INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(69913, 2001, 'bio', 'Time to defend what we have won', 'States, D', 'Institute of Biological Sciences, University of Wales, Aberystwyth SY23 3DD, UK. mendes@vt.edu', 'United Kingdom', 1, 'Computational Biology|Databases, Factual|Humans|Information Management|Societies, Scientific|Time Factors', 'Editorial', '10.1093/bioinformatics/17.4.299', '11301297', '-'),
(69914, 2001, 'bio', 'A relational schema for both array-based and SAGE gene expression experiments', 'Stoeckert, C|Pizarro, A|Manduchi, E|Gibson, M|Brunk, B|Crabtree, J|Schug, J|Shen-Orr, S|Overton, G C', 'Computational Biology and Informatics Laboratory, Center for Bioinformatics, University of Pennsylvania, 1313 Blockley Hall, 418 Guardian Drive, Philadelphia, PA 19104-6021, USA.|Computational Biology and Informatics Laboratory, Center for Bioinformatics, University of Pennsylvania, 1313 Blockley Hall, 418 Guardian Drive, Philadelphia, PA 19104-6021, USA.|Computational Biology and Informatics Laboratory, Center for Bioinformatics, University of Pennsylvania, 1313 Blockley Hall, 418 Guardian Drive, Philadelphia, PA 19104-6021, USA.|Computational Biology and Informatics Laboratory, Center for Bioinformatics, University of Pennsylvania, 1313 Blockley Hall, 418 Guardian Drive, Philadelphia, PA 19104-6021, USA.|Computational Biology and Informatics Laboratory, Center for Bioinformatics, University of Pennsylvania, 1313 Blockley Hall, 418 Guardian Drive, Philadelphia, PA 19104-6021, USA.|Computational Biology and Informatics Laboratory, Center for Bioinformatics, University of Pennsylvania, 1313 Blockley Hall, 418 Guardian Drive, Philadelphia, PA 19104-6021, USA.|Computational Biology and Informatics Laboratory, Center for Bioinformatics, University of Pennsylvania, 1313 Blockley Hall, 418 Guardian Drive, Philadelphia, PA 19104-6021, USA.|Computational Biology and Informatics Laboratory, Center for Bioinformatics, University of Pennsylvania, 1313 Blockley Hall, 418 Guardian Drive, Philadelphia, PA 19104-6021, USA.|Computational Biology and Informatics Laboratory, Center for Bioinformatics, University of Pennsylvania, 1313 Blockley Hall, 418 Guardian Drive, Philadelphia, PA 19104-6021, USA.', 'United States|United States|United States|United States|United States|United States|United States|United States|United States', 32, 'Databases, Factual|Gene Expression|Oligonucleotide Array Sequence Analysis', 'Journal Article', '10.1093/bioinformatics/17.4.300', '11301298', 'A relational schema is described for capturing highly parallel gene expression experiments using different technologies. This schema grew out of efforts to build a database for collaborators working on different biological systems and using different types of platforms in their gene expression experiments as well as different types of image quantification software. The tables are conceptually organized into three categories of information: Platform, Experiment (which includes image scanning and quantification), and Data. The strengths of the schema are: (i) integrating information on array elements using a gene index, (ii) describing samples using ontologies, (iii) reducing an experiment to a single RNA source for precise descriptions yet not losing the relationships between experiments done at the same time or for the same project, and (iv) maintaining both raw and processed (e.g. cleansed and normalized) data and recording how the data is processed. The result is a novel schema, which can hold both array and non-array data, is extensible for detailed experimental descriptions that are precise and consistent, and allows for meaningful comparisons of genes between experiments.'),
(69915, 2001, 'bio', 'Validating clustering for gene expression data', 'Yeung, K Y|Haynor, D R|Ruzzo, W L', 'Computer Science and Engineering, Box 352350, University of Washington, Seattle, WA 98195, USA.|Computer Science and Engineering, Box 352350, University of Washington, Seattle, WA 98195, USA.|Computer Science and Engineering, Box 352350, University of Washington, Seattle, WA 98195, USA.', 'United States|United States|United States', 495, 'Algorithms|Animals|Barrett Esophagus|Central Nervous System|Databases, Factual|Female|Gene Expression|Humans|Ovary|Rats|Saccharomyces cerevisiae|Software Validation', 'Journal Article', '10.1093/bioinformatics/17.4.309', '11301299', 'Many clustering algorithms have been proposed for the analysis of gene expression data, but little guidance is available to help choose among them. We provide a systematic framework for assessing the results of clustering algorithms. Clustering algorithms attempt to partition the genes into groups exhibiting similar patterns of variation in expression level. Our methodology is to apply a clustering algorithm to the data from all but one experimental condition. The remaining condition is used to assess the predictive power of the resulting clusters-meaningful clusters should exhibit less variation in the remaining condition than clusters formed by chance.'),
(69916, 2001, 'bio', 'Use of keyword hierarchies to interpret gene expression patterns', 'Masys, D R|Welsh, J B|Lynn Fink, J|Gribskov, M|Klacansky, I|Corbeil, J', 'Department of Medicine, UCSD Cancer Center, University of California, San Diego, San Diego, CA 92093, USA.|Department of Medicine, UCSD Cancer Center, University of California, San Diego, San Diego, CA 92093, USA.|Department of Medicine, UCSD Cancer Center, University of California, San Diego, San Diego, CA 92093, USA.|Department of Medicine, UCSD Cancer Center, University of California, San Diego, San Diego, CA 92093, USA.|Department of Medicine, UCSD Cancer Center, University of California, San Diego, San Diego, CA 92093, USA.|Department of Medicine, UCSD Cancer Center, University of California, San Diego, San Diego, CA 92093, USA.', 'United States|United States|United States|United States|United States|United States', 96, 'Abstracting and Indexing as Topic|Databases, Factual|Gene Expression Profiling|Information Storage and Retrieval|MEDLINE|Oligonucleotide Array Sequence Analysis', 'Journal Article', '10.1093/bioinformatics/17.4.319', '11301300', 'High-density microarray technology permits the quantitative and simultaneous monitoring of thousands of genes. The interpretation challenge is to extract relevant information from this large amount of data. A growing variety of statistical analysis approaches are available to identify clusters of genes that share common expression characteristics, but provide no information regarding the biological similarities of genes within clusters. The published literature provides a potential source of information to assist in interpretation of clustering results.'),
(69917, 2001, 'bio', 'A new approach to sequence comparison: normalized sequence alignment', 'Arslan, A N|Exc4x9fecioxc4x9flu , O|Pevzner, P A', 'Department of Computer Science, University of California, Santa Barbara, Santa Barbara, CA 93106, USA Department of Computer Science and Engineering, University of California, San Diego, San Diego, CA 92093, USA.|Department of Computer Science, University of California, Santa Barbara, Santa Barbara, CA 93106, USA Department of Computer Science and Engineering, University of California, San Diego, San Diego, CA 92093, USA.|Department of Computer Science, University of California, Santa Barbara, Santa Barbara, CA 93106, USA Department of Computer Science and Engineering, University of California, San Diego, San Diego, CA 92093, USA.', 'United States|United States|United States', 57, 'Algorithms|Sequence Alignment|Software', 'Journal Article', '10.1093/bioinformatics/17.4.327', '11301301', 'The Smith-Waterman algorithm for local sequence alignment is one of the most important techniques in computational molecular biology. This ingenious dynamic programming approach was designed to reveal the highly conserved fragments by discarding poorly conserved initial and terminal segments. However, the existing notion of local similarity has a serious flaw: it does not discard poorly conserved intermediate segments. The Smith-Waterman algorithm finds the local alignment with maximal score but it is unable to find local alignment with maximum degree of similarity (e.g. maximal percent of matches). Moreover, there is still no efficient algorithm that answers the following natural question: do two sequences share a (sufficiently long) fragment with more than 70% of similarity? As a result, the local alignment sometimes produces a mosaic of well-conserved fragments artificially connected by poorly-conserved or even unrelated fragments. This may lead to problems in comparison of long genomic sequences and comparative gene prediction as recently pointed out by Zhang et al. (Bioinformatics, 15, 1012-1019, 1999). In this paper we propose a new sequence comparison algorithm (normalized local alignment ) that reports the regions with maximum degree of similarity. The algorithm is based on fractional programming and its running time is O(n2log n). In practice, normalized local alignment is only 3-5 times slower than the standard Smith-Waterman algorithm.'),
(69918, 2001, 'bio', 'Limits of homology detection by pairwise sequence comparison', 'Spang, R|Vingron, M', 'Deutsches Krebsforschungszentrum, Theoretische Bioinformatik, Im Neuenheimer Feld 280, 69120 Heidelberg, Germany.|Deutsches Krebsforschungszentrum, Theoretische Bioinformatik, Im Neuenheimer Feld 280, 69120 Heidelberg, Germany.', 'Germany|Germany', 18, 'Computer Simulation|Databases, Factual|Mathematical Computing|Models, Statistical|Proteins|Sequence Alignment|Sequence Homology, Nucleic Acid', 'Journal Article', '10.1093/bioinformatics/17.4.338', '11301302', 'Noise in database searches resulting from random sequence similarities increases as the databases expand rapidly. The noise problems are not a technical shortcoming of the database search programs, but a logical consequence of the idea of homology searches. The effect can be observed in simulation experiments.'),
(69919, 2001, 'bio', 'NIFAS: visual analysis of domain evolution in proteins', 'Storm, C E|Sonnhammer, E L', 'Center for Genomics Research, Karolinska Institutet, 171 77 Stockholm, Sweden.|Center for Genomics Research, Karolinska Institutet, 171 77 Stockholm, Sweden.', 'Sweden|Sweden', 16, 'Computer Graphics|Evolution, Molecular|Humans|Image Processing, Computer-Assisted|Protein Kinase C|Protein Structure, Tertiary|Proteins|Software', 'Journal Article', '10.1093/bioinformatics/17.4.343', '11301303', 'Multi-domain proteins have evolved by insertions or deletions of distinct protein domains. Tracing the history of a certain domain combination can be important for functional annotation of multi-domain proteins, and for understanding the function of individual domains. In order to analyze the evolutionary history of the domains in modular proteins it is desirable to inspect a phylogenetic tree based on sequence divergence with the modular architecture of the sequences superimposed on the tree.'),
(69920, 2001, 'bio', 'Multi-class protein fold recognition using support vector machines and neural networks', 'Ding, C H|Dubchak, I', 'NERSC Division, Lawrence Berkeley National Laboratory, University of California, Berkeley, CA 94720, USA.|NERSC Division, Lawrence Berkeley National Laboratory, University of California, Berkeley, CA 94720, USA.', 'United States|United States', 674, 'Discriminant Analysis|Neural Networks (Computer)|Protein Folding|Proteins', 'Journal Article', '10.1093/bioinformatics/17.4.349', '11301304', 'Protein fold recognition is an important approach to structure discovery without relying on sequence similarity. We study this approach with new multi-class classification methods and examined many issues important for a practical recognition system.'),
(69921, 2001, 'bio', 'Mining literature for protein-protein interactions', 'Marcotte, E M|Xenarios, I|Eisenberg, D', 'Molecular Biology Institute, UCLA-DOE Laboratory of Structural Biology & Molecular Medicine, University of California at Los Angeles, PO Box 951570, Los Angeles, CA 90095-1570, USA.|Molecular Biology Institute, UCLA-DOE Laboratory of Structural Biology & Molecular Medicine, University of California at Los Angeles, PO Box 951570, Los Angeles, CA 90095-1570, USA.|Molecular Biology Institute, UCLA-DOE Laboratory of Structural Biology & Molecular Medicine, University of California at Los Angeles, PO Box 951570, Los Angeles, CA 90095-1570, USA.', 'United States|United States|United States', 216, 'Algorithms|Bayes Theorem|Information Storage and Retrieval|MEDLINE|Proteins', 'Journal Article', '10.1093/bioinformatics/17.4.359', '11301305', 'A central problem in bioinformatics is how to capture information from the vast current scientific literature in a form suitable for analysis by computer. We address the special case of information on protein-protein interactions, and show that the frequencies of words in Medline abstracts can be used to determine whether or not a given paper discusses protein-protein interactions. For those papers determined to discuss this topic, the relevant information can be captured for the Database of Interacting PROTEINS: Furthermore, suitable gene annotations can also be captured.'),
(69922, 2001, 'bio', 'Strategies for the development of a peptide computer', 'Hug, H|Schuler, R', 'Universitäts-Kinderklinik Ulm, Prittwitzstrasse 43, D-89075 Ulm, Germany.|Universitäts-Kinderklinik Ulm, Prittwitzstrasse 43, D-89075 Ulm, Germany.', 'Germany|Germany', 25, 'Antibodies|Antigen-Antibody Reactions|Computer Simulation|Computers|Models, Molecular|Peptides|Protein Binding|Proteins', 'Journal Article', '10.1093/bioinformatics/17.4.364', '11301306', 'We devise a computational model using protein-protein interactions.'),
(69923, 2001, 'bio', 'J-Express: exploring gene expression data using Java', 'Dysvik, B|Jonassen, I', 'Department of Informatics, University of Bergen, HIB, N5020 Bergen, Norway.|Department of Informatics, University of Bergen, HIB, N5020 Bergen, Norway.', 'Norway|Norway', 270, 'Databases, Factual|Gene Expression|Oligonucleotide Array Sequence Analysis|Software', 'Journal Article', '10.1093/bioinformatics/17.4.369', '11301307', 'J-Express is a Java application that allows the user to analyze gene expression (microarray) data in a flexible way giving access to multidimensional scaling, clustering, and visualization methods in an integrated manner. Specifically, J-Express includes implementations of hierarchical clustering, k-means, principal component analysis, and self-organizing maps. At present, it does not include methods for comparing two or more experiments for differentially expressed genes. The application is completely portable and requires only that a Java runtime environment 1.2 is installed on the system. Its efficiency allows interactive clustering of thousands of expression profiles on standard personal computers.'),
(69924, 2001, 'bio', 'DeFries-Fulker multiple regression analysis of sibship QTL data: a SAS macro', 'Lessem, J M|Cherny, S S|Lessem, J L', 'Institute for Behavioral Genetics, University of Colorado, Boulder, CO 80309-0447, USA.|Institute for Behavioral Genetics, University of Colorado, Boulder, CO 80309-0447, USA.|Institute for Behavioral Genetics, University of Colorado, Boulder, CO 80309-0447, USA.', 'United States|United States|United States', 15, 'Alleles|Chromosome Mapping|Genetic Linkage|Quantitative Trait, Heritable|Regression Analysis|Software', 'Journal Article', '10.1093/bioinformatics/17.4.371', '11301308', 'A SAS macro package for performing multipoint QTL mapping using the DeFries-Fulker multiple regression method is presented.'),
(69925, 2001, 'bio', 'Mocca: semi-automatic method for domain hunting', 'Notredame, C', 'Information Genetique et Structurale, CNRS-UMR 1889, 31 Ch. Joseph Aiguier, 13 402 Marseille, France.', 'France', 8, 'Automation|Databases, Factual|Information Storage and Retrieval|Protein Structure, Tertiary|Proteins|Software', 'Journal Article', '10.1093/bioinformatics/17.4.373', '11301309', 'Multiple OCCurrences Analysis (Mocca) is a new method for repeat extraction. It is based on the T-Coffee package (Notredame et al., JMB, 302, 205-217, 2000). Given a sequence or a set of sequences, and a library of local alignments, Mocca extracts every segment of sequence homologous to a pre-specified master. The implementation is meant for domain hunting and makes it fast and easy to test for new boundaries or extend known repeats in an interactive manner. Mocca is designed to deal with highly divergent protein repeats (less than 30% amino acid identity) of more than 30 amino acids.'),
(69926, 2001, 'bio', 'PALI: a database of alignments and phylogeny of homologous protein structures', 'Sujatha, S|Balaji, S|Srinivasan, N', 'Molecular Biophysics Unit, Indian Institute of Science, Bangalore 560 012, India.|Molecular Biophysics Unit, Indian Institute of Science, Bangalore 560 012, India.|Molecular Biophysics Unit, Indian Institute of Science, Bangalore 560 012, India.', 'India|India|India', 21, 'Databases, Factual|Internet|Phylogeny|Protein Structure, Quaternary|Proteins|Sequence Alignment', 'Journal Article', '10.1093/bioinformatics/17.4.375', '11301310', 'PALI is a database of structure-based sequence alignments and phylogenetic relationships derived on the basis of three-dimensional structures of homologous proteins. This database enables grouping of pairs of homologous protein structures on the basis of their sequence identity calculated from the structure-based alignment and PALI also enables association of a new sequence to a family and automatic generation of a dendrogram combining the query sequence and homologous protein structures.'),
(69927, 2001, 'bio', 'STRAP: editor for STRuctural Alignments of Proteins', 'Gille, C|Frömmel, C', 'Institute of Biochemistry, Charité, Medical Faculty of the Humboldt University, Monbijoustrasse 2a, 10117 Berlin, Germany.|Institute of Biochemistry, Charité, Medical Faculty of the Humboldt University, Monbijoustrasse 2a, 10117 Berlin, Germany.', 'Germany|Germany', 168, 'Cysteine Endopeptidases|Databases, Factual|Multienzyme Complexes|Proteasome Endopeptidase Complex|Proteins|Sequence Alignment|Software', 'Journal Article', '10.1093/bioinformatics/17.4.377', '11301311', 'STRAP is a comfortable and extensible tool for the generation and refinement of multiple alignments of protein sequences. Various sequence ordered input file formats are supported. These are the SwissProt-,GenBank-, EMBL-, DSSP- PDB-, MSF-, and plain ASCII text format. The special feature of STRAP is the simple visualization of spatial distances C(alpha)-atoms within the alignment. Thus structural information can easily be incorporated into the sequence alignment and can guide the alignment process in cases of low sequence similarities. Further STRAP is able to manage huge alignments comprising a lot of sequences. The protein viewers and modeling programs INSIGHT, RASMOL and WEBMOL are embedded into STRAP. STRAP is written in JAVA: The well-documented source code can be adapted easily to special requirements. STRAP may become the basis for complex alignment tools in the future.'),
(69928, 2001, 'bio', 'ProDDO: a database of disordered proteins from the Protein Data Bank (PDB)', 'Sim, K L|Uchida, T|Miyano, S', 'Human Genome Center, Institute of Medical Science, University of Tokyo, 4-6-1 Shirokanedai, Minato-ku, Tokyo 108-8639, Japan.|Human Genome Center, Institute of Medical Science, University of Tokyo, 4-6-1 Shirokanedai, Minato-ku, Tokyo 108-8639, Japan.|Human Genome Center, Institute of Medical Science, University of Tokyo, 4-6-1 Shirokanedai, Minato-ku, Tokyo 108-8639, Japan.', 'Japan|Japan|Japan', 11, 'Databases, Factual|Protein Structure, Secondary|Proteins', 'Journal Article', '10.1093/bioinformatics/17.4.379', '11301312', 'ProDDO represents a pre-screened database that denotes disorder (or possible disorder) in proteins from the PDB.'),
(69929, 2001, 'bio', 'MutaProt: a web interface for structural analysis of point mutations', 'Eyal, E|Najmanovich, R|Sobolev, V|Edelman, M', 'Department of Plant Sciences, Weizmann Institute of Science, Rehovot 76100, Israel.|Department of Plant Sciences, Weizmann Institute of Science, Rehovot 76100, Israel.|Department of Plant Sciences, Weizmann Institute of Science, Rehovot 76100, Israel.|Department of Plant Sciences, Weizmann Institute of Science, Rehovot 76100, Israel.', 'Israel|Israel|Israel|Israel', 8, 'Databases, Factual|Internet|Mutagenesis, Site-Directed|Point Mutation|Protein Conformation|Proteins|Software', 'Journal Article', '10.1093/bioinformatics/17.4.381', '11301313', 'A web-based tool, termed MutaProt, is described which analyses pairs of PDB files whose members differ in one, or two, amino acids. MutaProt examines the micro environment surrounding the exchanged residue(s) and can be searched by specifying a PDB ID, keywords, or any pair of amino acids. Detailed information about accessibility of the exchanged residue(s) and its atomic contacts are provided based on CSU software (Sobolev et al., Bioinformatics, 15, 327-332, 1999). An interactive 3D presentation of the superimposed regions around the mutation(s) is included. MutaProt is updated weekly.'),
(69930, 2001, 'bio', 'ATV: display and manipulation of annotated phylogenetic trees', 'Zmasek, C M|Eddy, S R', 'Howard Hughes Medical Institute, Department of Genetics, Washington University School of Medicine, St Louis, MO 63110, USA.|Howard Hughes Medical Institute, Department of Genetics, Washington University School of Medicine, St Louis, MO 63110, USA.', 'United States|United States', 213, 'Databases, Factual|Humans|Internet|Phylogeny|Proteins|Software', 'Journal Article', '10.1093/bioinformatics/17.4.383', '11301314', 'A Tree Viewer (ATV) is a Java tool for the display and manipulation of annotated phylogenetic trees. It can be utilized both as a standalone application and as an applet in a web browser.'),
(69931, 2001, 'bio', 'PhyloBLAST: facilitating phylogenetic analysis of BLAST results', 'Brinkman, F S|Wan, I|Hancock, R E|Rose, A M|Jones, S J', 'Department of Microbiology and Immunology, University of British Columbia, Vancouver, Canada V6T 1Z3.|Department of Microbiology and Immunology, University of British Columbia, Vancouver, Canada V6T 1Z3.|Department of Microbiology and Immunology, University of British Columbia, Vancouver, Canada V6T 1Z3.|Department of Microbiology and Immunology, University of British Columbia, Vancouver, Canada V6T 1Z3.|Department of Microbiology and Immunology, University of British Columbia, Vancouver, Canada V6T 1Z3.', 'Canada|Canada|Canada|Canada|Canada', 16, 'Internet|Phylogeny|Proteins|Sequence Analysis|Software', 'Journal Article', '10.1093/bioinformatics/17.4.385', '11301315', 'PhyloBLAST is an internet-accessed application based on CGI/Perl programming that compares a users protein sequence to a SwissProt/TREMBL database using BLAST2 and then allows phylogenetic analyses to be performed on selected sequences from the BLAST output. Flexible features such as ability to input your own multiple sequence alignment and use PHYLIP program options provide additional web-based phylogenetic analysis functionality beyond the analysis of a BLAST result.'),
(69932, 2001, 'bio', 'Bioinformatics needs to adopt statistical thinking', 'Vingron, M', 'Department of Microbiology and Immunology, University of British Columbia, Vancouver, Canada V6T 1Z3.', 'Canada', 18, 'Biometry|Computational Biology|Data Interpretation, Statistical|Databases, Factual|Humans|Sequence Alignment', 'Editorial', '10.1093/bioinformatics/17.5.389', '11331232', '-'),
(69933, 2001, 'bio', 'Comparison of genomic DNA sequences: solved and unsolved problems', 'Miller, W', 'Department of Computer Science and Engineering, Penn State, University Park, PA 16802, USA. webb@cse.psu.edu', 'United States', 0, 'Algorithms|Computational Biology|Sequence Alignment|Software', 'Comparative Study', '10.1093/bioinformatics/17.5.391', '11331233', 'The DNA sequences of entire genomes are being determined at a rapid rate. Whereas initial genome sequencing efforts were for organisms chosen to be widely spaced in the tree of life, there is a growing emphasis on projects to sequence a species that is sufficiently similar to an already-sequenced species to allow direct comparison of those two DNA sequences. This and other changes in genome sequencing strategies have created a strong need for new methods to compare genomic sequences.'),
(69934, 2001, 'bio', 'Analysis of temporal gene expression profiles: clustering by simulated annealing and determining the optimal number of clusters', 'Lukashin, A V|Fuchs, R', 'Biogen Inc., 14 Cambridge Center, Cambridge, MA 02142, USA. alex_lukashin@biogen.com|Biogen Inc., 14 Cambridge Center, Cambridge, MA 02142, USA. alex_lukashin@biogen.com', 'United States|United States', 146, 'Algorithms|Cell Cycle|Cluster Analysis|Computational Biology|Gene Expression Profiling|Oligonucleotide Array Sequence Analysis|Saccharomyces cerevisiae', 'Journal Article', '10.1093/bioinformatics/17.5.405', '11331234', 'Cluster analysis of genome-wide expression data from DNA microarray hybridization studies has proved to be a useful tool for identifying biologically relevant groupings of genes and samples. In the present paper, we focus on several important issues related to clustering algorithms that have not yet been fully studied.'),
(69935, 2001, 'bio', 'Retrieval and on-the-fly alignment of sequence fragments from the HIV database', 'Gaschen, B|Kuiken, C|Korber, B|Foley, B', 'HIV Database and Analysis Group (T10), Los Alamos National Laboratory, Mail stop K710, Los Alamos, NM 87545, USA. bkg@lanl.gov|HIV Database and Analysis Group (T10), Los Alamos National Laboratory, Mail stop K710, Los Alamos, NM 87545, USA. bkg@lanl.gov|HIV Database and Analysis Group (T10), Los Alamos National Laboratory, Mail stop K710, Los Alamos, NM 87545, USA. bkg@lanl.gov|HIV Database and Analysis Group (T10), Los Alamos National Laboratory, Mail stop K710, Los Alamos, NM 87545, USA. bkg@lanl.gov', 'United States|United States|United States|United States', 0, 'Algorithms|Computational Biology|Databases, Factual|HIV-1|Humans|Markov Chains|Sequence Alignment|Software|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/17.5.415', '11331235', 'The amount of HIV-1 sequence data generated (presently around 42000 sequences, of which more than 22000 are from the V3 region of the viral envelope) presents a challenge for anyone working on the analysis of these data. A major problem is obtaining the region of interest from the stored sequences, which often contain but are not limited to that region. In addition, multiple alignment programs generally cannot deal with the large numbers of sequences that are available for many HIV-1 regions. We set out to provide our users with a tool that will retrieve and create an initial alignment of the HIV sequences that are available for a given genomic region.'),
(69936, 2001, 'bio', 'Efficient large-scale sequence comparison by locality-sensitive hashing', 'Buhler, J', 'Department of Computer Science and Engineering, University of Washington, Seattle, WA 98195-2350, USA. jbuhler@cs.washington.edu', 'United States', 155, 'Algorithms|Animals|Chromosomes, Human, Pair 22|Computational Biology|DNA|Databases, Factual|Globins|Humans|Locus Control Region|Mice|Receptors, Antigen, T-Cell, alpha-beta|Sequence Alignment|Software', 'Comparative Study', '10.1093/bioinformatics/17.5.419', '11331236', 'Comparison of multimegabase genomic DNA sequences is a popular technique for finding and annotating conserved genome features. Performing such comparisons entails finding many short local alignments between sequences up to tens of megabases in length. To process such long sequences efficiently, existing algorithms find alignments by expanding around short runs of matching bases with no substitutions or other differences. Unfortunately, exact matches that are short enough to occur often in significant alignments also occur frequently by chance in the background sequence. Thus, these algorithms must trade off between efficiency and sensitivity to features without long exact matches.'),
(69937, 2001, 'bio', 'Analysis of genomic sequences by Chaos Game Representation', 'Almeida, J S|Carriço, J A|Maretzek, A|Noble, P A|Fletcher, M', 'ITQB/Universidade Nova Lisboa, PO Box 127, 2780 Oeiras, Portugal. almeidaj@musc.edu|ITQB/Universidade Nova Lisboa, PO Box 127, 2780 Oeiras, Portugal. almeidaj@musc.edu|ITQB/Universidade Nova Lisboa, PO Box 127, 2780 Oeiras, Portugal. almeidaj@musc.edu|ITQB/Universidade Nova Lisboa, PO Box 127, 2780 Oeiras, Portugal. almeidaj@musc.edu|ITQB/Universidade Nova Lisboa, PO Box 127, 2780 Oeiras, Portugal. almeidaj@musc.edu', 'Portugal|Portugal|Portugal|Portugal|Portugal', 0, 'Algorithms|Base Sequence|DNA, Bacterial|Escherichia coli|Game Theory|Genome|Genome, Bacterial|Nonlinear Dynamics|Sequence Alignment|Sequence Analysis, DNA|Threonine', 'Journal Article', '10.1093/bioinformatics/17.5.429', '11331237', 'Chaos Game Representation (CGR) is an iterative mapping technique that processes sequences of units, such as nucleotides in a DNA sequence or amino acids in a protein, in order to find the coordinates for their position in a continuous space. This distribution of positions has two properties: it is unique, and the source sequence can be recovered from the coordinates such that distance between positions measures similarity between the corresponding sequences. The possibility of using the latter property to identify succession schemes have been entirely overlooked in previous studies which raises the possibility that CGR may be upgraded from a mere representation technique to a sequence modeling tool.'),
(69938, 2001, 'bio', 'Unsupervised classification of noisy chromosomes', 'Chan, T Y', 'The University of Aizu, Aizu-Wakamatsu Shi, Fukushima Ken, 965-80 Japan. t-chan@u-aizu.ac.jp', 'Japan', 0, 'Algorithms|Chromosomes|Computational Biology|Models, Genetic|Pattern Recognition, Automated', 'Journal Article', '10.1093/bioinformatics/17.5.438', '11331238', 'Almost all methods of chromosome recognition assume supervised training, i.e. we are given correctly classified chromosomes to start the training phase. Noise, if any, is confined only in the representation of the chromosomes and not in the classification of the chromosomes. During the recognition phase, the problem is simply to calculate the string edit distance of the unknowns to the representatives chosen from the training phase and classify the unknowns accordingly.'),
(69939, 2001, 'bio', 'The utility of different representations of protein sequence for predicting functional class', 'King, R D|Karwath, A|Clare, A|Dehaspe, L', 'Department of Computer Science, University of Wales, Aberystwyth, Penglais, Aberystwyth, Ceredigion SY23 3DB, Wales, UK. rdk@aber.ac.uk|Department of Computer Science, University of Wales, Aberystwyth, Penglais, Aberystwyth, Ceredigion SY23 3DB, Wales, UK. rdk@aber.ac.uk|Department of Computer Science, University of Wales, Aberystwyth, Penglais, Aberystwyth, Ceredigion SY23 3DB, Wales, UK. rdk@aber.ac.uk|Department of Computer Science, University of Wales, Aberystwyth, Penglais, Aberystwyth, Ceredigion SY23 3DB, Wales, UK. rdk@aber.ac.uk', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom', 0, 'Bacterial Proteins|Computational Biology|Escherichia coli|Open Reading Frames|Proteins|Sequence Analysis, Protein|Software Design', 'Journal Article', '10.1093/bioinformatics/17.5.445', '11331239', 'Data Mining Prediction (DMP) is a novel approach to predicting protein functional class from sequence. DMP works even in the absence of a homologous protein of known function. We investigate the utility of different ways of representing protein sequence in DMP (residue frequencies, phylogeny, predicted structure) using the Escherichia coli genome as a model.'),
