontario, Canada M5S 1A8. Paul.Boutros@utoronto.ca|Department of Medical Biophysics, University of Toronto, Ontario, Canada M5S 1A8. Paul.Boutros@utoronto.ca', 'Canada|Canada', 83, 'Algorithms|Artificial Intelligence|Bayes Theorem|Cluster Analysis|Gene Expression Profiling|Models, Genetic|Models, Statistical|Oligonucleotide Array Sequence Analysis|Pattern Recognition, Automated', 'Journal Article', '10.1093/bib/6.4.331', '16420732', 'Clustering has become an integral part of microarray data analysis and interpretation. The algorithmic basis of clustering -- the application of unsupervised machine-learning techniques to identify the patterns inherent in a data set -- is well established. This review discusses the biological motivations for and applications of these techniques to integrating gene expression data with other biological information, such as functional annotation, promoter data and proteomic data.'),
(68317, 2005, 'bib', 'Evaluation of biomedical text-mining systems: lessons learned from information retrieval', 'Hersh, William', 'Department of Medical Informatics & Clinical Epidemiology, Oregon Health & Science University, 3181 SW Sam Jackson Park Rd, BICC, Portland, 97239, USA. hersh@ohsu.edu', 'United States', 19, 'Algorithms|Artificial Intelligence|Biomedical Research|Database Management Systems|Databases, Factual|Information Storage and Retrieval|Natural Language Processing|Periodicals as Topic', 'Comparative Study', '10.1093/bib/6.4.344', '16420733', 'Biomedical text-mining systems have great promise for improving the efficiency and productivity of biomedical researchers. However, such systems are still not in routine use. One impediment to their development is the lack of systematic and rigorous evaluation, comparable to the approaches developed for information retrieval systems. The developers of text-mining systems need to improve both test collections for system-oriented evaluation and undertake user-oriented evaluations to determine the most effective use of their systems for their intended audience.'),
(68318, 2005, 'bib', 'What makes a gene name? Named entity recognition in the biomedical literature', 'Leser, Ulf|Hakenberg, Jörg', 'Department for Computer Science, Humboldt-Universität zu Berlin, Rudower Chaussee 25, Germany. leser@informatik.hu-berlin.de|Department for Computer Science, Humboldt-Universität zu Berlin, Rudower Chaussee 25, Germany. leser@informatik.hu-berlin.de', 'Germany|Germany', 93, 'Algorithms|Artificial Intelligence|Biomedical Research|Database Management Systems|Databases, Factual|Genes|Information Storage and Retrieval|Natural Language Processing|Periodicals as Topic|Terminology as Topic', 'Journal Article', '10.1093/bib/6.4.357', '16420734', 'The recognition of biomedical concepts in natural text (named entity recognition, NER) is a key technology for automatic or semi-automatic analysis of textual resources. Precise NER tools are a prerequisite for many applications working on text, such as information retrieval, information extraction or document classification. Over the past years, the problem has achieved considerable attention in the bioinformatics community and experience has shown that NER in the life sciences is a rather difficult problem. Several systems and algorithms have been devised and implemented. In this paper, the problems and resources in NER research are described, the principal algorithms underlying most systems sketched, and the current state-of-the-art in the field surveyed.'),
(68319, 2005, 'bib', 'Software agents in molecular computational biology', 'Keele, John W|Wray, James E', 'United States Meat Animal Research Center, State Spur 18D, Clay Center, Nebraska 68933, USA. keele@email.marc.usda.gov|United States Meat Animal Research Center, State Spur 18D, Clay Center, Nebraska 68933, USA. keele@email.marc.usda.gov', 'United States|United States', 12, 'Algorithms|Animals|Artificial Intelligence|Chromosome Mapping|Computational Biology|Genomics|Molecular Biology|Sequence Analysis|Software|User-Computer Interface', 'Journal Article', '10.1093/bib/6.4.370', '16420735', 'Progress made in applying agent systems to molecular computational biology is reviewed and strategies by which to exploit agent technology to greater advantage are investigated. Communities of software agents could play an important role in helping genome scientists design reagents for future research. The advent of genome sequencing in cattle and swine increases the complexity of data analysis required to conduct research in livestock genomics. Databases are always expanding and semantic differences among data are common. Agent platforms have been developed to deal with generic issues such as agent communication, life cycle management and advertisement of services (white and yellow pages). This frees computational biologists from the drudgery of having to re-invent the wheel on these common chores, giving them more time to focus on biology and bioinformatics. Agent platforms that comply with the Foundation for Intelligent Physical Agents (FIPA) standards are able to interoperate. In other words, agents developed on different platforms can communicate and cooperate with one another if domain-specific higher-level communication protocol details are agreed upon between different agent developers. Many software agent platforms are peer-to-peer, which means that even if some of the agents and data repositories are temporarily unavailable, a subset of the goals of the system can still be met. Past use of software agents in bioinformatics indicates that an agent approach should prove fruitful. Examination of current problems in bioinformatics indicates that existing agent platforms should be adaptable to novel situations.'),
(68320, 2005, 'bib', 'Inferring network interactions within a cell', 'Carter, Greg W', 'Institute for Systems Biology, 1441 N. 34th Street, Seattle, WA 98103, USA. gcarter@systemsbiology.org', 'United States', 6, 'Algorithms|Computer Simulation|Gene Expression Profiling|Gene Expression Regulation, Fungal|Models, Biological|Protein Array Analysis|Protein Interaction Mapping|Saccharomyces cerevisiae|Saccharomyces cerevisiae Proteins|Signal Transduction', 'Journal Article', '10.1093/bib/6.4.380', '16420736', 'The continuing growth in high-throughput data acquisition has led to a proliferation of network models to represent and analyse biological systems. These networks involve distinct interaction types detected by a combination of methods, ranging from directly observed physical interactions based in biochemistry to interactions inferred from phenotype measurements, genomic expression and comparative genomics. The discovery of interactions increasingly requires a blend of experimental and computational methods. Considering yeast as a model system, recent analytical methods are reviewed here and specific aims are proposed to improve network interaction inference and facilitate predictive biological modelling.'),
(68321, 2005, 'bib', 'WinPop 25: software for representing population genetics phenomena', 'Nuin, Paulo A S', 'Division of Genomics and Proteomics in Cancer, Ontario Cancer Institute, University Health Network, 620 University Ave, Toronto, ON, Canada M5G 2M9.', 'Canada', 2, 'Algorithms|Computer Simulation|DNA Mutational Analysis|Genetics, Population|Humans|Models, Genetic|Software|User-Computer Interface', 'Journal Article', '10.1093/bib/6.4.390', '16420737', 'The curriculum for genetics courses is shifting from a classical to a more molecular genetics focus, increasing the importance of subjects such as population genetics. Population genetics is a computational and statistical field that requires a good understanding of the nature of stochastic events. It is a difficult field for biology students with a limited mathematical background and there is a need for visualisation tools to facilitate understanding by the use of practical examples. WinPop provides students and researchers with a visual tool to allow the simulation and representation of population genetics phenomena. WinPop is a user-friendly software meant for use in population genetics courses and basic research. WinPop 2.5 contains six different modules that represent and simulate population genetics models. Genotype and allele frequencies are calculated under the different models: panmixia, genetic drift, assortative matings, selection, gene flow and mutation. The programs interface presents information in Cartesian graphics and isosceles triangular coordinate systems, allowing the user to save graphical and textual data output from the simulations. WinPop is developed in Visual Basic 6.0 and uses Windows 95 and higher. WinPop 2.5 can be downloaded from http://www.genedrift.org/winpop.php.'),
(68322, 2005, 'bib', 'Detecting horizontal gene transfer with T-REX and RHOM programs', 'Li, Zuofeng|Wang, Li|Zhong, Yang', 'School of Life Sciences, Fudan University, Shanghai 200433, China and Shanghai Center for Bioinformation Technology, China.|School of Life Sciences, Fudan University, Shanghai 200433, China and Shanghai Center for Bioinformation Technology, China.|School of Life Sciences, Fudan University, Shanghai 200433, China and Shanghai Center for Bioinformation Technology, China.', 'China|China|China', 0, 'Algorithms|Animals|Chromosome Mapping|Computer Graphics|Conserved Sequence|Gene Transfer, Horizontal|Humans|Programming Languages|Sequence Alignment|Sequence Analysis, DNA|Sequence Homology, Nucleic Acid|Software|User-Computer Interface', 'Journal Article', '10.1093/bib/6.4.394', '16420738', 'As the Human Genome Project and other genome projects experience remarkable success and a flood of biological data is produced by means of high-throughout sequencing techniques, detection of horizontal gene transfer (HGT) becomes a promising field in bioinformatics. This review describes two freeware programs: T-REX for MS Windows and RHOM for Linux. T-REX is a graphical user interface program that offers functions to reconstruct the HGT network among the donor and receptor hosts from the gene and species distance matrices. RHOM is a set of command-line driven programs used to detect HGT in genomes. While T-REX impresses with a user-friendly interface and drawing of the reticulation network, the strength of RHOM is an extensive statistical framework of genome and the graphical display of the estimated sequence position probabilities for the candidate horizontally transferred genes.'),
(68323, 2006, 'bib', 'Statistical significance in biological sequence analysis', 'Mitrophanov, Alexander Yu|Borodovsky, Mark', 'School of Biology, Georgia Institute of Technology, Atlanta, GA 30332-0230, USA.|School of Biology, Georgia Institute of Technology, Atlanta, GA 30332-0230, USA.', 'Georgia|Georgia', 17, 'Computational Biology|Models, Genetic|Sequence Alignment|Sequence Analysis, DNA|Sequence Analysis, Protein', 'Journal Article', '10.1093/bib/bbk001', '16761361', 'One of the major goals of computational sequence analysis is to find sequence similarities, which could serve as evidence of structural and functional conservation, as well as of evolutionary relations among the sequences. Since the degree of similarity is usually assessed by the sequence alignment score, it is necessary to know if a score is high enough to indicate a biologically interesting alignment. A powerful approach to defining score cutoffs is based on the evaluation of the statistical significance of alignments. The statistical significance of an alignment score is frequently assessed by its P-value, which is the probability that this score or a higher one can occur simply by chance, given the probabilistic models for the sequences. In this review we discuss the general role of P-value estimation in sequence analysis, and give a description of theoretical methods and computational approaches to the estimation of statistical signifiance for important classes of sequence analysis problems. In particular, we concentrate on the P-value estimation techniques for single sequence studies (both score-based and score-free), global and local pairwise sequence alignments, multiple alignments, sequence-to-profile alignments and alignments built with hidden Markov models. We anticipate that the review will be useful both to researchers professionally working in bioinformatics as well as to biomedical scientists interested in using contemporary methods of DNA and protein sequence analysis.'),
(68324, 2006, 'bib', 'Estimation and control of multiple testing error rates for microarray studies', 'Pounds, Stanley B', 'Department of Biostatistics, MS 768, St Jude Childrens Research Hospital, Memphis, TN 38105, USA. stanley.pounds@stjude.org', 'United States', 31, 'Computational Biology|Gene Expression Profiling|Oligonucleotide Array Sequence Analysis|Protein Array Analysis|Reproducibility of Results', 'Journal Article', '10.1093/bib/bbk002', '16761362', 'The analysis of microarray data often involves performing a large number of statistical tests, usually at least one test per queried gene. Each test has a certain probability of reaching an incorrect inference, therefore, it is crucial to estimate or control error rates that measure the occurrence of erroneous conclusions in reporting and interpreting the results of a microarray study. In recent years, many innovative statistical methods have been developed to estimate or control various error rates for microarray studies. Researchers need guidance choosing the appropriate statistical methods for analysing these types of data sets. This review describes a family of methods that use a set of P-values to estimate or control the false discovery rate and similar error rates. Finally, these methods are classified in a manner that suggests the appropriate method for specific applications and diagnostic procedures that can identify problems in the analysis are described.'),
(68325, 2006, 'bib', 'Propagating uncertainty in microarray data analysis', 'Rattray, Magnus|Liu, Xuejun|Sanguinetti, Guido|Milo, Marta|Lawrence, Neil D', 'School of Computer Science, University of Manchester, Manchester M13 9PL, UK. magnus.rattary@manchester.ac.uk|School of Computer Science, University of Manchester, Manchester M13 9PL, UK. magnus.rattary@manchester.ac.uk|School of Computer Science, University of Manchester, Manchester M13 9PL, UK. magnus.rattary@manchester.ac.uk|School of Computer Science, University of Manchester, Manchester M13 9PL, UK. magnus.rattary@manchester.ac.uk|School of Computer Science, University of Manchester, Manchester M13 9PL, UK. magnus.rattary@manchester.ac.uk', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom', 21, 'Computational Biology|Oligonucleotide Array Sequence Analysis|Protein Array Analysis|Uncertainty', 'Journal Article', '10.1093/bib/bbk003', '16761363', 'Microarray technology is associated with many sources of experimental uncertainty. In this review we discuss a number of approaches for dealing with this uncertainty in the processing of data from microarray experiments. We focus here on the analysis of high-density oligonucleotide arrays, such as the popular Affymetrix GeneChip array, which contain multiple probes for each target. This set of probes can be used to determine an estimate for the target concentration and can also be used to determine the experimental uncertainty associated with this measurement. This measurement uncertainty can then be propagated through the downstream analysis using probabilistic methods. We give examples showing how these credibility intervals can be used to help identify differential expression, to combine information from replicated experiments and to improve the performance of principal component analysis.'),
(68326, 2006, 'bib', 'Studying statistical properties of regulatory DNA sequences, and their use in predicting regulatory regions in the eukaryotic genomes', 'Abnizova, Irina|Gilks, Walter R', 'MRC Biostatistics Unit. irina.abnizova@mrc-bsu.cam.ac.uk|MRC Biostatistics Unit. irina.abnizova@mrc-bsu.cam.ac.uk', '-|-', 3, 'Computational Biology|DNA|Genome|Predictive Value of Tests|Regulatory Sequences, Nucleic Acid|Sequence Analysis, DNA', 'Journal Article', '10.1093/bib/bbk004', '16761364', 'There are no well-known properties in regulatory DNA analogous to those in coding sequences, their spatial location is not regular, the consensus regulatory elements are often degenerate and there are no understandable rules governing their evolution. This makes it difficult to recognize regulatory regions within genome. We review developments in the statistical characterization of regulatory regions and methods of their recognition in eukaryotic genomes.'),
(68327, 2006, 'bib', 'Bioinformatics of alternative splicing and its regulation', 'Florea, Liliana', 'Department of Computer Science, George Washington University, Academic Center-Rm 714, Washington DC 20052, USA. florea@gwu.edu', 'United States', 23, 'Alternative Splicing|Computational Biology|Gene Expression Regulation|Regulatory Sequences, Nucleic Acid', 'Journal Article', '10.1093/bib/bbk005', '16761365', 'The sequencing of the human genome and ensuing wave of data generation have brought new light upon the extent and importance of alternative splicing as an RNA regulatory mechanism. Alternative splicing could potentially explain the complexity of protein repertoire during evolution, and defects in the splicing mechanism are responsible for diseases as complex as cancer. Among the challenges that rise in light of these discoveries are cataloguing splice variation in the human and other eukaryotic genomes, and identifying and characterizing the splicing regulatory elements that control their expression. Bioinformatics efforts tackling these two questions are just at the beginning. This article is a survey of these methods.'),
(68328, 2006, 'bib', 'Biological applications of the theory of birth-and-death processes', 'Novozhilov, Artem S|Karev, Georgy P|Koonin, Eugene V', 'National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Besthesda, MD 20894, USA.|National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Besthesda, MD 20894, USA.|National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Besthesda, MD 20894, USA.', 'United States|United States|United States', 76, 'Animals|Computational Biology|Computer Simulation|Evolution, Molecular|Humans|Models, Genetic', 'Journal Article', '10.1093/bib/bbk006', '16761366', 'In this review, we discuss applications of the theory of birth-and-death processes to problems in biology, primarily, those of evolutionary genomics. The mathematical principles of the theory of these processes are briefly described. Birth-and-death processes, with some straightforward additions such as innovation, are a simple, natural and formal framework for modeling a vast variety of biological processes such as population dynamics, speciation, genome evolution, including growth of paralogous gene families and horizontal gene transfer and somatic evolution of cancers. We further describe how empirical data, e.g. distributions of paralogous gene family size, can be used to choose the model that best reflects the actual course of evolution among different versions of birth-death-and-innovation models. We conclude that birth-and-death processes, thanks to their mathematical transparency, flexibility and relevance to fundamental biological processes, are going to be an indispensable mathematical tool for the burgeoning field of systems biology.'),
(68329, 2006, 'bib', 'Machine learning in bioinformatics', 'Larrañaga, Pedro|Calvo, Borja|Santana, Roberto|Bielza, Concha|Galdiano, Josu|Inza, Iñaki|Lozano, José A|Armañanzas, Rubén|Santafé, Guzmán|Pérez, Aritz|Robles, Victor', 'Intelligent Systems Group, Department of Computer Science and Artificial Intelligence, University of the Basque Country, Paseo Manuel de Lardizabal, 1, 20018 San Sebastian, Spain. pedro.larranaga@ehu.es|Intelligent Systems Group, Department of Computer Science and Artificial Intelligence, University of the Basque Country, Paseo Manuel de Lardizabal, 1, 20018 San Sebastian, Spain. pedro.larranaga@ehu.es|Intelligent Systems Group, Department of Computer Science and Artificial Intelligence, University of the Basque Country, Paseo Manuel de Lardizabal, 1, 20018 San Sebastian, Spain. pedro.larranaga@ehu.es|Intelligent Systems Group, Department of Computer Science and Artificial Intelligence, University of the Basque Country, Paseo Manuel de Lardizabal, 1, 20018 San Sebastian, Spain. pedro.larranaga@ehu.es|Intelligent Systems Group, Department of Computer Science and Artificial Intelligence, University of the Basque Country, Paseo Manuel de Lardizabal, 1, 20018 San Sebastian, Spain. pedro.larranaga@ehu.es|Intelligent Systems Group, Department of Computer Science and Artificial Intelligence, University of the Basque Country, Paseo Manuel de Lardizabal, 1, 20018 San Sebastian, Spain. pedro.larranaga@ehu.es|Intelligent Systems Group, Department of Computer Science and Artificial Intelligence, University of the Basque Country, Paseo Manuel de Lardizabal, 1, 20018 San Sebastian, Spain. pedro.larranaga@ehu.es|Intelligent Systems Group, Department of Computer Science and Artificial Intelligence, University of the Basque Country, Paseo Manuel de Lardizabal, 1, 20018 San Sebastian, Spain. pedro.larranaga@ehu.es|Intelligent Systems Group, Department of Computer Science and Artificial Intelligence, University of the Basque Country, Paseo Manuel de Lardizabal, 1, 20018 San Sebastian, Spain. pedro.larranaga@ehu.es|Intelligent Systems Group, Department of Computer Science and Artificial Intelligence, University of the Basque Country, Paseo Manuel de Lardizabal, 1, 20018 San Sebastian, Spain. pedro.larranaga@ehu.es|Intelligent Systems Group, Department of Computer Science and Artificial Intelligence, University of the Basque Country, Paseo Manuel de Lardizabal, 1, 20018 San Sebastian, Spain. pedro.larranaga@ehu.es', 'Spain|Spain|Spain|Spain|Spain|Spain|Spain|Spain|Spain|Spain|Spain', 94, 'Artificial Intelligence|Computational Biology|Genomics|Models, Theoretical|Proteomics', 'Journal Article', '10.1093/bib/bbk007', '16761367', 'This article reviews machine learning methods for bioinformatics. It presents modelling methods, such as supervised classification, clustering and probabilistic graphical models for knowledge discovery, as well as deterministic and stochastic heuristics for optimization. Applications in genomics, proteomics, systems biology, evolution and text mining are also shown.'),
(68330, 2006, 'bib', 'Pairwise sequence alignment--its all about us!', 'Mullan, Lisa', 'EMBL-European Bioinformatics Institute (EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK. lisa@ebi.ac.uk', 'United Kingdom', 4, 'Computational Biology|Sequence Alignment|Sequence Analysis, DNA|Sequence Analysis, Protein|Software', 'Journal Article', '10.1093/bib/bbk008', '16761368', '-'),
(68331, 2006, 'bib', 'Normalization and quantification of differential expression in gene expression microarrays', 'Steinhoff, Christine|Vingron, Martin', 'Max Planck Institute for Molecular Genetics, Department of Computational Molecular Biology, Ihnestr 73, D-14195 Berlin, Germany. steinhof@molgen.mpg.de|Max Planck Institute for Molecular Genetics, Department of Computational Molecular Biology, Ihnestr 73, D-14195 Berlin, Germany. steinhof@molgen.mpg.de', 'Germany|Germany', 59, 'Algorithms|Calibration|Data Interpretation, Statistical|Gene Expression Profiling|Guidelines as Topic|Internationality|Oligonucleotide Array Sequence Analysis|Reference Values', 'Journal Article', '10.1093/bib/bbl002', '16772260', 'Array-based gene expression studies frequently serve to identify genes that are expressed differently under two or more conditions. The actual analysis of the data, however, may be hampered by a number of technical and statistical problems. Possible remedies on the level of computational analysis lie in appropriate preprocessing steps, proper normalization of the data and application of statistical testing procedures in the derivation of differentially expressed genes. This review summarizes methods that are available for these purposes and provides a brief overview of the available software tools.'),
(68332, 2006, 'bib', 'Advances in the Exon-Intron Database (EID)', 'Shepelev, Valery|Fedorov, Alexei', 'Department of Medicine and Program in Bioinformatics and Proteomics/Genomics, Medical University of Ohio, Toledo OH 43614, USA.|Department of Medicine and Program in Bioinformatics and Proteomics/Genomics, Medical University of Ohio, Toledo OH 43614, USA.', 'United States|United States', 24, 'Base Sequence|Chromosome Mapping|DNA, Recombinant|Database Management Systems|Databases, Genetic|Documentation|Exons|Information Storage and Retrieval|Introns|Molecular Sequence Data|Sequence Alignment|Sequence Analysis, DNA|User-Computer Interface', 'Journal Article', '10.1093/bib/bbl003', '16772261', 'Investigation of exon-intron gene structures is a non-trivial task due to enormous expansions of the eukaryotic genomes, great variety of gene forms, and the imperfectness in sequence data. A number of available informational systems on various gene characteristics complement each other and are indispensable for many genomic studies. Among them, the Exon-Intron Database (EID) is a good choice for large-scale computational examination of exon/intron structure and splicing. It has many internal filters that control for sequence quality, consistency of gene descriptions, accordance to standards, and possible errors. New innovations in EID are described. The collection of exons and introns has been extended beyond coding regions and current versions of EID contain data on untranslated regions of gene sequences as well. Intron-less genes are included as a special part of EID. For species with entirely sequenced genomes, species-specific databases have been generated. A novel Mammalian Orthologous Intron Database (MOID) has been introduced which includes the full set of introns that come from orthologous genes that have the same positions relative to the reading frames. Examples of statistical analyses of gene sequences using EID are provided. We present the latest data on our comparison of intron positions in 11,025 orthologous genes of human, mouse and rat, and find no convincing cases of intron gain. We discuss relevant data-quality issues of genomic databases. In particular, 5% of genes in genomic databases contain internal stop codons. This fact is due to a combination of biological reasons and also to errors in sequence annotations. The EID is freely available at www.meduohio.edu/bioinfo/eid/.'),
(68333, 2006, 'bib', 'Computational aspects of systematic biology', 'Lilburn, Timothy G|Harrison, Scott H|Cole, James R|Garrity, George M', 'Department of Microbiology and Molecular Genetics at Michigan State University, East Lansing MI, USA.|Department of Microbiology and Molecular Genetics at Michigan State University, East Lansing MI, USA.|Department of Microbiology and Molecular Genetics at Michigan State University, East Lansing MI, USA.|Department of Microbiology and Molecular Genetics at Michigan State University, East Lansing MI, USA.', 'United States|United States|United States|United States', 1, 'Algorithms|Biological Evolution|Chromosome Mapping|Database Management Systems|Databases, Genetic|Genetics, Population|Information Storage and Retrieval|Models, Genetic|Phylogeny|Programming Languages|Systems Biology', 'Journal Article', '10.1093/bib/bbl005', '16772262', 'We review the resources available to systematic biologists who wish to use computers to build classifications. Algorithm development is in an early stage, and only a few examples of integrated applications for systematic biology are available. The availability of data is crucial if systematic biology is to enter the computer age.'),
(68334, 2006, 'bib', 'Metabolomics Standards Workshop and the development of international standards for reporting metabolomics experimental results', 'Castle, Arthur L|Fiehn, Oliver|Kaddurah-Daouk, Rima|Lindon, John C', 'Metabolomics and Informatics, National Institute of Diabetes & Digestive & Kidney Diseases, National Institutes of Health (NIH). 6707 Democracy Blvd, Room 791, MSC 5460, Bethesda, Maryland 20892-5460, USA. castlea@mail.nih.gov|Metabolomics and Informatics, National Institute of Diabetes & Digestive & Kidney Diseases, National Institutes of Health (NIH). 6707 Democracy Blvd, Room 791, MSC 5460, Bethesda, Maryland 20892-5460, USA. castlea@mail.nih.gov|Metabolomics and Informatics, National Institute of Diabetes & Digestive & Kidney Diseases, National Institutes of Health (NIH). 6707 Democracy Blvd, Room 791, MSC 5460, Bethesda, Maryland 20892-5460, USA. castlea@mail.nih.gov|Metabolomics and Informatics, National Institute of Diabetes & Digestive & Kidney Diseases, National Institutes of Health (NIH). 6707 Democracy Blvd, Room 791, MSC 5460, Bethesda, Maryland 20892-5460, USA. castlea@mail.nih.gov', 'United States|United States|United States|United States', 68, 'Cell Physiological Phenomena|Computational Biology|Documentation|Gene Expression Profiling|Guidelines as Topic|Information Dissemination|Information Storage and Retrieval|Internationality|Metabolism|Proteome|Proteomics|Signal Transduction', 'Consensus Development Conference, NIH', '10.1093/bib/bbl008', '16772263', 'Informatics standards and controlled vocabularies are essential for allowing information technology to help exchange, manage, interpret and compare large data collections. In a rapidly evolving field, the challenge is to work out how best to describe, but not prescribe, the use of these technologies and methods. A Metabolomics Standards Workshop was held by the US National Institutes of Health (NIH) to bring together multiple ongoing standards efforts in metabolomics with the NIH research community. The goals were to discuss metabolomics workflows (methods, technologies and data treatments) and the needs, challenges and potential approaches to developing a Metabolomics Standards Initiative that will help facilitate this rapidly growing field which has been a focus of the NIH roadmap effort. This report highlights specific aspects of what was presented and discussed at the 1st and 2nd August 2005 Metabolomics Standards Workshop.'),
(68335, 2006, 'bib', 'Flux balance analysis in the era of metabolomics', 'Lee, Jong Min|Gianchandani, Erwin P|Papin, Jason A', 'Department of Biomedical Engineering, University of Virginia, PO Box 800759, Charlottesville, VA 22908, USA.|Department of Biomedical Engineering, University of Virginia, PO Box 800759, Charlottesville, VA 22908, USA.|Department of Biomedical Engineering, University of Virginia, PO Box 800759, Charlottesville, VA 22908, USA.', 'United States|United States|United States', 71, 'Animals|Biotechnology|Cell Physiological Phenomena|Computational Biology|Energy Metabolism|Gene Expression Profiling|Gene Expression Regulation|Humans|Proteome|Proteomics|Signal Transduction', 'Journal Article', '10.1093/bib/bbl007', '16772264', 'Flux balance analysis (FBA) has emerged as an effective means to analyse biological networks in a quantitative manner. Much progress has been made on the extension of FBA to incorporate a priori biological knowledge, provide more practical descriptions of observed cell behaviours, and predict the outcome of network perturbations. Metabolomics is independently advancing as a set of high-throughput data acquisition tools providing dynamic profiles of metabolites in an unbiased manner. These data sets are neither yet sufficiently comprehensive nor accurate enough for generating large-scale kinetic models. Thus, there is a pressing need to develop quantitative techniques that can make use of the emerging data and embrace the associated uncertainties. This article reviews recent advances in FBA to meet this need and discusses the utility of FBA as a complement to metabolomics and the expected synergy as a result of combining these two techniques.'),
(68336, 2006, 'bib', 'Review: on the analysis and interpretation of correlations in metabolomic data', 'Steuer, Ralf', 'University Potsdam, Insitute of Physics, Am Neuen Palais 10, Haus 19, 14469 Potsdam, Germany. steuer@agnld.uni-potsdam.de', 'Germany', 150, 'Animals|Cell Physiological Phenomena|Computational Biology|Databases, Protein|Gene Expression Profiling|Gene Expression Regulation|Humans|Metabolism|Models, Biological|Proteome|Proteomics|Signal Transduction|Statistics as Topic', 'Journal Article', '10.1093/bib/bbl009', '16772265', 'A remarkable inherent feature of cellular metabolism is that the concentrations of a small but significant number of metabolites are strongly correlated when measurements of biological replicates are performed. This review seeks to summarize the recent efforts to elucidate the origin of these observed correlations and points out several aspects concerning their interpretation. It is argued that correlations between metabolites differ profoundly from their transcriptomic and proteomic counterparts, and a straightforward interpretation in terms of the underlying biochemical pathways will unavoidably fail. It is demonstrated that the comparative correlations analysis offers a way to exploit the observed correlations to obtain additional information about the physiological state of the system.'),