(69940, 2001, 'bio', 'Predicting protein--protein interactions from primary structure', 'Bock, J R|Gough, D A', 'Department of Bioengineering, 9500 Gilman Drive, University of California, San Diego, La Jolla, CA 92093-0412, USA.|Department of Bioengineering, 9500 Gilman Drive, University of California, San Diego, La Jolla, CA 92093-0412, USA.', 'United States|United States', 0, 'Artificial Intelligence|Chemical Phenomena|Chemistry, Physical|Computational Biology|Databases, Factual|Proteins|Proteome|Software', 'Journal Article', '10.1093/bioinformatics/17.5.455', '11331240', 'An ambitious goal of proteomics is to elucidate the structure, interactions and functions of all proteins within cells and organisms. The expectation is that this will provide a fuller appreciation of cellular processes and networks at the protein level, ultimately leading to a better understanding of disease mechanisms and suggesting new means for intervention. This paper addresses the question: can protein-protein interactions be predicted directly from primary structure and associated data? Using a diverse database of known protein interactions, a Support Vector Machine (SVM) learning system was trained to recognize and predict interactions based solely on primary structure and associated physicochemical properties.'),
(69941, 2001, 'bio', 'A graph layout algorithm for drawing metabolic pathways', 'Becker, M Y|Rojas, I', 'Scientific Databases and Visualization Group, European Media Laboratory, Schloss-Wolfsbrunnenweg 33, D-69118 Heidelberg, Germany. mywyb2@cam.ac.uk|Scientific Databases and Visualization Group, European Media Laboratory, Schloss-Wolfsbrunnenweg 33, D-69118 Heidelberg, Germany. mywyb2@cam.ac.uk', 'Germany|Germany', 77, 'Algorithms|Computational Biology|Computer Graphics|Metabolism|Models, Biological', 'Journal Article', '10.1093/bioinformatics/17.5.461', '11331241', 'A large amount of data on metabolic pathways is available in databases. The ability to visualise the complex data dynamically would be useful for building more powerful research tools to access the databases. Metabolic pathways are typically modelled as graphs in which nodes represent chemical compounds, and edges represent chemical reactions between compounds. Thus, the problem of visualising pathways can be formulated as a graph layout problem. Currently available visual interfaces to biochemical databases either use static images or cannot cope well with more complex, non-standard pathways.'),
(69942, 2001, 'bio', 'BioMolQuest: integrated database-based retrieval of protein structural and functional information', 'Bukhman, Y V|Skolnick, J', 'Laboratory of Computational Genomics, Donald Danforth Plant Science Center, 893 N. Warson Rd, St Louis, MO 63141, USA.|Laboratory of Computational Genomics, Donald Danforth Plant Science Center, 893 N. Warson Rd, St Louis, MO 63141, USA.', 'United States|United States', 16, 'Abstracting and Indexing as Topic|Algorithms|Computational Biology|Database Management Systems|Databases, Factual|Proteins|Software', 'Journal Article', '10.1093/bioinformatics/17.5.468', '11331242', 'Information about a particular protein or protein family is usually distributed among multiple databases and often in more than one entry in each database. Retrieval and organization of this information can be a laborious task. This task is complicated even further by the existence of alternative terms for the same concept.'),
(69943, 2001, 'bio', 'MAC5: Bayesian inference of phylogenetic trees from DNA sequences incorporating gaps', 'McGuire, G|Denham, M C|Balding, D J', 'Department of Applied Statistics, PO Box 240, University of Reading, Reading RG6 6FN, UK. gmcguire@hgmp.mrc.ac.uk|Department of Applied Statistics, PO Box 240, University of Reading, Reading RG6 6FN, UK. gmcguire@hgmp.mrc.ac.uk|Department of Applied Statistics, PO Box 240, University of Reading, Reading RG6 6FN, UK. gmcguire@hgmp.mrc.ac.uk', 'United Kingdom|United Kingdom|United Kingdom', 0, 'Algorithms|Bayes Theorem|Computational Biology|Models, Genetic|Phylogeny|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/17.5.479', '11331243', 'MAC5 implements MCMC sampling of the posterior distribution of tree topologies from DNA sequences containing gaps by using a five state model of evolution (the four nucleotides and the gap character).'),
(69944, 2001, 'bio', 'Cladogramer: incorporating haplotype frequency into cladogram analysis', 'Zhang, L|Heng, C K|Tan, T W', 'Kent Ridge Digital Laboratories, 21 Heng Mui Keng Terrace, Singapore 119613. lxzhang@krdl.org.sg|Kent Ridge Digital Laboratories, 21 Heng Mui Keng Terrace, Singapore 119613. lxzhang@krdl.org.sg|Kent Ridge Digital Laboratories, 21 Heng Mui Keng Terrace, Singapore 119613. lxzhang@krdl.org.sg', 'Singapore|Singapore|Singapore', 0, 'Computational Biology|Haplotypes|Mutation|Polymorphism, Genetic|Software|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/17.5.481', '11331244', 'We implement a program that incorporates polymorphic sites data, haplotype frequency arrays, and other factors, into cladogram estimation.'),
(69945, 2001, 'bio', 'A-Cell: graphical user interface for the construction of biochemical reaction models', 'Ichikawa, K', 'Corporate Research Center, Fuji Xerox, Co. Ltd, 430 Sakai Nakaimachi Ashigarakamigun Kanagawa, 259-0157, Japan. kazuhisa.ichikawa@fujixerox.co.jp', 'Japan', 27, 'Biochemistry|Computational Biology|Computer Simulation|Models, Biological|Software|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/17.5.483', '11331245', 'A-Cell is a tool for constructing models of complex and complicated biochemical reactions. An important feature of A-Cell is its graphical user interface for constructing biochemical reactions. In addition, it has a capability of importing previously constructed models, combining them, and constructing a comprehensive model. The simulation program for the model is automatically generated by A-cell.'),
(69946, 2001, 'bio', 'ExScript: an ex-centric approach to the description of transcript diversity', 'Hide, W', 'Corporate Research Center, Fuji Xerox, Co. Ltd, 430 Sakai Nakaimachi Ashigarakamigun Kanagawa, 259-0157, Japan. kazuhisa.ichikawa@fujixerox.co.jp', 'Japan', 0, 'Animals|Databases, Factual|Gene Expression|Gene Expression Profiling|Gene Library|Genetic Variation|Human Genome Project|Humans|Transcription, Genetic', 'Editorial', '10.1093/bioinformatics/17.6.485', '11395424', '-'),
(69947, 2001, 'bio', 'Uniform integration of genome mapping data using intersection graphs', 'Harley, E|Bonner, A|Goodman, N', 'Department of Computer Science, University of Toronto, Toronto, Ontario, Canada M5S 1A4. eharley@cs.toronto.edu|Department of Computer Science, University of Toronto, Toronto, Ontario, Canada M5S 1A4. eharley@cs.toronto.edu|Department of Computer Science, University of Toronto, Toronto, Ontario, Canada M5S 1A4. eharley@cs.toronto.edu', 'Canada|Canada|Canada', 0, 'Algorithms|Chromosome Mapping|Contig Mapping|DNA Fingerprinting|Data Display|Data Interpretation, Statistical|False Negative Reactions|Models, Genetic|Molecular Probes|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/17.6.487', '11395425', 'The methods for analyzing overlap data are distinct from those for analyzing probe data, making integration of the two forms awkward. Conversion of overlap data to probe-like data elements would facilitate comparison and uniform integration of overlap data and probe data using software developed for analysis of STS data.'),
(69948, 2001, 'bio', 'Aligning gene expression time series with time warping algorithms', 'Aach, J|Church, G M', 'Department of Genetics and Lipper Center for Computational Genetics, Harvard Medical School, 200 Longwood Ave, Boston, MA 02115, USA.|Department of Genetics and Lipper Center for Computational Genetics, Harvard Medical School, 200 Longwood Ave, Boston, MA 02115, USA.', 'United States|United States', 333, 'Algorithms|Cell Cycle|Chromosome Mapping|Cluster Analysis|Computer Graphics|Data Interpretation, Statistical|Fourier Analysis|Gene Expression Profiling|Mathematical Computing|RNA|Saccharomyces cerevisiae|Sequence Alignment|Time Factors', 'Comparative Study', '10.1093/bioinformatics/17.6.495', '11395426', 'motivation: Increasingly, biological processes are being studied through time series of RNA expression data collected for large numbers of genes. Because common processes may unfold at varying rates in different experiments or individuals, methods are needed that will allow corresponding expression states in different time series to be mapped to one another.'),
(69949, 2001, 'bio', 'A Bayesian framework for the analysis of microarray expression data: regularized t -test and statistical inferences of gene changes', 'Baldi, P|Long, A D', 'Department of Information and Computer Science, University of California at Irvine, Irvine, CA 92697-3425, USA. pfbaldi@ics.uci.edu|Department of Information and Computer Science, University of California at Irvine, Irvine, CA 92697-3425, USA. pfbaldi@ics.uci.edu', 'United States|United States', 0, 'Bayes Theorem|Computer Simulation|Data Interpretation, Statistical|Models, Genetic|Models, Statistical|Oligonucleotide Array Sequence Analysis|Programming Languages|Software', 'Journal Article', '10.1093/bioinformatics/17.6.509', '11395427', 'DNA microarrays are now capable of providing genome-wide patterns of gene expression across many different conditions. The first level of analysis of these patterns requires determining whether observed differences in expression are significant or not. Current methods are unsatisfactory due to the lack of a systematic framework that can accommodate noise, variability, and low replication often typical of microarray data.'),
(69950, 2001, 'bio', 'Missing value estimation methods for DNA microarrays', 'Troyanskaya, O|Cantor, M|Sherlock, G|Brown, P|Hastie, T|Tibshirani, R|Botstein, D|Altman, R B', 'Stanford Medical Informatics Stanford University School of Medicine, Stanford, CA, USA.|Stanford Medical Informatics Stanford University School of Medicine, Stanford, CA, USA.|Stanford Medical Informatics Stanford University School of Medicine, Stanford, CA, USA.|Stanford Medical Informatics Stanford University School of Medicine, Stanford, CA, USA.|Stanford Medical Informatics Stanford University School of Medicine, Stanford, CA, USA.|Stanford Medical Informatics Stanford University School of Medicine, Stanford, CA, USA.|Stanford Medical Informatics Stanford University School of Medicine, Stanford, CA, USA.|Stanford Medical Informatics Stanford University School of Medicine, Stanford, CA, USA.', 'United States|United States|United States|United States|United States|United States|United States|United States', 2006, 'Algorithms|Cell Cycle|Cluster Analysis|Data Display|Data Interpretation, Statistical|Gene Expression|Mathematical Computing|Multigene Family|Oligonucleotide Array Sequence Analysis|Saccharomyces cerevisiae|Sensitivity and Specificity|Software', 'Comparative Study', '10.1093/bioinformatics/17.6.520', '11395428', 'Gene expression microarray experiments can generate data sets with multiple missing expression values. Unfortunately, many algorithms for gene expression analysis require a complete matrix of gene array values as input. For example, methods such as hierarchical clustering and K-means clustering are not robust to missing data, and may lose effectiveness even with a few missing values. Methods for imputing missing data are needed, therefore, to minimize the effect of incomplete data sets on analyses, and to increase the range of data sets to which these algorithms can be applied. In this report, we investigate automated methods for estimating missing data.'),
(69951, 2001, 'bio', 'Database verification studies of SWISS-PROT and GenBank', 'Karp, P D|Paley, S|Zhu, J', 'Bioinformatics Research Group, SRI International, EK223, 333 Ravenswood Ave, Menlo Park, CA 94025, USA. pkarp@ai.sri.com|Bioinformatics Research Group, SRI International, EK223, 333 Ravenswood Ave, Menlo Park, CA 94025, USA. pkarp@ai.sri.com|Bioinformatics Research Group, SRI International, EK223, 333 Ravenswood Ave, Menlo Park, CA 94025, USA. pkarp@ai.sri.com', 'United States|United States|United States', 22, 'Algorithms|Base Sequence|Data Interpretation, Statistical|Databases, Factual|Escherichia coli|Gene Library|Haemophilus influenzae|Helicobacter pylori|Human Genome Project|Open Reading Frames|Protein Biosynthesis|Species Specificity', 'Evaluation Studies', '10.1093/bioinformatics/17.6.526', '11395429', 'We have studied the relationships among SWISS-PROT, TrEMBL, and GenBank with two goals. First is to determine whether users can reliably identify those proteins in SWISS-PROT whose functions were determined experimentally, as opposed to proteins whose functions were predicted computationally. If this information was present in reasonable quantities, it would allow researchers to decrease the propagation of incorrect function predictions during sequence annotation, and to assemble training sets for developing the next generation of sequence-analysis algorithms. Second is to assess the consistency between translated GenBank sequences and sequences in SWISS-PROT and TrEMBL.'),
(69952, 2001, 'bio', 'TreeGeneBrowser: phylogenetic data mining of gene sequences from public databases', 'Jakobsen, I B|Saleeba, J A|Poidinger, M|Littlejohn, T G', 'Australian Genomic Information Centre, University of Sydney, NSW 2006, Australia.|Australian Genomic Information Centre, University of Sydney, NSW 2006, Australia.|Australian Genomic Information Centre, University of Sydney, NSW 2006, Australia.|Australian Genomic Information Centre, University of Sydney, NSW 2006, Australia.', 'Australia|Australia|Australia|Australia', 0, 'Algorithms|Chromosome Mapping|Classification|Databases, Factual|Gene Library|Information Storage and Retrieval|Internet|National Library of Medicine (U.S.)|Phylogeny|Software|United States', 'Journal Article', '10.1093/bioinformatics/17.6.535', '11395431', 'Sequence databases represent an enormous resource of phylogenetic information, but there is a lack of tools for accessing that information in order to assess the amount of evolutionary information in these databases that may be suitable for phylogenetic reconstruction and for identifying areas of the taxonomy that are under-represented for specific gene sequences.'),
(69953, 2001, 'bio', 'SCORE: predicting the core of protein models', 'Deane, C M|Kaas, Q|Blundell, T L', 'Department of Biochemistry, University of Cambridge, Tennis Court Road, Cambridge CB2 1GA, UK. charlotte@cryst.bioc.cam.ac.uk|Department of Biochemistry, University of Cambridge, Tennis Court Road, Cambridge CB2 1GA, UK. charlotte@cryst.bioc.cam.ac.uk|Department of Biochemistry, University of Cambridge, Tennis Court Road, Cambridge CB2 1GA, UK. charlotte@cryst.bioc.cam.ac.uk', 'United Kingdom|United Kingdom|United Kingdom', 13, 'Algorithms|Conserved Sequence|Genetic Variation|Models, Molecular|Protein Structure, Tertiary|Proteins|Sequence Alignment', 'Journal Article', '10.1093/bioinformatics/17.6.541', '11395432', 'The prediction of the regions of homology models that can be restrained by or copied from the basis structures is a vital step in correct model generation, because these regions are the models most accurate part. However, there is no ideal method for the identification of their limits. In most algorithms their length depends on the number of family members and definitions of secondary structure.'),
(69954, 2001, 'bio', 'Prediction of quaternary structure from primary structure', 'Garian, R', 'School of Computational Sciences, George Mason University, USA. rgar@science.gmu.edu', 'United States', 46, 'Algorithms|Amino Acid Sequence|Amino Acids|Artificial Intelligence|Databases, Factual|Gene Library|Models, Molecular|Predictive Value of Tests|Protein Structure, Quaternary|Sensitivity and Specificity|Software', 'Journal Article', '10.1093/bioinformatics/17.6.551', '11395433', 'The sequence implies conformation principle has been the motivation for the construction of numerous systems of secondary and tertiary structure prediction. Computational experiments have shown that this principle can now be extended to quaternary structure prediction. This work appears to be the first effort to predict quaternary structure properties from sequence.'),
(69955, 2001, 'bio', 'Compositional symmetries in complete genomes', 'Qi, D|Cuticchia, A J', 'Bioinformatics Supercomputing Centre, The Hospital for Sick Children, 555 University Avenue, Toronto, Ontario, Canada M5G 1X8.|Bioinformatics Supercomputing Centre, The Hospital for Sick Children, 555 University Avenue, Toronto, Ontario, Canada M5G 1X8.', 'Canada|Canada', 41, 'DNA|Eukaryotic Cells|Gene Frequency|Genome|Oligonucleotides|Prokaryotic Cells', 'Journal Article', '10.1093/bioinformatics/17.6.557', '11395434', '-');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(69956, 2001, 'bio', 'Identification of novel families of membrane proteins from the model plant Arabidopsis thaliana', 'Ward, J M', 'Department of Plant Biology, University of Minnesota, 220 Bioscience Center, 1445 Gortner Ave, St Paul, MN, USA. jward@tc.umn.edu', 'United States', 0, 'ATP-Binding Cassette Transporters|Algorithms|Arabidopsis|Genome, Plant|Membrane Proteins|Multigene Family|Sequence Homology|Species Specificity', 'Journal Article', '10.1093/bioinformatics/17.6.560', '11395435', 'The completion of the Arabidopsis genome offers the first opportunity to analyze all of the membrane protein sequences of a plant. The majority of integral membrane proteins including transporters, channels, and pumps contain hydrophobic alpha-helices and can be selected based on TransMembrane Spanning (TMS) domain prediction. By clustering the predicted membrane proteins based on sequence, it is possible to sort the membrane proteins into families of known function, based on experimental evidence or homology, or unknown function. This provides a way to identify target sequences for future functional analysis.'),
(69957, 2001, 'bio', 'MAPS: a microarray project system for gene expression experiment information and data validation', 'Bushel, P R|Hamadeh, H|Bennett, L|Sieber, S|Martin, K|Nuwaysir, E F|Johnson, K|Reynolds, K|Paules, R S|Afshari, C A', 'Laboratory of Molecular Carcinogenesis, National Institute of Environmental Health Sciences, National Institutes of Health, PO Box 12233, Research Triangle Park, NC 27709, USA. bushel@niehs.nih.gov|Laboratory of Molecular Carcinogenesis, National Institute of Environmental Health Sciences, National Institutes of Health, PO Box 12233, Research Triangle Park, NC 27709, USA. bushel@niehs.nih.gov|Laboratory of Molecular Carcinogenesis, National Institute of Environmental Health Sciences, National Institutes of Health, PO Box 12233, Research Triangle Park, NC 27709, USA. bushel@niehs.nih.gov|Laboratory of Molecular Carcinogenesis, National Institute of Environmental Health Sciences, National Institutes of Health, PO Box 12233, Research Triangle Park, NC 27709, USA. bushel@niehs.nih.gov|Laboratory of Molecular Carcinogenesis, National Institute of Environmental Health Sciences, National Institutes of Health, PO Box 12233, Research Triangle Park, NC 27709, USA. bushel@niehs.nih.gov|Laboratory of Molecular Carcinogenesis, National Institute of Environmental Health Sciences, National Institutes of Health, PO Box 12233, Research Triangle Park, NC 27709, USA. bushel@niehs.nih.gov|Laboratory of Molecular Carcinogenesis, National Institute of Environmental Health Sciences, National Institutes of Health, PO Box 12233, Research Triangle Park, NC 27709, USA. bushel@niehs.nih.gov|Laboratory of Molecular Carcinogenesis, National Institute of Environmental Health Sciences, National Institutes of Health, PO Box 12233, Research Triangle Park, NC 27709, USA. bushel@niehs.nih.gov|Laboratory of Molecular Carcinogenesis, National Institute of Environmental Health Sciences, National Institutes of Health, PO Box 12233, Research Triangle Park, NC 27709, USA. bushel@niehs.nih.gov|Laboratory of Molecular Carcinogenesis, National Institute of Environmental Health Sciences, National Institutes of Health, PO Box 12233, Research Triangle Park, NC 27709, USA. bushel@niehs.nih.gov', 'United States|United States|United States|United States|United States|United States|United States|United States|United States|United States', 0, 'Computer Communication Networks|Data Display|Database Management Systems|Oligonucleotide Array Sequence Analysis|Programming Languages', 'Journal Article', '10.1093/bioinformatics/17.6.564', '11395436', 'MAPS is a MicroArray Project System for management and interpretation of microarray gene expression experiment information and data. Microarray project information is organized to track experiments and results that are: (1) validated by performing analysis on stored replicate gene expression data, and (2) queried according to the biological classifications of genes deposited on microarray chips.'),
(69958, 2001, 'bio', 'SVDMAN--singular value decomposition analysis of microarray data', 'Wall, M E|Dyck, P A|Brettin, T S', 'Bioscience Division, Los Alamos National Laboratory, Los Alamos, NM 87545, USA.|Bioscience Division, Los Alamos National Laboratory, Los Alamos, NM 87545, USA.|Bioscience Division, Los Alamos National Laboratory, Los Alamos, NM 87545, USA.', 'United States|United States|United States', 0, 'Algorithms|Cluster Analysis|Data Interpretation, Statistical|Oligonucleotide Array Sequence Analysis|Software', 'Comparative Study', '10.1093/bioinformatics/17.6.566', '11395437', 'We have developed two novel methods for Singular Value Decomposition analysis (SVD) of microarray data. The first is a threshold-based method for obtaining gene groups, and the second is a method for obtaining a measure of confidence in SVD analysis. Gene groups are obtained by identifying elements of the left singular vectors, or gene coefficient vectors, that are greater in magnitude than the threshold W N(-1/2), where N is the number of genes, and W is a weight factor whose default value is 3. The groups are non-exclusive and may contain genes of opposite (i.e. inversely correlated) regulatory response. The confidence measure is obtained by systematically deleting assays from the data set, interpolating the SVD of the reduced data set to reconstruct the missing assay, and calculating the Pearson correlation between the reconstructed assay and the original data. This confidence measure is applicable when each experimental assay corresponds to a value of parameter that can be interpolated, such as time, dose or concentration. Algorithms for the grouping method and the confidence measure are available in a software application called SVD Microarray ANalysis (SVDMAN). In addition to calculating the SVD for generic analysis, SVDMAN provides a new means for using microarray data to develop hypotheses for gene associations and provides a measure of confidence in the hypotheses, thus extending current SVD research in the area of global gene expression analysis.'),
(69959, 2001, 'bio', 'AMADA: analysis of microarray data', 'Xia, X|Xie, Z', 'Department of Ecology & Biodiversity, University of Hong Kong, Hong Kong, Peoples Republic of China. xxia@hkusua.hku.hk|Department of Ecology & Biodiversity, University of Hong Kong, Hong Kong, Peoples Republic of China. xxia@hkusua.hku.hk', 'China|China', 34, 'Algorithms|Cluster Analysis|Computer Graphics|Data Display|Data Interpretation, Statistical|Oligonucleotide Array Sequence Analysis|Software|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/17.6.569', '11395438', 'AMADA is a Windows program for identifying co-expressed genes from microarray data. It performs data transformation, principal component analysis, a variety of cluster analyses and extensive graphic functions for visualizing expression profiles.'),
(69960, 2001, 'bio', 'The HIB database of annotated UniGene clusters', 'Geier, B|Kastenmüller, G|Fellenberg, M|Mewes, H W|Morgenstern, B', 'MIPS Instituti für Bioinformatik GSF--Forschungszentrum für Umwelt und Gesundheit, GmbH Ingolstädter Landstrasse, 1 85764 Neuherberg, Germany.|MIPS Instituti für Bioinformatik GSF--Forschungszentrum für Umwelt und Gesundheit, GmbH Ingolstädter Landstrasse, 1 85764 Neuherberg, Germany.|MIPS Instituti für Bioinformatik GSF--Forschungszentrum für Umwelt und Gesundheit, GmbH Ingolstädter Landstrasse, 1 85764 Neuherberg, Germany.|MIPS Instituti für Bioinformatik GSF--Forschungszentrum für Umwelt und Gesundheit, GmbH Ingolstädter Landstrasse, 1 85764 Neuherberg, Germany.|MIPS Instituti für Bioinformatik GSF--Forschungszentrum für Umwelt und Gesundheit, GmbH Ingolstädter Landstrasse, 1 85764 Neuherberg, Germany.', 'Germany|Germany|Germany|Germany|Germany', 3, 'Data Collection|Databases, Factual|Genome, Human|Humans|Information Storage and Retrieval|Internet|Multigene Family|Sequence Analysis, Protein|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/17.6.571', '11395439', 'The HumanInfoBase (HIB) is a database of putative human gene transcripts. UniGene clusters are assembled, and the resulting consensus sequences are submitted to the PEDANT software system (Frishman,D., Albermann,K., Hani,J., Heumann,K., Metanomski,A., Zollner,A. and Mewes,H.-W., 2001, Bioinformatics, 17, 44--57) for fully automatic sequence analysis and annotation. Predicted transcripts are classified using a variety of functional and structural categories, and hyperlinks to various databases are provided for additional information. A WWW-based graphical user interface represents the assembly process as well as functionally important sites in the putative transcripts.'),
(69961, 2001, 'bio', 'SOAP, cleaning multiple alignments from unstable blocks', 'Löytynoja, A|Milinkovitch, M C', 'Evolutionary Genetics, Free University of Brussels (ULB), cp 300, Institute of Molecular Biology and Medicine, rue Jeener & Brachet 12, B-6041 Gosselies, Belgium. apl@dbm.ulb.ac.be|Evolutionary Genetics, Free University of Brussels (ULB), cp 300, Institute of Molecular Biology and Medicine, rue Jeener & Brachet 12, B-6041 Gosselies, Belgium. apl@dbm.ulb.ac.be', 'Belgium|Belgium', 0, 'Programming Languages|Sequence Alignment|Software|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/17.6.573', '11395440', 'SOAP is a stand-alone, multi-platform program to test the stability of a multiple alignment of molecular sequences.'),
(69962, 2001, 'bio', 'STOCHSIM: modelling of stochastic biomolecular processes', 'Le Novère, N|Shimizu, T S', 'Department of Zoology, University of Cambridge, Downing Street, Cambridge CB2 3EJ, UK. nl223@cus.cam.ac.uk|Department of Zoology, University of Cambridge, Downing Street, Cambridge CB2 3EJ, UK. nl223@cus.cam.ac.uk', 'United Kingdom|United Kingdom', 152, 'Algorithms|Computer Graphics|Computer Simulation|Data Display|Models, Chemical|Molecular Conformation|Proteins|Software|Stochastic Processes|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/17.6.575', '11395441', 'STOCHSIM is a stochastic simulator for chemical reactions. Molecules are represented as individual software objects that react according to probabilities derived from concentrations and rate constants. Version 1.2 of STOCHSIM provides a novel cross-platform graphical interface written in Perl/Tk. A simple two-dimensional spatial structure has also been implemented, in which nearest-neighbour interactions of molecules in a 2-D lattice can be simulated.'),
(69963, 2001, 'bio', 'SeWeR: a customizable and integrated dynamic HTML interface to bioinformatics services', 'Basu, M K', 'Centre for Cellular and Molecular Biology, Hyderabad 500007, India. curiouser@ccmb.ap.nic.in', 'India', 9, 'Computational Biology|Database Management Systems|Hypermedia|Information Services|Information Storage and Retrieval|Internet|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/17.6.577', '11395442', 'Sequence analysis using Web Resources (SeWeR) is an integrated, Dynamic HTML (DHTML) interface to commonly used bioinformatics services available on the World Wide Web. It is highly customizable, extendable, platform neutral, completely server-independent and can be hosted as a web page as well as being used as stand-alone software running within a web browser.'),
(69964, 2001, 'bio', 'Construction of DNA restriction maps based on a simplified experiment', 'Bxc5x82azewicz, J|Formanowicz, P|Kasprzak, M|Jaroszewski, M|Markiewicz, W T', 'Institute of Computing Science, Poznaxc5x84 University of Technology, Poland. blazewic@sol.put.poznan.pl|Institute of Computing Science, Poznaxc5x84 University of Technology, Poland. blazewic@sol.put.poznan.pl|Institute of Computing Science, Poznaxc5x84 University of Technology, Poland. blazewic@sol.put.poznan.pl|Institute of Computing Science, Poznaxc5x84 University of Technology, Poland. blazewic@sol.put.poznan.pl|Institute of Computing Science, Poznaxc5x84 University of Technology, Poland. blazewic@sol.put.poznan.pl', 'Poland|Poland|Poland|Poland|Poland', 23, 'Algorithms|Computational Biology|DNA|Data Interpretation, Statistical|Restriction Mapping', 'Journal Article', '10.1093/bioinformatics/17.5.398', '11396417', 'A formulation of a new problem of the restriction map construction based on a simplified digestion experiment and a development of an algorithm for solving both ideal and noisy data cases of the introduced problem.'),
(69965, 2001, 'bio', 'Reading the book of life', 'Searls, D B', 'Institute of Computing Science, Poznaxc5x84 University of Technology, Poland. blazewic@sol.put.poznan.pl', 'Poland', 17, 'Computational Biology|Genome, Human|Humans|Linguistics|Terminology as Topic', 'Editorial', '10.1093/bioinformatics/17.7.579', '11448875', '-'),
(69966, 2001, 'bio', 'LDB2000: sequence-based integrated maps of the human genome', 'Ke, X|Tapper, W|Collins, A', 'Human Genetics Research Division, University of Southampton, Duthie Building (Mailpoint 808), Southampton General Hospital, Tremona Road, Southampton SO16 6YD, UK.|Human Genetics Research Division, University of Southampton, Duthie Building (Mailpoint 808), Southampton General Hospital, Tremona Road, Southampton SO16 6YD, UK.|Human Genetics Research Division, University of Southampton, Duthie Building (Mailpoint 808), Southampton General Hospital, Tremona Road, Southampton SO16 6YD, UK.', 'United Kingdom|United Kingdom|United Kingdom', 9, 'Algorithms|Chromosome Mapping|Chromosomes, Human, Pair 21|Chromosomes, Human, Pair 22|Computational Biology|Genome, Human|Humans|Internet|Linkage Disequilibrium|Software', 'Journal Article', '10.1093/bioinformatics/17.7.581', '11448876', 'Integrated maps are useful for gene mapping and establishing the relationship between recombination and sequence. In this paper we describe algorithms and their implementation for constructing sequence-based integrated maps of the human chromosomes, which are presented in LDB2000, a web based resource. Gene mapping efforts are now focussing on linkage disequilibrium mapping and extension of the integrated map to represent the extent of linkage disequilibrium in different genomic regions would further increase the utility of these maps.'),
(69967, 2001, 'bio', 'Extending traditional query-based integration approaches for functional characterization of post-genomic data', 'Eckman, B A|Kosky, A S|Laroco , L A', 'Department of Bioinformatics, GlaxoSmithKline, King of Prussia, PA, USA. barbara_eckman@sbphrd.com|Department of Bioinformatics, GlaxoSmithKline, King of Prussia, PA, USA. barbara_eckman@sbphrd.com|Department of Bioinformatics, GlaxoSmithKline, King of Prussia, PA, USA. barbara_eckman@sbphrd.com', 'United States|United States|United States', 39, 'Animals|Computational Biology|DNA, Complementary|Database Management Systems|Databases as Topic|Gene Expression|Genome|Genome, Human|Humans|Internet|Mice|Phosphotransferases|Software', 'Journal Article', '10.1093/bioinformatics/17.7.587', '11448877', 'To identify and characterize regions of functional interest in genomic sequence requires full, flexible query access to an integrated, up-to-date view of all related information, irrespective of where it is stored (within an organization or across the Internet) and its format (traditional database, flat file, web site, results of runtime analysis). Wide-ranging multi-source queries often return unmanageably large result sets, requiring non-traditional approaches to exclude extraneous data.'),