(68337, 2006, 'bib', 'Metabolomics technology and bioinformatics', 'Shulaev, Vladimir', 'Virginia Bioinformatics Institute, Washington Street, Blacksburg, VA 24061, USA. vshulaev@vbi.vt.edu', 'United States', 239, 'Animals|Biotechnology|Cell Physiological Phenomena|Computational Biology|Gene Expression Profiling|Gene Expression Regulation|Humans|Metabolism|Proteome|Proteomics|Signal Transduction', 'Journal Article', '10.1093/bib/bbl012', '16772266', 'Metabolomics is the global analysis of all or a large number of cellular metabolites. Like other functional genomics research, metabolomics generates large amounts of data. Handling, processing and analysis of this data is a clear challenge and requires specialized mathematical, statistical and bioinformatics tools. Metabolomics needs for bioinformatics span through data and information management, raw analytical data processing, metabolomics standards and ontology, statistical analysis and data mining, data integration and mathematical modelling of metabolic networks within a framework of systems biology. The major approaches in metabolomics, along with the modern analytical tools used for data generation, are reviewed in the context of these specific bioinformatics needs.'),
(68338, 2006, 'bib', 'Automated protein function prediction--the genomic challenge', 'Friedberg, Iddo', 'Burnham Institute for Medical Research, Program in Bioinformatics and Systems Biology, La Jolla, CA 92037, USA. idoerg@burnham.org', 'United States', 241, 'Algorithms|Animals|Computational Biology|Databases, Protein|Electronic Data Processing|Genomics|Humans|Pattern Recognition, Automated|Proteins', 'Journal Article', '10.1093/bib/bbl004', '16772267', 'Overwhelmed with genomic data, biologists are facing the first big post-genomic question--what do all genes do? First, not only is the volume of pure sequence and structure data growing, but its diversity is growing as well, leading to a disproportionate growth in the number of uncharacterized gene products. Consequently, established methods of gene and protein annotation, such as homology-based transfer, are annotating less data and in many cases are amplifying existing erroneous annotation. Second, there is a need for a functional annotation which is standardized and machine readable so that function prediction programs could be incorporated into larger workflows. This is problematic due to the subjective and contextual definition of protein function. Third, there is a need to assess the quality of function predictors. Again, the subjectivity of the term function and the various aspects of biological function make this a challenging effort. This article briefly outlines the history of automated protein function prediction and surveys the latest innovations in all three topics.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(68339, 2007, 'bib', 'A hitchhikers guide to expressed sequence tag (EST) analysis', 'Nagaraj, Shivashankar H|Gasser, Robin B|Ranganathan, Shoba', 'Department of Chemistry and Biomolecular Sciences & Biotechnology Research Institute, Macquarie University, Sydney, Australia.|Department of Chemistry and Biomolecular Sciences & Biotechnology Research Institute, Macquarie University, Sydney, Australia.|Department of Chemistry and Biomolecular Sciences & Biotechnology Research Institute, Macquarie University, Sydney, Australia.', 'Australia|Australia|Australia', 193, 'Animals|Consensus Sequence|DNA, Complementary|Databases, Genetic|Databases, Nucleic Acid|Expressed Sequence Tags|Genomics|Humans|Software|Untranslated Regions', 'Journal Article', '10.1093/bib/bbl015', '16772268', 'Expressed sequence tag (EST) sequencing projects are underway for numerous organisms, generating millions of short, single-pass nucleotide sequence reads, accumulating in EST databases. Extensive computational strategies have been developed to organize and analyse both small- and large-scale EST data for gene discovery, transcript and single nucleotide polymorphism analysis as well as functional annotation of putative gene products. We provide an overview of the significance of ESTs in the genomic era, their properties and the applications of ESTs. Methods adopted for each step of EST analysis by various research groups have been compared. Challenges that lie ahead in organizing and analysing the ever increasing EST data have also been identified. The most appropriate software tools for EST pre-processing, clustering and assembly, database matching and functional annotation have been compiled (available online from http://biolinfo.org/EST). We propose a road map for EST analysis to accelerate the effective analyses of EST data sets. An investigation of EST analysis platforms reveals that they all terminate prior to downstream functional annotation including gene ontologies, motif/pattern analysis and pathway mapping.'),
(68340, 2007, 'bib', 'Partial least squares: a versatile tool for the analysis of high-dimensional genomic data', 'Boulesteix, Anne-Laure|Strimmer, Korbinian', 'Department of Medical Statistics and Epidemiology, Technical University of Munich, Ismaningerstrasse 22, D-81675 Munich, Germany. anne-laure.boulesteix@tum.de|Department of Medical Statistics and Epidemiology, Technical University of Munich, Ismaningerstrasse 22, D-81675 Munich, Germany. anne-laure.boulesteix@tum.de', 'Germany|Germany', 423, 'Animals|Computational Biology|Data Interpretation, Statistical|Genomics|Humans|Least-Squares Analysis|Models, Statistical|Multivariate Analysis|Software|Survival Analysis', 'Journal Article', '10.1093/bib/bbl016', '16772269', 'Partial least squares (PLS) is an efficient statistical regression technique that is highly suited for the analysis of genomic and proteomic data. In this article, we review both the theory underlying PLS as well as a host of bioinformatics applications of PLS. In particular, we provide a systematic comparison of the PLS approaches currently employed, and discuss analysis problems as diverse as, e.g. tumor classification from transcriptome data, identification of relevant genes, survival analysis and modeling of gene networks and transcription factor activities.'),
(68341, 2007, 'bib', 'Agents in bioinformatics, computational and systems biology', 'Merelli, Emanuela|Armano, Giuliano|Cannata, Nicola|Corradini, Flavio|dInverno, Mark|Doms, Andreas|Lord, Phillip|Martin, Andrew|Milanesi, Luciano|Möller, Steffen|Schroeder, Michael|Luck, Michael', 'Dipartimento di Matematica e Informatica Via Madonna delle Carceri 9 62032 Camerino, Italy. emanuela.merelli@unicam.it|Dipartimento di Matematica e Informatica Via Madonna delle Carceri 9 62032 Camerino, Italy. emanuela.merelli@unicam.it|Dipartimento di Matematica e Informatica Via Madonna delle Carceri 9 62032 Camerino, Italy. emanuela.merelli@unicam.it|Dipartimento di Matematica e Informatica Via Madonna delle Carceri 9 62032 Camerino, Italy. emanuela.merelli@unicam.it|Dipartimento di Matematica e Informatica Via Madonna delle Carceri 9 62032 Camerino, Italy. emanuela.merelli@unicam.it|Dipartimento di Matematica e Informatica Via Madonna delle Carceri 9 62032 Camerino, Italy. emanuela.merelli@unicam.it|Dipartimento di Matematica e Informatica Via Madonna delle Carceri 9 62032 Camerino, Italy. emanuela.merelli@unicam.it|Dipartimento di Matematica e Informatica Via Madonna delle Carceri 9 62032 Camerino, Italy. emanuela.merelli@unicam.it|Dipartimento di Matematica e Informatica Via Madonna delle Carceri 9 62032 Camerino, Italy. emanuela.merelli@unicam.it|Dipartimento di Matematica e Informatica Via Madonna delle Carceri 9 62032 Camerino, Italy. emanuela.merelli@unicam.it|Dipartimento di Matematica e Informatica Via Madonna delle Carceri 9 62032 Camerino, Italy. emanuela.merelli@unicam.it|Dipartimento di Matematica e Informatica Via Madonna delle Carceri 9 62032 Camerino, Italy. emanuela.merelli@unicam.it', 'Italy|Italy|Italy|Italy|Italy|Italy|Italy|Italy|Italy|Italy|Italy|Italy', 70, 'Artificial Intelligence|Computational Biology|Genetic Diseases, Inborn|Humans|Information Management|Models, Biological|Protein Structure, Secondary|Semantics|Software|Stem Cells|Systems Biology', 'Congress', '10.1093/bib/bbl014', '16772270', 'The adoption of agent technologies and multi-agent systems constitutes an emerging area in bioinformatics. In this article, we report on the activity of the Working Group on Agents in Bioinformatics (BIOAGENTS) founded during the first AgentLink III Technical Forum meeting on the 2nd of July, 2004, in Rome. The meeting provided an opportunity for seeding collaborations between the agent and bioinformatics communities to develop a different (agent-based) approach of computational frameworks both for data analysis and management in bioinformatics and for systems modelling and simulation in computational and systems biology. The collaborations gave rise to applications and integrated tools that we summarize and discuss in context of the state of the art in this area. We investigate on future challenges and argue that the field should still be explored from many perspectives ranging from bio-conceptual languages for agent-based simulation, to the definition of bio-ontology-based declarative languages to be used by information agents, and to the adoption of agents for computational grids.'),
(68342, 2006, 'bib', 'Navigating the HapMap', 'Barnes, Michael R', 'Molecular Genetic Informatics, Discovery and Analysis Bioinformatics, GlaxoSmithKline Pharmaceuticals, Harlow, Essex, UK. Michael.R.Barnes@gsk.com', 'United Kingdom', 17, 'Alleles|Chromosome Mapping|Databases, Genetic|Genome, Human|Haplotypes|Humans|Internet|Linkage Disequilibrium|Polymorphism, Single Nucleotide|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bib/bbl021', '16877472', 'With the availability of the HapMap--a resource which describes common patterns of linkage disequilibrium (LD) in four different human population samples, we now have a powerful tool to help dissect the role of genetic variation in the biology of the genome. HapMap is entirely complimentary to the human genome map and so it is particularly fitting that it should be viewed in a full genomic context. However, characterization of high resolution LD across the genome can be a challenging task, owing in part to the sheer volume of data and the inherent dimensionality that its analysis entails. However, a number of tools are now available to make this task easier for researchers. This review will examine tools for viewing and analysing haplotype and LD data, enabling a number of tasks, including identification of optimal sets of haplotype tagging single nucleotide polymorphisms (SNPs), drawing links between associated SNPs and putative causal alleles, or simply viewing LD and haplotypes across a gene or region of interest. The data generated by the HapMap also has other important applications, informing, for example, on the demographic history and evidence of selection in human populations and on previously undetected regulatory relationships and gene networks. All of these properties make the HapMap no less an important resource than the human genome sequence itself and so this makes it essential viewing for all in the field of human biology.'),
(68343, 2006, 'bib', 'Graph-based methods for analysing networks in cell biology', 'Aittokallio, Tero|Schwikowski, Benno', 'Systems Biology Group, Institut Pasteur, 25-28 Rue du Dr Roux, FR-75724 Paris, France. teanai@pasteur.fr|Systems Biology Group, Institut Pasteur, 25-28 Rue du Dr Roux, FR-75724 Paris, France. teanai@pasteur.fr', 'France|France', 256, 'Artificial Intelligence|Cluster Analysis|Computational Biology|Computer Simulation|Databases, Protein|Models, Biological|Pattern Recognition, Automated|Software', 'Journal Article', '10.1093/bib/bbl022', '16880171', 'Availability of large-scale experimental data for cell biology is enabling computational methods to systematically model the behaviour of cellular networks. This review surveys the recent advances in the field of graph-driven methods for analysing complex cellular networks. The methods are outlined on three levels of increasing complexity, ranging from methods that can characterize global or local structural properties of networks to methods that can detect groups of interconnected nodes, called motifs or clusters, potentially involved in common elementary biological functions. We also briefly summarize recent approaches to data integration and network inference through graph-based formalisms. Finally, we highlight some challenges in the field and offer our personal view of the key future trends and developments in graph-based analysis of large-scale datasets.'),
(68344, 2007, 'bib', 'Statistically designing microarrays and microarray experiments to enhance sensitivity and specificity', 'Hsu, Jason C|Chang, Jane|Wang, Tao|Steingrímsson, Eiríkur|Magnússon, Magnús Karl|Bergsteinsdottir, Kristin', 'Department of Statistics, The Ohio State University, 1958 Neil Avenue Columbus, Ohio 43210, USA. Hsu.1@osu.edu|Department of Statistics, The Ohio State University, 1958 Neil Avenue Columbus, Ohio 43210, USA. Hsu.1@osu.edu|Department of Statistics, The Ohio State University, 1958 Neil Avenue Columbus, Ohio 43210, USA. Hsu.1@osu.edu|Department of Statistics, The Ohio State University, 1958 Neil Avenue Columbus, Ohio 43210, USA. Hsu.1@osu.edu|Department of Statistics, The Ohio State University, 1958 Neil Avenue Columbus, Ohio 43210, USA. Hsu.1@osu.edu|Department of Statistics, The Ohio State University, 1958 Neil Avenue Columbus, Ohio 43210, USA. Hsu.1@osu.edu', 'United States|United States|United States|United States|United States|United States', 10, 'Breast Neoplasms|Female|Gene Expression Profiling|Humans|Middle Aged|Models, Statistical|Oligonucleotide Array Sequence Analysis|Random Allocation|Reproducibility of Results|Research Design|Sensitivity and Specificity', 'Journal Article', '10.1093/bib/bbl023', '16899493', 'Gene expression signatures from microarray experiments promise to provide important prognostic tools for predicting disease outcome or response to treatment. A number of microarray studies in various cancers have reported such gene signatures. However, the overlap of gene signatures in the same disease has been limited so far, and some reported signatures have not been reproduced in other populations. Clearly, the methods used for verifying novel gene signatures need improvement. In this article, we describe an experiment in which microarrays and sample hybridization are designed according to the statistical principles of randomization, replication and blocking. Our results show that such designs provide unbiased estimation of differential expression levels as well as powerful tests for them.'),
(68345, 2006, 'bib', 'Open source tools and toolkits for bioinformatics: significance, and where are we?', 'Stajich, Jason E|Lapp, Hilmar', 'Department of Plant and Microbial Biology, University of California Berkeley, Berkeley, CA 94720, USA. jason.stajich@duke.edu|Department of Plant and Microbial Biology, University of California Berkeley, Berkeley, CA 94720, USA. jason.stajich@duke.edu', 'United States|United States', 32, 'Algorithms|Computational Biology|Databases, Factual|Software', 'Journal Article', '10.1093/bib/bbl026', '16899494', 'This review summarizes important work in open-source bioinformatics software that has occurred over the past couple of years. The survey is intended to illustrate how programs and toolkits whose source code has been developed or released under an Open Source license have changed informatics-heavy areas of life science research. Rather than creating a comprehensive list of all tools developed over the last 2-3 years, we use a few selected projects encompassing toolkit libraries, analysis tools, data analysis environments and interoperability standards to show how freely available and modifiable open-source software can serve as the foundation for building important applications, analysis workflows and resources.'),
(68346, 2006, 'bib', 'Bio-ontologies: current trends and future directions', 'Bodenreider, Olivier|Stevens, Robert', 'National Library of Medicine, Bethesda, MD 20894, USA. olivier@nlm.nih.gov|National Library of Medicine, Bethesda, MD 20894, USA. olivier@nlm.nih.gov', 'United States|United States', 249, 'Animals|Artificial Intelligence|Genomics|Humans|Natural Language Processing|Systems Biology|Vocabulary, Controlled', 'Journal Article', '10.1093/bib/bbl027', '16899495', 'In recent years, as a knowledge-based discipline, bioinformatics has been made more computationally amenable. After its beginnings as a technology advocated by computer scientists to overcome problems of heterogeneity, ontology has been taken up by biologists themselves as a means to consistently annotate features from genotype to phenotype. In medical informatics, artifacts called ontologies have been used for a longer period of time to produce controlled lexicons for coding schemes. In this article, we review the current position in ontologies and how they have become institutionalized within biomedicine. As the field has matured, the much older philosophical aspects of ontology have come into play. With this and the institutionalization of ontology has come greater formality. We review this trend and what benefits it might bring to ontologies and their use within biomedicine.'),
(68347, 2006, 'bib', 'The Life Sciences Semantic Web is full of creeps!', 'Good, Benjamin M|Wilkinson, Mark D', 'James Hogg iCAPTURE Centre for Cardiovascular and Pulmonary Research, Vancouver, BC.|James Hogg iCAPTURE Centre for Cardiovascular and Pulmonary Research, Vancouver, BC.', '-|-', 67, 'Computational Biology|Databases, Factual|Internet|Semantics|User-Computer Interface', 'Journal Article', '10.1093/bib/bbl025', '16899496', 'The Semantic Web for the Life Sciences (SWLS), when realized, will dramatically improve our ability to conduct bioinformatics analyses using the vast and growing stores of web-accessible resources. This ability will be achieved through the widespread acceptance and application of standards for naming, representing, describing and accessing biological information. The W3C-led Semantic Web initiative has established most, if not all, of the standards and technologies needed to achieve a unified, global SWLS. Unfortunately, the bioinformatics community has, thus far, appeared reluctant to fully adopt them. Rather, we are seeing what could be described as semantic creep-timid, piecemeal and ad hoc adoption of parts of standards by groups that should be stridently taking a leadership role for the community. We suggest that, at this point, the primary hindrances to the creation of the SWLS may be social rather than technological in nature, and that, like the original Web, the establishment of the SWLS will depend primarily on the will and participation of its consumers.'),
(68348, 2006, 'bib', 'Metabolomics and the challenges ahead', 'Mendes, Pedro', 'James Hogg iCAPTURE Centre for Cardiovascular and Pulmonary Research, Vancouver, BC.', '-', 11, 'Animals|Cell Physiological Phenomena|Gene Expression Profiling|Gene Expression Regulation|Humans|Metabolism|Models, Biological|Oligonucleotide Array Sequence Analysis|Proteome|Proteomics|Signal Transduction', 'Editorial', '10.1093/bib/bbl010', '16914436', '-'),
(68349, 2007, 'bib', 'HCOP: a searchable database of human orthology predictions', 'Eyre, Tina A|Wright, Mathew W|Lush, Michael J|Bruford, Elspeth A', 'HUGO Gene Nomenclature Committee (HGNC), Department of Biology, University College London, Wolfson House, 4 Stephenson Way, London.|HUGO Gene Nomenclature Committee (HGNC), Department of Biology, University College London, Wolfson House, 4 Stephenson Way, London.|HUGO Gene Nomenclature Committee (HGNC), Department of Biology, University College London, Wolfson House, 4 Stephenson Way, London.|HUGO Gene Nomenclature Committee (HGNC), Department of Biology, University College London, Wolfson House, 4 Stephenson Way, London.', '-|-|-|-', 48, 'Databases, Genetic|Evolution, Molecular|Genes|Genomics|Humans|Sequence Homology, Nucleic Acid|Terminology as Topic', 'Journal Article', '10.1093/bib/bbl030', '16951416', 'The HUGO Gene Nomenclature Committee (HGNC) Comparison of Orthology Predictions (HCOP) search tool combines the human, mouse, rat and chicken orthology assertions made by PhIGs, HomoloGene, Ensembl, Inparanoid, Mouse Genome Informatics (MGI) and HGNC, enabling users to identify predicted ortholog pairs for a specified gene or genes. The HCOP resource provides a useful method to integrate, compare and access a variety of disparate sources of human orthology data. The HCOP search tool, data and documentation are available at http://www.gene.ucl.ac.uk/hcop.'),
(68350, 2006, 'bib', 'Statistical methods in genetics', 'Montana, Giovanni', 'Department of Mathematics, Imperial College London, London, UK. g.montana@imperial.ac.uk', 'United Kingdom', 4, 'Genetic Markers|Genetic Predisposition to Disease|Genetic Variation|Genotype|Humans|Linkage Disequilibrium|Models, Genetic|Models, Statistical', 'Journal Article', '10.1093/bib/bbl028', '16963464', 'In recent years, a very large variety of statistical methodologies, at various levels of complexity, have been put forward to analyse genotype data and detect genetic variations that may be responsible for increasing the susceptibility to disease. This review provides a concise account of a number of selected statistical methods for population-based association mapping, from single-marker tests of association to multi-marker data mining techniques for gene-gene interaction detection.'),
(68351, 2006, 'bib', 'Advanced computing for systems biology', 'Burrage, Kevin|Hood, Lindsay|Ragan, Mark A', 'ARC Centre in Bioinformatics and Institute for Molecular Bioscience, The University of Queensland, Brisbane 4072, Australia.|ARC Centre in Bioinformatics and Institute for Molecular Bioscience, The University of Queensland, Brisbane 4072, Australia.|ARC Centre in Bioinformatics and Institute for Molecular Bioscience, The University of Queensland, Brisbane 4072, Australia.', 'Australia|Australia|Australia', 9, 'Animals|Cells|Computational Biology|Computer Simulation|Genomics|Metabolic Networks and Pathways|Models, Biological|Systems Biology', 'Journal Article', '10.1093/bib/bbl033', '17000735', 'Systems biology is based on computational modelling and simulation of large networks of interacting components. Models may be intended to capture processes, mechanisms, components and interactions at different levels of fidelity. Input data are often large and geographically disperse, and may require the computation to be moved to the data, not vice versa. In addition, complex system-level problems require collaboration across institutions and disciplines. Grid computing can offer robust, scaleable solutions for distributed data, compute and expertise. We illustrate some of the range of computational and data requirements in systems biology with three case studies: one requiring large computation but small data (orthologue mapping in comparative genomics), a second involving complex terabyte data (the Visible Cell project) and a third that is both computationally and data-intensive (simulations at multiple temporal and spatial scales). Authentication, authorisation and audit systems are currently not well scalable and may present bottlenecks for distributed collaboration particularly where outcomes may be commercialised. Challenges remain in providing lightweight standards to facilitate the penetration of robust, scalable grid-type computing into diverse user communities to meet the evolving demands of systems biology.'),
(68352, 2006, 'bib', 'Strategies for dealing with incomplete information in the modeling of molecular interaction networks', 'de Jong, Hidde|Ropers, Delphine', 'INRIA Rhône-Alpes, 655 avenue de lEurope, Montbonnot, 38334 Saint Ismier Cedex, France. Hidde.de-Jong@inrialpes.fr|INRIA Rhône-Alpes, 655 avenue de lEurope, Montbonnot, 38334 Saint Ismier Cedex, France. Hidde.de-Jong@inrialpes.fr', 'France|France', 3, 'Animals|Computer Simulation|Gene Expression Regulation|Models, Biological|Models, Genetic', 'Journal Article', '10.1093/bib/bbl034', '17003073', 'Modelers of molecular interaction networks encounter the paradoxical situation that while large amounts of data are available, these are often insufficient for the formulation and analysis of mathematical models describing the network dynamics. In particular, information on the reaction mechanisms and numerical values of kinetic parameters are usually not available for all but a few well-studied model systems. In this article we review two strategies that have been proposed for dealing with incomplete information in the study of molecular interaction networks: parameter sensitivity analysis and model simplification. These strategies are based on the biologically justified intuition that essential properties of the system dynamics are robust against moderate changes in the value of kinetic parameters or even in the rate laws describing the interactions. Although advanced measurement techniques can be expected to relieve the problem of incomplete information to some extent, the strategies discussed in this article will retain their interest as tools providing an initial characterization of essential properties of the network dynamics.'),
(68353, 2007, 'bib', 'The WWWH of remote homolog detection: the state of the art', 'Fariselli, Piero|Rossi, Ivan|Capriotti, Emidio|Casadio, Rita', 'Biocomputing Group, Department of Biology, University of Bologna, via Irnerio 42, 40126 Bologna, Italy. piero@biocomp.unibo.it|Biocomputing Group, Department of Biology, University of Bologna, via Irnerio 42, 40126 Bologna, Italy. piero@biocomp.unibo.it|Biocomputing Group, Department of Biology, University of Bologna, via Irnerio 42, 40126 Bologna, Italy. piero@biocomp.unibo.it|Biocomputing Group, Department of Biology, University of Bologna, via Irnerio 42, 40126 Bologna, Italy. piero@biocomp.unibo.it', 'Italy|Italy|Italy|Italy', 27, 'Algorithms|Amino Acid Sequence|Conserved Sequence|Molecular Sequence Data|Proteins|Review Literature as Topic|Sequence Alignment|Sequence Analysis, Protein|Sequence Homology, Amino Acid', 'Journal Article', '10.1093/bib/bbl032', '17003074', 'The detection of remote homolog pairs of proteins using computational methods is a pivotal problem in structural bioinformatics, aiming to compute protein folds on the basis of information in the database of known structures. In the last 25 years, several methods have been developed to tackle this problem, based on different approaches including sequence-sequence alignments and/or structure comparison. In this article, we will briefly discuss When, Why, Where and How (WWWH) to perform remote homology search, reviewing some of the most widely adopted computational approaches. The specific aim is highlighting the basic criteria implemented by different research groups and commenting on the status of the art as well as on still-open questions.'),
(68354, 2006, 'bib', 'Mining literature for systems biology', 'Roberts, Phoebe M', 'Biogen Idec Inc., 14 Cambridge Center, Cambridge MA 02142, USA. Phoebe.Roberts@biogenidec.com', 'United States', 13, 'Animals|Databases as Topic|Databases, Genetic|Humans|Information Storage and Retrieval|Medical Subject Headings|Periodicals as Topic|Systems Biology|Vocabulary, Controlled', 'Journal Article', '10.1093/bib/bbl037', '17032698', 'Currently, literature is integrated in systems biology studies in three ways. Hand-curated pathways have been sufficient for assembling models in numerous studies. Second, literature is frequently accessed in a derived form, such as the concepts represented by the Medical Subject Headings (MeSH) and Gene Ontologies (GO), or functional relationships captured in protein-protein interaction (PPI) databases, both of these are convenient, consistent reductions of more complex concepts expressed as free text in the literature. Moreover, their contents are easily integrated into computational processes required for dealing with large data sets. Last, mining text directly for specific types of information is on the rise as text analytics methods become more accurate and accessible. These uses of literature, specifically manual curation, derived concepts captured in ontologies and databases, and indirect and direct application of text mining, will be discussed as they pertain to systems biology.'),
(68355, 2006, 'bib', 'Resources for integrative systems biology: from data through databases to networks and dynamic system models', 'Ng, Aylwin|Bursteinas, Borisas|Gao, Qiong|Mollison, Ewan|Zvelebil, Marketa', 'Bioinformatics and Systems Biology Group, Ludwig Institute for Cancer Research, University College London Branch, 91 Riding House Street, London W1W 7BS, UK.|Bioinformatics and Systems Biology Group, Ludwig Institute for Cancer Research, University College London Branch, 91 Riding House Street, London W1W 7BS, UK.|Bioinformatics and Systems Biology Group, Ludwig Institute for Cancer Research, University College London Branch, 91 Riding House Street, London W1W 7BS, UK.|Bioinformatics and Systems Biology Group, Ludwig Institute for Cancer Research, University College London Branch, 91 Riding House Street, London W1W 7BS, UK.|Bioinformatics and Systems Biology Group, Ludwig Institute for Cancer Research, University College London Branch, 91 Riding House Street, London W1W 7BS, UK.', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom', 68, 'Animals|Computer Simulation|Data Collection|Databases, Genetic|Gene Expression|Genomics|Humans|Metabolic Networks and Pathways|Models, Biological|Models, Molecular|Phenotype|Proteomics|Software|Systems Biology', 'Journal Article', '10.1093/bib/bbl036', '17040977', 'In systems biology, biologically relevant quantitative modelling of physiological processes requires the integration of experimental data from diverse sources. Recent developments in high-throughput methodologies enable the analysis of the transcriptome, proteome, interactome, metabolome and phenome on a previously unprecedented scale, thus contributing to the deluge of experimental data held in numerous public databases. In this review, we describe some of the databases and simulation tools that are relevant to systems biology and discuss a number of key issues affecting data integration and the challenges these pose to systems-level research.'),
(68356, 2007, 'bib', 'Methods and protocols for prediction of immunogenic epitopes', 'Tong, Joo Chuan|Tan, Tin Wee|Ranganathan, Shoba', 'Institute of Infocomm Research, Singapore.|Institute of Infocomm Research, Singapore.|Institute of Infocomm Research, Singapore.', 'Singapore|Singapore|Singapore', 78, 'Algorithms|Antigen-Antibody Complex|Binding Sites|Epitope Mapping|Epitopes, T-Lymphocyte|HLA Antigens|Peptides|Protein Binding|Protein Interaction Mapping|Sequence Analysis, Protein', 'Journal Article', '10.1093/bib/bbl038', '17077136', 'T-cell recognition of peptide/major histocompatibility complex (MHC) is a prerequisite for cellular immunity. Recently, there has been an influx of bioinformatics tools to facilitate the identification of T-cell epitopes to specific MHC alleles. This article examines existing computational strategies for the study of peptide/MHC interactions. The most important bioinformatics tools and methods with relevance to the study of peptide/MHC interactions have been reviewed. We have also provided guidelines for predicting antigenic peptides based on the availability of existing experimental data.'),
(68357, 2007, 'bib', 'Enrichment analysis in high-throughput genomics - accounting for dependency in the NULL', 'Gold, David L|Coombes, Kevin R|Wang, Jing|Mallick, Bani', 'Department of Statistics, Texas A&M University, 3134 TAMU, College Statio, TX 77843-3143, USA. dlgold@tamu.edu|Department of Statistics, Texas A&M University, 3134 TAMU, College Statio, TX 77843-3143, USA. dlgold@tamu.edu|Department of Statistics, Texas A&M University, 3134 TAMU, College Statio, TX 77843-3143, USA. dlgold@tamu.edu|Department of Statistics, Texas A&M University, 3134 TAMU, College Statio, TX 77843-3143, USA. dlgold@tamu.edu', 'United States|United States|United States|United States', 14, 'Data Interpretation, Statistical|Databases, Protein|Gene Expression Profiling|Genomics|Information Storage and Retrieval|Protein Interaction Mapping|Proteome|Signal Transduction', 'Journal Article', '10.1093/bib/bbl019', '17077137', 'Translating the overwhelming amount of data generated in high-throughput genomics experiments into biologically meaningful evidence, which may for example point to a series of biomarkers or hint at a relevant pathway, is a matter of great interest in bioinformatics these days. Genes showing similar experimental profiles, it is hypothesized, share biological mechanisms that if understood could provide clues to the molecular processes leading to pathological events. It is the topic of further study to learn if or how a priori information about the known genes may serve to explain coexpression. One popular method of knowledge discovery in high-throughput genomics experiments, enrichment analysis (EA), seeks to infer if an interesting collection of genes is enriched for a Consortium particular set of a priori Gene Ontology Consortium (GO) classes. For the purposes of statistical testing, the conventional methods offered in EA software implicitly assume independence between the GO classes. Genes may be annotated for more than one biological classification, and therefore the resulting test statistics of enrichment between GO classes can be highly dependent if the overlapping gene sets are relatively large. There is a need to formally determine if conventional EA results are robust to the independence assumption. We derive the exact null distribution for testing enrichment of GO classes by relaxing the independence assumption using well-known statistical theory. In applications with publicly available data sets, our test results are similar to the conventional approach which assumes independence. We argue that the independence assumption is not detrimental.'),