(69968, 2001, 'bio', 'Identifying the 3-terminal exon in human DNA', 'Tabaska, J E|Davuluri, R V|Zhang, M Q', 'Cold Spring Harbor Laboratory, PO Box 100, Cold Spring Harbor, NY 11724, USA.|Cold Spring Harbor Laboratory, PO Box 100, Cold Spring Harbor, NY 11724, USA.|Cold Spring Harbor Laboratory, PO Box 100, Cold Spring Harbor, NY 11724, USA.', 'United States|United States|United States', 5, 'Algorithms|Bayes Theorem|Computational Biology|DNA|DNA, Recombinant|Discriminant Analysis|Exons|Humans|Poly A|Software', 'Journal Article', '10.1093/bioinformatics/17.7.602', '11448878', 'We present JTEF, a new program for finding 3 terminal exons in human DNA sequences. This program is based on quadratic discriminant analysis, a standard non-linear statistical pattern recognition method. The quadratic discriminant functions used for building the algorithm were trained on a set of 3 terminal exons of type 3tuexon (those containing the true STOP codon).'),
(69969, 2001, 'bio', 'Identifying target sites for cooperatively binding factors', 'GuhaThakurta, D|Stormo, G D', 'Department of Genetics, Washington University School of Medicine, 4566 Scott Avenue, Campus Box: 8232, St Louis, MO 63110, USA. dg@genetics.wustl.edu|Department of Genetics, Washington University School of Medicine, 4566 Scott Avenue, Campus Box: 8232, St Louis, MO 63110, USA. dg@genetics.wustl.edu', 'United States|United States', 143, 'Algorithms|Bacterial Proteins|Base Sequence|Binding Sites|Computational Biology|DNA|DNA, Bacterial|DNA-Binding Proteins|Databases as Topic|Escherichia coli|Genes, Fungal|Genome, Bacterial|Saccharomyces cerevisiae|Software|Transcription Factors|Transcriptional Activation', 'Comparative Study', '10.1093/bioinformatics/17.7.608', '11448879', 'Transcriptional activation in eukaryotic organisms normally requires combinatorial interactions of multiple transcription factors. Though several methods exist for identification of individual protein binding site patterns in DNA sequences, there are few methods for discovery of binding site patterns for cooperatively acting factors. Here we present an algorithm, Co-Bind (for COperative BINDing), for discovering DNA target sites for cooperatively acting transcription factors. The method utilizes a Gibbs sampling strategy to model the cooperativity between two transcription factors and defines position weight matrices for the binding sites. Sequences from both the training set and the entire genome are taken into account, in order to discriminate against commonly occurring patterns in the genome, and produce patterns which are significant only in the training set.'),
(69970, 2001, 'bio', 'Conformational model for binding site recognition by the Ecoli MetJ transcription factor', 'Liu, R|Blackwell, T W|States, D J', 'Center for Computational Biology and Department of Genetics, Washington University School of Medicine, 700 S. Euclid Ave, St Louis, MO 63110, USA.|Center for Computational Biology and Department of Genetics, Washington University School of Medicine, 700 S. Euclid Ave, St Louis, MO 63110, USA.|Center for Computational Biology and Department of Genetics, Washington University School of Medicine, 700 S. Euclid Ave, St Louis, MO 63110, USA.', 'United States|United States|United States', 28, 'Bacterial Proteins|Base Sequence|Binding Sites|Computational Biology|Crystallography|DNA, Bacterial|Escherichia coli|Escherichia coli Proteins|Genome, Bacterial|Models, Molecular|Nucleic Acid Conformation|Protein Conformation|Protein Structure, Tertiary|Repressor Proteins', 'Comparative Study', '10.1093/bioinformatics/17.7.622', '11448880', 'Current methods for identifying sequence specific binding sites in DNA sequence using position specific weight matrices are limited in both sensitivity and specificity. Double strand DNA helix exhibits sequence dependent variations in conformation. Interactions between macromolecules result from complementarity of the two tertiary structures. We hypothesize that this conformational variation plays a role in transcription factor binding site recognition, and that the use of this structure information will improve the predictive power of transcription factor binding site models.'),
(69971, 2001, 'bio', 'Automated image analysis for array hybridization experiments', 'Steinfath, M|Wruck, W|Seidel, H|Lehrach, H|Radelof, U|OBrien, J', 'Max-Planck-Institute for Molecular Genetics Berlin-Dahlem, Ihnestrasse 73, D-14195 Berlin, Germany. steinfat@molgen.mpg.de|Max-Planck-Institute for Molecular Genetics Berlin-Dahlem, Ihnestrasse 73, D-14195 Berlin, Germany. steinfat@molgen.mpg.de|Max-Planck-Institute for Molecular Genetics Berlin-Dahlem, Ihnestrasse 73, D-14195 Berlin, Germany. steinfat@molgen.mpg.de|Max-Planck-Institute for Molecular Genetics Berlin-Dahlem, Ihnestrasse 73, D-14195 Berlin, Germany. steinfat@molgen.mpg.de|Max-Planck-Institute for Molecular Genetics Berlin-Dahlem, Ihnestrasse 73, D-14195 Berlin, Germany. steinfat@molgen.mpg.de|Max-Planck-Institute for Molecular Genetics Berlin-Dahlem, Ihnestrasse 73, D-14195 Berlin, Germany. steinfat@molgen.mpg.de', 'Germany|Germany|Germany|Germany|Germany|Germany', 83, 'Algorithms|Computational Biology|DNA Fingerprinting|Image Interpretation, Computer-Assisted|Molecular Probes|Nucleic Acid Hybridization|Oligonucleotide Array Sequence Analysis|Software', 'Journal Article', '10.1093/bioinformatics/17.7.634', '11448881', 'Image analysis is a major part of data evaluation for array hybridization experiments in molecular biology. The program presented here is designed to analyze automatically images from hybridization experiments with various arrangements: different kinds of probes (oligonucleotides or complex probes), different supports (nylon filters or glass slides), different labeling of probes (radioactively or fluorescently). The program is currently applied to oligonucleotide fingerprinting projects and complex hybridizations. The only precondition for the use of the program is that the targets are arrayed in a grid, which can be approximately transformed to an orthogonal equidistant grid by a projective mapping.'),
(69972, 2001, 'bio', 'Semi-automated update and cleanup of structural RNA alignment databases', 'Gorodkin, J|Zwieb, C|Knudsen, B', 'Department of Ecology and Genetics, The Institute of Biological Sciences, University of Aarhus, Building 540, Ny Munkegade, DK-8000 Aarhus C, Denmark.|Department of Ecology and Genetics, The Institute of Biological Sciences, University of Aarhus, Building 540, Ny Munkegade, DK-8000 Aarhus C, Denmark.|Department of Ecology and Genetics, The Institute of Biological Sciences, University of Aarhus, Building 540, Ny Munkegade, DK-8000 Aarhus C, Denmark.', 'Denmark|Denmark|Denmark', 14, 'Base Sequence|Computational Biology|Databases as Topic|Molecular Sequence Data|RNA|RNA, Messenger|Sequence Alignment|Sequence Analysis, RNA|Sequence Homology, Nucleic Acid|Software', 'Journal Article', '10.1093/bioinformatics/17.7.642', '11448882', 'We have developed a series of programs which assist in maintenance of structural RNA databases. A main program BLASTs the RNA database against GenBank and automatically extends and realigns the sequences to include the entire range of the RNA query sequences. After manual update of the database, other programs can examine base pair consistency and phylogenetic support. The output can be applied iteratively to refine the structural alignment of the RNA database. Using these tools, the number of potential misannotations per sequence was reduced from 20 to 3 in the Signal Recognition Particle RNA database.'),
(69973, 2001, 'bio', 'Evaluation of methods for the prediction of membrane spanning regions', 'Möller, S|Croning, M D|Apweiler, R', 'EMBL-Outstation European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK. moeller@ebi.ac.uk|EMBL-Outstation European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK. moeller@ebi.ac.uk|EMBL-Outstation European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK. moeller@ebi.ac.uk', 'United Kingdom|United Kingdom|United Kingdom', 700, 'Computational Biology|Databases as Topic|Membrane Proteins|Models, Molecular|Protein Conformation|Protein Sorting Signals|Software|Solubility', 'Comparative Study', '10.1093/bioinformatics/17.7.646', '11448883', 'A variety of tools are available to predict the topology of transmembrane proteins. To date no independent evaluation of the performance of these tools has been published. A better understanding of the strengths and weaknesses of the different tools would guide both the biologist and the bioinformatician to make better predictions of membrane protein topology.'),
(69974, 2001, 'bio', 'An integrated system for high throughput TaqMan based SNP genotyping', 'Hampe, J|Wollstein, A|Lu, T|Frevel, H J|Will, M|Manaster, C|Schreiber, S', '1st Medical Department, Christian-Albrechts-University, D-24105, Kiel, Germany. J.Hampe@mucosa.de|1st Medical Department, Christian-Albrechts-University, D-24105, Kiel, Germany. J.Hampe@mucosa.de|1st Medical Department, Christian-Albrechts-University, D-24105, Kiel, Germany. J.Hampe@mucosa.de|1st Medical Department, Christian-Albrechts-University, D-24105, Kiel, Germany. J.Hampe@mucosa.de|1st Medical Department, Christian-Albrechts-University, D-24105, Kiel, Germany. J.Hampe@mucosa.de|1st Medical Department, Christian-Albrechts-University, D-24105, Kiel, Germany. J.Hampe@mucosa.de|1st Medical Department, Christian-Albrechts-University, D-24105, Kiel, Germany. J.Hampe@mucosa.de', 'Germany|Germany|Germany|Germany|Germany|Germany|Germany', 0, 'Clinical Laboratory Information Systems|Computational Biology|Genetic Linkage|Genotype|Humans|Pedigree|Phenotype|Polymerase Chain Reaction|Polymorphism, Single Nucleotide|Software|Taq Polymerase', 'Journal Article', '10.1093/bioinformatics/17.7.654', '11448884', 'We have developed an integrated laboratory information system that allows the flexible handling of pedigree, phenotype and genotype information. Specifically, it includes client applications for an integrated data import from TaqMan typing files, Mendel checking, data export, handling of pedigree and phenotype information and analysis features.'),
(69975, 2001, 'bio', 'MEDUSA: large scale automatic selection and visual assessment of PCR primer pairs', 'Podowski, R M|Sonnhammer, E L', 'Center for Genomics Research, Karolinska Institutet, S-171 77 Stockholm, Sweden.|Center for Genomics Research, Karolinska Institutet, S-171 77 Stockholm, Sweden.', 'Sweden|Sweden', 8, 'Base Sequence|Computational Biology|DNA Primers|Genomics|Molecular Sequence Data|Polymerase Chain Reaction|Software', 'Journal Article', '10.1093/bioinformatics/17.7.656', '11448885', 'MEDUSA is a tool for automatic selection and visual assessment of PCR primer pairs, developed to assist large scale gene expression analysis projects. The system allows specification of constraints of the location and distances between the primers in a pair. For instance, primers in coding, non-coding, exon/intron-spanning regions might be selected. Medusa applies these constraints as a filter to primers predicted by three external programs, and displays the resulting primer pairs graphically in the Blixem (Sonnhammer and Durbin, COMPUT: Appl. Biosci. 10, 301-307, 1994, http://www.cgr.ki.se/cgr/groups/sonnhammer/Blixem.html) viewer.'),
(69976, 2001, 'bio', 'Visualization of expression clusters using Sammons non-linear mapping', 'Ewing, R M|Cherry, J M', 'Carnegie Institution of Washington, Department of Plant Biology, 260 Panama Street, Stanford, CA 94305, USA. ewing@genome.stanford.edu|Carnegie Institution of Washington, Department of Plant Biology, 260 Panama Street, Stanford, CA 94305, USA. ewing@genome.stanford.edu', 'Panama|Panama', 20, 'Arabidopsis|Chromosome Mapping|Cluster Analysis|Computational Biology|Gene Expression|Gene Expression Profiling|Genes, Plant|Nonlinear Dynamics|Oligonucleotide Array Sequence Analysis|Software', 'Journal Article', '10.1093/bioinformatics/17.7.658', '11448886', 'A method of exploratory analysis and visualization of multi-dimensional gene expression data using Sammons Non-Linear Mapping (NLM) is presented.'),
(69977, 2001, 'bio', 'ADAPTSITE: detecting natural selection at single amino acid sites', 'Suzuki, Y|Gojobori, T|Nei, M', 'Institute of Molecular Evolutionary Genetics and Department of Biology, Pennsylvania State University, 328 Mueller Laboratory, University Park, PA 16802, USA. yis1@psu.edu|Institute of Molecular Evolutionary Genetics and Department of Biology, Pennsylvania State University, 328 Mueller Laboratory, University Park, PA 16802, USA. yis1@psu.edu|Institute of Molecular Evolutionary Genetics and Department of Biology, Pennsylvania State University, 328 Mueller Laboratory, University Park, PA 16802, USA. yis1@psu.edu', 'United States|United States|United States', 64, 'Algorithms|Amino Acid Sequence|Computational Biology|Evolution, Molecular|Proteins|Selection, Genetic|Software', 'Journal Article', '10.1093/bioinformatics/17.7.660', '11448887', 'ADAPTSITE is a program package for detecting natural selection at single amino acid sites, using a multiple alignment of protein-coding sequences for a given phylogenetic tree. The program infers ancestral codons at all interior nodes, and computes the total numbers of synonymous (c(S)) and nonsynonymous (c(N)) substitutions as well as the average numbers of synonymous (s(S)) and nonsynonymous (s(N)) sites for each codon site. The probabilities of occurrence of synonymous and nonsynonymous substitutions are approximated by s(S) / (s(S) + s(N)) and s(N) / (s(S) + s(N)), respectively. The null hypothesis of selective neutrality is tested for each codon site, assuming a binomial distribution for the probability of obtaining c(S) and c(N).'),
(69978, 2001, 'bio', 'PAL: an object-oriented programming library for molecular evolution and phylogenetics', 'Drummond, A|Strimmer, K', 'School of Biological Sciences, University of Auckland, 3A Symonds Street, Auckland, New Zealand. a.drummond@auckland.ac.nz|School of Biological Sciences, University of Auckland, 3A Symonds Street, Auckland, New Zealand. a.drummond@auckland.ac.nz', 'New Zealand|New Zealand', 0, 'Computational Biology|Evolution, Molecular|Likelihood Functions|Phylogeny|Software', 'Journal Article', '10.1093/bioinformatics/17.7.662', '11448888', 'Phylogenetic Analysis Library (PAL) is a collection of Java classes for use in molecular evolution and phylogenetics. PAL provides a modular environment for the rapid construction of both special-purpose and general analysis programs. PAL version 1.1 consists of 145 public classes or interfaces in 13 packages, including classes for models of character evolution, maximum-likelihood estimation, and the coalescent, with a total of more than 27000 lines of code. The PAL project is set up as a collaborative project to facilitate contributions from other researchers. AVAILIABILTY: The program is free and is available at http://www.pal-project.org. It requires Java 1.1 or later. PAL is licensed under the GNU General Public License.'),
(69979, 2001, 'bio', 'T-REX: reconstructing and visualizing phylogenetic trees and reticulation networks', 'Makarenkov, V', 'Département de Sciences Biologiques, Universitéde Montréal, CP 6128, succ. Centre-ville, Montréal, Québec H3C 3J7, Canada. makarenv@magellan.umontreal.ca', 'Canada', 109, 'Algorithms|Computational Biology|Phylogeny|Software', 'Journal Article', '10.1093/bioinformatics/17.7.664', '11448889', 'T-REX (tree and reticulogram reconstruction) is an application to reconstruct phylogenetic trees and reticulation networks from distance matrices. The application includes a number of tree fitting methods like NJ, UNJ or ADDTREE which have been very popular in phylogenetic analysis. At the same time, the software comprises several new methods of phylogenetic analysis such as: tree reconstruction using weights, tree inference from incomplete distance matrices or modeling a reticulation network for a collection of objects or species. T-REX also allows the user to visualize obtained tree or network structures using Hierarchical, Radial or Axial types of tree drawing and manipulate them interactively.'),
(69980, 2001, 'bio', 'A Java applet for visualizing protein-protein interaction', 'Mrowka, R', 'Johannes-Müller-Institut für Physiologie, Charité, Humboldt-Universität zu Berlin, Tucholsky Str. 2, D-10117 Berlin, Germany. Mrowka@rz.hu-berlin.de', 'Germany', 0, 'Algorithms|Computational Biology|Fungal Proteins|Proteins|Saccharomyces cerevisiae|Software|Two-Hybrid System Techniques', 'Journal Article', '10.1093/bioinformatics/17.7.669', '11448890', 'A web applet for browsing protein-protein interactions was implemented. It enables the display of interaction relationships, based upon neighboring distance and biological function.'),
(69981, 2001, 'bio', 'An efficient algorithm for finding short approximate non-tandem repeats', 'Adebiyi, E F|Jiang, T|Kaufmann, M', 'Wilhelm-Schickard-Institut für Informatik, Universität Tübingen, Sand 13, Tübingen, 72076, Germany. adebiyi@informatik.uni-tuebingen.de|Wilhelm-Schickard-Institut für Informatik, Universität Tübingen, Sand 13, Tübingen, 72076, Germany. adebiyi@informatik.uni-tuebingen.de|Wilhelm-Schickard-Institut für Informatik, Universität Tübingen, Sand 13, Tübingen, 72076, Germany. adebiyi@informatik.uni-tuebingen.de', 'Germany|Germany|Germany', 22, 'Algorithms|Computational Biology|Databases, Genetic|Repetitive Sequences, Nucleic Acid', 'Journal Article', '10.1093/bioinformatics/17.suppl_1.s5', '11472987', 'We study the problem of approximate non-tandem repeat extraction. Given a long subject string S of length N over a finite alphabet Sigma and a threshold D, we would like to find all short substrings of S of length P that repeat with at most D differences, i.e., insertions, deletions, and mismatches. We give a careful theoretical characterization of the set of seeds (i.e., some maximal exact repeats) required by the algorithm, and prove a sublinear bound on their expected numbers. Using this result, we present a sub-quadratic algorithm for finding all short (i.e., of length O(log N)) approximate repeats. The running time of our algorithm is O(DN(3pow(epsilon)-1)log N), where epsilon = D/P and pow(epsilon) is an increasing, concave function that is 0 when epsilon = 0 and about 0.9 for DNA and protein sequences.'),
(69982, 2001, 'bio', 'SCOPE: a probabilistic model for scoring tandem mass spectra against a peptide database', 'Bafna, V|Edwards, N', 'Informatics Research, Celera Genomics, 45 W. Gude Drive, Rockville, MD 20850, USA. Vineet.Bafna@Celera.Com|Informatics Research, Celera Genomics, 45 W. Gude Drive, Rockville, MD 20850, USA. Vineet.Bafna@Celera.Com', 'United States|United States', 0, 'Amino Acid Sequence|Computational Biology|Databases, Protein|Mass Spectrometry|Models, Statistical|Molecular Sequence Data|Peptide Fragments|Peptides|Proteome|Software|Spectrometry, Mass, Matrix-Assisted Laser Desorption-Ionization|Stochastic Processes', 'Journal Article', '10.1093/bioinformatics/17.suppl_1.s13', '11472988', 'Proteomics, or the direct analysis of the expressed protein components of a cell, is critical to our understanding of cellular biological processes in normal and diseased tissue. A key requirement for its success is the ability to identify proteins in complex mixtures. Recent technological advances in tandem mass spectrometry has made it the method of choice for high-throughput identification of proteins. Unfortunately, the software for unambiguously identifying peptide sequences has not kept pace with the recent hardware improvements in mass spectrometry instruments. Critical for reliable high-throughput protein identification, scoring functions evaluate the quality of a match between experimental spectra and a database peptide. Current scoring function technology relies heavily on ad-hoc parameterization and manual curation by experienced mass spectrometrists. In this work, we propose a two-stage stochastic model for the observed MS/MS spectrum, given a peptide. Our model explicitly incorporates fragment ion probabilities, noisy spectra, and instrument measurement error. We describe how to compute this probability based score efficiently, using a dynamic programming technique. A prototype implementation demonstrates the effectiveness of the model.'),
(69983, 2001, 'bio', 'Fast optimal leaf ordering for hierarchical clustering', 'Bar-Joseph, Z|Gifford, D K|Jaakkola, T S', 'Laboratory for Computer Science, MIT, 545 Technology Square, Cambridge, MA 02139, USA.|Laboratory for Computer Science, MIT, 545 Technology Square, Cambridge, MA 02139, USA.|Laboratory for Computer Science, MIT, 545 Technology Square, Cambridge, MA 02139, USA.', 'United States|United States|United States', 221, 'Algorithms|Cell Cycle|Cluster Analysis|Computational Biology|Databases, Genetic|Gene Expression Profiling|Genes, Fungal|Multigene Family|Saccharomyces cerevisiae', 'Journal Article', '10.1093/bioinformatics/17.suppl_1.s22', '11472989', 'We present the first practical algorithm for the optimal linear leaf ordering of trees that are generated by hierarchical clustering. Hierarchical clustering has been extensively used to analyze gene expression data, and we show how optimal leaf ordering can reveal biological structure that is not observed with an existing heuristic ordering method. For a tree with n leaves, there are 2(n-1) linear orderings consistent with the structure of the tree. Our optimal leaf ordering algorithm runs in time O(n(4)), and we present further improvements that make the running time of our algorithm practical.'),
(69984, 2001, 'bio', 'Separating real motifs from their artifacts', 'Blanchette, M|Sinha, S', 'Department of Computer Science and Engineering, University of Washington, Box 352350, Seattle, WA 98195-2350, USA. blanchem@cs.washington.edu|Department of Computer Science and Engineering, University of Washington, Box 352350, Seattle, WA 98195-2350, USA. blanchem@cs.washington.edu', 'United States|United States', 32, 'Algorithms|Base Sequence|Binding Sites|Computational Biology|DNA|DNA, Fungal|Databases, Nucleic Acid|Genes, Fungal|Multigene Family|Probability|Saccharomyces cerevisiae|Software', 'Journal Article', '10.1093/bioinformatics/17.suppl_1.s30', '11472990', 'The typical output of many computational methods to identify binding sites is a long list of motifs containing some real motifs (those most likely to correspond to the actual binding sites) along with a large number of random variations of these. We present a statistical method to separate real motifs from their artifacts. This produces a short list of high quality motifs that is sufficient to explain the over-representation of all motifs in the given sequences. Using synthetic data sets, we show that the output of our method is very accurate. On various sets of upstream sequences in S. cerevisiae, our program identifies several known binding sites, as well as a number of significant novel motifs.'),
(69985, 2001, 'bio', 'Probe selection algorithms with applications in the analysis of microbial communities', 'Borneman, J|Chrobak, M|Della Vedova, G|Figueroa, A|Jiang, T', 'Department of Plant Pathology, University of California, Riverside, CA 92521, USA.|Department of Plant Pathology, University of California, Riverside, CA 92521, USA.|Department of Plant Pathology, University of California, Riverside, CA 92521, USA.|Department of Plant Pathology, University of California, Riverside, CA 92521, USA.|Department of Plant Pathology, University of California, Riverside, CA 92521, USA.', 'United States|United States|United States|United States|United States', 15, 'Algorithms|Computational Biology|DNA Fingerprinting|DNA, Ribosomal|Genetics, Microbial|Molecular Probe Techniques|Oligonucleotide Array Sequence Analysis|Oligonucleotide Probes', 'Journal Article', '10.1093/bioinformatics/17.suppl_1.s39', '11472991', 'We propose two efficient heuristics for minimizing the number of oligonucleotide probes needed for analyzing populations of ribosomal RNA gene (rDNA) clones by hybridization experiments on DNA microarrays. Such analyses have applications in the study of microbial communities. Unlike in the classical SBH (sequencing by hybridization) procedure, where multiple probes are on a DNA chip, in our applications we perform a series of experiments, each one consisting of applying a single probe to a DNA microarray containing a large sample of rDNA sequences from the studied population. The overall cost of the analysis is thus roughly proportional to the number of experiments, underscoring the need for minimizing the number of probes. Our algorithms are based on two well-known optimization techniques, i.e. simulated annealing and Lagrangian relaxation, and our preliminary tests demonstrate that both algorithms are able to find satisfactory probe sets for real rDNA data.'),
(69986, 2001, 'bio', 'Visualizing associations between genome sequences and gene expression data using genome-mean expression profiles', 'Chiang, D Y|Brown, P O|Eisen, M B', 'Dept. of Molecular and Cell Biology, U. of California, Berkeley, CA 94720, USA.|Dept. of Molecular and Cell Biology, U. of California, Berkeley, CA 94720, USA.|Dept. of Molecular and Cell Biology, U. of California, Berkeley, CA 94720, USA.', 'United States|United States|United States', 75, 'Algorithms|Base Sequence|Cluster Analysis|Computational Biology|DNA, Fungal|Gene Expression Profiling|Genome|Genome, Fungal|Oligonucleotide Array Sequence Analysis|Saccharomyces cerevisiae|Software', 'Journal Article', '10.1093/bioinformatics/17.suppl_1.s49', '11472992', 'The combination of genome-wide expression patterns and full genome sequences offers a great opportunity to further our understanding of the mechanisms and logic of transcriptional regulation. Many methods have been described that identify sequence motifs enriched in transcription control regions of genes that share similar gene expression patterns. Here we present an alternative approach that evaluates the transcriptional information contained by specific sequence motifs by computing for each motif the mean expression profile of all genes that contain the motif in their transcription control regions. These genome-mean expression profiles (GMEPs) are valuable for visualizing the relationship between genome sequences and gene expression data, and for characterizing the transcriptional importance of specific sequence motifs. Analysis of GMEPs calculated from a dataset of 519 whole-genome microarray experiments in Saccharomyces cerevisiae show a significant correlation between GMEPs of motifs that are reverse complements, a result that supports the relationship between GMEPs and transcriptional regulation. Hierarchical clustering of GMEPs identifies clusters of motifs that correspond to binding sites of well-characterized transcription factors. The GMEPs of these clustered motifs have patterns of variation across conditions that reflect the known activities of these transcription factors. Software that computed GMEPs from sequence and gene expression data is available under the terms of the Gnu Public License from http://rana.lbl.gov/.'),
(69987, 2001, 'bio', 'Gene recognition based on DAG shortest paths', 'Chuang, J S|Roth, D', 'Department of Computer Science, University of Illinois at Urbana-Champaign, Digital Computing Laboratory, Urbana, Illinois 61801, USA. jsc@ocf.berkeley.edu|Department of Computer Science, University of Illinois at Urbana-Champaign, Digital Computing Laboratory, Urbana, Illinois 61801, USA. jsc@ocf.berkeley.edu', 'United States|United States', 7, 'Computational Biology|DNA|Databases, Nucleic Acid|Genes|Genetic Techniques|Software', 'Evaluation Studies', '10.1093/bioinformatics/17.suppl_1.s56', '11472993', 'We describe DAGGER, an ab initio gene recognition program which combines the output of high dimensional signal sensors in an intuitive gene model based on directed acyclic graphs. In the first stage, candidate start, donor, acceptor, and stop sites are scored using the SNoW learning architecture. These sites are then used to generate a directed acyclic graph in which each source-sink path represents a possible gene structure. Training sequences are used to optimize an edge weighting function so that the shortest source-sink path maximizes exon-level prediction accuracy. Experimental evaluation of prediction accuracy on two benchmark data sets demonstrates that DAGGERis competitive with ab initio gene finding programs based on Hidden Markov Models.'),
(69988, 2001, 'bio', 'Using mixtures of common ancestors for estimating the probabilities of discrete events in biological sequences', 'Eskin, E|Grundy, W N|Singer, Y', 'Department of Computer Science, Columbia University, New York, NY, 10027, USA. eeskin@cs.columbia.edu|Department of Computer Science, Columbia University, New York, NY, 10027, USA. eeskin@cs.columbia.edu|Department of Computer Science, Columbia University, New York, NY, 10027, USA. eeskin@cs.columbia.edu', 'United States|United States|United States', 5, 'Amino Acid Sequence|Bayes Theorem|Computational Biology|Evolution, Molecular|Models, Genetic|Probability|Proteins|Sequence Alignment', 'Journal Article', '10.1093/bioinformatics/17.suppl_1.s65', '11472994', 'Accurately estimating probabilities from observations is important for probabilistic-based approaches to problems in computational biology. In this paper we present a biologically-motivated method for estimating probability distributions over discrete alphabets from observations using a mixture model of common ancestors. The method is an extension of substitution matrix-based probability estimation methods. In contrast to previous such methods, our method has a simple Bayesian interpretation and has the advantage over Dirichlet mixtures that it is both effective and simple to compute for large alphabets. The method is applied to estimate amino acid probabilities based on observed counts in an alignment and is shown to perform comparably to previous methods. The method is also applied to estimate probability distributions over protein families and improves protein classification accuracy.'),
(69989, 2001, 'bio', 'GENIES: a natural-language processing system for the extraction of molecular pathways from journal articles', 'Friedman, C|Kra, P|Yu, H|Krauthammer, M|Rzhetsky, A', 'Computer Science Dept, Queens College CUNY, Flushing, NY, 11367, USA. friedman.carol@dmi.columbia.edu|Computer Science Dept, Queens College CUNY, Flushing, NY, 11367, USA. friedman.carol@dmi.columbia.edu|Computer Science Dept, Queens College CUNY, Flushing, NY, 11367, USA. friedman.carol@dmi.columbia.edu|Computer Science Dept, Queens College CUNY, Flushing, NY, 11367, USA. friedman.carol@dmi.columbia.edu|Computer Science Dept, Queens College CUNY, Flushing, NY, 11367, USA. friedman.carol@dmi.columbia.edu', 'United States|United States|United States|United States|United States', 350, 'Artificial Intelligence|Computational Biology|Molecular Biology|Natural Language Processing|Periodicals as Topic|Pilot Projects', 'Evaluation Studies', '10.1093/bioinformatics/17.suppl_1.s74', '11472995', 'Systems that extract structured information from natural language passages have been highly successful in specialized domains. The time is opportune for developing analogous applications for molecular biology and genomics. We present a system, GENIES, that extracts and structures information about cellular pathways from the biological literature in accordance with a knowledge model that we developed earlier. We implemented GENIES by modifying an existing medical natural language processing system, MedLEE, and performed a preliminary evaluation study. Our results demonstrate the value of the underlying techniques for the purpose of acquiring valuable knowledge from biological journals.'),
(69990, 2001, 'bio', 'An insight into domain combinations', 'Apic, G|Gough, J|Teichmann, S A', 'MRC Laboratory of Molecular Biology, Hills Road, Cambridge, CB2 2QH, UK. apic@mrc-lmb.cam.ac.uk|MRC Laboratory of Molecular Biology, Hills Road, Cambridge, CB2 2QH, UK. apic@mrc-lmb.cam.ac.uk|MRC Laboratory of Molecular Biology, Hills Road, Cambridge, CB2 2QH, UK. apic@mrc-lmb.cam.ac.uk', 'United Kingdom|United Kingdom|United Kingdom', 87, 'Computational Biology|Databases, Protein|Genome|Peptides|Phylogeny|Protein Structure, Tertiary', 'Journal Article', '10.1093/bioinformatics/17.suppl_1.s83', '11472996', 'Domains are the building blocks of all globular proteins, and are units of compact three-dimensional structure as well as evolutionary units. There is a limited repertoire of domain families, so that these domain families are duplicated and combined in different ways to form the set of proteins in a genome. Proteins are gene products. The processes that produce new genes are duplication and recombination as well as gene fusion and fission. We attempt to gain an overview of these processes by studying the structural domains in the proteins of seven genomes from the three kingdoms of life: Eubacteria, Archaea and Eukaryota. We use here the domain and superfamily definitions in Structural Classification of Proteins Database (SCOP) in order to map pairs of adjacent domains in genome sequences in terms of their superfamily combinations. We find 624 out of the 764 superfamilies in SCOP in these genomes, and the 624 families occur in 585 pairwise combinations. Most families are observed in combination with one or two other families, while a few families are very versatile in their combinatorial behaviour. This type of pattern can be described by a scale-free network. Finally, we study domain repeats and we compare the set of the domain combinations in the genomes to those in PDB, and discuss the implications for structural genomics.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(69991, 2001, 'bio', 'Promoter prediction in the human genome', 'Hannenhalli, S|Levy, S', 'Informatics Research, Celera Genomics, 45 West Gude Drive, Rockville, MD-20850, USA. Sridhar.Hannenhalli@celera.com|Informatics Research, Celera Genomics, 45 West Gude Drive, Rockville, MD-20850, USA. Sridhar.Hannenhalli@celera.com', 'United States|United States', 88, 'Binding Sites|Computational Biology|CpG Islands|DNA|Genetic Techniques|Genome, Human|Humans|Promoter Regions, Genetic', 'Journal Article', '10.1093/bioinformatics/17.suppl_1.s90', '11472997', 'Computational prediction of eukaryotic poIII promoters has been one of the most elusive problems despite considerable effort devoted to the study. Researchers have looked for various types of signals around the transcriptional start site (TSS), viz. oligo-nucleotide statistics, potential binding sites for core factors, clusters of binding sites, proximity to CpG islands etc. The proximity of CpG islands to gene starts is now a well established fact, although until recently, it was based on very little genomic data. In this work we explore the possibility of enhancing the promoter prediction accuracy by combining CpG island information with a few other, biologically motivated, seemingly independent signals, that cover most of the known knowledge. We benchmarked the method on a much larger genomic datasets compared to previous studies. We were able to improve slightly upon current prediction accuracy. Furthermore, we observe that CpG islands are the most dominant signals and the other signals do not improve the prediction. This suggests that the computational prediction of promoters for genes with no associated CpG-island (typically having tissue-specific expression) looking only at the immediate neighborhood of the TSS may not even be possible. We suggest some biological experiments and studies to better understand the biology of transcription.'),
(69992, 2001, 'bio', 'Disambiguating proteins, genes, and RNA in text: a machine learning approach', 'Hatzivassiloglou, V|Duboué, P A|Rzhetsky, A', 'Department of Computer Science, Columbia University, 1214 Amsterdam Avenue, New York, NY 10027, USA. vh@cs.columbia.edu|Department of Computer Science, Columbia University, 1214 Amsterdam Avenue, New York, NY 10027, USA. vh@cs.columbia.edu|Department of Computer Science, Columbia University, 1214 Amsterdam Avenue, New York, NY 10027, USA. vh@cs.columbia.edu', 'United States|United States|United States', 121, 'Algorithms|Artificial Intelligence|Bayes Theorem|Computational Biology|Data Collection|Genes|Natural Language Processing|Proteins|RNA', 'Comparative Study', '10.1093/bioinformatics/17.suppl_1.s97', '11472998', 'We present an automated system for assigning protein, gene, or mRNA class labels to biological terms in free text. Three machine learning algorithms and several extended ways for defining contextual features for disambiguation are examined, and a fully unsupervised manner for obtaining training examples is proposed. We train and evaluate our system over a collection of 9 million words of molecular biology journal articles, obtaining accuracy rates up to 85%.'),
(69993, 2001, 'bio', 'Identifying splits with clear separation: a new class discovery method for gene expression data', 'von Heydebreck, A|Huber, W|Poustka, A|Vingron, M', 'Division of Computational Molecular Biology, Max-Planck-Institute for Molecular Genetics, Ihnestr. 73, D-14195 Berlin, Germany. heydebre@molgen.mpg.de|Division of Computational Molecular Biology, Max-Planck-Institute for Molecular Genetics, Ihnestr. 73, D-14195 Berlin, Germany. heydebre@molgen.mpg.de|Division of Computational Molecular Biology, Max-Planck-Institute for Molecular Genetics, Ihnestr. 73, D-14195 Berlin, Germany. heydebre@molgen.mpg.de|Division of Computational Molecular Biology, Max-Planck-Institute for Molecular Genetics, Ihnestr. 73, D-14195 Berlin, Germany. heydebre@molgen.mpg.de', 'Germany|Germany|Germany|Germany', 48, 'Algorithms|Computational Biology|Databases, Factual|Gene Expression|Gene Expression Profiling|Humans|Leukemia|Melanoma|Oligonucleotide Array Sequence Analysis|Oncogenes', 'Journal Article', '10.1093/bioinformatics/17.suppl_1.s107', '11472999', 'We present a new class discovery method for microarray gene expression data. Based on a collection of gene expression profiles from different tissue samples, the method searches for binary class distinctions in the set of samples that show clear separation in the expression levels of specific subsets of genes. Several mutually independent class distinctions may be found, which is difficult to obtain from most commonly used clustering algorithms. Each class distinction can be biologically interpreted in terms of its supporting genes. The mathematical characterization of the favored class distinctions is based on statistical concepts. By analyzing three data sets from cancer gene expression studies, we demonstrate that our method is able to detect biologically relevant structures, for example cancer subtypes, in an unsupervised fashion.'),
(69994, 2001, 'bio', 'GEST: a gene expression search tool based on a novel Bayesian similarity metric', 'Hunter, L|Taylor, R C|Leach, S M|Simon, R', 'Center for Computational Pharmacology, Department of Pharmacology, School of Medicine, C236, University of Colorado Health Sciences Center, 4200 E. Ninth Avenue, Denver CO 80206, USA. larry.hunter@uchsc.edu|Center for Computational Pharmacology, Department of Pharmacology, School of Medicine, C236, University of Colorado Health Sciences Center, 4200 E. Ninth Avenue, Denver CO 80206, USA. larry.hunter@uchsc.edu|Center for Computational Pharmacology, Department of Pharmacology, School of Medicine, C236, University of Colorado Health Sciences Center, 4200 E. Ninth Avenue, Denver CO 80206, USA. larry.hunter@uchsc.edu|Center for Computational Pharmacology, Department of Pharmacology, School of Medicine, C236, University of Colorado Health Sciences Center, 4200 E. Ninth Avenue, Denver CO 80206, USA. larry.hunter@uchsc.edu', 'United States|United States|United States|United States', 52, 'Bayes Theorem|Computational Biology|Databases, Genetic|Gene Expression Profiling|Genes, Fungal|Saccharomyces cerevisiae|Software', 'Journal Article', '10.1093/bioinformatics/17.suppl_1.s115', '11473000', 'Gene expression array technology has made possible the assay of expression levels of tens of thousands of genes at a time, large databases of such measurements are currently under construction. One important use of such databases is the ability to search for experiments that have similar gene expression levels as a query, potentially identifying previously unsuspected relationships among cellular states. Such searches depend crucially on the metric used to assess the similarity between pairs of experiments. The complex joint distribution of gene expression levels, particularly their correlational structure and non-normality, make simple similarity metrics such as Euclidean distance or correlational similarity scores suboptimal for use in this application. We present a similarity metric for gene expression array experiments that takes into account the complex joint distribution of expression values. We provide a computationally tractable approximation to this measure, and have implemented a database search tool based on it. We discuss implementation issues and efficiency, and we compare our new metric to other standard metrics.'),
(69995, 2001, 'bio', 'Probabilistic divergence measures for detecting interspecies recombination', 'Husmeier, D|Wright, F', 'Biomathematics & Statistics Scotland, SCRI, Invergowrie, Dundee, DD2 5DA, UK. dirk@bioss.ac.uk|Biomathematics & Statistics Scotland, SCRI, Invergowrie, Dundee, DD2 5DA, UK. dirk@bioss.ac.uk', 'United Kingdom|United Kingdom', 24, 'Computational Biology|DNA|Dengue Virus|Genetic Techniques|Hepatitis B virus|Humans|Markov Chains|Models, Genetic|Models, Statistical|Monte Carlo Method|Phylogeny|Recombination, Genetic|Sequence Alignment|Species Specificity', 'Journal Article', '10.1093/bioinformatics/17.suppl_1.s123', '11473001', 'This paper proposes a graphical method for detecting interspecies recombination in multiple alignments of DNA sequences. A fixed-size window is moved along a given DNA sequence alignment. For every position, the marginal posterior probability over tree topologies is determined by means of a Markov chain Monte Carlo simulation. Two probabilistic divergence measures are plotted along the alignment, and are used to identify recombinant regions. The method is compared with established detection methods on a set of synthetic benchmark sequences and two real-world DNA sequence alignments.'),
(69996, 2001, 'bio', 'Design of a compartmentalized shotgun assembler for the human genome', 'Huson, D H|Reinert, K|Kravitz, S A|Remington, K A|Delcher, A L|Dew, I M|Flanigan, M|Halpern, A L|Lai, Z|Mobarry, C M|Sutton, G G|Myers, E W', 'Informatics Research, Celera Genomics, 45 West Gude Drive, Rockville 20850, USA.|Informatics Research, Celera Genomics, 45 West Gude Drive, Rockville 20850, USA.|Informatics Research, Celera Genomics, 45 West Gude Drive, Rockville 20850, USA.|Informatics Research, Celera Genomics, 45 West Gude Drive, Rockville 20850, USA.|Informatics Research, Celera Genomics, 45 West Gude Drive, Rockville 20850, USA.|Informatics Research, Celera Genomics, 45 West Gude Drive, Rockville 20850, USA.|Informatics Research, Celera Genomics, 45 West Gude Drive, Rockville 20850, USA.|Informatics Research, Celera Genomics, 45 West Gude Drive, Rockville 20850, USA.|Informatics Research, Celera Genomics, 45 West Gude Drive, Rockville 20850, USA.|Informatics Research, Celera Genomics, 45 West Gude Drive, Rockville 20850, USA.|Informatics Research, Celera Genomics, 45 West Gude Drive, Rockville 20850, USA.|Informatics Research, Celera Genomics, 45 West Gude Drive, Rockville 20850, 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', 49, 'Chromosomes, Artificial, Bacterial|Cloning, Molecular|Computational Biology|Databases, Nucleic Acid|Genome, Human|Humans|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/17.suppl_1.s132', '11473002', 'Two different strategies for determining the human genome are currently being pursued: one is the clone-by-clone approach, employed by the publicly funded project, and the other is the whole genome shotgun assembler approach, favored by researchers at Celera Genomics. An interim strategy employed at Celera, called compartmentalized shotgun assembly, makes use of preliminary data produced by both approaches. In this paper we describe the design, implementation and operation of the compartmentalized shotgun assembler.'),
(69997, 2001, 'bio', 'Integrating genomic homology into gene structure prediction', 'Korf, I|Flicek, P|Duan, D|Brent, M R', 'Department of Computer Science, Washington University, Campus Box 1045, St. Louis, MO, 63130, USA. ikorf@cs.wustl.edu|Department of Computer Science, Washington University, Campus Box 1045, St. Louis, MO, 63130, USA. ikorf@cs.wustl.edu|Department of Computer Science, Washington University, Campus Box 1045, St. Louis, MO, 63130, USA. ikorf@cs.wustl.edu|Department of Computer Science, Washington University, Campus Box 1045, St. Louis, MO, 63130, USA. ikorf@cs.wustl.edu', 'United States|United States|United States|United States', 328, 'Algorithms|Animals|Base Sequence|Computational Biology|Conserved Sequence|DNA|Evolution, Molecular|Genome|Genome, Human|Humans|Mice|Models, Statistical|Sensitivity and Specificity|Sequence Alignment|Sequence Homology, Nucleic Acid', 'Comparative Study', '10.1093/bioinformatics/17.suppl_1.s140', '11473003', 'TWINSCAN is a new gene-structure prediction system that directly extends the probability model of GENSCAN, allowing it to exploit homology between two related genomes. Separate probability models are used for conservation in exons, introns, splice sites, and UTRs, reflecting the differences among their patterns of evolutionary conservation. TWINSCAN is specifically designed for the analysis of high-throughput genomic sequences containing an unknown number of genes. In experiments on high-throughput mouse sequences, using homologous sequences from the human genome, TWINSCAN shows notable improvement over GENSCAN in exon sensitivity and specificity and dramatic improvement in exact gene sensitivity and specificity. This improvement can be attributed entirely to modeling the patterns of evolutionary conservation in genomic sequence.'),
(69998, 2001, 'bio', 'Generating protein interaction maps from incomplete data: application to fold assignment', 'Lappe, M|Park, J|Niggemann, O|Holm, L', 'Structural Genomics Group, The European Bioinformatics Institute, EMBL Outstation, Cambridge CB10 1SD, UK. lappe@ebi.ac.uk|Structural Genomics Group, The European Bioinformatics Institute, EMBL Outstation, Cambridge CB10 1SD, UK. lappe@ebi.ac.uk|Structural Genomics Group, The European Bioinformatics Institute, EMBL Outstation, Cambridge CB10 1SD, UK. lappe@ebi.ac.uk|Structural Genomics Group, The European Bioinformatics Institute, EMBL Outstation, Cambridge CB10 1SD, UK. lappe@ebi.ac.uk', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom', 25, 'Algorithms|Cluster Analysis|Computational Biology|Data Interpretation, Statistical|Databases, Protein|Fungal Proteins|Protein Folding|Proteins|Two-Hybrid System Techniques', 'Comparative Study', '10.1093/bioinformatics/17.suppl_1.s149', '11473004', 'We present a framework to generate comprehensive overviews of protein-protein interactions. In the post-genomic view of cellular function, each biological entity is seen in the context of a complex network of interactions. Accordingly, we model functional space by representing protein-protein-interaction data as undirected graphs. We suggest a general approach to generate interaction maps of cellular networks in the presence of huge amounts of fragmented and incomplete data, and to derive representations of large networks which hide clutter while keeping the essential architecture of the interaction space. This is achieved by contracting the graphs according to domain-specific hierarchical classifications. The key concept here is the notion of induced interaction, which allows the integration, comparison and analysis of interaction data from different sources and different organisms at a given level of abstraction.'),
(69999, 2001, 'bio', 'Feature selection for DNA methylation based cancer classification', 'Model, F|Adorján, P|Olek, A|Piepenbrock, C', 'Epigenomics AG, Kastanienallee 24, D-10435 Berlin, Germany. Fabian.Model@epigenomics.com|Epigenomics AG, Kastanienallee 24, D-10435 Berlin, Germany. Fabian.Model@epigenomics.com|Epigenomics AG, Kastanienallee 24, D-10435 Berlin, Germany. Fabian.Model@epigenomics.com|Epigenomics AG, Kastanienallee 24, D-10435 Berlin, Germany. Fabian.Model@epigenomics.com', 'Germany|Germany|Germany|Germany', 102, 'Computational Biology|CpG Islands|DNA Methylation|DNA, Neoplasm|Humans|Leukemia, Myeloid, Acute|Neoplasms|Oligonucleotide Array Sequence Analysis|Precursor Cell Lymphoblastic Leukemia-Lymphoma|Principal Component Analysis', 'Comparative Study', '10.1093/bioinformatics/17.suppl_1.s157', '11473005', 'Molecular portraits, such as mRNA expression or DNA methylation patterns, have been shown to be strongly correlated with phenotypical parameters. These molecular patterns can be revealed routinely on a genomic scale. However, class prediction based on these patterns is an under-determined problem, due to the extreme high dimensionality of the data compared to the usually small number of available samples. This makes a reduction of the data dimensionality necessary. Here we demonstrate how phenotypic classes can be predicted by combining feature selection and discriminant analysis. By comparing several feature selection methods we show that the right dimension reduction strategy is of crucial importance for the classification performance. The techniques are demonstrated by methylation pattern based discrimination between acute lymphoblastic leukemia and acute myeloid leukemia.'),
(70000, 2001, 'bio', 'New approaches for reconstructing phylogenies from gene order data', 'Moret, B M|Wang, L S|Warnow, T|Wyman, S K', 'Department of Computer Science, University of New Mexico, Albuquerque, NM 87131, USA. moret@cs.unm.edu|Department of Computer Science, University of New Mexico, Albuquerque, NM 87131, USA. moret@cs.unm.edu|Department of Computer Science, University of New Mexico, Albuquerque, NM 87131, USA. moret@cs.unm.edu|Department of Computer Science, University of New Mexico, Albuquerque, NM 87131, USA. moret@cs.unm.edu', 'Mexico|Mexico|Mexico|Mexico', 97, 'Biological Evolution|Chloroplasts|Computational Biology|Databases, Genetic|Genetic Techniques|Genome, Plant|Magnoliopsida|Models, Genetic|Phylogeny', 'Journal Article', '10.1093/bioinformatics/17.suppl_1.s165', '11473006', 'We report on new techniques we have developed for reconstructing phylogenies on whole genomes. Our mathematical techniques include new polynomial-time methods for bounding the inversion length of a candidate tree and new polynomial-time methods for estimating genomic distances which greatly improve the accuracy of neighbor-joining analyses. We demonstrate the power of these techniques through an extensive performance study based on simulating genome evolution under a wide range of model conditions. Combining these new tools with standard approaches (fast reconstruction with neighbor-joining, exploration of all possible refinements of strict consensus trees, etc.) has allowed us to analyze datasets that were previously considered computationally impractical. In particular, we have conducted a complete phylogenetic analysis of a subset of the Campanulaceae family, confirming various conjectures about the relationships among members of the subset and about the principal mechanism of evolution for their chloroplast genome. We give representative results of the extensive experimentation we conducted on both real and simulated datasets in order to validate and characterize our approaches. We find that our techniques provide very accurate reconstructions of the true tree topology even when the data are generated by processes that include a significant fraction of transpositions and when the data are close to saturation.'),
(70001, 2001, 'bio', 'Prediction of the coupling specificity of G protein coupled receptors to their G proteins', 'Möller, S|Vilo, J|Croning, M D', 'EMBL-EBI, European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SD, UK.|EMBL-EBI, European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SD, UK.|EMBL-EBI, European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SD, UK.', 'United Kingdom|United Kingdom|United Kingdom', 0, 'Amino Acid Sequence|Binding Sites|Computational Biology|Databases, Protein|GTP-Binding Proteins|Ligands|Models, Biological|Pattern Recognition, Automated|Receptors, Cell Surface|Signal Transduction', 'Journal Article', '10.1093/bioinformatics/17.suppl_1.s174', '11473007', 'G protein coupled receptors (GPCRs) are found in great numbers in most eukaryotic genomes. They are responsible for sensing a staggering variety of structurally diverse ligands, with their activation resulting in the initiation of a variety of cellular signalling cascades. The physiological response that is observed following receptor activation is governed by the guanine nucleotide-binding proteins (G proteins) to which a particular receptor chooses to couple. Previous investigations have demonstrated that the specificity of the receptor-G protein interaction is governed by the intracellular domains of the receptor. Despite many studies it has proven very difficult to predict de novo, from the receptor sequence alone, the G proteins to which a GPCR is most likely to couple. We have used a data-mining approach, combining pattern discovery with membrane topology prediction, to find patterns of amino acid residues in the intracellular domains of GPCR sequences that are specific for coupling to a particular functional class of G proteins. A prediction system was then built, being based upon these discovered patterns. We can report this approach was successful in the prediction of G protein coupling specificity of unknown sequences. Such predictions should be of great use in providing in silico characterisation of newly cloned receptor sequences and for improving the annotation of GPCRs stored in protein sequence databases.'),
(70002, 2001, 'bio', 'Non-symmetric score matrices and the detection of homologous transmembrane proteins', 'Müller, T|Rahmann, S|Rehmsmeier, M', 'Theoretische Bioinformatik (TBI), Deutsches Krebsforschungszentrum, Im Neuenheimer Feld 280, Heidelberg, D-61920, Germany. muelle_t@molgen.mpg.de|Theoretische Bioinformatik (TBI), Deutsches Krebsforschungszentrum, Im Neuenheimer Feld 280, Heidelberg, D-61920, Germany. muelle_t@molgen.mpg.de|Theoretische Bioinformatik (TBI), Deutsches Krebsforschungszentrum, Im Neuenheimer Feld 280, Heidelberg, D-61920, Germany. muelle_t@molgen.mpg.de', 'Germany|Germany|Germany', 0, 'Computational Biology|Computer Simulation|Databases, Protein|Membrane Proteins|Models, Statistical|Receptors, Cell Surface|Sequence Alignment', 'Journal Article', '10.1093/bioinformatics/17.suppl_1.s182', '11473008', 'Given a transmembrane protein, we wish to find related ones by a database search. Due to the strongly hydrophobic amino acid composition of transmembrane domains, suboptimal results are obtained when general-purpose scoring matrices such as BLOSUM are used. Recently, a transmembrane-specific score matrix called PHAT was shown to perform much better than BLOSUM. In this article, we derive a transmembrane score matrix family, called SLIM, which has several distinguishing features. In contrast to currently used matrices, SLIM is non-symmetric. The asymmetry arises because different background compositions are assumed for the transmembrane query and the unknown database sequences. We describe the mathematical model behind SLIM in detail and show that SLIM outperforms PHAT both on simulated data and in a realistic setting. Since non-symmetric score matrices are a new concept in database search methods, we discuss some important theoretical and practical issues.'),
(70003, 2001, 'bio', 'Designing fast converging phylogenetic methods', 'Nakhleh, L|Roshan, U|St  John, K|Sun, J|Warnow, T', 'Department of Computer Sciences, University of Texas at Austin, Austin, Texas 78712, USA.|Department of Computer Sciences, University of Texas at Austin, Austin, Texas 78712, USA.|Department of Computer Sciences, University of Texas at Austin, Austin, Texas 78712, USA.|Department of Computer Sciences, University of Texas at Austin, Austin, Texas 78712, USA.|Department of Computer Sciences, University of Texas at Austin, Austin, Texas 78712, USA.', 'United States|United States|United States|United States|United States', 36, 'Computational Biology|Computer Simulation|Databases, Genetic|Genetic Techniques|Models, Genetic|Models, Statistical|Phylogeny|Software', 'Comparative Study', '10.1093/bioinformatics/17.suppl_1.s190', '11473009', 'Absolute fast converging phylogenetic reconstruction methods are provably guaranteed to recover the true tree with high probability from sequences that grow only polynomially in the number of leaves, once the edge lengths are bounded arbitrarily from above and below. Only a few methods have been determined to be absolute fast converging, these have all been developed in just the last few years, and most are polynomial time. In this paper, we compare pre-existing fast converging methods as well as some new polynomial time methods that we have developed. Our study, based upon simulating evolution under a wide range of model conditions, establishes that our new methods outperform both neighbor joining and the previous fast converging methods, returning very accurate large trees, when these other methods do poorly.'),
(70004, 2001, 'bio', 'Joint modeling of DNA sequence and physical properties to improve eukaryotic promoter recognition', '-', '-', '-', 106, 'Animals|Chemical Phenomena|Chemistry, Physical|Computational Biology|DNA|Databases, Nucleic Acid|Drosophila|Likelihood Functions|Markov Chains|Models, Genetic|Models, Statistical|Neural Networks (Computer)|Promoter Regions, Genetic|Stochastic Processes', 'Journal Article', '10.1093/bioinformatics/17.suppl_1.s199', '11473010', 'We present an approach to integrate physical properties of DNA, such as DNA bendability or GC content, into our probabilistic promoter recognition system McPROMOTER. In the new model, a promoter is represented as a sequence of consecutive segments represented by joint likelihoods for DNA sequence and profiles of physical properties. Sequence likelihoods are modeled with interpolated Markov chains, physical properties with Gaussian distributions. The background uses two joint sequence/profile models for coding and non-coding sequences, each consisting of a mixture of a sense and an anti-sense submodel. On a large Drosophila test set, we achieved a reduction of about 30% of false positives when compared with a model solely based on sequence likelihoods.'),
(70005, 2001, 'bio', 'An algorithm for finding signals of unknown length in DNA sequences', 'Pavesi, G|Mauri, G|Pesole, G', 'Department of Computer Science, Systems and Communication, University of Milan-Bicocca, Via Bicocca degli Arcimboldi 8, Milan, I-20126, Italy. pavesi@disco.unimib.it|Department of Computer Science, Systems and Communication, University of Milan-Bicocca, Via Bicocca degli Arcimboldi 8, Milan, I-20126, Italy. pavesi@disco.unimib.it|Department of Computer Science, Systems and Communication, University of Milan-Bicocca, Via Bicocca degli Arcimboldi 8, Milan, I-20126, Italy. pavesi@disco.unimib.it', 'Italy|Italy|Italy', 255, 'Algorithms|Computational Biology|DNA|DNA Mutational Analysis|Genetic Techniques|Pattern Recognition, Automated|Sequence Analysis, DNA|Software', 'Evaluation Studies', '10.1093/bioinformatics/17.suppl_1.s207', '11473011', 'Pattern discovery in unaligned DNA sequences is a challenging problem in both computer science and molecular biology. Several different methods and techniques have been proposed so far, but in most of the cases signals in DNA sequences are very complicated and avoid detection. Exact exhaustive methods can solve the problem only for short signals with a limited number of mutations. In this work, we extend exhaustive enumeration also to longer patterns. More in detail, the basic version of algorithm presented in this paper, given as input a set of sequences and an error ratio epsilon < 1, finds all patterns that occur in at least q sequences of the set with at most epsilonm mutations, where m is the length of the pattern. The only restriction is imposed on the location of mutations along the signal. That is, a valid occurrence of a pattern can present at most [epsiloni] mismatches in the first i nucleotides, and so on. However, we show how the algorithm can be used also when no assumption can be made on the position of mutations. In this case, it is also possible to have an estimate of the probability of finding a signal according to the signal length, the error ratio, and the input parameters. Finally, we discuss some significance measures that can be used to sort the patterns output by the algorithm.'),
(70006, 2001, 'bio', 'Inferring subnetworks from perturbed expression profiles', 'Peer, D|Regev, A|Elidan, G|Friedman, N', 'School of Computer Science & Engineering, Hebrew University, Jerusalem, 91904, Israel. danab@cs.huji.ac.il|School of Computer Science & Engineering, Hebrew University, Jerusalem, 91904, Israel. danab@cs.huji.ac.il|School of Computer Science & Engineering, Hebrew University, Jerusalem, 91904, Israel. danab@cs.huji.ac.il|School of Computer Science & Engineering, Hebrew University, Jerusalem, 91904, Israel. danab@cs.huji.ac.il', 'Israel|Israel|Israel|Israel', 358, 'Bayes Theorem|Computational Biology|Databases, Genetic|Gene Expression Profiling|Genes, Fungal|Models, Genetic|Models, Statistical|Mutation|Saccharomyces cerevisiae', 'Journal Article', '10.1093/bioinformatics/17.suppl_1.s215', '11473012', 'Genome-wide expression profiles of genetic mutants provide a wide variety of measurements of cellular responses to perturbations. Typical analysis of such data identifies genes affected by perturbation and uses clustering to group genes of similar function. In this paper we discover a finer structure of interactions between genes, such as causality, mediation, activation, and inhibition by using a Bayesian network framework. We extend this framework to correctly handle perturbations, and to identify significant subnetworks of interacting genes. We apply this method to expression data of S. cerevisiae mutants and uncover a variety of structured metabolic, signaling and regulatory pathways.'),