(68358, 2007, 'bib', 'Evolving research trends in bioinformatics', 'Perez-Iratxeta, Carolina|Andrade-Navarro, Miguel A|Wren, Jonathan D', 'Ottawa Health Research Institute, Ontario, Canada.|Ottawa Health Research Institute, Ontario, Canada.|Ottawa Health Research Institute, Ontario, Canada.', 'Canada|Canada|Canada', 31, 'Computational Biology|Forecasting|Genomics|MEDLINE|Periodicals as Topic|Research', 'Journal Article', '10.1093/bib/bbl035', '17077138', 'The cross-disciplinary nature of bioinformatics entails co-evolution with other biomedical disciplines, whereby some bioinformatics applications become popular in certain disciplines and, in turn, these disciplines influence the focus of future bioinformatics development efforts. We observe here that the growth of computational approaches within various biomedical disciplines is not merely a reflection of a general extended usage of computers and the Internet, but due to the production of useful bioinformatics databases and methods for the rest of the biomedical scientific community. We have used the abstracts stored both in the MEDLINE database of biomedical literature and in NIH-funded project grants, to quantify two effects. First, we examine the biomedical literature as a whole and find that the use of computational methods has become increasingly prevalent across biomedical disciplines over the past three decades, while use of databases and the Internet have been rapidly increasing over the past decade. Second, we study the recent trends in the use of bioinformatics topics. We observe that molecular sequence databases are a widely adopted contribution in biomedicine from the field of bioinformatics, and that microarray analysis is one of the major new topics engaged by the bioinformatics community. Via this analysis, we were able to identify areas of rapid growth in the use of informatics to aid in curriculum planning, development of computational infrastructure and strategies for workforce education and funding.'),
(68359, 2006, 'bib', 'Dynamic modelling and analysis of biochemical networks: mechanism-based models and model-based experiments', 'van Riel, Natal A W', 'Biomodeling and Bioinformatics, Department of Biomedical Engineering, Eindhoven University of Technology, PO Box 513, 5600 MB Eindhoven, The Netherlands. N.A.W.v.Riel@tue.nl', 'Netherlands', 62, 'Algorithms|Animals|Computational Biology|Computer Simulation|Humans|Metabolic Networks and Pathways|Models, Biological|Monte Carlo Method|Research Design|Systems Biology', 'Journal Article', '10.1093/bib/bbl040', '17107967', 'Systems biology applies quantitative, mechanistic modelling to study genetic networks, signal transduction pathways and metabolic networks. Mathematical models of biochemical networks can look very different. An important reason is that the purpose and application of a model are essential for the selection of the best mathematical framework. Fundamental aspects of selecting an appropriate modelling framework and a strategy for model building are discussed. Concepts and methods from system and control theory provide a sound basis for the further development of improved and dedicated computational tools for systems biology. Identification of the network components and rate constants that are most critical to the output behaviour of the system is one of the major problems raised in systems biology. Current approaches and methods of parameter sensitivity analysis and parameter estimation are reviewed. It is shown how these methods can be applied in the design of model-based experiments which iteratively yield models that are decreasingly wrong and increasingly gain predictive power.'),
(68360, 2006, 'bib', 'Computational methodologies for modelling, analysis and simulation of signalling networks', 'Gilbert, David|Fuss, Hendrik|Gu, Xu|Orton, Richard|Robinson, Steve|Vyshemirsky, Vladislav|Kurth, Mary Jo|Downes, C Stephen|Dubitzky, Werner', 'Bioinformatics Research Centre, A416, Davidson Building University of Glasgow, Glasgow G12 8QQ, Scotland, UK. drg@dcs.gla.ac.uk|Bioinformatics Research Centre, A416, Davidson Building University of Glasgow, Glasgow G12 8QQ, Scotland, UK. drg@dcs.gla.ac.uk|Bioinformatics Research Centre, A416, Davidson Building University of Glasgow, Glasgow G12 8QQ, Scotland, UK. drg@dcs.gla.ac.uk|Bioinformatics Research Centre, A416, Davidson Building University of Glasgow, Glasgow G12 8QQ, Scotland, UK. drg@dcs.gla.ac.uk|Bioinformatics Research Centre, A416, Davidson Building University of Glasgow, Glasgow G12 8QQ, Scotland, UK. drg@dcs.gla.ac.uk|Bioinformatics Research Centre, A416, Davidson Building University of Glasgow, Glasgow G12 8QQ, Scotland, UK. drg@dcs.gla.ac.uk|Bioinformatics Research Centre, A416, Davidson Building University of Glasgow, Glasgow G12 8QQ, Scotland, UK. drg@dcs.gla.ac.uk|Bioinformatics Research Centre, A416, Davidson Building University of Glasgow, Glasgow G12 8QQ, Scotland, UK. drg@dcs.gla.ac.uk|Bioinformatics Research Centre, A416, Davidson Building University of Glasgow, Glasgow G12 8QQ, Scotland, UK. drg@dcs.gla.ac.uk', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom', 69, 'Algorithms|Animals|Cell Communication|Cell Cycle|Cell Physiological Phenomena|Computational Biology|Computer Simulation|Humans|Models, Biological|Receptor Protein-Tyrosine Kinases|Signal Transduction|Software|Systems Biology', 'Journal Article', '10.1093/bib/bbl043', '17116646', 'This article is a critical review of computational techniques used to model, analyse and simulate signalling networks. We propose a conceptual framework, and discuss the role of signalling networks in three major areas: signal transduction, cellular rhythms and cell-to-cell communication. In order to avoid an overly abstract and general discussion, we focus on three case studies in the areas of receptor signalling and kinase cascades, cell-cycle regulation and wound healing. We report on a variety of modelling techniques and associated tools, in addition to the traditional approach based on ordinary differential equations (ODEs), which provide a range of descriptive and analytical powers. As the field matures, we expect a wider uptake of these alternative approaches for several reasons, including the need to take into account low protein copy numbers and noise and the great complexity of cellular organisation. An advantage offered by many of these alternative techniques, which have their origins in computing science, is the ability to perform sophisticated model analysis which can better relate predicted behaviour and observations.'),
(68361, 2006, 'bib', 'Application of wavelet-based tools to study the dynamics of biological processes', 'Pavlov, Alexey N|Makarov, Valeri A|Mosekilde, Erik|Sosnovtseva, Olga V', 'Department of Physics, Saratov State University, Astrakhanskaya Str. 83, 410026 Saratov, Russia. pavlov@chaos.ssu.runnet.ru|Department of Physics, Saratov State University, Astrakhanskaya Str. 83, 410026 Saratov, Russia. pavlov@chaos.ssu.runnet.ru|Department of Physics, Saratov State University, Astrakhanskaya Str. 83, 410026 Saratov, Russia. pavlov@chaos.ssu.runnet.ru|Department of Physics, Saratov State University, Astrakhanskaya Str. 83, 410026 Saratov, Russia. pavlov@chaos.ssu.runnet.ru', '-|-|-|-', 33, 'Animals|Computer Simulation|Hypertension|Mathematical Computing|Metabolic Networks and Pathways|Microscopy, Interference|Nephrons|Neurons|Oscillometry|Rats|Rats, Inbred SHR|Signal Processing, Computer-Assisted|Snails|Software|Trigeminal Nuclei', 'Journal Article', '10.1093/bib/bbl041', '17118945', 'The article makes use of three different examples (sensory information processing in the rat trigeminal complex, intracellular interaction in snail neurons and multimodal dynamics in nephron autoregulation) to demonstrate how modern approaches to time-series analysis based on the wavelet-transform can provide information about the underlying complex biological processes.'),
(68362, 2006, 'bib', 'Understanding the computational methodologies of systems biology', 'Dubitzky, Werner', 'Department of Physics, Saratov State University, Astrakhanskaya Str. 83, 410026 Saratov, Russia. pavlov@chaos.ssu.runnet.ru', '-', 3, 'Animals|Computational Biology|Humans|Systems Biology', 'Editorial', '10.1093/bib/bbl044', '17132621', '-'),
(68363, 2006, 'bib', 'Representing, storing and accessing molecular interaction data: a review of models and tools', 'Strömbäck, Lena|Jakoniene, Vaida|Tan, He|Lambrix, Patrick', 'Department of Computer and Information Science, Linköpings Universitet, Sweden. lestr@ida.liu.se|Department of Computer and Information Science, Linköpings Universitet, Sweden. lestr@ida.liu.se|Department of Computer and Information Science, Linköpings Universitet, Sweden. lestr@ida.liu.se|Department of Computer and Information Science, Linköpings Universitet, Sweden. lestr@ida.liu.se', 'Sweden|Sweden|Sweden|Sweden', 24, 'Computational Biology|Computer Simulation|Data Collection|Data Interpretation, Statistical|Database Management Systems|Metabolic Networks and Pathways|Models, Biological|Programming Languages|Proteomics|Software|Systems Biology', 'Journal Article', '10.1093/bib/bbl039', '17132622', 'One important aim within systems biology is to integrate disparate pieces of information, leading to discovery of higher-level knowledge about important functionality within living organisms. This makes standards for representation of data and technology for exchange and integration of data important key points for development within the area. In this article, we focus on the recent developments within the field. We compare the recent updates to the three standard representations for exchange of data SBML, PSI MI and BioPAX. In addition, we give an overview of available tools for these three standards and a discussion on how these developments support possibilities for data exchange and integration.'),
(68364, 2006, 'bib', 'Circumventing the cut-off for enrichment analysis', 'Rubin, Eitan', 'Department of Computer and Information Science, Linköpings Universitet, Sweden. lestr@ida.liu.se', 'Sweden', 7, 'Algorithms|Gene Expression Profiling|Oligonucleotide Array Sequence Analysis|Software', 'News', '10.1093/bib/bbl013', '17201027', 'Three tools for threshold-free enrichment analysis of microarray data are introduced: GSEA (gene set enrichment analysis), ermineJ and DRIM (discovering rank imbalanced motifs). GSEA offers an interface to a specific algorithm and a well-defined pipeline for the identifying enrichment in diverse gene sets and the creation of signature profiles. ermineJ offers a combined front end to three different algorithms, two of which perform a cut-off-free enrichment analysis. DRIM comprises an implementation of a new algorithm and is specifically designed for the search of new transcription-factor-binding sites based on expression patterns. Together, these tools demonstrate an emerging trend in high-throughput data analysis-the joint analysis of raw results with external knowledge.'),
(68365, 2007, 'bib', 'The growth of bioinformatics', 'Brusic, Vladimir', 'Department of Computer and Information Science, Linköpings Universitet, Sweden. lestr@ida.liu.se', 'Sweden', 1, 'Computational Biology|Genomics|MEDLINE|Periodicals as Topic', 'Editorial', '10.1093/bib/bbm008', '17384431', '-'),
(68366, 2007, 'bib', 'Forensic DNA and bioinformatics', 'Bianchi, Lucia|Liò, Pietro', 'University of Cambridge, Cambridge, UK.|University of Cambridge, Cambridge, UK.', 'United Kingdom|United Kingdom', 10, 'Biometry|Computational Biology|DNA|DNA Fingerprinting|DNA Mutational Analysis|Databases, Genetic|Forensic Genetics|Linkage Disequilibrium|Sequence Analysis, DNA', 'Journal Article', '10.1093/bib/bbm006', '17384432', 'The field of forensic science is increasingly based on biomolecular data and many European countries are establishing forensic databases to store DNA profiles of crime scenes of known offenders and apply DNA testing. The field is boosted by statistical and technological advances such as DNA microarray sequencing, TFT biosensors, machine learning algorithms, in particular Bayesian networks, which provide an effective way of evidence organization and inference. The aim of this article is to discuss the state of art potentialities of bioinformatics in forensic DNA science. We also discuss how bioinformatics will address issues related to privacy rights such as those raised from large scale integration of crime, public health and population genetic susceptibility-to-diseases databases.'),
(68367, 2007, 'bib', 'Bayesian methods in bioinformatics and computational systems biology', 'Wilkinson, Darren J', 'School of Mathematics and Statistics, Newcastle University, Newcastle upon Tyne, NE1 7RU, UK. d.j.wilkinson@ncl.ac.uk', 'United Kingdom', 122, 'Artificial Intelligence|Bayes Theorem|Computational Biology|Gene Expression Profiling|Oligonucleotide Array Sequence Analysis|Pattern Recognition, Automated|Proteome|Sequence Analysis|Signal Transduction|Systems Biology', 'Journal Article', '10.1093/bib/bbm007', '17430978', 'Bayesian methods are valuable, inter alia, whenever there is a need to extract information from data that are uncertain or subject to any kind of error or noise (including measurement error and experimental error, as well as noise or random variation intrinsic to the process of interest). Bayesian methods offer a number of advantages over more conventional statistical techniques that make them particularly appropriate for complex data. It is therefore no surprise that Bayesian methods are becoming more widely used in the fields of genetics, genomics, bioinformatics and computational systems biology, where making sense of complex noisy data is the norm. This review provides an introduction to the growing literature in this area, with particular emphasis on recent developments in Bayesian bioinformatics relevant to computational systems biology.'),
(68368, 2007, 'bib', 'A practical guide to the art of RNA gene prediction', 'Meyer, Irmtraud M', 'Bioinformatics Centre and Department of Computer Science, University of British Columbia, 2366 Main Mall, Vancouver, BC, Canada V6T 1Z4. irmtraud.meyer@cantab.net', 'Canada', 13, 'Algorithms|Base Sequence|Chromosome Mapping|Molecular Sequence Data|RNA, Untranslated|Sequence Alignment|Sequence Analysis, RNA|Transcription, Genetic', 'Journal Article', '10.1093/bib/bbm011', '17483123', 'This review introduces the different strategies and computational methods that can be used in order to predict RNA genes. It discusses our current view of RNA genes as well as recent computational analyses of RNA genes and concludes with an outlook to future directions in algorithm development and data analyses.'),
(68369, 2007, 'bib', 'High-throughput modeling and analysis of protein structural dynamics', 'Liu, Xiong|Karimi, Hassan A', 'Wilmer Institute, Johns Hopkins University School of Medicine, Baltimore, MD 21287, USA. xliu33@jhmi.edu|Wilmer Institute, Johns Hopkins University School of Medicine, Baltimore, MD 21287, USA. xliu33@jhmi.edu', 'United States|United States', 3, 'Algorithms|Amino Acid Sequence|Computer Graphics|Models, Chemical|Models, Molecular|Molecular Sequence Data|Protein Conformation|Proteins|Sequence Analysis, Protein|Software|User-Computer Interface', 'Journal Article', '10.1093/bib/bbm014', '17485424', 'Protein function is a dynamic property closely related to the conformational mechanisms of protein structure in its physiological environment. To understand and control the function of target proteins, it becomes increasingly important to develop methods and tools for predicting collective motions at the molecular level. In this article, we review computational methods for predicting conformational dynamics and discuss software tools for data analysis. In particular, we discuss a high-throughput, web-based system called iGNM for protein structural dynamics. iGNM contains a database of protein motions for more than 20 000 PDB structures and supports online calculations for newly deposited PDB structures or user-modified structures. iGNM allows dynamics analysis of protein structures ranging from enzymes to large complexes and assemblies, and enables the exploration of protein sequence-structure-dynamics-function relations.'),