(70007, 2001, 'bio', 'Fragment assembly with double-barreled data', 'Pevzner, P A|Tang, H', 'Department of Computer Science and Engineering, University of California at San Diego, La Jolla, CA 92093, USA. ppevzner@ucsd.edu|Department of Computer Science and Engineering, University of California at San Diego, La Jolla, CA 92093, USA. ppevzner@ucsd.edu', 'United States|United States', 78, 'Algorithms|Computational Biology|Genetic Techniques|Genome|Models, Genetic|Nucleic Acid Hybridization|Sequence Analysis, DNA', 'Journal Article', '10.1093/bioinformatics/17.suppl_1.s225', '11473013', 'For the last twenty years fragment assembly was dominated by the overlap - layout - consensus algorithms that are used in all currently available assembly tools. However, the limits of these algorithms are being tested in the era of genomic sequencing and it is not clear whether they are the best choice for large-scale assemblies. Although the overlap - layout - consensus approach proved to be useful in assembling clones, it faces difficulties in genomic assemblies: the existing algorithms make assembly errors even in bacterial genomes. We abandoned the overlap - layout - consensus approach in favour of a new Eulerian Superpath approach that outperforms the existing algorithms for genomic fragment assembly (Pevzner et al. 2001 InProceedings of the Fifth Annual International Conference on Computational Molecular Biology (RECOMB-01), 256-26). In this paper we describe our new EULER-DB algorithm that, similarly to the Celera assembler takes advantage of clone-end sequencing by using the double-barreled data. However, in contrast to the Celera assembler, EULER-DB does not mask repeats but uses them instead as a powerful tool for contig ordering. We also describe a new approach for the Copy Number Problem: How many times a given repeat is present in the genome?. For long nearly-perfect repeats this question is notoriously difficult and some copies of such repeats may be lost in genomic assemblies. We describe our EULER-CN algorithm for the Copy Number Problem that proved to be successful in difficult sequencing projects.'),
(70008, 2001, 'bio', 'Improved prediction of the number of residue contacts in proteins by recurrent neural networks', 'Pollastri, G|Baldi, P|Fariselli, P|Casadio, R', 'Department of Information and Computer Science, Institute for Genomics and Bioinformatics, University of California, Irvine, Irvine, CA 92697-3425, USA.|Department of Information and Computer Science, Institute for Genomics and Bioinformatics, University of California, Irvine, Irvine, CA 92697-3425, USA.|Department of Information and Computer Science, Institute for Genomics and Bioinformatics, University of California, Irvine, Irvine, CA 92697-3425, USA.|Department of Information and Computer Science, Institute for Genomics and Bioinformatics, University of California, Irvine, Irvine, CA 92697-3425, USA.', 'United States|United States|United States|United States', 42, 'Amino Acid Sequence|Computational Biology|Databases, Protein|Molecular Structure|Neural Networks (Computer)|Proteins', 'Journal Article', '10.1093/bioinformatics/17.suppl_1.s234', '11473014', 'Knowing the number of residue contacts in a protein is crucial for deriving constraints useful in modeling protein folding, protein structure, and/or scoring remote homology searches. Here we use an ensemble of bi-directional recurrent neural network architectures and evolutionary information to improve the state-of-the-art in contact prediction using a large corpus of curated data. The ensemble is used to discriminate between two different states of residue contacts, characterized by a contact number higher or lower than the average value of the residue distribution. The ensemble achieves performances ranging from 70.1% to 73.1% depending on the radius adopted to discriminate contacts (6Ato 12A). These performances represent gains of 15% to 20% over the base line statistical predictors always assigning an aminoacid to the most numerous state, 3% to 7% better than any previous method. Combination of different radius predictors further improves the performance. SERVER: http://promoter.ics.uci.edu/BRNN-PRED/.'),
(70009, 2001, 'bio', 'Rich probabilistic models for gene expression', 'Segal, E|Taskar, B|Gasch, A|Friedman, N|Koller, D', 'Computer Science Department, Stanford University, Stanford 94305, USA. eran@cs.stanford.edu|Computer Science Department, Stanford University, Stanford 94305, USA. eran@cs.stanford.edu|Computer Science Department, Stanford University, Stanford 94305, USA. eran@cs.stanford.edu|Computer Science Department, Stanford University, Stanford 94305, USA. eran@cs.stanford.edu|Computer Science Department, Stanford University, Stanford 94305, USA. eran@cs.stanford.edu', 'United States|United States|United States|United States|United States', 166, 'Algorithms|Artificial Intelligence|Cluster Analysis|Computational Biology|Databases, Genetic|Gene Expression Profiling|Genes, Fungal|Genetic Techniques|Models, Statistical|Saccharomyces cerevisiae', 'Journal Article', '10.1093/bioinformatics/17.suppl_1.s243', '11473015', 'Clustering is commonly used for analyzing gene expression data. Despite their successes, clustering methods suffer from a number of limitations. First, these methods reveal similarities that exist over all of the measurements, while obscuring relationships that exist over only a subset of the data. Second, clustering methods cannot readily incorporate additional types of information, such as clinical data or known attributes of genes. To circumvent these shortcomings, we propose the use of a single coherent probabilistic model, that encompasses much of the rich structure in the genomic expression data, while incorporating additional information such as experiment type, putative binding sites, or functional information. We show how this model can be learned from the data, allowing us to discover patterns in the data and dependencies between the gene expression patterns and additional attributes. The learned model reveals context-specific relationships, that exist only over a subset of the experiments in the dataset. We demonstrate the power of our approach on synthetic data and on two real-world gene expression data sets for yeast. For example, we demonstrate a novel functionality that falls naturally out of our framework: predicting the cluster of the array resulting from a gene mutation based only on the genes expression pattern in the context of other mutations.'),
(70010, 2001, 'bio', 'Designing better phages', 'Skiena, S S', 'Applied Algorithm Laboratory, Dept. of Computer Science, State University of New York, Stony Brook, NY 11794-4400, USA. skiena@cs.sunysb.edu', 'United States', 15, 'Algorithms|Bacteriophages|Binding Sites|Computational Biology|DNA Restriction Enzymes|Evolution, Molecular|Genetic Code|Genetic Engineering|Genome, Viral|Models, Genetic', 'Journal Article', '10.1093/bioinformatics/17.suppl_1.s253', '11473016', 'We propose a method to engineer the genome of bacteriophages to increase their effectiveness as antibacterial agents. Specifically, we exploit the redundancy of the triplet code to design genomes that avoid restriction sites while producing the same proteins as wild-type phages. We give an efficient algorithm to minimize the number of restriction sites against sets of cutter sequences, and demonstrate that that phage genomes can be significantly protected against surprisingly large sets of enzymes with no loss of function. Finally, we develop a model to explain why evolution has failed to eliminate many possible restriction sites despite selective pressure, thus motivating the need for genome-level sequence engineering.'),
(70011, 2001, 'bio', 'Probabilistic approaches to the use of higher order clone relationships in physical map assembly', 'States, D J|Nowotny, V|Blackwell, T W', 'Center for Computational Biology and the Department of Genetics, Washington University, Saint Louis, MO 63110, USA. states@ccb.wustl.edu|Center for Computational Biology and the Department of Genetics, Washington University, Saint Louis, MO 63110, USA. states@ccb.wustl.edu|Center for Computational Biology and the Department of Genetics, Washington University, Saint Louis, MO 63110, USA. states@ccb.wustl.edu', 'United States|United States|United States', 0, 'Cloning, Molecular|Computational Biology|Genetic Techniques|Models, Statistical|Physical Chromosome Mapping|Restriction Mapping|Sequence Tagged Sites', 'Evaluation Studies', '10.1093/bioinformatics/17.suppl_1.s262', '11473017', 'Physical map assembly is the inference of genome structure from experimental data. Map assembly depends on the integration of diverse data including sequence tagged site (STS) marker content, clone sizing, and restriction digest fingerprints (RDF). As experimentally measured data, these are uncertain and error prone. Physical map assembly from error free data is straightforward and can be accomplished in linear time in the number of clones, but the assembly of an optimal map from error prone data is an NP-hard problem. We present an alternative approach to physical map assembly that is based on a probabilistic view of the data and seeks to identify those features of the map that can be reliably inferred from the available data. With this approach, we achieve a number of goals. These include the use of multiple data sources, appropriate representation of uncertainties in the underlying data, the use of clone length information in fingerprint map assembly, and the use of higher order information in map assembly. By higher order information, we mean relationships that are not expressible in terms of neighbouring clone relationships. These include triplet and multiple clone overlaps, the uniqueness of STS position, and fingerprint marker locations. In a probabilistic view of physical mapping, we assert that all of the many possible map assemblies are equally likely a priori. Given experimental data, we can only state which assemblies are more likely than others given the experimental observations. Parameters of interest are then derived as likelihood weighted averages over map assemblies. Ideally these averages should be sums or integrals over all possible map assemblies, but computationally this is not feasible for real-world map assembly problems. Instead, sampling is used to asymptotically approach the desired parameters. Software implementing our probabilistic approach to mapping has been written. Assembly of mixed RDF and STS maps containing up to 60 clones can be accomplished on a desktop PC with run times under an hour.'),
(70012, 2001, 'bio', 'Computational expansion of genetic networks', 'Tanay, A|Shamir, R', 'School of Computer Science, Tel-Aviv University, Tel-Aviv, 69978, Israel.|School of Computer Science, Tel-Aviv University, Tel-Aviv, 69978, Israel.', 'Israel|Israel', 34, 'Algorithms|Computational Biology|Ergosterol|Gene Expression Profiling|Genes, Fungal|Genetic Techniques|Models, Genetic|Saccharomyces cerevisiae|Software|Transcription Factors|Transcription, Genetic', 'Journal Article', '10.1093/bioinformatics/17.suppl_1.s270', '11473018', 'We present a new methodology for computational analysis of gene and protein networks. The aim is to generate new educated hypotheses on gene functions and on the logic of the biological network circuitry, based on gene expression profiles. The framework supports the incorporation of biologically motivated network constraints and rules to improve specificity. Since current data is insufficient for de-novo reconstruction, the method receives as input a known pathway core and suggests likely expansions to it. Network modeling is combinatorial, yet data can be probabilistic. At the heart of the approach are a fitness function which estimates the quality of suggested network expansions given the core and the data, and a specificity measure of the expansions. The approach has been implemented in an interactive software tool called GENESYS. We report encouraging results in preliminary analysis of yeast ergosterol pathway based on transcription profiles. In particular, the analysis suggests a novel ergosterol transcription factor.'),
(70013, 2001, 'bio', 'Separation of samples into their constituents using gene expression data', 'Venet, D|Pecasse, F|Maenhaut, C|Bersini, H', 'I.R.I.B.H.N., Campus Hopital Erasme, Route de Lennik 808 Bat C-CP602, Brussels, B-1070, Belgium. davenet@ulb.ac.be|I.R.I.B.H.N., Campus Hopital Erasme, Route de Lennik 808 Bat C-CP602, Brussels, B-1070, Belgium. davenet@ulb.ac.be|I.R.I.B.H.N., Campus Hopital Erasme, Route de Lennik 808 Bat C-CP602, Brussels, B-1070, Belgium. davenet@ulb.ac.be|I.R.I.B.H.N., Campus Hopital Erasme, Route de Lennik 808 Bat C-CP602, Brussels, B-1070, Belgium. davenet@ulb.ac.be', 'Belgium|Belgium|Belgium|Belgium', 77, 'Algorithms|Cells|Colonic Neoplasms|Computational Biology|Computer Simulation|Gene Expression Profiling|Humans|Models, Genetic|Principal Component Analysis|RNA, Messenger', 'Journal Article', '10.1093/bioinformatics/17.suppl_1.s279', '11473019', 'Gene expression measurements are a powerful tool in molecular biology, but when applied to heterogeneous samples containing more than one cellular type the results are difficult to interpret. We present here a new approach to this problem allowing to deduce the gene expression profile of the various cellular types contained in a set of samples directly from the measurements taken on the whole sample.'),
(70014, 2001, 'bio', '0jpy: a software tool for low complexity proteins and protein domains', 'Wise, M J', 'Centre for Communications Systems Research, 10 Downing Street, Cambridge, CB2 3DS, England. M.Wise@ccsr.cam.ac.uk', '-', 0, 'Algorithms|Amino Acid Sequence|Computational Biology|Molecular Sequence Data|Protein Structure, Tertiary|Proteins|Software|Species Specificity', 'Journal Article', '10.1093/bioinformatics/17.suppl_1.s288', '11473020', 'Low complexity proteins and protein domains have sequences which appear highly non-random. Over the years, these sequences have been routinely filtered out during sequence similarity searches because interest has been focused on globular proteins, and inclusion of these domains can severely skew search results. However, early work on these proteins and more recent studies of the related area of repeated protein sequences suggests that low complexity protein domains have function and therefore are in need of further investigation. 0j.py is a new tool for demarcating low complexity protein domains more accurately than has been possible to date. The paper describes 0j.py and its use in revealing proteins with repeated and poly-amino-acid peptides. Statistical methods are then employed to to examine the distribution of these proteins across species, while keyword clustering is used to suggest roles performed by proteins through the use of low complexity domains.'),
(70015, 2001, 'bio', 'Protein-protein interaction map inference using interacting domain profile pairs', 'Wojcik, J|Schächter, V', 'Hybrigenics S.A., 180 avenue Daumesnil, 75012 Paris, France.|Hybrigenics S.A., 180 avenue Daumesnil, 75012 Paris, France.', 'France|France', 163, 'Algorithms|Amino Acid Sequence|Binding Sites|Computational Biology|Databases, Protein|Escherichia coli Proteins|Macromolecular Substances|Molecular Sequence Data|Molecular Structure|Peptide Mapping|Protein Structure, Tertiary|Proteins|Software|Software Design', 'Journal Article', '10.1093/bioinformatics/17.suppl_1.s296', '11473021', 'A number of predictive methods have been designed to predict protein interaction from sequence or expression data. On the experimental front, however, high-throughput proteomics technologies are starting to yield large volumes of protein-protein interaction data. High-quality experimental protein interaction maps constitute the natural dataset upon which to build interaction predictions. Thus the motivation to develop the first interaction-based protein interaction map prediction algorithm. A technique to predict protein-protein interaction maps across organisms is introduced, the interaction-domain pair profile method. The method uses a high-quality protein interaction map with interaction domain information as input to predict an interaction map in another organism. It combines sequence similarity searches with clustering based on interaction patterns and interaction domain information. We apply this approach to the prediction of an interaction map of Escherichia coli from the recently published interaction map of the human gastric pathogen Helicobacter pylori. Results are compared with predictions of a second inference method based only on full-length protein sequence similarity - the naive method. The domain-based method is shown to i) eliminate a significant amount of false-positives of the naive method that are the consequences of multi-domain proteins, ii) increase the sensitivity compared to the naive method by identifying new potential interactions.'),
(70016, 2001, 'bio', 'CLIFF: clustering of high-dimensional microarray data via iterative feature filtering using normalized cuts', 'Xing, E P|Karp, R M', 'Division of Computer Science, University of California, Berkeley, Berkeley, CA 94720, USA. epxing@cs.berkeley.edu|Division of Computer Science, University of California, Berkeley, Berkeley, CA 94720, USA. epxing@cs.berkeley.edu', 'United States|United States', 155, 'Algorithms|Cluster Analysis|Computational Biology|Gene Expression Profiling|Humans|Leukemia|Markov Chains|Oligonucleotide Array Sequence Analysis', 'Comparative Study', '10.1093/bioinformatics/17.suppl_1.s306', '11473022', 'We present CLIFF, an algorithm for clustering biological samples using gene expression microarray data. This clustering problem is difficult for several reasons, in particular the sparsity of the data, the high dimensionality of the feature (gene) space, and the fact that many features are irrelevant or redundant. Our algorithm iterates between two computational processes, feature filtering and clustering. Given a reference partition that approximates the correct clustering of the samples, our feature filtering procedure ranks the features according to their intrinsic discriminability, relevance to the reference partition, and irredundancy to other relevant features, and uses this ranking to select the features to be used in the following round of clustering. Our clustering algorithm, which is based on the concept of a normalized cut, clusters the samples into a new reference partition on the basis of the selected features. On a well-studied problem involving 72 leukemia samples and 7130 genes, we demonstrate that CLIFF outperforms standard clustering approaches that do not consider the feature selection issue, and produces a result that is very close to the original expert labeling of the sample set.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(70017, 2001, 'bio', 'Molecular classification of multiple tumor types', 'Yeang, C H|Ramaswamy, S|Tamayo, P|Mukherjee, S|Rifkin, R M|Angelo, M|Reich, M|Lander, E|Mesirov, J|Golub, T', 'Center for Genome Research, MIT Whitehead Institute, One Kendall Square, Cambridge, MA 02139, USA. chyeang@mit.edu|Center for Genome Research, MIT Whitehead Institute, One Kendall Square, Cambridge, MA 02139, USA. chyeang@mit.edu|Center for Genome Research, MIT Whitehead Institute, One Kendall Square, Cambridge, MA 02139, USA. chyeang@mit.edu|Center for Genome Research, MIT Whitehead Institute, One Kendall Square, Cambridge, MA 02139, USA. chyeang@mit.edu|Center for Genome Research, MIT Whitehead Institute, One Kendall Square, Cambridge, MA 02139, USA. chyeang@mit.edu|Center for Genome Research, MIT Whitehead Institute, One Kendall Square, Cambridge, MA 02139, USA. chyeang@mit.edu|Center for Genome Research, MIT Whitehead Institute, One Kendall Square, Cambridge, MA 02139, USA. chyeang@mit.edu|Center for Genome Research, MIT Whitehead Institute, One Kendall Square, Cambridge, MA 02139, USA. chyeang@mit.edu|Center for Genome Research, MIT Whitehead Institute, One Kendall Square, Cambridge, MA 02139, USA. chyeang@mit.edu|Center for Genome Research, MIT Whitehead Institute, One Kendall Square, Cambridge, MA 02139, USA. chyeang@mit.edu', 'United States|United States|United States|United States|United States|United States|United States|United States|United States|United States', 0, 'Algorithms|Computational Biology|Confidence Intervals|Databases, Genetic|Gene Expression Profiling|Humans|Neoplasms', 'Journal Article', '10.1093/bioinformatics/17.suppl_1.s316', '11473023', 'Using gene expression data to classify tumor types is a very promising tool in cancer diagnosis. Previous works show several pairs of tumor types can be successfully distinguished by their gene expression patterns (Golub et al. 1999, Ben-Dor et al. 2000, Alizadeh et al. 2000). However, the simultaneous classification across a heterogeneous set of tumor types has not been well studied yet. We obtained 190 samples from 14 tumor classes and generated a combined expression dataset containing 16063 genes for each of those samples. We performed multi-class classification by combining the outputs of binary classifiers. Three binary classifiers (k-nearest neighbors, weighted voting, and support vector machines) were applied in conjunction with three combination scenarios (one-vs-all, all-pairs, hierarchical partitioning). We achieved the best cross validation error rate of 18.75% and the best test error rate of 21.74% by using the one-vs-all support vector machine algorithm. The results demonstrate the feasibility of performing clinically useful classification from samples of multiple tumor types.'),
(70018, 2001, 'bio', 'Centralization: a new method for the normalization of gene expression data', 'Zien, A|Aigner, T|Zimmer, R|Lengauer, T', 'SCAI - Institute for Algorithms and Scientific Computing, GMD - German National Research Center for Information Technology, Schloss Birlinghoven, Sankt Augustin, 53754, Germany. Alexander.Zien@gmd.de|SCAI - Institute for Algorithms and Scientific Computing, GMD - German National Research Center for Information Technology, Schloss Birlinghoven, Sankt Augustin, 53754, Germany. Alexander.Zien@gmd.de|SCAI - Institute for Algorithms and Scientific Computing, GMD - German National Research Center for Information Technology, Schloss Birlinghoven, Sankt Augustin, 53754, Germany. Alexander.Zien@gmd.de|SCAI - Institute for Algorithms and Scientific Computing, GMD - German National Research Center for Information Technology, Schloss Birlinghoven, Sankt Augustin, 53754, Germany. Alexander.Zien@gmd.de', 'Germany|Germany|Germany|Germany', 94, 'Computational Biology|Gene Expression Profiling|Genetic Techniques|Models, Genetic|Oligonucleotide Array Sequence Analysis|RNA, Messenger', 'Journal Article', '10.1093/bioinformatics/17.suppl_1.s323', '11473024', 'Microarrays measure values that are approximately proportional to the numbers of copies of different mRNA molecules in samples. Due to technical difficulties, the constant of proportionality between the measured intensities and the numbers of mRNA copies per cell is unknown and may vary for different arrays. Usually, the data are normalized (i.e., array-wise multiplied by appropriate factors) in order to compensate for this effect and to enable informative comparisons between different experiments. Centralization is a new two-step method for the computation of such normalization factors that is both biologically better motivated and more robust than standard approaches. First, for each pair of arrays the quotient of the constants of proportionality is estimated. Second, from the resulting matrix of pairwise quotients an optimally consistent scaling of the samples is computed.'),
(70019, 2001, 'bio', 'Generation of a database containing discordant intron positions in eukaryotic genes (MIDB)', 'Sakharkar, M K|Tan, T W|de Souza, S J', 'Bioinformatics Centre, National University of Singapore, Singapore. meena@bic.nus.edu.sg|Bioinformatics Centre, National University of Singapore, Singapore. meena@bic.nus.edu.sg|Bioinformatics Centre, National University of Singapore, Singapore. meena@bic.nus.edu.sg', 'Singapore|Singapore|Singapore', 0, 'Amino Acid Sequence|Animals|Base Sequence|Computational Biology|Databases, Genetic|Databases, Nucleic Acid|Databases, Protein|Evolution, Molecular|Internet|Introns|Molecular Sequence Data|Sequence Homology, Amino Acid|Sequence Homology, Nucleic Acid', 'Journal Article', '10.1093/bioinformatics/17.8.671', '11524368', 'Intron sliding is the relocation of intron-exon boundaries over short distances and is often also referred to as intron slippage or intron migration or intron drift. We have generated a database containing discordant intron positions in homologous genes (MIDB--Mismatched Intron DataBase). Discordant intron positions are those that are either closely located in homologous genes (within a window of 10 nucleotides) or an intron position that is present in one gene but not in any of its homologs. The MIDB database aims at systematically collecting information about mismatched introns in the genes from GenBank and organizing it into a form useful for understanding the genomics and dynamics of introns thereby helping understand the evolution of genes.'),
(70020, 2001, 'bio', 'Representation of amino acids as five-bit or three-bit patterns for filtering protein databases', 'Coghlan, A|Mac Dónaill, D A|Buttimore, N H', 'Department of Genetics, University of Dublin, Trinity College, Dublin 2, Ireland.|Department of Genetics, University of Dublin, Trinity College, Dublin 2, Ireland.|Department of Genetics, University of Dublin, Trinity College, Dublin 2, Ireland.', 'Ireland|Ireland|Ireland', 0, 'Algorithms|Amino Acid Sequence|Amino Acid Substitution|Amino Acids|Bacteriophages|Computational Biology|Conserved Sequence|Databases, Protein|Escherichia coli|Escherichia coli Proteins|Porins|Sequence Alignment|Sequence Homology, Amino Acid|Viral Proteins', 'Journal Article', '10.1093/bioinformatics/17.8.676', '11524369', 'We propose representing amino acids by bit-patterns so they may be used in a filter algorithm for similarity searches over protein databases, to rapidly eliminate non-homologous regions of database sequences. The filter algorithm would be based on dynamic programming optimization. It would have the advantage over previous filter algorithms that its substitution scoring function distinguishes between conservative and non-conservative amino acid substitutions.'),
(70021, 2001, 'bio', 'Amino acid similarity matrices based on force fields', 'Dosztányi, Z|Torda, A E', 'Research School of Chemistry, Australian National University, Canberra, ACT 0200, Australia. zsuzsa@rsc.anu.edu.au|Research School of Chemistry, Australian National University, Canberra, ACT 0200, Australia. zsuzsa@rsc.anu.edu.au', 'Australia|Australia', 18, 'Amino Acid Substitution|Chemical Phenomena|Chemistry, Physical|Computational Biology|Mutation|Proteins|Sequence Alignment|Thermodynamics', 'Comparative Study', '10.1093/bioinformatics/17.8.686', '11524370', 'We propose a general method for deriving amino acid substitution matrices from low resolution force fields. Unlike current popular methods, the approach does not rely on evolutionary arguments or alignment of sequences or structures. Instead, residues are computationally mutated and their contribution to the total energy/score is collected. The average of these values over each position within a set of proteins results in a substitution matrix.'),
(70022, 2001, 'bio', 'AL2CO: calculation of positional conservation in a protein sequence alignment', 'Pei, J|Grishin, N V', 'Department of Biochemistry, University of Texas Southwestern Medical Center, 5323 Harry Hines Blvd, Dallas, TX 75390-9050, USA.|Department of Biochemistry, University of Texas Southwestern Medical Center, 5323 Harry Hines Blvd, Dallas, TX 75390-9050, USA.', 'United States|United States', 287, 'Algorithms|Amino Acid Sequence|Computational Biology|Conserved Sequence|Entropy|Models, Molecular|Protein Conformation|Proteins|Quality Control|Sequence Alignment|Software', 'Comparative Study', '10.1093/bioinformatics/17.8.700', '11524371', 'Amino acid sequence alignments are widely used in the analysis of protein structure, function and evolutionary relationships. Proteins within a superfamily usually share the same fold and possess related functions. These structural and functional constraints are reflected in the alignment conservation patterns. Positions of functional and/or structural importance tend to be more conserved. Conserved positions are usually clustered in distinct motifs surrounded by sequence segments of low conservation. Poorly conserved regions might also arise from the imperfections in multiple alignment algorithms and thus indicate possible alignment errors. Quantification of conservation by attributing a conservation index to each aligned position makes motif detection more convenient. Mapping these conservation indices onto a protein spatial structure helps to visualize spatial conservation features of the molecule and to predict functionally and/or structurally important sites. Analysis of conservation indices could be a useful tool in detection of potentially misaligned regions and will aid in improvement of multiple alignments.'),
(70023, 2001, 'bio', 'Evaluation of protein multiple alignments by SAM-T99 using the BAliBASE multiple alignment test set', 'Karplus, K|Hu, B', 'Computer Engineering, University of California, Santa Cruz, 59064, USA.|Computer Engineering, University of California, Santa Cruz, 59064, USA.', 'United States|United States', 26, 'Computational Biology|Databases, Protein|Markov Chains|Proteins|Sequence Alignment|Software', 'Comparative Study', '10.1093/bioinformatics/17.8.713', '11524372', 'SAM-T99 is an iterative hidden Markov model-based method for finding proteins similar to a single target sequence and aligning them. One of its main uses is to produce multiple alignments of homologs of the target sequence. Previous tests of SAM-T99 and its predecessors have concentrated on the quality of the searches performed, not on the quality of the multiple alignment. In this paper we report on tests of multiple alignment quality, comparing SAM-T99 to the standard multiple aligner, CLUSTALW.'),
(70024, 2001, 'bio', 'Support vector machine approach for protein subcellular localization prediction', 'Hua, S|Sun, Z', 'Institute of Bioinformatics, State Key Laboratory of Biomembrane and Membrane Biotechnology, Department of Biological Sciences and Biotechnology, Tsinghua University, Beijing 100084, Peoples Republic of China. huasj00@mails.tsinghua.edu.cn|Institute of Bioinformatics, State Key Laboratory of Biomembrane and Membrane Biotechnology, Department of Biological Sciences and Biotechnology, Tsinghua University, Beijing 100084, Peoples Republic of China. huasj00@mails.tsinghua.edu.cn', 'China|China', 166, 'Amino Acid Sequence|Amino Acids|Computational Biology|Computer Simulation|Databases, Protein|Internet|Models, Biological|Proteins|Software|Subcellular Fractions', 'Comparative Study', '10.1093/bioinformatics/17.8.721', '11524373', 'Subcellular localization is a key functional characteristic of proteins. A fully automatic and reliable prediction system for protein subcellular localization is needed, especially for the analysis of large-scale genome sequences.'),
(70025, 2001, 'bio', 'The Bioinformatics Template Library--generic components for biocomputing', 'Pitt, W R|Williams, M A|Steven, M|Sweeney, B|Bleasby, A J|Moss, D S', 'School of Crystallography, Birkbeck College, Malet Street, London WC1E 7HX, UK.|School of Crystallography, Birkbeck College, Malet Street, London WC1E 7HX, UK.|School of Crystallography, Birkbeck College, Malet Street, London WC1E 7HX, UK.|School of Crystallography, Birkbeck College, Malet Street, London WC1E 7HX, UK.|School of Crystallography, Birkbeck College, Malet Street, London WC1E 7HX, UK.|School of Crystallography, Birkbeck College, Malet Street, London WC1E 7HX, UK.', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom', 5, 'Algorithms|Computational Biology|Libraries|Programming Languages|Software', 'Journal Article', '10.1093/bioinformatics/17.8.729', '11524374', 'The efficiency of bioinformatics programmers can be greatly increased through the provision of ready-made software components that can be rapidly combined, with additional bespoke components where necessary, to create finished programs. The new standard for C++ includes an efficient and easy to use library of generic algorithms and data-structures, designed to facilitate low-level component programming. The extension of this library to include functionality that is specifically useful in compute-intensive tasks in bioinformatics and molecular modelling could provide an effective standard for the design of reusable software components within the biocomputing community.'),