(68370, 2007, 'bib', 'Boca: an open-source RDF store for building Semantic Web applications', 'Feigenbaum, Lee|Martin, Sean|Roy, Matthew N|Szekely, Benjamin|Yung, Wing C', 'IBM Internet Technology, Cambridge, MA, USA. feigenbl@us.ibm.com|IBM Internet Technology, Cambridge, MA, USA. feigenbl@us.ibm.com|IBM Internet Technology, Cambridge, MA, USA. feigenbl@us.ibm.com|IBM Internet Technology, Cambridge, MA, USA. feigenbl@us.ibm.com|IBM Internet Technology, Cambridge, MA, USA. feigenbl@us.ibm.com', 'United States|United States|United States|United States|United States', 6, 'Humans|Information Dissemination|Information Storage and Retrieval|Internet|Research Personnel|Semantics|Software|User-Computer Interface', 'Journal Article', '10.1093/bib/bbm017', '17491005', 'This article presents the design goals and features of the open-source Boca RDF server in the context of a community of cancer-tumor modeling investigators. Boca supplements the desirable data features of the Semantic Web with important enterprise and application features to power a new generation of Semantic-Web-based applications. The data features enable the integration and retrieval of tremendous quantities of diverse data. The enterprise features promote data integrity, fidelity, provenance and robustness. The application features provide for collaborative applications and dynamic user interfaces.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(68371, 2007, 'bib', 'Using provenance to manage knowledge of in silico experiments', 'Stevens, Robert|Zhao, Jun|Goble, Carole', 'BioHealth and Information Management Groups, School of Computer Science, University of Manchester, Oxford Road, Manchester, UK, M13 9PL. Robert.Stevens@manchester.ac.uk|BioHealth and Information Management Groups, School of Computer Science, University of Manchester, Oxford Road, Manchester, UK, M13 9PL. Robert.Stevens@manchester.ac.uk|BioHealth and Information Management Groups, School of Computer Science, University of Manchester, Oxford Road, Manchester, UK, M13 9PL. Robert.Stevens@manchester.ac.uk', '-|-|-', 4, 'Computational Biology|Humans|Information Storage and Retrieval|Internet|Knowledge|Research Design|Software', 'Journal Article', '10.1093/bib/bbm015', '17502335', 'This article offers a briefing in one of the knowledge management issues of in silico experimentation in bioinformatics. Recording of the provenance of an experiment-what was done, where, how and why, etc. is an important aspect of scientific best practice that should be extended to in silico experimentation. We will do this in the context of eScience which has been part of the move of bioinformatics towards an industrial setting. Despite the computational nature of bioinformatics, these analyses are scientific and thus necessitate their own versions of typical scientific rigour. Just as recording who, what, why, when, where and how of an experiment is central to the scientific process in laboratory science, so it should be in silico science. The generation and recording of these aspects, or provenance, of an experiment are necessary knowledge management goals if we are to introduce scientific rigour into routine bioinformatics. In Silico experimental protocols should themselves be a form of managing the knowledge of how to perform bioinformatics analyses. Several systems now exist that offer support for the generation and collection of provenance information about how a particular in silico experiment was run, what results were generated, how they were generated, etc. In reviewing provenance support, we will review one of the important knowledge management issues in bioinformatics.'),
(68372, 2007, 'bib', 'Knowledge networks in the age of the Semantic Web', 'Neumann, Eric|Prusak, Larry', 'Teranode Corporation, USA. eneumann@teranode.com|Teranode Corporation, USA. eneumann@teranode.com', 'United States|United States', 21, 'Information Dissemination|Information Storage and Retrieval|Internet|Knowledge|Medical Informatics|Semantics|Software', 'Journal Article', '10.1093/bib/bbm013', '17502336', 'The Web has become the major medium for various communities to share their knowledge. To this end, it provides an optimal environment for knowledge networks. The web offers global connectivity that is virtually instantaneous, and whose resources and documents can easily be indexed for easy searching. In the coupled realms of biomedical research and healthcare, this has become especially important where today many thousands of communities already exist that connect across academia, hospitals and industry. These communities also rely on several forms of knowledge assets, including publications, experimental data, domain-specific vocabularies and policies. Web-based communities will be one of the earlier beneficiaries of the emerging Semantic Web. With the new standards and technologies of the Semantic Web, effective utilization of knowledge networks will expand profoundly, fostering new levels of innovation and knowledge.'),
(68373, 2007, 'bib', 'SenseLab: new developments in disseminating neuroscience information', 'Crasto, Chiquito J|Marenco, Luis N|Liu, Nian|Morse, Thomas M|Cheung, Kei-Hoi|Lai, Peter C|Bahl, Gautam|Masiar, Peter|Lam, Hugo Y K|Lim, Ernest|Chen, Huajin|Nadkarni, Prakash|Migliore, Michele|Miller, Perry L|Shepherd, Gordon M', 'Department of Genetics, University of Alabama at Birmingham, Birmingham, Alabama, USA. ccrasto@genetics.uab.edu|Department of Genetics, University of Alabama at Birmingham, Birmingham, Alabama, USA. ccrasto@genetics.uab.edu|Department of Genetics, University of Alabama at Birmingham, Birmingham, Alabama, USA. ccrasto@genetics.uab.edu|Department of Genetics, University of Alabama at Birmingham, Birmingham, Alabama, USA. ccrasto@genetics.uab.edu|Department of Genetics, University of Alabama at Birmingham, Birmingham, Alabama, USA. ccrasto@genetics.uab.edu|Department of Genetics, University of Alabama at Birmingham, Birmingham, Alabama, USA. ccrasto@genetics.uab.edu|Department of Genetics, University of Alabama at Birmingham, Birmingham, Alabama, USA. ccrasto@genetics.uab.edu|Department of Genetics, University of Alabama at Birmingham, Birmingham, Alabama, USA. ccrasto@genetics.uab.edu|Department of Genetics, University of Alabama at Birmingham, Birmingham, Alabama, USA. ccrasto@genetics.uab.edu|Department of Genetics, University of Alabama at Birmingham, Birmingham, Alabama, USA. ccrasto@genetics.uab.edu|Department of Genetics, University of Alabama at Birmingham, Birmingham, Alabama, USA. ccrasto@genetics.uab.edu|Department of Genetics, University of Alabama at Birmingham, Birmingham, Alabama, USA. ccrasto@genetics.uab.edu|Department of Genetics, University of Alabama at Birmingham, Birmingham, Alabama, USA. ccrasto@genetics.uab.edu|Department of Genetics, University of Alabama at Birmingham, Birmingham, Alabama, USA. ccrasto@genetics.uab.edu|Department of Genetics, University of Alabama at Birmingham, Birmingham, Alabama, USA. ccrasto@genetics.uab.edu', 'United States|United States|United States|United States|United States|United States|United States|United States|United States|United States|United States|United States|United States|United States|United States', 22, 'Databases, Factual|Humans|Information Dissemination|Information Storage and Retrieval|Internet|Neurosciences|Software|Systems Integration', 'Journal Article', '10.1093/bib/bbm018', '17510162', 'This article presents the latest developments in neuroscience information dissemination through the SenseLab suite of databases: NeuronDB, CellPropDB, ORDB, OdorDB, OdorMapDB, ModelDB and BrainPharm. These databases include information related to: (i) neuronal membrane properties and neuronal models, and (ii) genetics, genomics, proteomics and imaging studies of the olfactory system. We describe here: the new features for each database, the evolution of SenseLabs unifying database architecture and instances of SenseLab database interoperation with other neuroscience online resources.'),
(68374, 2007, 'bib', 'Alzforum and SWAN: the present and future of scientific web communities', 'Clark, Tim|Kinoshita, June', 'Alzheimer Research Forum, Boston, MA, USA.|Alzheimer Research Forum, Boston, MA, USA.', 'United States|United States', 15, 'Alzheimer Disease|Computer Communication Networks|Humans|Information Dissemination|Internet|Research|Research Personnel|Residence Characteristics|Semantics|Software|User-Computer Interface', 'Journal Article', '10.1093/bib/bbm012', '17510163', 'Scientists drove the early development of the World Wide Web, primarily as a means for rapid communication, document sharing and data access. They have been far slower to adopt the web as a medium for building research communities. Yet, web-based communities hold great potential for accelerating the pace of scientific research. In this article, we will describe the 10-year experience of the Alzheimer Research Forum (Alzforum), a unique example of a thriving scientific web community, and explain the features that contributed to its success. We will then outline the SWAN (Semantic Web Applications in Neuromedicine) project, in which Alzforum curators are collaborating with informatics researchers to develop novel approaches that will enable communities to share richly contextualized information about scientific data, claims and hypotheses.'),
(68375, 2007, 'bib', 'Improving life sciences information retrieval using semantic web technology', 'Quan, Dennis', 'IBM, San Jose, CA 95141, USA. dennisq@us.ibm.com', 'United States', 18, 'Drug Design|Humans|Information Dissemination|Information Storage and Retrieval|Information Systems|Internet|Semantics|Software', 'Journal Article', '10.1093/bib/bbm016', '17526593', 'The ability to retrieve relevant information is at the heart of every aspect of research and development in the life sciences industry. Information is often distributed across multiple systems and recorded in a way that makes it difficult to piece together the complete picture. Differences in data formats, naming schemes and network protocols amongst information sources, both public and private, must be overcome, and user interfaces not only need to be able to tap into these diverse information sources but must also assist users in filtering out extraneous information and highlighting the key relationships hidden within an aggregated set of information. The Semantic Web community has made great strides in proposing solutions to these problems, and many efforts are underway to apply Semantic Web techniques to the problem of information retrieval in the life sciences space. This article gives an overview of the principles underlying a Semantic Web-enabled information retrieval system: creating a unified abstraction for knowledge using the RDF semantic network model, designing semantic lenses that extract contextually relevant subsets of information, and assembling semantic lenses into powerful information displays. Furthermore, concrete examples of how these principles can be applied to life science problems including a scenario involving a drug discovery dashboard prototype called BioDash are provided.'),
(68376, 2007, 'bib', 'Combining experiments with multi-cell agent-based modeling to study biological tissue patterning', 'Thorne, Bryan C|Bailey, Alexander M|Peirce, Shayn M', 'Department of Biomedical Engineering, University of Virginia, PO Box 800759, Charlottesville, VA 22908, USA. bthorne@virginia.edu|Department of Biomedical Engineering, University of Virginia, PO Box 800759, Charlottesville, VA 22908, USA. bthorne@virginia.edu|Department of Biomedical Engineering, University of Virginia, PO Box 800759, Charlottesville, VA 22908, USA. bthorne@virginia.edu', 'United States|United States|United States', 97, 'Animals|Computer Simulation|Humans|Models, Biological|Regenerative Medicine|Tissue Engineering', 'Journal Article', '10.1093/bib/bbm024', '17584763', 'Agent-based modeling (ABM), also termed Individual-based modeling (IBM), is a computational approach that simulates the interactions of autonomous entities (agents, or individual cells) with each other and their local environment to predict higher level emergent patterns. A literature-derived rule set governs the actions of each individual agent. While this technique has been widely used in the ecological and social sciences, it has only recently been applied in biomedical research. The purpose of this review is to provide an introduction to ABM as it has been used to study complex multi-cell biological phenomena, underscore the importance of coupling models with experimental work, and outline future challenges for the ABM field and its application to biomedical research. We highlight a number of published examples of ABM, focusing on work that has combined experimental with ABM analyses and how this pairing produces new understanding. We conclude with suggestions for moving forward with this parallel approach.'),
(68377, 2007, 'bib', 'Current trends in computational inference from mass spectrometry-based proteomics', 'Webb-Robertson, Bobbie-Jo M|Cannon, William R', 'Department of Computational Biology & Bioinformatics, Pacific Northwest National Laboratory, P.O. BOX 999, Richland, WA 99352, USA. bj@pnl.gov|Department of Computational Biology & Bioinformatics, Pacific Northwest National Laboratory, P.O. BOX 999, Richland, WA 99352, USA. bj@pnl.gov', 'United States|United States', 26, 'Computational Biology|Forecasting|Gene Expression Profiling|Mass Spectrometry|Peptide Mapping|Protein Interaction Mapping|Proteome|Proteomics|Sequence Analysis, Protein', 'Journal Article', '10.1093/bib/bbm023', '17584764', 'Mass spectrometry offers a high-throughput approach to quantifying the proteome associated with a biological sample and hence has become the primary approach of proteomic analyses. Computation is tightly coupled to this advanced technological platform as a required component of not only peptide and protein identification, but quantification and functional inference, such as protein modifications and interactions. Proteomics faces several key computational challenges such as identification of proteins and peptides from tandem mass spectra as well as their quantitation. In addition, the application of proteomics to systems biology requires understanding the functional proteome, including how the dynamics of the cell change in response to protein modifications and complex interactions between biomolecules. This review presents an overview of recently developed methods and their impact on these core computational challenges currently facing proteomics.'),
(68378, 2007, 'bib', 'An integrative approach to understanding mechanosensation', 'Poirier, Christopher C|Iglesias, Pablo A', 'Department of Electrical & Computer Engineering, The Johns Hopkins University, 3400North Charles Street, Baltimore, MD 21218, USA.|Department of Electrical & Computer Engineering, The Johns Hopkins University, 3400North Charles Street, Baltimore, MD 21218, USA.', 'United States|United States', 5, 'Animals|Finite Element Analysis|Humans|Ion Channels|Mechanoreceptors|Models, Biological|Sensation|Systems Integration', 'Journal Article', '10.1093/bib/bbm025', '17591637', 'The ability for a living organism to sense and respond to its external environment is crucial to its survival. Understanding mechanosensation, the mechanism by which organisms react in response to mechanical stimuli, presents many interesting and challenging problems for both experimental and computational biologists. A major difficulty in studying mechanosensors is their inherent multiscale nature. The systems involved in mechanosesnsing can span eight orders of magnitude in length scale and up to 10 orders of magnitude in time scale. Trying to ascertain information across these length and time scales simultaneously is challenging. This problem has led to the need to approach these types of problems using an integrative approach, combining both computational and experimental biology. This review classifies the major types of mechanosensors and explains methods that have been employed in understanding their behavior, both using modeling and experimental techniques. Multiscale modeling methods combined with experimental techniques in an integrative approach are suggested as ways of undertaking the study of such systems.'),
(68379, 2007, 'bib', 'Informatics challenges in structured RNA', 'Laederach, Alain', 'Department of Genetics, 300 Pasteur Drive, Stanford University, Stanford, CA 94305, USA. alain@helix.stanford.edu', 'United States', 10, 'Computational Biology|Models, Chemical|Models, Molecular|Nucleic Acid Conformation|RNA|RNA Interference|Structure-Activity Relationship', 'Journal Article', '10.1093/bib/bbm026', '17611237', 'The world of regulatory RNAs is fast expanding into mainstream molecular biology as both a subject of intense mechanistic study and as a tool for functional characterization. The RNA world is one of complex structures that carry out catalysis, sense metabolites and synthesize proteins. The dynamic and structural nature of RNAs presents a whole new set of informatics challenges to the computational community. The ability to relate structure and dynamics to function will be key to understanding this complex world. I review several important classes of structured RNAs that present our community with a series of biologically novel informatics challenges. I also review available informatics tools that have been recently developed in the field.'),