(70026, 2001, 'bio', 'Genotype transposer: automated genotype manipulation for linkage disequilibrium analysis', 'Cox, D G|Canzian, F', 'Genome Analysis Group, International Agency for Research on Cancer, 69008 Lyon, France. cox@iarc.fr|Genome Analysis Group, International Agency for Research on Cancer, 69008 Lyon, France. cox@iarc.fr', 'France|France', 54, 'Computational Biology|Genetics, Medical|Genotype|Haplotypes|Humans|Linkage Disequilibrium|Polymorphism, Single Nucleotide|Software|Software Design', 'Journal Article', '10.1093/bioinformatics/17.8.738', '11524375', 'The purpose of this work is to provide the modern molecular geneticist with tools to perform more efficient and more accurate analysis of the genotype data they produce. By using Microsoft Excel macros written in Visual Basic, we can translate genotype data into a form readable by the versatile software Arlequin, read the Arlequin output, calculate statistics of linkage disequilibrium, and put the results in a format for viewing with the software GOLD.'),
(70027, 2001, 'bio', 'Polylink: to support two-point linkage analysis in autotetraploids', 'He, Y|Xu, X|Tobutt, K R|Ridout, M S', 'Department of Electronics, University of Kent at Canterbury, Canterbury, Kent CT2 7NF, UK.|Department of Electronics, University of Kent at Canterbury, Canterbury, Kent CT2 7NF, UK.|Department of Electronics, University of Kent at Canterbury, Canterbury, Kent CT2 7NF, UK.|Department of Electronics, University of Kent at Canterbury, Canterbury, Kent CT2 7NF, UK.', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom', 3, 'Computational Biology|Genetic Linkage|Likelihood Functions|Models, Genetic|Phenotype|Plants|Polyploidy|Software', 'Journal Article', '10.1093/bioinformatics/17.8.740', '11524376', 'Polylink runs under Microsoft Windows (95 or later). It performs various calculations that are useful for investigating two-point linkage analysis for autopolyploids, based on the random chromosome pairing model. These include calculation of offspring phenotypic probabilities as functions of the recombination fraction, calculation of theoretical standard errors for the maximum likelihood estimator of and numerical computation of maximum likelihood estimates. It also includes simulation facilities.'),
(70028, 2001, 'bio', 'GRR: graphical representation of relationship errors', 'Abecasis, G R|Cherny, S S|Cookson, W O|Cardon, L R', 'Wellcome Trust Centre for Human Genetics, University of Oxford, Roosevelt Drive, Oxford OX3 7RZ, UK. goncalo@well.ox.ac.uk|Wellcome Trust Centre for Human Genetics, University of Oxford, Roosevelt Drive, Oxford OX3 7RZ, UK. goncalo@well.ox.ac.uk|Wellcome Trust Centre for Human Genetics, University of Oxford, Roosevelt Drive, Oxford OX3 7RZ, UK. goncalo@well.ox.ac.uk|Wellcome Trust Centre for Human Genetics, University of Oxford, Roosevelt Drive, Oxford OX3 7RZ, UK. goncalo@well.ox.ac.uk', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom', 174, 'Alleles|Computational Biology|Computer Graphics|Databases, Genetic|Genetic Linkage|Genetics, Medical|Genotype|Humans', 'Journal Article', '10.1093/bioinformatics/17.8.742', '11524377', 'A graphical tool for verifying assumed relationships between individuals in genetic studies is described. GRR can detect many common errors using genotypes from many markers.'),
(70029, 2001, 'bio', 'Multi-query sequence BLAST output examination with MuSeqBox', 'Xing, L|Brendel, V', 'Department of Zoology and Genetics, Iowa State University, Ames, IA 50011, USA.|Department of Zoology and Genetics, Iowa State University, Ames, IA 50011, USA.', 'United States|United States', 13, 'Alternative Splicing|Computational Biology|Databases, Genetic|Expressed Sequence Tags|Open Reading Frames|Sequence Analysis|Software|Zea mays', 'Journal Article', '10.1093/bioinformatics/17.8.744', '11524378', 'MuSeqBox is a program to parse BLAST output and store attributes of BLAST hits in tabular form. The user can apply a number of selection criteria to filter out hits with particular attributes. MuSeqBox provides a powerful annotation tool for large sets of query sequences that are simultaneously compared against a database with any of the standard stand-alone or network-client BLAST programs. We discuss such application to the problem of annotation and analysis of EST collections.'),
(70030, 2001, 'bio', 'DBAli: a database of protein structure alignments', 'Martí-Renom, M A|Ilyin, V A|Sali, A', 'Laboratories of Molecular Biophysics, Pels Family Center for Biochemistry and Structural Biology, The Rockefeller University, 1230 York Ave, New York, NY 10021, USA.|Laboratories of Molecular Biophysics, Pels Family Center for Biochemistry and Structural Biology, The Rockefeller University, 1230 York Ave, New York, NY 10021, USA.|Laboratories of Molecular Biophysics, Pels Family Center for Biochemistry and Structural Biology, The Rockefeller University, 1230 York Ave, New York, NY 10021, USA.', 'United States|United States|United States', 36, 'Computational Biology|Databases, Protein|Proteins|Sequence Alignment|Software|Software Design', 'Journal Article', '10.1093/bioinformatics/17.8.746', '11524379', 'The DBAli database includes approximately 35000 alignments of pairs of protein structures from SCOP (Lo Conte et al., Nucleic Acids Res., 28, 257-259, 2000) and CE (Shindyalov and Bourne, Protein Eng., 11, 739-747, 1998). DBAli is linked to several resources, including Compare3D (Shindyalov and Bourne, http://www.sdsc.edu/pb/software.htm, 1999) and ModView (Ilyin and Sali, http://guitar.rockefeller.edu/ModView/, 2001) for visualizing sequence alignments and structure superpositions. A flexible search of DBAli by protein sequence and structure properties allows construction of subsets of alignments suitable for a number of applications, such as benchmarking of sequence-sequence and sequence-structure alignment methods under a variety of conditions.'),
(70031, 2001, 'bio', 'HOMSTRAD: adding sequence information to structure-based alignments of homologous protein families', 'de Bakker, P I|Bateman, A|Burke, D F|Miguel, R N|Mizuguchi, K|Shi, J|Shirai, H|Blundell, T L', 'Department of Biochemistry, University of Cambridge, 80 Tennis Court Road, Cambridge CB2 1GA, UK.|Department of Biochemistry, University of Cambridge, 80 Tennis Court Road, Cambridge CB2 1GA, UK.|Department of Biochemistry, University of Cambridge, 80 Tennis Court Road, Cambridge CB2 1GA, UK.|Department of Biochemistry, University of Cambridge, 80 Tennis Court Road, Cambridge CB2 1GA, UK.|Department of Biochemistry, University of Cambridge, 80 Tennis Court Road, Cambridge CB2 1GA, UK.|Department of Biochemistry, University of Cambridge, 80 Tennis Court Road, Cambridge CB2 1GA, UK.|Department of Biochemistry, University of Cambridge, 80 Tennis Court Road, Cambridge CB2 1GA, UK.|Department of Biochemistry, University of Cambridge, 80 Tennis Court Road, Cambridge CB2 1GA, UK.', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom', 48, 'Computational Biology|Databases, Protein|Proteins|Sequence Alignment', 'Journal Article', '10.1093/bioinformatics/17.8.748', '11524380', 'summary: We describe an extension to the Homologous Structure Alignment Database (HOMSTRAD, Mizuguchi et al., Protein Sci., 7, 2469-2471, 1998a) to include homologous sequences derived from the protein families database Pfam (Bateman et al., Nucleic Acids Res., 28, 263-266, 2000). HOMSTRAD is integrated with the server FUGUE (Shi et al., submitted, 2001) for recognition and alignment of homologues, benefitting from the combination of abundant sequence information and accurate structure-based alignments. AVAILABILITY The HOMSTRAD database is available at: http://www-cryst.bioc.cam.ac.uk/homstrad/. Query sequences can be submitted to the homology recognition/alignment server FUGUE at: http://www-cryst.bioc.cam.ac.uk/fugue/.'),
(70032, 2001, 'bio', 'Structure prediction meta server', 'Bujnicki, J M|Elofsson, A|Fischer, D|Rychlewski, L', 'Bioinformatics Laboratory, International Institute of Molecular and Cell Biology, Warsaw, Poland.|Bioinformatics Laboratory, International Institute of Molecular and Cell Biology, Warsaw, Poland.|Bioinformatics Laboratory, International Institute of Molecular and Cell Biology, Warsaw, Poland.|Bioinformatics Laboratory, International Institute of Molecular and Cell Biology, Warsaw, Poland.', 'Poland|Poland|Poland|Poland', 0, 'Amino Acid Sequence|Computational Biology|Databases, Protein|Protein Folding|Protein Structure, Secondary|Protein Structure, Tertiary|Proteins|Software', 'Journal Article', '10.1093/bioinformatics/17.8.750', '11524381', 'The Structure Prediction Meta Server offers a convenient way for biologists to utilize various high quality structure prediction servers available worldwide. The meta server translates the results obtained from remote services into uniform format, which are consequently used to request a jury prediction from a remote consensus server Pcons.'),
(70033, 2001, 'bio', 'Easier threading through web-based comparisons and cross-validations', 'Douguet, D|Labesse, G', 'Centre de Biochimie Structurale, INSERM U554 - CNRS UMR5048 - Université Montpellier 1 15, Ave Charles Flahault, 34060 Montpellier Cedex, France. douguet@cbs.univ-montp1.fr|Centre de Biochimie Structurale, INSERM U554 - CNRS UMR5048 - Université Montpellier 1 15, Ave Charles Flahault, 34060 Montpellier Cedex, France. douguet@cbs.univ-montp1.fr', 'France|France', 0, 'Amino Acid Sequence|Computational Biology|Databases, Protein|Internet|Molecular Sequence Data|Protein Structure, Secondary|Proteins|Sequence Alignment|Sequence Homology, Amino Acid|User-Computer Interface', 'Comparative Study', '10.1093/bioinformatics/17.8.752', '11524382', 'We have developed a WWW server for the integration and comparison of protein structure predictions performed by five different servers. Users submit an amino acid sequence to a selected set of these prediction methods. Results are gathered on a web-based page in order to facilitate comparison and analysis. All the alignments are further evaluated through a common threading tool making their comparisons easier.'),
(70034, 2001, 'bio', 'MRBAYES: Bayesian inference of phylogenetic trees', 'Huelsenbeck, J P|Ronquist, F', 'Department of Biology, University of Rochester, Rochester, NY 14627, USA. johnh@brahms.biology.rochester.edu|Department of Biology, University of Rochester, Rochester, NY 14627, USA. johnh@brahms.biology.rochester.edu', 'United States|United States', 0, 'Algorithms|Bayes Theorem|Computational Biology|Markov Chains|Phylogeny|Software', 'Journal Article', '10.1093/bioinformatics/17.8.754', '11524383', 'The program MRBAYES performs Bayesian inference of phylogeny using a variant of Markov chain Monte Carlo.'),
(70035, 2001, 'bio', 'SIR: a simple indexing and retrieval system for biological flat file databases', 'Ramu, C', 'European Molecular Biological Laboratory, 1, Meyerhofstrasse, Postfach 10.2209, Heidelberg, Germany. Chenna@EMBL-Heidelberg.de', 'Germany', 0, 'Abstracting and Indexing as Topic|Computational Biology|Databases, Genetic|Databases, Protein|Information Storage and Retrieval|Software', 'Journal Article', '10.1093/bioinformatics/17.8.756', '11524384', 'SIR is a Simple Indexing and Retrieval tool for indexing and searching biological flat file databases. SIR is a cross-platform solution entirely written in Python. Since the package is very small and installation is trivial, this would be an ideal solution for database providers to provide a custom retrieval tool to access them.'),
(70036, 2001, 'bio', 'Training for bioinformatics and computational biology', 'Pearson, W R', 'European Molecular Biological Laboratory, 1, Meyerhofstrasse, Postfach 10.2209, Heidelberg, Germany. Chenna@EMBL-Heidelberg.de', 'Germany', 12, 'Computational Biology|Protein Engineering', 'Editorial', '10.1093/bioinformatics/17.9.761', '11590093', '-'),
(70037, 2001, 'bio', 'Principal component analysis for clustering gene expression data', 'Yeung, K Y|Ruzzo, W L', 'Computer Science and Engineering, Box 352350, University of Washington, Seattle, WA 98195, USA. kayee@cs.washington.edu|Computer Science and Engineering, Box 352350, University of Washington, Seattle, WA 98195, USA. kayee@cs.washington.edu', 'United States|United States', 0, 'Algorithms|Cluster Analysis|Female|Gene Expression|Genes|Genes, Fungal|Genes, Neoplasm|Genes, cdc|Humans|Models, Statistical|Oligonucleotide Array Sequence Analysis|Ovarian Neoplasms|Ovary|Saccharomyces cerevisiae', 'Comparative Study', '10.1093/bioinformatics/17.9.763', '11590094', 'There is a great need to develop analytical methodology to analyze and to exploit the information contained in gene expression data. Because of the large number of genes and the complexity of biological networks, clustering is a useful exploratory technique for analysis of gene expression data. Other classical techniques, such as principal component analysis (PCA), have also been applied to analyze gene expression data. Using different data analysis techniques and different clustering algorithms to analyze the same data set can lead to very different conclusions. Our goal is to study the effectiveness of principal components (PCs) in capturing cluster structure. Specifically, using both real and synthetic gene expression data sets, we compared the quality of clusters obtained from the original data to the quality of clusters obtained after projecting onto subsets of the principal component axes.'),
(70038, 2001, 'bio', 'A non-parametric approach to translating gene region heterogeneity associated with phenotype into location heterogeneity', 'Kowalski, J', 'Department of Biostatistics, Harvard School of Public Health, Boston, MA 02115, USA.', 'United States', 0, 'Clinical Trials, Phase II as Topic|Factor Analysis, Statistical|Genes, Viral|Genetic Heterogeneity|Genetic Markers|Genome, Viral|HIV Protease|HIV-1|Multicenter Studies as Topic|Phenotype|Protein Biosynthesis|Randomized Controlled Trials as Topic|Sequence Analysis, DNA|Statistics, Nonparametric|Viral Structural Proteins', 'Journal Article', '10.1093/bioinformatics/17.9.775', '11590095', 'The analysis of genetic data poses statistical problems in the form of high dimensionality with small sample sizes. The construction of a composite gene region (sequence pair) heterogeneity measure is one technique for reducing the dimensionality of the problem. This approach however is not without cost, since the contribution of locations to observed gene region differences between groups becomes entangled in this summary measure. This is problematic since it is of scientific interest to identify locations that together depict phenotype.'),
(70039, 2001, 'bio', 'Comprehensive comparison between locations of orthologous genes on archaeal and bacterial genomes', 'Horimoto, K|Fukuchi, S|Mori, K', 'Laboratory of Mathematics, Saga Medical School, 5-1-1 Nabeshima, Saga 849-8501, Japan. horimoto@post.saga-med.ac.jp|Laboratory of Mathematics, Saga Medical School, 5-1-1 Nabeshima, Saga 849-8501, Japan. horimoto@post.saga-med.ac.jp|Laboratory of Mathematics, Saga Medical School, 5-1-1 Nabeshima, Saga 849-8501, Japan. horimoto@post.saga-med.ac.jp', 'Japan|Japan|Japan', 0, 'Chromosome Mapping|Chromosomes, Archaeal|Chromosomes, Bacterial|Gene Order|Genetic Markers|Genome, Archaeal|Genome, Bacterial|Sequence Analysis, DNA|Sequence Homology, Nucleic Acid', 'Comparative Study', '10.1093/bioinformatics/17.9.791', '11590096', 'Following an extensive search for orthologous genes between the complete genomes from archaea and bacteria, the spatial association of the orthologs has been investigated in terms of synteny, the conservation of the order of neighboring genes. However, the relationships between the relative locations of remote orthologs over entire genomes have not been shown.'),
(70040, 2001, 'bio', 'Evolutionary HMMs: a Bayesian approach to multiple alignment', 'Holmes, I|Bruno, W J', 'Group T10, Los Alamos National Laboratory, NM 87545, USA. ihh@fruitfly.org|Group T10, Los Alamos National Laboratory, NM 87545, USA. ihh@fruitfly.org', 'United States|United States', 119, 'Algorithms|Bayes Theorem|Computer Simulation|Databases, Protein|Evolution, Molecular|Humans|Markov Chains|Models, Statistical|Proteins|Sequence Alignment|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/17.9.803', '11590097', 'We review proposed syntheses of probabilistic sequence alignment, profiling and phylogeny. We develop a multiple alignment algorithm for Bayesian inference in the links model proposed by Thorne et al. (1991, J. Mol. Evol., 33, 114-124). The algorithm, described in detail in Section 3, samples from and/or maximizes the posterior distribution over multiple alignments for any number of DNA or protein sequences, conditioned on a phylogenetic tree. The individual sampling and maximization steps of the algorithm require no more computational resources than pairwise alignment.'),
(70041, 2001, 'bio', 'A simple algorithm to infer gene duplication and speciation events on a gene tree', 'Zmasek, C M|Eddy, S R', 'Howard Hughes Medical Institute, Department of Genetics, Washington University School of Medicine, St Louis, MO 63110, USA.|Howard Hughes Medical Institute, Department of Genetics, Washington University School of Medicine, St Louis, MO 63110, USA.', 'United States|United States', 140, 'Algorithms|Animals|Cattle|Chick Embryo|Computational Biology|Drosophila melanogaster|Evolution, Molecular|Gene Duplication|Humans|Mice|Models, Genetic|Phylogeny|Rats|Sequence Homology, Nucleic Acid|Software|Species Specificity|Swine|Xenopus laevis', 'Journal Article', '10.1093/bioinformatics/17.9.821', '11590098', 'When analyzing protein sequences using sequence similarity searches, orthologous sequences (that diverged by speciation) are more reliable predictors of a new proteins function than paralogous sequences (that diverged by gene duplication), because duplication enables functional diversification. The utility of phylogenetic information in high-throughput genome annotation (phylogenomics) is widely recognized, but existing approaches are either manual or indirect (e.g. not based on phylogenetic trees). Our goal is to automate phylogenomics using explicit phylogenetic inference. A necessary component is an algorithm to infer speciation and duplication events in a given gene tree.'),
(70042, 2001, 'bio', 'Knowledge representation of signal transduction pathways', 'Fukuda, K|Takagi, T', 'Human Genome Center, Institute of Medical Science, University of Tokyo, 4-6-1 Shirokanedai, Minato-ku, Tokyo 108-8639, Japan. fukuda-cbrc@aist.go.jp|Human Genome Center, Institute of Medical Science, University of Tokyo, 4-6-1 Shirokanedai, Minato-ku, Tokyo 108-8639, Japan. fukuda-cbrc@aist.go.jp', 'Japan|Japan', 0, 'Amino Acid Motifs|Animals|Cell Line|Computational Biology|Conserved Sequence|DNA-Binding Proteins|Humans|Models, Biological|Protein Structure, Tertiary|Signal Transduction|Smad Proteins|Trans-Activators', 'Journal Article', '10.1093/bioinformatics/17.9.829', '11590099', 'Signal transduction is the common term used to define a diverse topic that encompasses a large body of knowledge about the biochemical mechanisms. Since most of the knowledge of signal transduction resides in scientific articles and is represented by texts in natural language or by diagrams, there is the need of a knowledge representation model for signal transduction pathways that can be as readily processed by a computer as it is easily understood by humans.'),
(70043, 2001, 'bio', 'PIRA PCR designer for restriction analysis of single nucleotide polymorphisms', 'Ke, X|Collins, A|Ye, S', 'Division of Human Genetics, University of Southampton, Duthie Building, Southampton General Hospital, Southampton SO16 6YD, UK. xke@soton.ac.uk|Division of Human Genetics, University of Southampton, Duthie Building, Southampton General Hospital, Southampton SO16 6YD, UK. xke@soton.ac.uk|Division of Human Genetics, University of Southampton, Duthie Building, Southampton General Hospital, Southampton SO16 6YD, UK. xke@soton.ac.uk', 'United Kingdom|United Kingdom|United Kingdom', 0, 'Base Pair Mismatch|DNA Primers|Mutation|Polymerase Chain Reaction|Polymorphism, Single Nucleotide|Restriction Mapping|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/17.9.838', '11590100', 'Primer-introduced restriction analysis (PIRA-PCR) is widely used to detect Single Nucleotide Polymorphisms (SNPs). To create artificial Restriction Fragment Length Polymorphism (RFLP), a mismatch is usually introduced near the end of the primer that is close to the mutation of interest. We describe in this report a www-based computer program that screens for the suitable mismatches, designs the primers, lists the appropriate restriction enzymes and other related information.'),
(70044, 2001, 'bio', 'Detecting the impact of sequencing errors on SAGE data', 'Colinge, J|Feger, G', 'Serono Pharmaceutical Research Institute, Ch. des Aulx 14, CH-1228 Plan-les-Ouates, Switzerland.|Serono Pharmaceutical Research Institute, Ch. des Aulx 14, CH-1228 Plan-les-Ouates, Switzerland.', 'Switzerland|Switzerland', 24, 'Gene Expression Profiling|Gene Library|RNA, Messenger|Sequence Analysis, DNA', 'Journal Article', '10.1093/bioinformatics/17.9.840', '11590101', 'SAGE data are obtained by sequencing short DNA tags. Due to the mistakes in DNA sequencing, SAGE data contain errors. We propose a new approach to identify tags whose abundance is biased by sequencing errors. This approach is based on a concept of neighbourhood: abundant tags can contaminate tags whose sequence is very close. The application of our approach reveals that moderately abundant tags can be generated by sequencing errors uniquely. It also allows for detecting correct rare tags.'),
(70045, 2001, 'bio', 'GeneMachine: gene prediction and sequence annotation', 'Makalowska, I|Ryan, J F|Baxevanis, A D', 'Genome Technology Branch, National Human Genome Research Institute, National Institutes of Health, Building 49, Room 4A-22, Bethesda, MD 20892, USA.|Genome Technology Branch, National Human Genome Research Institute, National Institutes of Health, Building 49, Room 4A-22, Bethesda, MD 20892, USA.|Genome Technology Branch, National Human Genome Research Institute, National Institutes of Health, Building 49, Room 4A-22, Bethesda, MD 20892, USA.', 'United States|United States|United States', 2, 'Animals|Computational Biology|Data Interpretation, Statistical|Databases, Genetic|Genes|Humans|Internet|Open Reading Frames|Repetitive Sequences, Nucleic Acid|Sequence Analysis, DNA|Sequence Homology, Nucleic Acid', 'Comparative Study', '10.1093/bioinformatics/17.9.843', '11590102', 'A number of free-standing programs have been developed in order to help researchers find potential coding regions and deduce gene structure for long stretches of what is essentially anonymous DNA. As these programs apply inherently different criteria to the question of what is and is not a coding region, multiple algorithms should be used in the course of positional cloning and positional candidate projects to assure that all potential coding regions within a previously-identified critical region are identified.'),
(70046, 2001, 'bio', 'CHROMA: consensus-based colouring of multiple alignments for publication', 'Goodstadt, L|Ponting, C P', 'MRC Functional Genetics Unit, University of Oxford, Department of Human Anatomy and Genetics, South Parks Road, Oxford OX1 3QX, UK. CHROMA@lg.ndirect.co.uk|MRC Functional Genetics Unit, University of Oxford, Department of Human Anatomy and Genetics, South Parks Road, Oxford OX1 3QX, UK. CHROMA@lg.ndirect.co.uk', 'United Kingdom|United Kingdom', 59, 'Amino Acid Sequence|Consensus Sequence|Internet|Molecular Sequence Data|Publications|Sequence Alignment|Software|Word Processing', 'Journal Article', '10.1093/bioinformatics/17.9.845', '11590103', 'CHROMA annotates multiple protein sequence alignments by consensus to produce formatted and coloured text suitable for incorporation into other documents for publication. The package is designed to be flexible and reliable, and has a simple-to-use graphical user interface running under Microsoft Windows. Both the executables and source code for CHROMA running under Windows and Linux (portable command-line only) are freely available at http://www.lg.ndirect.co.uk/chroma. Software enquiries should be directed to CHROMA@lg.ndirect.co.uk.'),
(70047, 2001, 'bio', 'InterProScan--an integration platform for the signature-recognition methods in InterPro', 'Zdobnov, E M|Apweiler, R', 'EMBL Outstation, European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK. Evgueni.Zdobnov@EBI.ac.uk|EMBL Outstation, European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK. Evgueni.Zdobnov@EBI.ac.uk', 'United Kingdom|United Kingdom', 1896, 'Database Management Systems|Databases, Protein', 'Journal Article', '10.1093/bioinformatics/17.9.847', '11590104', 'InterProScan is a tool that scans given protein sequences against the protein signatures of the InterPro member databases, currently--PROSITE, PRINTS, Pfam, ProDom and SMART. The number of signature databases and their associated scanning tools as well as the further refinement procedures make the problem complex. InterProScan is designed to be a scalable and extensible system with a robust internal architecture.'),
(70048, 2001, 'bio', 'The HMMTOP transmembrane topology prediction server', 'Tusnády, G E|Simon, I', 'Institute of Enzymology, BRC, Hungarian Academy of Sciences, H-1518 Budapest, PO Box 7, Hungary. tusi@enzim.hu|Institute of Enzymology, BRC, Hungarian Academy of Sciences, H-1518 Budapest, PO Box 7, Hungary. tusi@enzim.hu', 'Hungary|Hungary', 0, 'Algorithms|Computer Simulation|Database Management Systems|Databases, Protein|Humans|Markov Chains|Membrane Proteins|Models, Statistical|Protein Structure, Secondary|Software', 'Journal Article', '10.1093/bioinformatics/17.9.849', '11590105', 'The HMMTOP transmembrane topology prediction server predicts both the localization of helical transmembrane segments and the topology of transmembrane proteins. Recently, several improvements have been introduced to the original method. Now, the user is allowed to submit additional information about segment localization to enhance the prediction power. This option improves the prediction accuracy as well as helps the interpretation of experimental results, i.e. in epitope insertion experiments.'),
(70049, 2001, 'bio', 'VISTRAJ: exploring protein conformational space', 'Salama, J J|Feldman, H J|Hogue, C W', 'MDS Proteomics Inc., 480 University Ave, Toronto, Ontario, Canada.|MDS Proteomics Inc., 480 University Ave, Toronto, Ontario, Canada.|MDS Proteomics Inc., 480 University Ave, Toronto, Ontario, Canada.', 'Canada|Canada|Canada', 1, 'Amino Acid Substitution|Computer Simulation|Databases, Protein|Internet|Protein Conformation|Sequence Homology, Amino Acid|Software', 'Journal Article', '10.1093/bioinformatics/17.9.851', '11590106', 'VISTRAJ is an application which allows 3D visualization, manipulation and editing of protein conformational space using probabilistic maps of this space called trajectory distributions. Trajectory distributions serve as input to FOLDTRAJ which samples protein structures based on the represented conformational space. VISTRAJ also allows FOLDTRAJ to be used as a tool for homology model creation, and structures may be generated containing post-translationally modified amino acids.'),
(70050, 2001, 'bio', 'BioLayout--an automatic graph layout algorithm for similarity visualization', 'Enright, A J|Ouzounis, C A', 'Computational Genomics Group, Research Programme, The European Bioinformatics Institute, EMBL Cambridge Outstation, Cambridge CB10 1SD, UK. enright@ebi.ac.uk|Computational Genomics Group, Research Programme, The European Bioinformatics Institute, EMBL Cambridge Outstation, Cambridge CB10 1SD, UK. enright@ebi.ac.uk', 'United Kingdom|United Kingdom', 0, 'Algorithms|Amino Acid Sequence|Computer Graphics|Databases, Protein|Image Processing, Computer-Assisted|Imaging, Three-Dimensional|Software', 'Journal Article', '10.1093/bioinformatics/17.9.853', '11590107', 'Graph layout is extensively used in the field of mathematics and computer science, however these ideas and methods have not been extended in a general fashion to the construction of graphs for biological data. To this end, we have implemented a version of the Fruchterman Rheingold graph layout algorithm, extensively modified for the purpose of similarity analysis in biology. This algorithm rapidly and effectively generates clear two (2D) or three-dimensional (3D) graphs representing similarity relationships such as protein sequence similarity. The implementation of the algorithm is general and applicable to most types of similarity information for biological data.'),
(70051, 2001, 'bio', 'BioWAP, mobile Internet service for bioinformatics', 'Riikonen, P|Boberg, J|Salakoski, T|Vihinen, M', 'Institute of Medical Technology, FIN-33014 University of Tampere, Finland.|Institute of Medical Technology, FIN-33014 University of Tampere, Finland.|Institute of Medical Technology, FIN-33014 University of Tampere, Finland.|Institute of Medical Technology, FIN-33014 University of Tampere, Finland.', 'Finland|Finland|Finland|Finland', 6, 'Computational Biology|Internet', 'Journal Article', '10.1093/bioinformatics/17.9.855', '11590108', 'We have developed a new Internet service, which provides mobile access to bioinformatics databases and software tools. The BioWAP service facilitates access to basic bioinformatics databases and analysis tools from everywhere without a PC or a laptop computer. Both open source bioinformatics program suites and Internet services, which are not designed for mobile Internet access, were utilized in the BioWAP service.'),
(70052, 2001, 'bio', 'Critical assessment of microarray data analysis: the 2001 challenge', 'Johnson, K F|Lin, S M', 'Duke Bioinformatics Shared Resource, Duke University Medical Center, Durham, NC 27710, USA. camda01@canctr.mc.duke.edu|Duke Bioinformatics Shared Resource, Duke University Medical Center, Durham, NC 27710, USA. camda01@canctr.mc.duke.edu', 'United States|United States', 0, 'Computational Biology|Databases, Genetic|Gene Expression Profiling|Oligonucleotide Array Sequence Analysis', 'Congress', '10.1093/bioinformatics/17.9.857', '11590109', 'We initiated the Critical Assessment of Microarray Data Analysis (CAMDA) conference to stimulate and evaluate the development of advanced data analysis techniques for microarrays. A standard data set has been released for this data analysis challenge. The goal of this challenge is to assess the performance of different analytical methods and at the same time to determine how such methods should be evaluated. We hope this effort will catalyze the discussion of microarray data analysis among the research community of biologists, statisticians, mathematicians, and computer scientists.'),
(70053, 2001, 'bio', 'Dealing with errors in interactive sequencing by hybridization', 'Phan, V T|Skiena, S S', 'Department of Computer Science, State University of New York, Stony Brook, NY 11794-4400, USA.|Department of Computer Science, State University of New York, Stony Brook, NY 11794-4400, USA.', 'United States|United States', 0, 'Algorithms|Computational Biology|False Negative Reactions|False Positive Reactions|Models, Statistical|Nucleic Acid Hybridization|Oligonucleotide Probes|Sequence Analysis, DNA', 'Journal Article', '10.1093/bioinformatics/17.10.862', '11673230', 'A realistic approach to sequencing by hybridization must deal with realistic sequencing errors. The results of such a method can surely be applied to similar sequencing tasks.'),