(68380, 2007, 'bib', 'Current progress in computational metabolomics', 'Wishart, David S', 'Department of Computing Science, Department of Biological Sciences and National Institute for Nanotechnology (NRC-NINT), University of Alberta, Edmonton Alberta, Canada T6G 2E8. david.wishart@ualberta.ca', 'Canada', 136, 'Combinatorial Chemistry Techniques|Computational Biology|Databases, Protein|Gene Expression Profiling|Metabolism|Protein Interaction Mapping|Proteome|Proteomics', 'Journal Article', '10.1093/bib/bbm030', '17626065', 'Being a relatively new addition to the omics field, metabolomics is still evolving its own computational infrastructure and assessing its own computational needs. Due to its strong emphasis on chemical information and because of the importance of linking that chemical data to biological consequences, metabolomics must combine elements of traditional bioinformatics with traditional cheminformatics. This is a significant challenge as these two fields have evolved quite separately and require very different computational tools and skill sets. This review is intended to familiarize readers with the field of metabolomics and to outline the needs, the challenges and the recent progress being made in four areas of computational metabolomics: (i) metabolomics databases, (ii) metabolomics LIMS, (iii) spectral analysis tools for metabolomics and (iv) metabolic modeling.'),
(68381, 2007, 'bib', 'Petri net modelling of biological networks', 'Chaouiya, Claudine', 'TAGC, INSERM ERM, 206, Université de la Méditerranée, Campus Scientifique de Luminy - Case 928, 13288 Marseille Cedex 09, France. chaouiya@univmed.fr', 'France', 243, 'Animals|Drosophila Proteins|Models, Genetic|Signal Transduction|Systems Biology', 'Journal Article', '10.1093/bib/bbm029', '17626066', 'Mathematical modelling is increasingly used to get insights into the functioning of complex biological networks. In this context, Petri nets (PNs) have recently emerged as a promising tool among the various methods employed for the modelling and analysis of molecular networks. PNs come with a series of extensions, which allow different abstraction levels, from purely qualitative to more complex quantitative models. Noteworthily, each of these models preserves the underlying graph, which depicts the interactions between the biological components. This article intends to present the basics of the approach and to foster the potential role PNs could play in the development of the computational systems biology.'),
(68382, 2007, 'bib', 'Dynamical roles of biological regulatory circuits', 'Thieffry, Denis', 'INSERM ERM 206 - TAGC & Université de la Méditerranée, Campus Scientifique de Luminy, case 928, 13288 Marseille, Cedex 09 France. thieffry@tagc.univ-mrs.fr', 'France', 75, 'Algorithms|Bacteria|Feedback, Physiological|Homeostasis|Models, Genetic|Systems Biology', 'Journal Article', '10.1093/bib/bbm028', '17626067', 'Regulatory circuits are found at the basis of all non-trivial dynamical properties of biological networks. More specifically, positive circuits are involved in the generation of multiple differentiated states, whereas negative circuits can generate cyclic or homeostatic behaviours. These notions are briefly reviewed, from initial biological formulations to mathematical formalisations, further encompassing their application to the design of synthetic regulatory systems. Finally, current challenges for the analysis of increasingly complex regulatory networks are indicated, as well as prospects for our understanding of development and evolution.'),
(68383, 2007, 'bib', 'Combined experimental and computational approaches to study the regulatory elements in eukaryotic genes', 'Kolchanov, Nikolay A|Merkulova, Tatyana I|Ignatieva, Elena V|Ananko, Elena A|Oshchepkov, Dmitry Yu|Levitsky, Viktor G|Vasiliev, Gennady V|Klimova, Nataly V|Merkulov, Vasily M|Charles Hodgman, T', 'Institute of Cytology and Genetics of the Siberian Branch of the Russian Academy of Sciences (ICG SB RAS), Novosibirsk, Russia. eananko@bionet.nsc.ru|Institute of Cytology and Genetics of the Siberian Branch of the Russian Academy of Sciences (ICG SB RAS), Novosibirsk, Russia. eananko@bionet.nsc.ru|Institute of Cytology and Genetics of the Siberian Branch of the Russian Academy of Sciences (ICG SB RAS), Novosibirsk, Russia. eananko@bionet.nsc.ru|Institute of Cytology and Genetics of the Siberian Branch of the Russian Academy of Sciences (ICG SB RAS), Novosibirsk, Russia. eananko@bionet.nsc.ru|Institute of Cytology and Genetics of the Siberian Branch of the Russian Academy of Sciences (ICG SB RAS), Novosibirsk, Russia. eananko@bionet.nsc.ru|Institute of Cytology and Genetics of the Siberian Branch of the Russian Academy of Sciences (ICG SB RAS), Novosibirsk, Russia. eananko@bionet.nsc.ru|Institute of Cytology and Genetics of the Siberian Branch of the Russian Academy of Sciences (ICG SB RAS), Novosibirsk, Russia. eananko@bionet.nsc.ru|Institute of Cytology and Genetics of the Siberian Branch of the Russian Academy of Sciences (ICG SB RAS), Novosibirsk, Russia. eananko@bionet.nsc.ru|Institute of Cytology and Genetics of the Siberian Branch of the Russian Academy of Sciences (ICG SB RAS), Novosibirsk, Russia. eananko@bionet.nsc.ru|Institute of Cytology and Genetics of the Siberian Branch of the Russian Academy of Sciences (ICG SB RAS), Novosibirsk, Russia. eananko@bionet.nsc.ru', '-|-|-|-|-|-|-|-|-|-', 23, 'Animals|Computational Biology|Eukaryotic Cells|Gene Expression Regulation|Humans|Models, Biological|Regulatory Elements, Transcriptional', 'Journal Article', '10.1093/bib/bbm027', '17627963', 'The recognition of transcription factor binding sites (TFBSs) is the first step on the way to deciphering the DNA regulatory code. There is a large variety of experimental approaches providing information on TFBS location in genomic sequences. Many computational approaches to TFBS recognition based on the experimental data obtained are available, each having its own advantages and shortcomings. This article provides short review of approaches to computational recognition of TFBS in genomic sequences and methods of experimental verification of predicted sites. We also present a case study of the interplay between experimental and theoretical approaches to the successful prediction of Steroidogenic Factor 1 (SF1).'),
(68384, 2007, 'bib', 'Protein interactions and disease: computational approaches to uncover the etiology of diseases', 'Kann, Maricel G', 'National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Department of Health and Human Services, Bethesda, MD 20894, USA. kann@mail.nih.gov', 'United States', 140, 'Biomarkers|Computational Biology|Genetic Predisposition to Disease|Genetic Testing|Humans|Protein Interaction Mapping|Proteins', 'Journal Article', '10.1093/bib/bbm031', '17638813', 'The genomic era has been characterised by vast amounts of data from diverse sources, creating a need for new tools to extract biologically meaningful information. Bioinformatics is, for the most part, responding to that need. The sparseness of the genomic data associated with diseases, however, creates a new challenge. Understanding the complex interplay between genes and proteins requires integration of data from a wide variety of sources, i.e. gene expression, genetic linkage, protein interaction, and protein structure among others. Thus, computational tools have become critical for the integration, representation and visualization of heterogeneous biomedical data. Furthermore, several bioinformatics methods have been developed to formulate predictions about the functional role of genes and proteins, including their role in diseases. After an introduction to the complex interplay between proteins and genetic diseases, this review explores recent approaches to the understanding of the mechanisms of disease at the molecular level. Finally, because most known mechanisms leading to disease involve some form of protein interaction, this review focuses on the recent methodologies for understanding diseases through their underlying protein interactions. Recent contributions from genetics, protein structure and protein interaction network analyses to the understanding of diseases are discussed here.'),
(68385, 2007, 'bib', 'Integrative biology--the way forward', 'Hodgman, Charlie', 'National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Department of Health and Human Services, Bethesda, MD 20894, USA. kann@mail.nih.gov', 'United States', 4, 'Animals|Computational Biology|Forecasting|Humans|Models, Biological|Systems Biology', 'Editorial', '10.1093/bib/bbm036', '17640922', '-'),
(68386, 2007, 'bib', 'Statistical software for gene mapping by admixture linkage disequilibrium', 'Montana, Giovanni|Hoggart, Clive', 'Imperial College London, Department of Mathematics, South Kensington Campus, 180 Queens Gate, London SW7 2AZ. g.montana@imperial.ac.uk|Imperial College London, Department of Mathematics, South Kensington Campus, 180 Queens Gate, London SW7 2AZ. g.montana@imperial.ac.uk', '-|-', 7, 'Algorithms|Chromosome Mapping|Data Interpretation, Statistical|Genetic Variation|Linkage Disequilibrium|Sequence Analysis, DNA|Software', 'Comparative Study', '10.1093/bib/bbm035', '17640923', 'Admixture mapping is a statistical methodology that detects genetic variants in recently admixed populations that are responsible for ethnic differences in disease risk. Three software packages are now available for admixture mapping and we provide a brief overview of the statistical methods and other principal features they implement.'),
(68387, 2007, 'bib', 'Towards a calculus of biomolecular complexes at equilibrium', 'Mjolsness, Eric', 'Department of Computer Science, University of California, Irvine, CA 92617, USA. emj@uci.edu', 'United States', 5, 'Animals|Binding Sites|Computational Biology|Humans|Models, Chemical|Models, Genetic|Multiprotein Complexes|Protein Binding', 'Journal Article', '10.1093/bib/bbm034', '17640924', 'An overview is presented of the construction and use of algebraic partition functions to represent the equilibrium statistical mechanics of multimolecular complexes and their action within a larger regulatory network. Unlike many applications of equilibrium statistical mechanics, multimolecular complexes may operate with various subsets of their components present and connected to the others, the rest remaining in solution. Thus they are variable-structure systems. This aspect of their behavior may be accounted for by the use of fugacity variables as a representation within the partition functions. Four principles are proposed by which the combinatorics of molecular complex construction can be reflected in the construction of their partition functions. The corresponding algebraic operations on partition functions are multiplication, addition, function composition and a less commonly used operation called contraction. Each has a natural interpretation in terms of probability distributions on multimolecular structures. Possible generalizations to nonequilibrium statistical mechanics are briefly discussed.'),
(68388, 2007, 'bib', 'Modelling and simulation techniques for membrane biology', 'Burrage, Kevin|Hancock, John|Leier, André|Nicolau, Dan V', 'The Advanced Computational Modelling Centre and the ARC Centre in Bioinformatics, The University of Queensland, Brisbane 4072, Australia. kb@maths.uq.edu.au|The Advanced Computational Modelling Centre and the ARC Centre in Bioinformatics, The University of Queensland, Brisbane 4072, Australia. kb@maths.uq.edu.au|The Advanced Computational Modelling Centre and the ARC Centre in Bioinformatics, The University of Queensland, Brisbane 4072, Australia. kb@maths.uq.edu.au|The Advanced Computational Modelling Centre and the ARC Centre in Bioinformatics, The University of Queensland, Brisbane 4072, Australia. kb@maths.uq.edu.au', 'Australia|Australia|Australia|Australia', 33, 'Animals|Computer Simulation|Kinetics|Membrane Microdomains|Models, Chemical|Models, Statistical|Monte Carlo Method', 'Journal Article', '10.1093/bib/bbm033', '17693422', 'One of the most important aspects of Computational Cell Biology is the understanding of the complicated dynamical processes that take place on plasma membranes. These processes are often so complicated that purely temporal models cannot always adequately capture the dynamics. On the other hand, spatial models can have large computational overheads. In this article, we review some of these issues with respect to chemistry, membrane microdomains and anomalous diffusion and discuss how to select appropriate modelling and simulation paradigms based on some or all the following aspects: discrete, continuous, stochastic, delayed and complex spatial processes.'),
(68389, 2007, 'bib', 'Biodiversity informatics: organizing and linking information across the spectrum of life', 'Sarkar, Indra Neil', 'MBLWHOI Library, Marine Biological Laboratory, Woods Hole, MA 02543, USA. sarkar@mbl.edu', 'United States', 45, 'Biodiversity|Computational Biology|Databases, Genetic|Information Dissemination|Population Dynamics', 'Journal Article', '10.1093/bib/bbm037', '17704120', 'Biological knowledge can be inferred from three major levels of information: molecules, organisms and ecologies. Bioinformatics is an established field that has made significant advances in the development of systems and techniques to organize contemporary molecular data, biodiversity informatics is an emerging discipline that strives to develop methods to organize knowledge at the organismal level extending back to the earliest dates of recorded natural history. Furthermore, while bioinformatics studies generally focus on detailed examinations of key model organisms, biodiversity informatics aims to develop over-arching hypotheses that span the entire tree of life. Biodiversity informatics is presented here as a discipline that unifies biological information from a range of contemporary and historical sources across the spectrum of life using organisms as the linking thread. The present review primarily focuses on the use of organism names as a universal metadata element to link and integrate biodiversity data across a range of data sources.'),
(68390, 2007, 'bib', 'BioManager: the use of a bioinformatics web application as a teaching tool in undergraduate bioinformatics training', 'Cattley, Sonia|Arthur, Jonathan W', 'Sydney Bioinformatics, Medical Foundation Building (K25), The University of Sydney, Sydney NSW 2006, Australia. scattley@angis.org.au|Sydney Bioinformatics, Medical Foundation Building (K25), The University of Sydney, Sydney NSW 2006, Australia. scattley@angis.org.au', 'Australia|Australia', 20, 'Computational Biology|Computer-Assisted Instruction|Education, Medical, Undergraduate|Internet|Software|Software Design|Teaching', 'Journal Article', '10.1093/bib/bbm039', '17715151', 'The completion of the human genome project, and other genome sequencing projects, has spearheaded the emergence of the field of bioinformatics. Using computer programs to analyse DNA and protein information has become an important area of life science research and development. While it is not necessary for most life science researchers to develop specialist bioinformatic skills (including software development), basic skills in the application of common bioinformatics software and the effective interpretation of results are increasingly required by all life science researchers. Training in bioinformatics is increasingly occurring within the university system as part of existing undergraduate science and specialist degrees. One difficulty in bioinformatics education is the sheer number of software programs required in order to provide a thorough grounding in the subject to the student. Teaching requires either a well-maintained internal server with all the required software, properly interfacing with student terminals, and with sufficient capacity to handle multiple simultaneous requests, or it requires the individual installation and maintenance of every piece of software on each computer. In both cases, there are difficult issues regarding site maintenance and accessibility. In this article, we discuss the use of BioManager, a web-based bioinformatics application integrating a variety of common bioinformatics tools, for teaching, including its role as the main bioinformatics training tool in some Australian and international universities. We discuss some of the issues with using a bioinformatics resource primarily created for research in an undergraduate teaching environment.'),