(70054, 2001, 'bio', 'Enrichment of regulatory signals in conserved non-coding genomic sequence', 'Levy, S|Hannenhalli, S|Workman, C', 'Informatics Research, Celera Genomics Corporation, 45 West Gude Drive, Rockville, MD 20850, USA.|Informatics Research, Celera Genomics Corporation, 45 West Gude Drive, Rockville, MD 20850, USA.|Informatics Research, Celera Genomics Corporation, 45 West Gude Drive, Rockville, MD 20850, USA.', 'United States|United States|United States', 106, 'Algorithms|Animals|Binding Sites|Computational Biology|Conserved Sequence|DNA|Genes, Regulator|Genome, Human|Humans|Mice|Models, Genetic|Sequence Alignment|Sequence Analysis, DNA|Transcription Factors', 'Journal Article', '10.1093/bioinformatics/17.10.871', '11673231', 'Whole genome shotgun sequencing strategies generate sequence data prior to the application of assembly methodologies that result in contiguous sequence. Sequence reads can be employed to indicate regions of conservation between closely related species for which only one genome has been assembled. Consequently, by using pairwise sequence alignments methods it is possible to identify novel, non-repetitive, conserved segments in non-coding sequence that exist between the assembled human genome and mouse whole genome shotgun sequencing fragments. Conserved non-coding regions identify potentially functional DNA that could be involved in transcriptional regulation.'),
(70055, 2001, 'bio', 'Detection of cis-element clusters in higher eukaryotic DNA', 'Frith, M C|Hansen, U|Weng, Z', 'Bioinformatics Program, Boston University, 44 Cummington St, Boston, MA 02215, USA.|Bioinformatics Program, Boston University, 44 Cummington St, Boston, MA 02215, USA.|Bioinformatics Program, Boston University, 44 Cummington St, Boston, MA 02215, USA.', 'United States|United States|United States', 189, 'Algorithms|Animals|Binding Sites|Cluster Analysis|Computational Biology|DNA|Genes, Regulator|Genome, Human|Humans|Markov Chains|Muscles|Promoter Regions, Genetic|Sensitivity and Specificity|Sequence Analysis, DNA|Software|Transcription Factors', 'Journal Article', '10.1093/bioinformatics/17.10.878', '11673232', 'Computational prediction and analysis of transcription regulatory regions in DNA sequences has the potential to accelerate greatly our understanding of how cellular processes are controlled. We present a hidden Markov model based method for detecting regulatory regions in DNA sequences, by searching for clusters of cis-elements.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(70056, 2001, 'bio', 'Presence of ATG triplets in 5 untranslated regions of eukaryotic cDNAs correlates with a weak context of the start codon', 'Rogozin, I B|Kochetov, A V|Kondrashov, F A|Koonin, E V|Milanesi, L', 'National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD 20804, USA. rogozin@ncbi.nlm.nih.gov|National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD 20804, USA. rogozin@ncbi.nlm.nih.gov|National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD 20804, USA. rogozin@ncbi.nlm.nih.gov|National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD 20804, USA. rogozin@ncbi.nlm.nih.gov|National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD 20804, USA. rogozin@ncbi.nlm.nih.gov', 'United States|United States|United States|United States|United States', 0, '5 Untranslated Regions|Alternative Splicing|Animals|Base Composition|Base Sequence|Codon, Initiator|Computational Biology|DNA, Complementary|Humans|Linear Models|Models, Genetic|RNA, Messenger|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/17.10.890', '11673233', 'The context of the start codon (typically, AUG) and the features of the 5 Untranslated Regions (5 UTRs) are important for understanding translation regulation in eukaryotic mRNAs and for accurate prediction of the coding region in genomic and cDNA sequences. The presence of AUG triplets in 5 UTRs (upstream AUGs) might effect the initiation rate and, in the context of gene prediction, could reduce the accuracy of the identification of the authentic start. To reveal potential connections between the presence of upstream AUGs and other features of 5 UTRs, such as their length and the start codon context, we undertook a systematic analysis of the available eukaryotic 5 UTR sequences.'),
(70057, 2001, 'bio', 'An analysis of gene-finding programs for Neurospora crassa', 'Kraemer, E|Wang, J|Guo, J|Hopkins, S|Arnold, J', 'Computer Science Department, The University of Georgia, Athens, GA 30602, USA. eileen@cs.uga.edu|Computer Science Department, The University of Georgia, Athens, GA 30602, USA. eileen@cs.uga.edu|Computer Science Department, The University of Georgia, Athens, GA 30602, USA. eileen@cs.uga.edu|Computer Science Department, The University of Georgia, Athens, GA 30602, USA. eileen@cs.uga.edu|Computer Science Department, The University of Georgia, Athens, GA 30602, USA. eileen@cs.uga.edu', 'Georgia|Georgia|Georgia|Georgia|Georgia', 0, 'Algorithms|Computational Biology|Cosmids|Genes, Fungal|Neurospora crassa|Predictive Value of Tests|Sequence Analysis, DNA|Software', 'Evaluation Studies', '10.1093/bioinformatics/17.10.901', '11673234', 'Computational gene identification plays an important role in genome projects. The approaches used in gene identification programs are often tuned to one particular organism, and accuracy for one organism or class of organism does not necessarily translate to accurate predictions for other organisms. In this paper we evaluate five computer programs on their ability to locate coding regions and to predict gene structure in Neurospora crassa. One of these programs (FFG) was designed specifically for gene-finding in N.crassa, but the model parameters have not yet been fully tuned, and the program should thus be viewed as an initial prototype. The other four programs were neither designed nor tuned for N.crassa.'),
(70058, 2001, 'bio', 'DIANA-EST: a statistical analysis', 'Hatzigeorgiou, A G|Fiziev, P|Reczko, M', 'Metagen GmbH, Ihnestr.63, 14195 Berlin, Germany. artemis@pcbi.upenn.edu|Metagen GmbH, Ihnestr.63, 14195 Berlin, Germany. artemis@pcbi.upenn.edu|Metagen GmbH, Ihnestr.63, 14195 Berlin, Germany. artemis@pcbi.upenn.edu', 'Germany|Germany|Germany', 0, 'Computational Biology|Consensus Sequence|DNA, Complementary|Expressed Sequence Tags|Neural Networks (Computer)|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/17.10.913', '11673235', 'Expressed Sequence Tags (ESTs) are next to cDNA sequences as the most direct way to locate in silico the genes of the genome and determine their structure. Currently ESTs make up more than 60% of all the database entries. The goal of this work is the development of a new program called DNA Intelligent Analysis for ESTs (DIANA-EST) based on a combination of Artificial Neural Networks (ANN) and statistics for the characterization of the coding regions within ESTs and the reconstruction of the encoded protein.'),
(70059, 2001, 'bio', 'Automatic rule generation for protein annotation with the C45 data mining algorithm applied on SWISS-PROT', 'Kretschmann, E|Fleischmann, W|Apweiler, R', 'The EMBL Outstation, The European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK. kretsch@ebi.ac.uk|The EMBL Outstation, The European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK. kretsch@ebi.ac.uk|The EMBL Outstation, The European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK. kretsch@ebi.ac.uk', 'United Kingdom|United Kingdom|United Kingdom', 0, 'Algorithms|Computational Biology|Databases, Protein|Proteins|Reproducibility of Results|Software', 'Journal Article', '10.1093/bioinformatics/17.10.920', '11673236', 'The gap between the amount of newly submitted protein data and reliable functional annotation in public databases is growing. Traditional manual annotation by literature curation and sequence analysis tools without the use of automated annotation systems is not able to keep up with the ever increasing quantity of data that is submitted. Automated supplements to manually curated databases such as TrEMBL or GenPept cover raw data but provide only limited annotation. To improve this situation automatic tools are needed that support manual annotation, automatically increase the amount of reliable information and help to detect inconsistencies in manually generated annotations.'),
(70060, 2001, 'bio', 'Markovian domain fingerprinting: statistical segmentation of protein sequences', 'Bejerano, G|Seldin, Y|Margalit, H|Tishby, N', 'School of Computer Science & Engineering, The Hebrew University, Jerusalem 91904, Israel.|School of Computer Science & Engineering, The Hebrew University, Jerusalem 91904, Israel.|School of Computer Science & Engineering, The Hebrew University, Jerusalem 91904, Israel.|School of Computer Science & Engineering, The Hebrew University, Jerusalem 91904, Israel.', 'Israel|Israel|Israel|Israel', 10, 'Algorithms|Computational Biology|DNA Topoisomerases, Type II|Glutathione Transferase|Homeodomain Proteins|Markov Chains|Peptide Mapping|Protein Structure, Tertiary|Proteins|Sequence Alignment|Transcription Factors', 'Comparative Study', '10.1093/bioinformatics/17.10.927', '11673237', 'Characterization of a protein family by its distinct sequence domains is crucial for functional annotation and correct classification of newly discovered proteins. Conventional Multiple Sequence Alignment (MSA) based methods find difficulties when faced with heterogeneous groups of proteins. However, even many families of proteins that do share a common domain contain instances of several other domains, without any common underlying linear ordering. Ignoring this modularity may lead to poor or even false classification results. An automated method that can analyze a group of proteins into the sequence domains it contains is therefore highly desirable.'),
(70061, 2001, 'bio', 'Clustering protein sequences--structure prediction by transitive homology', 'Bolten, E|Schliep, A|Schneckener, S|Schomburg, D|Schrader, R', 'Institut für Biochemie, Universität zu Köln, Weyertal 80, D-50937 Köln, Germany. e.bolten@science-factory.com|Institut für Biochemie, Universität zu Köln, Weyertal 80, D-50937 Köln, Germany. e.bolten@science-factory.com|Institut für Biochemie, Universität zu Köln, Weyertal 80, D-50937 Köln, Germany. e.bolten@science-factory.com|Institut für Biochemie, Universität zu Köln, Weyertal 80, D-50937 Köln, Germany. e.bolten@science-factory.com|Institut für Biochemie, Universität zu Köln, Weyertal 80, D-50937 Köln, Germany. e.bolten@science-factory.com', 'Germany|Germany|Germany|Germany|Germany', 65, 'Algorithms|Cluster Analysis|Computational Biology|Databases, Protein|Proteins|Sensitivity and Specificity|Sequence Alignment|Sequence Homology, Amino Acid|Software', 'Journal Article', '10.1093/bioinformatics/17.10.935', '11673238', 'It is widely believed that for two proteins Aand Ba sequence identity above some threshold implies structural similarity due to a common evolutionary ancestor. Since this is only a sufficient, but not a necessary condition for structural similarity, the question remains what other criteria can be used to identify remote homologues. Transitivity refers to the concept of deducing a structural similarity between proteins A and C from the existence of a third protein B, such that A and B as well as B and C are homologues, as ascertained if the sequence identity between A and B as well as that between B and C is above the aforementioned threshold. It is not fully understood if transitivity always holds and whether transitivity can be extended ad infinitum.'),
(70062, 2001, 'bio', 'Predicting class II MHC/peptide multi-level binding with an iterative stepwise discriminant analysis meta-algorithm', 'Mallios, R R', 'Office of Sponsored Projects and Research, University of California, San Francisco, 2615 East Clinton Avenue, Fresno, CA 93703, USA. ronna@ucsfresno.edu', 'United States', 0, 'Algorithms|Computational Biology|Databases, Protein|Discriminant Analysis|HLA-DR1 Antigen|Histocompatibility Antigens Class II|Humans|Peptides|Protein Binding|Software', 'Comparative Study', '10.1093/bioinformatics/17.10.942', '11673239', 'Predicting peptides that bind to both Major Histocompatibility Complex (MHC) molecules and T cell receptors provides crucial information for vaccine development. An agretope is that portion of a peptide that interacts with an MHC molecule. The identification and prediction of agretopes is the first step towards vaccine design.'),
(70063, 2001, 'bio', 'Determining the minimum number of types necessary to represent the sizes of protein atoms', 'Tsai, J|Voss, N|Gerstein, M', 'Department of Biochemistry & Biophysics, 103 Biochemistry/Biophysics Building, Texas A&M University, 2128 TAMU, College Station, TX 77843-2128, USA. JerryTsai@TAMU.edu|Department of Biochemistry & Biophysics, 103 Biochemistry/Biophysics Building, Texas A&M University, 2128 TAMU, College Station, TX 77843-2128, USA. JerryTsai@TAMU.edu|Department of Biochemistry & Biophysics, 103 Biochemistry/Biophysics Building, Texas A&M University, 2128 TAMU, College Station, TX 77843-2128, USA. JerryTsai@TAMU.edu', 'United States|United States|United States', 15, 'Carbon|Cluster Analysis|Computational Biology|Databases, Protein|Hydrogen|Molecular Structure|Proteins|Software', 'Journal Article', '10.1093/bioinformatics/17.10.949', '11673240', 'Traditionally, for packing calculations people have collected atoms together into a number of distinct types. These, in fact, often represent a heavy atom and its associated hydrogens (i.e. a united atom). Also, atom typing is usually done according to basic chemistry, giving rise to 20-30 protein atom types, such as carbonyl carbons, methyl groups, and hydroxyl groups. No one has yet investigated how similar in packing these chemically derived types are. Here we address this question in detail, using Voronoi volume calculations on a set of high-resolution crystal structures.'),
(70064, 2001, 'bio', 'Prediction of disulfide connectivity in proteins', 'Fariselli, P|Casadio, R', 'CIRB Biocomputing Unit, Laboratory of Biophysics, Department of Biology, University of Bologna, via Irnerio 42, 40126 Bologna, Italy. Piero@biocomp.unibo.it|CIRB Biocomputing Unit, Laboratory of Biophysics, Department of Biology, University of Bologna, via Irnerio 42, 40126 Bologna, Italy. Piero@biocomp.unibo.it', 'Italy|Italy', 0, 'Algorithms|Computational Biology|Databases, Protein|Disulfides|Molecular Structure|Monte Carlo Method|Odds Ratio|Protein Folding|Proteins', 'Journal Article', '10.1093/bioinformatics/17.10.957', '11673241', 'A major problem in protein structure prediction is the correct location of disulfide bridges in cysteine-rich proteins. In protein-folding prediction, the location of disulfide bridges can strongly reduce the search in the conformational space. Therefore the correct prediction of the disulfide connectivity starting from the protein residue sequence may also help in predicting its 3D structure.'),
(70065, 2001, 'bio', 'Sequence and structural aspects of functional diversification in class I alpha-mannosidase evolution', 'Jordan, I K|Bishop, G R|Gonzalez, D S', 'National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD 20894, USA.|National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD 20894, USA.|National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD 20894, USA.', 'United States|United States|United States', 0, 'Amino Acid Sequence|Animals|Computational Biology|Endoplasmic Reticulum|Evolution, Molecular|Gene Duplication|Golgi Apparatus|Humans|Mannosidases|Models, Molecular|Molecular Sequence Data|Molecular Structure|Phylogeny|Protein Structure, Tertiary|Selection, Genetic|Sequence Homology, Amino Acid|Static Electricity|alpha-Mannosidase', 'Comparative Study', '10.1093/bioinformatics/17.10.965', '11673242', 'Class I alpha-mannosidases comprise a homologous and functionally diverse family of glycoside hydrolases. Phylogenetic analysis based on an amino acid sequence alignment of the catalytic domain of class I alpha-mannosidases reveals four well-supported phylogenetic groups within this family. These groups include a number of paralogous members generated by gene duplications that occurred as far back as the initial divergence of the crown-group of eukaryotes. Three of the four phylogenetic groups consist of enzymes that have group-specific biochemical specificity and/or sites of activity. An attempt has been made to uncover the role that natural selection played in the sequence and structural divergence between the phylogenetically and functionally distinct Endoplasmic Reticulum (ER) and Golgi apparatus groups.'),
(70066, 2001, 'bio', 'Model-based clustering and data transformations for gene expression data', 'Yeung, K Y|Fraley, C|Murua, A|Raftery, A E|Ruzzo, W L', 'Computer Science and Engineering, Box 352350, University of Washington, Seattle, WA 98195, USA. kayee@cs.washington.edu|Computer Science and Engineering, Box 352350, University of Washington, Seattle, WA 98195, USA. kayee@cs.washington.edu|Computer Science and Engineering, Box 352350, University of Washington, Seattle, WA 98195, USA. kayee@cs.washington.edu|Computer Science and Engineering, Box 352350, University of Washington, Seattle, WA 98195, USA. kayee@cs.washington.edu|Computer Science and Engineering, Box 352350, University of Washington, Seattle, WA 98195, USA. kayee@cs.washington.edu', 'United States|United States|United States|United States|United States', 0, 'Algorithms|Cell Cycle|Cluster Analysis|Computational Biology|Data Interpretation, Statistical|Databases, Nucleic Acid|Female|Gene Expression Profiling|Humans|Models, Statistical|Ovary|Saccharomyces cerevisiae', 'Journal Article', '10.1093/bioinformatics/17.10.977', '11673243', 'Clustering is a useful exploratory technique for the analysis of gene expression data. Many different heuristic clustering algorithms have been proposed in this context. Clustering algorithms based on probability models offer a principled alternative to heuristic algorithms. In particular, model-based clustering assumes that the data is generated by a finite mixture of underlying probability distributions such as multivariate normal distributions. The issues of selecting a good clustering method and determining the correct number of clusters are reduced to model selection problems in the probability framework. Gaussian mixture models have been shown to be a powerful tool for clustering in many applications.'),
(70067, 2001, 'bio', 'Birth of scale-free molecular networks and the number of distinct DNA and protein domains per genome', 'Rzhetsky, A|Gomez, S M', 'Columbia Genome Center, Columbia University, New York, NY 10032, USA. ar345@columbia.edu|Columbia Genome Center, Columbia University, New York, NY 10032, USA. ar345@columbia.edu', 'United States|United States', 0, 'Biological Evolution|Computational Biology|DNA|Databases, Nucleic Acid|Databases, Protein|Fractals|Genomics|Humans|Models, Genetic|Proteins|Stochastic Processes', 'Journal Article', '10.1093/bioinformatics/17.10.988', '11673244', 'Current growth in the field of genomics has provided a number of exciting approaches to the modeling of evolutionary mechanisms within the genome. Separately, dynamical and statistical analyses of networks such as the World Wide Web and the social interactions existing between humans have shown that these networks can exhibit common fractal properties-including the property of being scale-free. This work attempts to bridge these two fields and demonstrate that the fractal properties of molecular networks are linked to the fractal properties of their underlying genomes.'),
(70068, 2001, 'bio', 'Nucleosome formation potential of eukaryotic DNA: calculation and promoters analysis', 'Levitsky, V G|Podkolodnaya, O A|Kolchanov, N A|Podkolodny, N L', 'Laboratory of Theoretical Genetics, Institute of Cytology & Genetics, 630090, Lavrentiev Ave. 10, Novosibirsk, Russia. levitsky@bionet.nsc.ru|Laboratory of Theoretical Genetics, Institute of Cytology & Genetics, 630090, Lavrentiev Ave. 10, Novosibirsk, Russia. levitsky@bionet.nsc.ru|Laboratory of Theoretical Genetics, Institute of Cytology & Genetics, 630090, Lavrentiev Ave. 10, Novosibirsk, Russia. levitsky@bionet.nsc.ru|Laboratory of Theoretical Genetics, Institute of Cytology & Genetics, 630090, Lavrentiev Ave. 10, Novosibirsk, Russia. levitsky@bionet.nsc.ru', '-|-|-|-', 0, 'Algorithms|Base Composition|Computational Biology|DNA|Eukaryotic Cells|Gene Expression|Histones|Nucleosomes|Promoter Regions, Genetic|Software', 'Journal Article', '10.1093/bioinformatics/17.11.998', '11724728', 'A rapid growth in the number of genes with known sequences calls for developing automated tools for their classification and analysis. It became clear that nucleosome packaging of eukaryotic DNA is very important for gene functioning. Automated computer tools for characterization of nucleosome packaging density could be useful for studying of gene regulation and genome annotation.'),
(70069, 2001, 'bio', 'Gene recognition in eukaryotic DNA by comparison of genomic sequences', 'Novichkov, P S|Gelfand, M S|Mironov, A A', 'State Scientific Center GosNIIGenetika, 1 Dorozhny pr. 1, Moscow 113545, Russia.|State Scientific Center GosNIIGenetika, 1 Dorozhny pr. 1, Moscow 113545, Russia.|State Scientific Center GosNIIGenetika, 1 Dorozhny pr. 1, Moscow 113545, Russia.', '-|-|-', 0, 'Algorithms|Animals|Computational Biology|DNA|Drosophila|Eukaryotic Cells|Exons|Genome|Humans|Mice|Sequence Alignment|Software|Xenopus', 'Comparative Study', '10.1093/bioinformatics/17.11.1011', '11724729', 'Sequencing of complete eukaryotic genomes and large syntenic fragments of genomes makes it possible to apply genomic comparison for gene recognition.'),
(70070, 2001, 'bio', 'Combining frequency and positional information to predict transcription factor binding sites', 'Kiexc5x82basa, S M|Korbel, J O|Beule, D|Schuchhardt, J|Herzel, H', 'Innovationskolleg Theoretische Biologie, Humboldt-Universitxc3xa4t zu Berlin, Invalidenstrasse 43, D-10115 Berlin, Germany. s.kielbasa@itb.biologie.hu-berlin.de|Innovationskolleg Theoretische Biologie, Humboldt-Universitxc3xa4t zu Berlin, Invalidenstrasse 43, D-10115 Berlin, Germany. s.kielbasa@itb.biologie.hu-berlin.de|Innovationskolleg Theoretische Biologie, Humboldt-Universitxc3xa4t zu Berlin, Invalidenstrasse 43, D-10115 Berlin, Germany. s.kielbasa@itb.biologie.hu-berlin.de|Innovationskolleg Theoretische Biologie, Humboldt-Universitxc3xa4t zu Berlin, Invalidenstrasse 43, D-10115 Berlin, Germany. s.kielbasa@itb.biologie.hu-berlin.de|Innovationskolleg Theoretische Biologie, Humboldt-Universitxc3xa4t zu Berlin, Invalidenstrasse 43, D-10115 Berlin, Germany. s.kielbasa@itb.biologie.hu-berlin.de', 'Germany|Germany|Germany|Germany|Germany', 30, 'Algorithms|Base Sequence|Binding Sites|Computational Biology|DNA|DNA, Fungal|Databases, Genetic|Humans|Promoter Regions, Genetic|Saccharomyces cerevisiae|Software|Transcription Factors', 'Journal Article', '10.1093/bioinformatics/17.11.1019', '11724730', 'Even though a number of genome projects have been finished on the sequence level, still only a small proportion of DNA regulatory elements have been identified. Growing amounts of gene expression data provide the possibility of finding coregulated genes by clustering methods. By analysis of the promoter regions of those genes, rather weak signals of transcription factor binding sites may be detected.'),
(70071, 2001, 'bio', 'Thermodynamic database for protein-nucleic acid interactions (ProNIT)', 'Prabakaran, P|An, J|Gromiha, M M|Selvaraj, S|Uedaira, H|Kono, H|Sarai, A', 'RIKEN Tsukuba Institute, 3-1-1 Koyadai, Tsukuba, Ibaraki 305-0074, Japan.|RIKEN Tsukuba Institute, 3-1-1 Koyadai, Tsukuba, Ibaraki 305-0074, Japan.|RIKEN Tsukuba Institute, 3-1-1 Koyadai, Tsukuba, Ibaraki 305-0074, Japan.|RIKEN Tsukuba Institute, 3-1-1 Koyadai, Tsukuba, Ibaraki 305-0074, Japan.|RIKEN Tsukuba Institute, 3-1-1 Koyadai, Tsukuba, Ibaraki 305-0074, Japan.|RIKEN Tsukuba Institute, 3-1-1 Koyadai, Tsukuba, Ibaraki 305-0074, Japan.|RIKEN Tsukuba Institute, 3-1-1 Koyadai, Tsukuba, Ibaraki 305-0074, Japan.', 'Japan|Japan|Japan|Japan|Japan|Japan|Japan', 49, 'Amino Acid Sequence|Bacteriophage lambda|Base Sequence|Computational Biology|Computer Simulation|DNA-Binding Proteins|Databases, Genetic|Macromolecular Substances|Models, Molecular|Molecular Sequence Data|Nucleic Acids|Operator Regions, Genetic|Protein Binding|Proteins|Repressor Proteins|Software|Thermodynamics|User-Computer Interface|Viral Proteins|Viral Regulatory and Accessory Proteins', 'Journal Article', '10.1093/bioinformatics/17.11.1027', '11724731', 'Protein-nucleic acid interactions are fundamental to the regulation of gene expression. In order to elucidate the molecular mechanism of protein-nucleic acid recognition and analyze the gene regulation network, not only structural data but also quantitative binding data are necessary. Although there are structural databases for proteins and nucleic acids, there exists no database for their experimental binding data. Thus, we have developed a Thermodynamic Database for Protein-Nucleic Acid Interactions (ProNIT).'),
(70072, 2001, 'bio', 'Detection of conserved physico-chemical characteristics of proteins by analyzing clusters of positions with co-ordinated substitutions', 'Afonnikov, D A|Oshchepkov, D Y|Kolchanov, N A', 'Laboratory of Theoretical Genetics, Institute of Cytology and Genetics, 10 Lavrentyev Ave., Novosibirsk 630090, Russia. ada.bionet.nsc.ru|Laboratory of Theoretical Genetics, Institute of Cytology and Genetics, 10 Lavrentyev Ave., Novosibirsk 630090, Russia. ada.bionet.nsc.ru|Laboratory of Theoretical Genetics, Institute of Cytology and Genetics, 10 Lavrentyev Ave., Novosibirsk 630090, Russia. ada.bionet.nsc.ru', '-|-|-', 0, 'Algorithms|Amino Acid Sequence|Amino Acid Substitution|Binding Sites|Chemical Phenomena|Chemistry, Physical|Computational Biology|Conserved Sequence|Evolution, Molecular|Homeodomain Proteins|Models, Molecular|Protein Structure, Tertiary|Proteins|Software', 'Journal Article', '10.1093/bioinformatics/17.11.1035', '11724732', 'It is known that the physico-chemical characteristics of proteins underlying specific folding of the polypeptide chain and the protein function are evolutionary conserved. Detection of such characteristics while analyzing homologous sequences would expand essentially the knowledge on protein function, structure, and evolution. These characteristics are maintained constant, in particular, by co-ordinated substitutions. In this process, the destabilizing effect of a substitution may be compensated by another substitution at a different position within the same protein, making the overall change in this protein characteristic insignificant. Consequently, the patterns of co-ordinated substitutions contain important information on conserved physico-chemical properties of proteins, requiring their investigation and development of the corresponding methods and software for correlation analysis of protein sequences available to a wide range of users.'),
(70073, 2001, 'bio', 'MDB: a database system utilizing automatic construction of modules and STAR-derived universal language', 'Migliavacca, E|Adzhubei, A A|Peitsch, M C', 'GlaxoSmithKline, R & D, 10 Route de lAéroport, CH-1215, Geneva, Switzerland. em96794@gsk.com|GlaxoSmithKline, R & D, 10 Route de lAéroport, CH-1215, Geneva, Switzerland. em96794@gsk.com|GlaxoSmithKline, R & D, 10 Route de lAéroport, CH-1215, Geneva, Switzerland. em96794@gsk.com', 'Switzerland|Switzerland|Switzerland', 3, 'Computational Biology|Computer Simulation|Databases, Protein|Programming Languages|Proteins|Software', 'Journal Article', '10.1093/bioinformatics/17.11.1047', '11724733', 'The value of information greatly increases if stored in databases. The objective was to construct a multi-purpose database system primarily designed to store and provide access to three-dimensional structures of biological molecules including theoretical models.'),
(70074, 2001, 'bio', 'The TRANSPATH signal transduction database: a knowledge base on signal transduction networks', 'Schacherer, F|Choi, C|Götze, U|Krull, M|Pistor, S|Wingender, E', 'GBF German Research Centre for Biotechnology Biobase Biological Databases GmbH, Halchtersche Strasse 33, D-38304 Wolfenbüttel, Germany. frs@biobase.de|GBF German Research Centre for Biotechnology Biobase Biological Databases GmbH, Halchtersche Strasse 33, D-38304 Wolfenbüttel, Germany. frs@biobase.de|GBF German Research Centre for Biotechnology Biobase Biological Databases GmbH, Halchtersche Strasse 33, D-38304 Wolfenbüttel, Germany. frs@biobase.de|GBF German Research Centre for Biotechnology Biobase Biological Databases GmbH, Halchtersche Strasse 33, D-38304 Wolfenbüttel, Germany. frs@biobase.de|GBF German Research Centre for Biotechnology Biobase Biological Databases GmbH, Halchtersche Strasse 33, D-38304 Wolfenbüttel, Germany. frs@biobase.de|GBF German Research Centre for Biotechnology Biobase Biological Databases GmbH, Halchtersche Strasse 33, D-38304 Wolfenbüttel, Germany. frs@biobase.de', 'Germany|Germany|Germany|Germany|Germany|Germany', 16, 'Algorithms|Artificial Intelligence|Computational Biology|Computer Graphics|Computer Simulation|Databases, Factual|Signal Transduction', 'Journal Article', '10.1093/bioinformatics/17.11.1053', '11724734', 'TRANSPATH is an information system on gene-regulatory pathways, and an extension module to the TRANSFAC database system (Wingender et al., Nucleic Acids Res., 28, 316-319, 2000). It focuses on pathways involved in the regulation of transcription factors in different species, mainly human, mouse and rat. Elements of the relevant signal transduction pathways like complexes, signaling molecules, and their states are stored together with information about their interaction in an object-oriented database. The database interface provides clickable maps and automatically generated pathway cascades as additional ways to explore the data. All information is validated with references to the original publications. Also, references to other databases are provided (TRANSFAC, SWISS-PROT, EMBL, PubMed and others).'),
(70075, 2001, 'bio', 'Concepts to automate the theoretical design of effective antisense oligonucleotides', 'Far, R K|Nedbal, W|Sczakiel, G', 'Medizinische Universität zu Lübeck, Institut für Molekulare Medizin, Ratzeburger Allee 160, D-23538 Lübeck, Germany.|Medizinische Universität zu Lübeck, Institut für Molekulare Medizin, Ratzeburger Allee 160, D-23538 Lübeck, Germany.|Medizinische Universität zu Lübeck, Institut für Molekulare Medizin, Ratzeburger Allee 160, D-23538 Lübeck, Germany.', 'Germany|Germany|Germany', 15, 'Algorithms|Computational Biology|Computer-Aided Design|Drug Design|Oligonucleotides, Antisense|Software Design|Thermodynamics', 'Journal Article', '10.1093/bioinformatics/17.11.1058', '11724735', 'Among the large number of possible antisense species against a given target RNA, only a small number shows effective suppression of the target gene in living cells. In the case of short-chain antisense oligonucleotides (asON) which usually comprise less than approximately 25 nucleotides, local structures of the target RNA seem to be of particular importance for the extent of gene suppression. Experimental approaches to identify promising local target sequences and, hence, complementary asON sequences, have provided tools to define asON that are biologically active at higher than statistical probability. However, experimental protocols are expensive, time consuming, and are associated with intrinsic basic and technical limitations. As insights into the structure-function relationship of asON as well as the role of sequence motifs increase, it becomes feasible to consider computer-based theoretical approaches for the design of effective asON. In the following we describe how individual steps of the theoretical design of asON may be automated by establishing and implementing suitable algorithms.'),