(68391, 2007, 'bib', 'Current progress in bioinformatics 2007', 'Altman, Russ B', 'Sydney Bioinformatics, Medical Foundation Building (K25), The University of Sydney, Sydney NSW 2006, Australia. scattley@angis.org.au', 'Australia', 0, 'Biodiversity|Computational Biology|Mass Spectrometry|Models, Molecular|Natural Language Processing|Proteomics', 'Editorial', '10.1093/bib/bbm041', '17724063', '-'),
(68392, 2007, 'bib', 'Current progress in network research: toward reference networks for key model organisms', 'Srinivasan, Balaji S|Shah, Nigam H|Flannick, Jason A|Abeliuk, Eduardo|Novak, Antal F|Batzoglou, Serafim', '318 Campus Drive, Clark Center S251, Stanford, CA 94305, USA. balajis@stanford.edu|318 Campus Drive, Clark Center S251, Stanford, CA 94305, USA. balajis@stanford.edu|318 Campus Drive, Clark Center S251, Stanford, CA 94305, USA. balajis@stanford.edu|318 Campus Drive, Clark Center S251, Stanford, CA 94305, USA. balajis@stanford.edu|318 Campus Drive, Clark Center S251, Stanford, CA 94305, USA. balajis@stanford.edu|318 Campus Drive, Clark Center S251, Stanford, CA 94305, USA. balajis@stanford.edu', 'United States|United States|United States|United States|United States|United States', 35, 'Animals|Computational Biology|Computer Simulation|Forecasting|Gene Expression Profiling|Humans|Models, Biological|Protein Interaction Mapping|Proteome|Research|Signal Transduction', 'Journal Article', '10.1093/bib/bbm038', '17728341', 'The collection of multiple genome-scale datasets is now routine, and the frontier of research in systems biology has shifted accordingly. Rather than clustering a single dataset to produce a static map of functional modules, the focus today is on data integration, network alignment, interactive visualization and ontological markup. Because of the intrinsic noisiness of high-throughput measurements, statistical methods have been central to this effort. In this review, we briefly survey available datasets in functional genomics, review methods for data integration and network alignment, and describe recent work on using network models to guide experimental validation. We explain how the integration and validation steps spring from a Bayesian description of network uncertainty, and conclude by describing an important near-term milestone for systems biology: the construction of a set of rich reference networks for key model organisms.'),
(68393, 2008, 'bib', 'Computational methods for the comparative quantification of proteins in label-free LCn-MS experiments', 'Wong, Jason W H|Sullivan, Matthew J|Cagney, Gerard', 'Conway Institute of Biomolecular and Biomedical Research, University College Dublin, Belfield, Dublin 4, Ireland. jason.wong@ucd.ie|Conway Institute of Biomolecular and Biomedical Research, University College Dublin, Belfield, Dublin 4, Ireland. jason.wong@ucd.ie|Conway Institute of Biomolecular and Biomedical Research, University College Dublin, Belfield, Dublin 4, Ireland. jason.wong@ucd.ie', 'Ireland|Ireland|Ireland', 63, 'Chromatography, Liquid|Computational Biology|Data Interpretation, Statistical|Mass Spectrometry|Proteins|Proteomics|Sequence Analysis, Protein|Software', 'Journal Article', '10.1093/bib/bbm046', '17905794', 'Liquid chromatography (LC) coupled to electrospray mass spectrometry (MS) is well established in high-throughput proteomics. The technology enables rapid identification of large numbers of proteins in a relatively short time. Comparative quantification of identified proteins from different samples is often regarded as the next step in proteomics experiments enabling the comparison of protein expression in different proteomes. Differential labeling of samples using stable isotope incorporation or conjugation is commonly used to compare protein levels between samples but these procedures are difficult to carry out in the laboratory and for large numbers of samples. Recently, comparative quantification of label-free LC(n)-MS proteomics data has emerged as an alternative approach. In this review, we discuss different computational approaches for extracting comparative quantitative information from label-free LC(n)-MS proteomics data. The procedure for computationally recovering the quantitative information is described. Furthermore, statistical tests used to evaluate the relevance of results will also be discussed.'),
(68394, 2007, 'bib', 'A microarray analysis for differential gene expression in the soybean genome using Bioconductor and R', 'Gregory Alvord, W|Roayaei, Jean A|Quiñones, Octavio A|Schneider, Katherine T', 'Statistical Consulting Services, Data Management Services, Inc. (DMS), National Cancer Institute at Frederick (NCI-Frederick), PO Box B, Frederick, MD 21702-1201, USA. gwa@css.ncifcrf.gov|Statistical Consulting Services, Data Management Services, Inc. (DMS), National Cancer Institute at Frederick (NCI-Frederick), PO Box B, Frederick, MD 21702-1201, USA. gwa@css.ncifcrf.gov|Statistical Consulting Services, Data Management Services, Inc. (DMS), National Cancer Institute at Frederick (NCI-Frederick), PO Box B, Frederick, MD 21702-1201, USA. gwa@css.ncifcrf.gov|Statistical Consulting Services, Data Management Services, Inc. (DMS), National Cancer Institute at Frederick (NCI-Frederick), PO Box B, Frederick, MD 21702-1201, USA. gwa@css.ncifcrf.gov', 'United States|United States|United States|United States', 34, 'Gene Expression Profiling|Genome, Plant|Oligonucleotide Array Sequence Analysis|Plant Proteins|Programming Languages|Proteome|Software|Soybeans', 'Journal Article', '10.1093/bib/bbm043', '17906332', 'This article describes specific procedures for conducting quality assessment of Affymetrix GeneChip(R) soybean genome data and for performing analyses to determine differential gene expression using the open-source R programming environment in conjunction with the open-source Bioconductor software. We describe procedures for extracting those Affymetrix probe set IDs related specifically to the soybean genome on the Affymetrix soybean chip and demonstrate the use of exploratory plots including images of raw probe-level data, boxplots, density plots and M versus A plots. RNA degradation and recommended procedures from Affymetrix for quality control are discussed. An appropriate probe-level model provides an excellent quality assessment tool. To demonstrate this, we discuss and display chip pseudo-images of weights, residuals and signed residuals and additional probe-level modeling plots that may be used to identify aberrant chips. The Robust Multichip Averaging (RMA) procedure was used for background correction, normalization and summarization of the AffyBatch probe-level data to obtain expression level data and to discover differentially expressed genes. Examples of boxplots and MA plots are presented for the expression level data. Volcano plots and heatmaps are used to demonstrate the use of (log) fold changes in conjunction with ordinary and moderated t-statistics for determining interesting genes. We show, with real data, how implementation of functions in R and Bioconductor successfully identified differentially expressed genes that may play a role in soybean resistance to a fungal pathogen, Phakopsora pachyrhizi. Complete source code for performing all quality assessment and statistical procedures may be downloaded from our web source: http://css.ncifcrf.gov/services/download/MicroarraySoybean.zip.'),
(68395, 2007, 'bib', 'Discovering and detecting transposable elements in genome sequences', 'Bergman, Casey M|Quesneville, Hadi', 'Faculty of Life Sciences, University of Manchester, Michael Smith Building, Oxford Road, Manchester, M13 9PT, UK. casey.bergman@manchester.ac.uk|Faculty of Life Sciences, University of Manchester, Michael Smith Building, Oxford Road, Manchester, M13 9PT, UK. casey.bergman@manchester.ac.uk', 'United Kingdom|United Kingdom', 108, 'Algorithms|Base Sequence|Chromosome Mapping|DNA Mutational Analysis|DNA Transposable Elements|Evolution, Molecular|Molecular Sequence Data|Sequence Alignment|Sequence Analysis, DNA', 'Journal Article', '10.1093/bib/bbm048', '17932080', 'The contribution of transposable elements (TEs) to genome structure and evolution as well as their impact on genome sequencing, assembly, annotation and alignment has generated increasing interest in developing new methods for their computational analysis. Here we review the diversity of innovative approaches to identify and annotate TEs in the post-genomic era, covering both the discovery of new TE families and the detection of individual TE copies in genome sequences. These approaches span a broad spectrum in computational biology including de novo, homology-based, structure-based and comparative genomic methods. We conclude that the integration and visualization of multiple approaches and the development of new conceptual representations for TE annotation will further advance the computational analysis of this dynamic component of the genome.'),
(68396, 2007, 'bib', 'Informatics in neuroscience', 'French, Leon|Pavlidis, Paul', 'UBC Bioinformatics Centre (UBiC), 177, Michael Smith Laboratories, 2185 East Mall, University of British Columbia, Vancouver BC V6T1Z4, Canada.|UBC Bioinformatics Centre (UBiC), 177, Michael Smith Laboratories, 2185 East Mall, University of British Columbia, Vancouver BC V6T1Z4, Canada.', 'Canada|Canada', 6, 'Animals|Brain|Computational Biology|Humans|Models, Neurological|Nerve Tissue Proteins|Neurons|Neurosciences', 'Journal Article', '10.1093/bib/bbm047', '17932081', 'The application of informatics to neuroscience goes far beyond traditional bioinformatics modalities such as DNA sequences. In this review, we describe how informatics is being used to study the nervous system at multiple levels, spanning scales from molecules to behavior. The continuing development of standards for data exchange and interoperability, together with increasing awareness and acceptance of the importance of data sharing, are among the key efforts required to advance the field.'),
(68397, 2008, 'bib', 'Biobanking for Europe', 'Yuille, Martin|van Ommen, Gert-Jan|Bréchot, Christian|Cambon-Thomsen, Anne|Dagher, Georges|Landegren, Ulf|Litton, Jan-Eric|Pasterk, Markus|Peltonen, Leena|Taussig, Mike|Wichmann, H-Erich|Zatloukal, Kurt', 'The University of Manchester, School of Translational Medicine, CIGMR Oxford Road, Manchester M13 9PT, UK. Martin.Yuille@manchester.ac.uk|The University of Manchester, School of Translational Medicine, CIGMR Oxford Road, Manchester M13 9PT, UK. Martin.Yuille@manchester.ac.uk|The University of Manchester, School of Translational Medicine, CIGMR Oxford Road, Manchester M13 9PT, UK. Martin.Yuille@manchester.ac.uk|The University of Manchester, School of Translational Medicine, CIGMR Oxford Road, Manchester M13 9PT, UK. Martin.Yuille@manchester.ac.uk|The University of Manchester, School of Translational Medicine, CIGMR Oxford Road, Manchester M13 9PT, UK. Martin.Yuille@manchester.ac.uk|The University of Manchester, School of Translational Medicine, CIGMR Oxford Road, Manchester M13 9PT, UK. Martin.Yuille@manchester.ac.uk|The University of Manchester, School of Translational Medicine, CIGMR Oxford Road, Manchester M13 9PT, UK. Martin.Yuille@manchester.ac.uk|The University of Manchester, School of Translational Medicine, CIGMR Oxford Road, Manchester M13 9PT, UK. Martin.Yuille@manchester.ac.uk|The University of Manchester, School of Translational Medicine, CIGMR Oxford Road, Manchester M13 9PT, UK. Martin.Yuille@manchester.ac.uk|The University of Manchester, School of Translational Medicine, CIGMR Oxford Road, Manchester M13 9PT, UK. Martin.Yuille@manchester.ac.uk|The University of Manchester, School of Translational Medicine, CIGMR Oxford Road, Manchester M13 9PT, UK. Martin.Yuille@manchester.ac.uk|The University of Manchester, School of Translational Medicine, CIGMR Oxford Road, Manchester M13 9PT, UK. Martin.Yuille@manchester.ac.uk', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom', 153, 'Biological Specimen Banks|Europe|European Union|Interinstitutional Relations|Specimen Handling', 'Journal Article', '10.1093/bib/bbm050', '17959611', 'Biobanks are well-organized resources comprising biological samples and associated information that are accessible to scientific investigation. Across Europe, millions of samples with related data are held in different types of collections. While individual collections can be well organized and accessible, the resources are subject to fragmentation, insecurity of funding and incompleteness. To address these issues, a Biobanking and BioMolecular Resources Infrastructure (BBMRI) is to be developed across Europe, thereby implementing a European roadmap for research infrastructures that was developed by a forum of EU member states and that has been received by the European Commission. In this review, we describe the work involved in preparing for the construction of BBMRI in a European and global context.'),
(68398, 2007, 'bib', 'Frontiers of biomedical text mining: current progress', 'Zweigenbaum, Pierre|Demner-Fushman, Dina|Yu, Hong|Cohen, Kevin B', 'LIMSI-CNRS, BP 133, 91403 Orsay Cedex, France. pz@limsi.fr|LIMSI-CNRS, BP 133, 91403 Orsay Cedex, France. pz@limsi.fr|LIMSI-CNRS, BP 133, 91403 Orsay Cedex, France. pz@limsi.fr|LIMSI-CNRS, BP 133, 91403 Orsay Cedex, France. pz@limsi.fr', 'France|France|France|France', 176, 'Abstracting and Indexing as Topic|Artificial Intelligence|Biology|Databases, Bibliographic|Forecasting|Natural Language Processing|Periodicals as Topic|Vocabulary, Controlled', 'Journal Article', '10.1093/bib/bbm045', '17977867', 'It is now almost 15 years since the publication of the first paper on text mining in the genomics domain, and decades since the first paper on text mining in the medical domain. Enormous progress has been made in the areas of information retrieval, evaluation methodologies and resource construction. Some problems, such as abbreviation-handling, can essentially be considered solved problems, and others, such as identification of gene mentions in text, seem likely to be solved soon. However, a number of problems at the frontiers of biomedical text mining continue to present interesting challenges and opportunities for great improvements and interesting research. In this article we review the current state of the art in biomedical text mining or BioNLP in general, focusing primarily on papers published within the past year.'),
(68399, 2008, 'bib', 'Correlated substitution analysis and the prediction of amino acid structural contacts', 'Horner, David S|Pirovano, Walter|Pesole, Graziano', 'Department of Biomolecular Sciences and Biotechnology, University of Milan, via Celoria 26, 20133 Milano, Italy. David.horner@unimi.it|Department of Biomolecular Sciences and Biotechnology, University of Milan, via Celoria 26, 20133 Milano, Italy. David.horner@unimi.it|Department of Biomolecular Sciences and Biotechnology, University of Milan, via Celoria 26, 20133 Milano, Italy. David.horner@unimi.it', 'Italy|Italy|Italy', 50, 'Amino Acid Sequence|Amino Acid Substitution|Amino Acids|Binding Sites|Computer Simulation|Models, Chemical|Models, Molecular|Molecular Sequence Data|Protein Binding|Proteins|Sequence Analysis, Protein', 'Journal Article', '10.1093/bib/bbm052', '18000015', 'It has long been suspected that analysis of correlated amino acid substitutions should uncover pairs or clusters of sites that are spatially proximal in mature protein structures. Accordingly, methods based on different mathematical principles such as information theory, correlation coefficients and maximum likelihood have been developed to identify co-evolving amino acids from multiple sequence alignments. Sets of pairs of sites whose behaviour is identified by these methods as correlated are often significantly enriched in pairs of spatially proximal residues. However, relatively high levels of false-positive predictions typically render such methods, in isolation, of little use in the ab initio prediction of protein structure. Misleading signal (or problems with the estimation of significance levels) can be caused by phylogenetic correlations between homologous sequences and from correlation due to factors other than spatial proximity (for example, correlation of sites which are not spatially close but which are involved in common functional properties of the protein). In recent years, several workers have suggested that information from correlated substitutions should be combined with other sources of information (secondary structure, solvent accessibility, evolutionary rates) in an attempt to reduce the proportion of false-positive predictions. We review methods for the detection of correlated amino acid substitutions, compare their relative performance in contact prediction and predict future directions in the field.');