(70076, 2001, 'bio', 'Nucleosome formation potential of exons, introns, and Alu repeats', 'Levitsky, V G|Podkolodnaya, O A|Kolchanov, N A|Podkolodny, N L', 'Laboratory of Theoretical Genetics, Institute of Cytology & Genetics, 630090, Lavrentiev ave. 10, Novosibirsk, Russia.|Laboratory of Theoretical Genetics, Institute of Cytology & Genetics, 630090, Lavrentiev ave. 10, Novosibirsk, Russia.|Laboratory of Theoretical Genetics, Institute of Cytology & Genetics, 630090, Lavrentiev ave. 10, Novosibirsk, Russia.|Laboratory of Theoretical Genetics, Institute of Cytology & Genetics, 630090, Lavrentiev ave. 10, Novosibirsk, Russia.', '-|-|-|-', 0, 'Alu Elements|Computational Biology|Databases, Nucleic Acid|Exons|Humans|Introns|Nucleosomes|Software', 'Journal Article', '10.1093/bioinformatics/17.11.1062', '11724736', 'A program for constructing nucleosome formation potential profile was applied for investigation of exons, introns, and repetitive sequences. The program is available at http://wwwmgs.bionet.nsc.ru/mgs/programs/recon/. We have demonstrated that introns and repetitive sequences exhibit higher nucleosome formation potentials than exons. This fact may be explained by functional saturation of exons with genetic code, hindering the localization of efficient nucleosome positioning sites.'),
(70077, 2001, 'bio', 'Segmentation of long genomic sequences into domains with homogeneous composition with BASIO software', 'Ramensky, V E|Makeev, V J|Roytberg, M A|Tumanyan, V G', 'Engelhardt Institute of Molecular Biology, Vavilova, 32 Moscow 117984, Russia. ramensky@imb.ac.ru|Engelhardt Institute of Molecular Biology, Vavilova, 32 Moscow 117984, Russia. ramensky@imb.ac.ru|Engelhardt Institute of Molecular Biology, Vavilova, 32 Moscow 117984, Russia. ramensky@imb.ac.ru|Engelhardt Institute of Molecular Biology, Vavilova, 32 Moscow 117984, Russia. ramensky@imb.ac.ru', '-|-|-|-', 6, 'Algorithms|Animals|Computational Biology|Genome|Genomics|Plasmodium falciparum|Saccharomyces cerevisiae|Software', 'Journal Article', '10.1093/bioinformatics/17.11.1065', '11724737', 'We present a software system BASIO that allows one to segment a sequence into regions with homogeneous nucleotide composition at a desired length scale. The system can work with arbitrary alphabet and therefore can be applied to various (e.g. protein) sequences. Several sequences of complete genomes of eukaryotes are used to demonstrate the efficiency of the software.'),
(70078, 2001, 'bio', 'Selection of optimal DNA oligos for gene expression arrays', 'Li, F|Stormo, G D', 'Department of Genetics, Washington University School of Medicine, St Louis, MO 63110, USA. lif@ural.wustl.edu|Department of Genetics, Washington University School of Medicine, St Louis, MO 63110, USA. lif@ural.wustl.edu', 'United States|United States', 176, 'Algorithms|Base Sequence|Computational Biology|Databases, Nucleic Acid|Nucleic Acid Hybridization|Oligonucleotide Array Sequence Analysis|Oligonucleotide Probes|Software|Thermodynamics', 'Journal Article', '10.1093/bioinformatics/17.11.1067', '11724738', 'High density DNA oligo microarrays are widely used in biomedical research. Selection of optimal DNA oligos that are deposited on the microarrays is critical. Based on sequence information and hybridization free energy, we developed a new algorithm to select optimal short (20-25 bases) or long (50 or 70 bases) oligos from genes or open reading frames (ORFs) and predict their hybridization behavior. Having optimized probes for each gene is valuable for two reasons. By minimizing background hybridization they provide more accurate determinations of true expression levels. Having optimum probes minimizes the number of probes needed per gene, thereby decreasing the cost of each microarray, raising the number of genes on each chip and increasing its usage.'),
(70079, 2001, 'bio', 'Database-driven multi locus sequence typing (MLST) of bacterial pathogens', 'Chan, M S|Maiden, M C|Spratt, B G', 'The Wellcome Trust Centre for the Epidemiology of Infectious Disease, University of Oxford, South Parks Road, Oxford OX1 3FY, UK. mchan@molbiol.ox.ac.uk|The Wellcome Trust Centre for the Epidemiology of Infectious Disease, University of Oxford, South Parks Road, Oxford OX1 3FY, UK. mchan@molbiol.ox.ac.uk|The Wellcome Trust Centre for the Epidemiology of Infectious Disease, University of Oxford, South Parks Road, Oxford OX1 3FY, UK. mchan@molbiol.ox.ac.uk', 'United Kingdom|United Kingdom|United Kingdom', 0, 'Bacteria|Bacterial Typing Techniques|Computational Biology|Database Management Systems|Databases, Nucleic Acid|Genes, Bacterial|Internet|Sequence Analysis, DNA|Software|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/17.11.1077', '11724739', 'Multi Locus Sequence Typing (MLST) is a newly developed typing method for bacteria based on the sequence determination of internal fragments of seven house-keeping genes. It has proved useful in characterizing and monitoring disease-causing and antibiotic resistant lineages of bacteria. The strength of this approach is that unlike data obtained using most other typing methods, sequence data are unambiguous, can be held on a central database and be queried through a web server.'),
(70080, 2001, 'bio', 'Automatic analysis of agarose gel images', 'Adiga, P S|Bhomra, A|Turri, M G|Nicod, A|Datta, S R|Jeavons, P|Mott, R|Flint, J', 'Psychiatric Genetics Group, Wellcome Trust Centre for Human Genetics, University of Oxford, Roosevelt Drive, Headington, Oxford OX3 7BN, UK.|Psychiatric Genetics Group, Wellcome Trust Centre for Human Genetics, University of Oxford, Roosevelt Drive, Headington, Oxford OX3 7BN, UK.|Psychiatric Genetics Group, Wellcome Trust Centre for Human Genetics, University of Oxford, Roosevelt Drive, Headington, Oxford OX3 7BN, UK.|Psychiatric Genetics Group, Wellcome Trust Centre for Human Genetics, University of Oxford, Roosevelt Drive, Headington, Oxford OX3 7BN, UK.|Psychiatric Genetics Group, Wellcome Trust Centre for Human Genetics, University of Oxford, Roosevelt Drive, Headington, Oxford OX3 7BN, UK.|Psychiatric Genetics Group, Wellcome Trust Centre for Human Genetics, University of Oxford, Roosevelt Drive, Headington, Oxford OX3 7BN, UK.|Psychiatric Genetics Group, Wellcome Trust Centre for Human Genetics, University of Oxford, Roosevelt Drive, Headington, Oxford OX3 7BN, UK.|Psychiatric Genetics Group, Wellcome Trust Centre for Human Genetics, University of Oxford, Roosevelt Drive, Headington, Oxford OX3 7BN, UK.', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom', 12, 'Animals|Computational Biology|DNA|Electrophoresis, Agar Gel|Genetic Markers|Image Processing, Computer-Assisted|Polymorphism, Genetic|Sequence Analysis, DNA|Software|Software Design', 'Journal Article', '10.1093/bioinformatics/17.11.1084', '11724740', 'Automatic tools to speed up routine biological processes are very much sought after in bio-medical research. Much repetitive work in molecular biology, such as allele calling in genetic analysis, can be made semi-automatic or task specific automatic by using existing techniques from computer science and signal processing. Computerized analysis is reproducible and avoids various forms of human error. Semi-automatic techniques with an interactive check on the results speed up the analysis and reduce the error.'),
(70081, 2001, 'bio', 'Towards computer aided design (CAD) of useful microorganisms', 'Tomita, M', 'Psychiatric Genetics Group, Wellcome Trust Centre for Human Genetics, University of Oxford, Roosevelt Drive, Headington, Oxford OX3 7BN, UK.', 'United Kingdom', 9, 'Cells|Computational Biology|Computer Simulation|Escherichia coli|Humans|Japan|Models, Genetic|Mycoplasma', 'Editorial', '10.1093/bioinformatics/17.12.1091', '11751216', '-'),
(70082, 2001, 'bio', 'DNA sequence quality trimming and vector removal', 'Chou, H H|Holmes, M H', 'Department of Zoology and Genetics, Department of Computer Science, Iowa State University, Ames, IA 50011, USA. hhchou@iastate.edu|Department of Zoology and Genetics, Department of Computer Science, Iowa State University, Ames, IA 50011, USA. hhchou@iastate.edu', 'United States|United States', 0, 'Algorithms|Consensus Sequence|Genetic Vectors|RNA Splicing|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/17.12.1093', '11751217', 'Most sequence comparison methods assume that the data being compared are trustworthy, but this is not the case with raw DNA sequences obtained from automatic sequencing machines. Nevertheless, sequence comparisons need to be done on them in order to remove vector splice sites and contaminants. This step is necessary before other genomic data processing stages can be carried out, such as fragment assembly or EST clustering. A specialized tool is therefore needed to solve this apparent dilemma.'),
(70083, 2001, 'bio', 'Tricross : using dot-plots in sequence-id space to detect uncataloged intergenic features', 'Ray, W C|Munson, R S|Daniels, C J', 'Childrens Research Institute, The Ohio State University, 700 Childrens Dr., Columbus, OH 43205, USA. ray@biosci.ohio-state.edu|Childrens Research Institute, The Ohio State University, 700 Childrens Dr., Columbus, OH 43205, USA. ray@biosci.ohio-state.edu|Childrens Research Institute, The Ohio State University, 700 Childrens Dr., Columbus, OH 43205, USA. ray@biosci.ohio-state.edu', 'United States|United States|United States', 5, 'Base Sequence|DNA, Archaeal|DNA, Intergenic|Genome, Archaeal|Molecular Sequence Data|Pyrococcus|Software', 'Journal Article', '10.1093/bioinformatics/17.12.1105', '11751218', 'The process of determining the functional sequence content of an organism is confounded by several factors. Large protein coding sequences are relatively easy to find by statistical methods. Smaller proteins however may escape detection due to their size falling below some arbitrary researcher-defined minimum cutoff, or the inability to precisely define a promoter, or translational start (Delcher et al., Nucleic Acids Res., 27, 4636-4641, 1999). Promoter and regulatory sequences themselves are difficult to define due to a significant amount of allowable sequence variation, as well as a probable lack of any completely accurate whole-organismal gene catalogs to date. Finally, certain genes coding functional RNAs may have insufficient structural or sequence constraints to be detectable by normal sequence structure/pattern searching methods (Eddy and Rivas, Bioinformatics, 16, 583-605, 2000). In those cases where there are multiple closely related organisms that have been sequenced, there is additional information that may be used in the investigation of sequence content-that being the possible conserved nature of functional sequences between the organisms. We present a method for the utilization of this conserved information to detect genes and other potentially functional sequences that may be missed by standard ORF-calling, RNA finding, and pattern matching software. The tricross programs produce a multi-way cross comparison of three sets of sequences, determine which are conserved in all three sets, and produce a graphical (Virtual Reality Modelling Language-VRML, (ISO/IEC 14772-1: 1997, VDC), 1997) representation as well as alignments of all sequence triples found. The software can also be applied to a pair of sequence sets, though the noise in the results increases.'),
(70084, 2001, 'bio', 'A higher-order background model improves the detection of promoter regulatory elements by Gibbs sampling', 'Thijs, G|Lescot, M|Marchal, K|Rombauts, S|De Moor, B|Rouzé, P|Moreau, Y', 'ESAT-SISTA/COSIC, KULeuven, Kasteelpark Arenberg 10, 3001 Leuven-Heverlee, Belgium. gert.thijs@esat.kuleuven.ac.be|ESAT-SISTA/COSIC, KULeuven, Kasteelpark Arenberg 10, 3001 Leuven-Heverlee, Belgium. gert.thijs@esat.kuleuven.ac.be|ESAT-SISTA/COSIC, KULeuven, Kasteelpark Arenberg 10, 3001 Leuven-Heverlee, Belgium. gert.thijs@esat.kuleuven.ac.be|ESAT-SISTA/COSIC, KULeuven, Kasteelpark Arenberg 10, 3001 Leuven-Heverlee, Belgium. gert.thijs@esat.kuleuven.ac.be|ESAT-SISTA/COSIC, KULeuven, Kasteelpark Arenberg 10, 3001 Leuven-Heverlee, Belgium. gert.thijs@esat.kuleuven.ac.be|ESAT-SISTA/COSIC, KULeuven, Kasteelpark Arenberg 10, 3001 Leuven-Heverlee, Belgium. gert.thijs@esat.kuleuven.ac.be|ESAT-SISTA/COSIC, KULeuven, Kasteelpark Arenberg 10, 3001 Leuven-Heverlee, Belgium. gert.thijs@esat.kuleuven.ac.be', 'Belgium|Belgium|Belgium|Belgium|Belgium|Belgium|Belgium', 297, 'Algorithms|Arabidopsis|Computer Simulation|DNA, Intergenic|Gene Expression|Models, Genetic|Probability|Promoter Regions, Genetic|Transcription, Genetic', 'Journal Article', '10.1093/bioinformatics/17.12.1113', '11751219', 'Transcriptome analysis allows detection and clustering of genes that are coexpressed under various biological circumstances. Under the assumption that coregulated genes share cis-acting regulatory elements, it is important to investigate the upstream sequences controlling the transcription of these genes. To improve the robustness of the Gibbs sampling algorithm to noisy data sets we propose an extension of this algorithm for motif finding with a higher-order background model.'),
(70085, 2001, 'bio', 'A probabilistic method for identifying start codons in bacterial genomes', 'Suzek, B E|Ermolaeva, M D|Schreiber, M|Salzberg, S L', 'Department of Computer Science, Johns Hopkins University, Baltimore, MD 21218, USA.|Department of Computer Science, Johns Hopkins University, Baltimore, MD 21218, USA.|Department of Computer Science, Johns Hopkins University, Baltimore, MD 21218, USA.|Department of Computer Science, Johns Hopkins University, Baltimore, MD 21218, USA.', 'United States|United States|United States|United States', 145, 'Algorithms|Binding Sites|Codon, Initiator|Genome, Bacterial|Models, Genetic|Models, Statistical|Ribosomes', 'Journal Article', '10.1093/bioinformatics/17.12.1123', '11751220', 'As the pace of genome sequencing has accelerated, the need for highly accurate gene prediction systems has grown. Computational systems for identifying genes in prokaryotic genomes have sensitivities of 98-99% or higher (Delcher et al., Nucleic Acids Res., 27, 4636-4641, 1999). These accuracy figures are calculated by comparing the locations of verified stop codons to the predictions. Determining the accuracy of start codon prediction is more problematic, however, due to the relatively small number of start sites that have been confirmed by independent, non-computational methods. Nonetheless, the accuracy of gene finders at predicting the exact gene boundaries at both the 5 and 3 ends of genes is of critical importance for microbial genome annotation, especially in light of the important signaling information that is sometimes found on the 5 end of a protein coding region. In this paper we propose a probabilistic method to improve the accuracy of gene identification systems at finding precise translation start sites. The new system, RBSfinder, is tested on a validated set of genes from Escherichia coli, for which it improves the accuracy of start site locations predicted by computational gene finding systems from the range 67-77% to 90% correct.'),
(70086, 2001, 'bio', 'Gene selection for sample classification based on gene expression data: study of sensitivity to choice of parameters of the GA/KNN method', 'Li, L|Weinberg, C R|Darden, T A|Pedersen, L G', 'Biostatistics Branch Laboratory of Structural Biology, National Institute of Environmental Health Sciences, Research Triangle Park, NC 27709, USA. LI3@niehs.nih.gov|Biostatistics Branch Laboratory of Structural Biology, National Institute of Environmental Health Sciences, Research Triangle Park, NC 27709, USA. LI3@niehs.nih.gov|Biostatistics Branch Laboratory of Structural Biology, National Institute of Environmental Health Sciences, Research Triangle Park, NC 27709, USA. LI3@niehs.nih.gov|Biostatistics Branch Laboratory of Structural Biology, National Institute of Environmental Health Sciences, Research Triangle Park, NC 27709, USA. LI3@niehs.nih.gov', 'United States|United States|United States|United States', 474, 'Algorithms|Colon|Colonic Neoplasms|Databases, Factual|Gene Expression|Humans|Lymphoma, B-Cell|Reproducibility of Results|Sensitivity and Specificity', 'Journal Article', '10.1093/bioinformatics/17.12.1131', '11751221', 'We recently introduced a multivariate approach that selects a subset of predictive genes jointly for sample classification based on expression data. We tested the algorithm on colon and leukemia data sets. As an extension to our earlier work, we systematically examine the sensitivity, reproducibility and stability of gene selection/sample classification to the choice of parameters of the algorithm.'),
(70087, 2001, 'bio', 'Statistical estimation of cluster boundaries in gene expression profile data', 'Horimoto, K|Toh, H', 'Laboratory of Mathematics, Saga Medical School, 5-1-1 Nabeshima, Saga, Saga 849-8501, Japan. horimoto@post.saga-med.ac.jp|Laboratory of Mathematics, Saga Medical School, 5-1-1 Nabeshima, Saga, Saga 849-8501, Japan. horimoto@post.saga-med.ac.jp', 'Japan|Japan', 45, 'Analysis of Variance|Data Interpretation, Statistical|Databases, Factual|Gene Expression|Gene Expression Profiling|Genes, Fungal|Regression Analysis|Saccharomyces cerevisiae', 'Journal Article', '10.1093/bioinformatics/17.12.1143', '11751222', 'Gene expression profile data are rapidly accumulating due to advances in microarray techniques. The abundant data are analyzed by clustering procedures to extract the useful information about the genes inherent in the data. In the clustering analyses, the systematic determination of the boundaries of gene clusters, instead of by visual inspection and biological knowledge, still remains challenging.'),
(70088, 2001, 'bio', 'Differential expression of genes coding for ribosomal proteins in different human tissues', 'Bortoluzzi, S|dAlessi, F|Romualdi, C|Danieli, G A', 'Department of Biology CRIBI Biotechnology Centre, University of Padua, Via G. Colombo 3, 35131 Italy.|Department of Biology CRIBI Biotechnology Centre, University of Padua, Via G. Colombo 3, 35131 Italy.|Department of Biology CRIBI Biotechnology Centre, University of Padua, Via G. Colombo 3, 35131 Italy.|Department of Biology CRIBI Biotechnology Centre, University of Padua, Via G. Colombo 3, 35131 Italy.', 'Italy|Italy|Italy|Italy', 83, 'Adult|Data Interpretation, Statistical|Databases, Nucleic Acid|Expressed Sequence Tags|Gene Expression Profiling|Gene Expression Regulation|Humans|Ribosomal Proteins', 'Journal Article', '10.1093/bioinformatics/17.12.1152', '11751223', 'To perform a computational and statistical study on a large set of gene expression data pertaining six adult human tissues (brain, liver, skeletal muscle, ovary, retina and uterus) for analyzing the expression of ribosomal protein genes.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(70089, 2001, 'bio', 'Estimation of P-values for global alignments of protein sequences', 'Webber, C|Barton, G J', 'EMBL-European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK.|EMBL-European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK.', 'United Kingdom|United Kingdom', 31, 'Databases, Protein|Mathematical Computing|Probability|Proteins|Sequence Alignment|Sequence Analysis, Protein|Software', 'Journal Article', '10.1093/bioinformatics/17.12.1158', '11751224', 'The global alignment of protein sequence pairs is often used in the classification and analysis of full-length sequences. The calculation of a Z-score for the comparison gives a length and composition corrected measure of the similarity between the sequences. However, the Z-score alone, does not indicate the likely biological significance of the similarity. In this paper, all pairs of domains from 250 sequences belonging to different SCOP folds were aligned and Z-scores calculated. The distribution of Z-scores was fitted with a peak distribution from which the probability of obtaining a given Z-score from the global alignment of two protein sequences of unrelated fold was calculated. A similar analysis was applied to subsequence pairs found by the Smith-Waterman algorithm. These analyses allow the probability that two protein sequences share the same fold to be estimated by global sequence alignment.'),
(70090, 2001, 'bio', 'Minimum conflict: a divide-and-conquer approach to phylogeny estimation', 'Fuellen, G|Wägele, J W|Giegerich, R', 'Ruhr-Universität Bochum, Lehrstuhl für spez. Zoologie, D-44780 Bochum, Germany. fuellen@alum.mit.edu|Ruhr-Universität Bochum, Lehrstuhl für spez. Zoologie, D-44780 Bochum, Germany. fuellen@alum.mit.edu|Ruhr-Universität Bochum, Lehrstuhl für spez. Zoologie, D-44780 Bochum, Germany. fuellen@alum.mit.edu', 'Germany|Germany|Germany', 12, 'Algorithms|Animals|Base Sequence|Crustacea|Evolution, Molecular|Molecular Sequence Data|Phylogeny', 'Journal Article', '10.1093/bioinformatics/17.12.1168', '11751225', 'Fast and reliable phylogeny estimation is rapidly gaining importance as more and more genomic sequence information is becoming available, and the study of the evolution of genes and genomes accelerates our understanding in biology and medicine alike. Branch attraction phenomena due to unequal amounts of evolutionary change in different parts of the phylogeny are one major problem for current methods, placing the species that evolved fast in one part of the phylogenetic tree, and the species that evolved slowly in the other.'),
(70091, 2001, 'bio', 'Taxonomy workbench', 'Wildpaner, M|Schneider, G|Schleiffer, A|Eisenhaber, F', 'Research Institute of Molecular Pathology, Dr Bohr-Gasse 7, A-1030 Vienna, Austria.|Research Institute of Molecular Pathology, Dr Bohr-Gasse 7, A-1030 Vienna, Austria.|Research Institute of Molecular Pathology, Dr Bohr-Gasse 7, A-1030 Vienna, Austria.|Research Institute of Molecular Pathology, Dr Bohr-Gasse 7, A-1030 Vienna, Austria.', 'Austria|Austria|Austria|Austria', 4, 'Databases, Nucleic Acid|Databases, Protein|Nucleic Acids|Proteins|Software', 'Journal Article', '10.1093/bioinformatics/17.12.1179', '11751226', 'At advanced stages of working with user-defined protein and gene sequence collections, it is frequently necessary to link these data to the taxonomic tree and to extract subsets in accordance with taxonomic considerations. Since no general automatic tools had been available, this was a tedious manual effort. Our taxonomy workbench allows processing of sequence sets, mapping of these sets onto the taxonomic tree, collection of taxonomic subsets from them and printing of the whole tree or some part of it. As a side effect, the system enables queries to and navigation within the taxonomy database.'),
(70092, 2001, 'bio', 'How to reconstruct a large genetic network from n gene perturbations in fewer than n(2) easy steps', 'Wagner, A', 'The Santa Fe Institute, Department of Biology, University of New Mexico, 167A Castetter Hall, Albuquerque, NM 817131-1091, USA. wagnera@unm.edu', 'Mexico', 104, 'Algorithms|Gene Expression Regulation|Genes', 'Journal Article', '10.1093/bioinformatics/17.12.1183', '11751227', 'The reconstruction of genetic networks is the holy grail of functional genomics. Its core task is to identify the causal structure of a gene network, that is, to distinguish direct from indirect regulatory interactions among gene products. In other words, to reconstruct a genetic network is to identify, for each network gene, which other genes and their activity the gene influences directly. Crucial to this task are perturbations of gene activity. Genomic technology permits large-scale experiments perturbing the activity of many genes and assessing the effect of each perturbation on all other genes in a genome. However, such experiments cannot distinguish between direct and indirect effects of a genetic perturbation.'),
(70093, 2001, 'bio', 'Visualizing plant metabolomic correlation networks using clique-metabolite matrices', 'Kose, F|Weckwerth, W|Linke, T|Fiehn, O', 'Max Planck Institute of Molecular Plant Physiology, Department of Lothar Willmitzer, Postfach, 14424 Potsdam, Germany. kose@mpimp-golm.mpg.de|Max Planck Institute of Molecular Plant Physiology, Department of Lothar Willmitzer, Postfach, 14424 Potsdam, Germany. kose@mpimp-golm.mpg.de|Max Planck Institute of Molecular Plant Physiology, Department of Lothar Willmitzer, Postfach, 14424 Potsdam, Germany. kose@mpimp-golm.mpg.de|Max Planck Institute of Molecular Plant Physiology, Department of Lothar Willmitzer, Postfach, 14424 Potsdam, Germany. kose@mpimp-golm.mpg.de', 'Germany|Germany|Germany|Germany', 127, 'Algorithms|Image Processing, Computer-Assisted|Plants', 'Journal Article', '10.1093/bioinformatics/17.12.1198', '11751228', 'Today, metabolite levels in biological samples can be determined using multiparallel, fast, and precise metabolomic approaches. Correlations between the levels of various metabolites can be searched to gain information about metabolic links. Such correlations are the net result of direct enzymatic conversions and of indirect cellular regulation over transcriptional or biochemical processes. In order to visualize metabolic networks derived from correlation lists graphically, each metabolite pair may be represented as vertices connected by an edge. However, graph complexity rapidly increases with the number of edges and vertices. To gain structural information from metabolite correlation networks, improvements in clarity are needed.'),
(70094, 2001, 'bio', 'SPiD: a subtilis protein interaction database', 'Hoebeke, M|Chiapello, H|Noirot, P|Bessières, P', 'Unité Mathématique, Informatique & Génome, INRA CRV, Route de St. Cyr, F-78026 Versailles Cedex, France. hoebeke@versailles.inra.fr|Unité Mathématique, Informatique & Génome, INRA CRV, Route de St. Cyr, F-78026 Versailles Cedex, France. hoebeke@versailles.inra.fr|Unité Mathématique, Informatique & Génome, INRA CRV, Route de St. Cyr, F-78026 Versailles Cedex, France. hoebeke@versailles.inra.fr|Unité Mathématique, Informatique & Génome, INRA CRV, Route de St. Cyr, F-78026 Versailles Cedex, France. hoebeke@versailles.inra.fr', 'France|France|France|France', 15, 'Bacillus subtilis|Bacterial Proteins|Databases, Protein|Internet|Two-Hybrid System Techniques', 'Journal Article', '10.1093/bioinformatics/17.12.1209', '11751229', 'Protein-protein interactions are a potential source of valuable clues in determining the functional role of as yet uncharacterized gene products in metabolic pathways. Graph-like structures emerging from the accumulation of interaction data make it difficult to maintain a consistent and global overview by hand. Bioinformatics tools are needed to perform this graph visualization while maintaining a link to the experimental data.'),
(70095, 2001, 'bio', 'A neural network classifier capable of recognizing the patterns of all major subcellular structures in fluorescence microscope images of HeLa cells', 'Boland, M V|Murphy, R F', 'Center for Light Microscope Imaging and Biotechnology, Biomedical and Health Engineering Program, Carnegie Mellon University, 4400 Fifth Ave., Pittsburgh, PA 15213, USA.|Center for Light Microscope Imaging and Biotechnology, Biomedical and Health Engineering Program, Carnegie Mellon University, 4400 Fifth Ave., Pittsburgh, PA 15213, USA.', 'United States|United States', 357, 'Algorithms|HeLa Cells|Humans|Image Processing, Computer-Assisted|Microscopy, Fluorescence|Neural Networks (Computer)|Proteins|Subcellular Fractions', 'Journal Article', '10.1093/bioinformatics/17.12.1213', '11751230', 'Assessment of protein subcellular location is crucial to proteomics efforts since localization information provides a context for a proteins sequence, structure, and function. The work described below is the first to address the subcellular localization of proteins in a quantitative, comprehensive manner.'),
(70096, 2001, 'bio', 'Evolution of amino acid biosynthesis and enzymes with broad substrate specificity', 'Nishida, H', 'Institute of Molecular and Cellular Biosciences, The University of Tokyo, Tokyo 113-0032, Japan. hnishida@iam.u-tokyo.ac.jp', 'Japan', 4, 'Amino Acids|Bacterial Proteins|Enzymes|Escherichia coli|Evolution, Molecular|Phylogeny|Substrate Specificity', 'Journal Article', '10.1093/bioinformatics/17.12.1224', '11751231', 'I selected 82 proteins that were related to amino acid biosynthesis in the genome of Escherichia coli. I then searched the extensive sequence homology for each of the selected proteins from among the proteins of E.coli. The result showed that 30 proteins of the selected proteins had extensive sequence homology within the selected proteins, and 21 proteins had extensive sequence homology to proteins outside the selected proteins. In addition, the enzymes with broad substrate specificity play an important role in the amino acid biosynthesis. I demonstrate here that some substrate-specific enzymes evolved from an ancestor enzyme with broad substrate specificity.'),
(70097, 2001, 'bio', 'MELTING, computing the melting temperature of nucleic acid duplex', 'Le Novère, N', 'Department of Zoology, University of Cambridge, Downing Street, Cambridge CB2 3EJ, UK. nl223@cus.cam.ac.uk', 'United Kingdom', 0, 'Mathematical Computing|Nucleic Acids|Software|Temperature|Thermodynamics', 'Journal Article', '10.1093/bioinformatics/17.12.1226', '11751232', 'MELTING computes the enthalpy and entropy of an oligonucleotide duplex helix-coil transition, and then its melting temperature. The program uses the method of nearest-neighbours. The set of thermodynamic parameters can be easily customized. The program provides several correction methods for the concentration of salt. MELTING is a free program, available at no cost and open-source. Perl scripts are provided to show how MELTING can be used to construct more ambitious programs.'),
(70098, 2001, 'bio', 'Progenetixnet: an online repository for molecular cytogenetic aberration data', 'Baudis, M|Cleary, M L', 'Medizinische Klinik und Poliklinik V der Universität Heidelberg, Germany. mbaudis@stanford.edu|Medizinische Klinik und Poliklinik V der Universität Heidelberg, Germany. mbaudis@stanford.edu', 'Germany|Germany', 140, 'Chromosome Aberrations|Cytogenetics|Databases, Nucleic Acid|Internet', 'Journal Article', '10.1093/bioinformatics/17.12.1228', '11751233', 'Through sequencing projects and, more recently, array-based expression analysis experiments, a wealth of genetic data has become accessible via online resources. In contrast, few of the (molecular-) cytogenetic aberration data collected in the last decades are available in a format suitable for data mining procedures. www.progenetix.net is a new online repository for previously published chromosomal aberration data, allowing the addition of band-specific information about chromosomal imbalances to oncologic data analysis efforts.'),
(70099, 2001, 'bio', 'Sequence type analysis and recombinational tests (START)', 'Jolley, K A|Feil, E J|Chan, M S|Maiden, M C', 'Department of Zoology, University of Oxford, South Parks Road, Oxford OX1 3PS, UK. keith.jolley@ceid.ox.ac.uk|Department of Zoology, University of Oxford, South Parks Road, Oxford OX1 3PS, UK. keith.jolley@ceid.ox.ac.uk|Department of Zoology, University of Oxford, South Parks Road, Oxford OX1 3PS, UK. keith.jolley@ceid.ox.ac.uk|Department of Zoology, University of Oxford, South Parks Road, Oxford OX1 3PS, UK. keith.jolley@ceid.ox.ac.uk', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom', 423, 'Databases, Nucleic Acid|Genes, Bacterial|Recombination, Genetic|Sequence Analysis|Software', 'Journal Article', '10.1093/bioinformatics/17.12.1230', '11751234', 'The 32-bit Windows application START is implemented using Visual Basic and C(++) and performs analyses to aid in the investigation of bacterial population structure using multilocus sequence data. These analyses include data summary, lineage assignment, and tests for recombination and selection.'),
(70100, 2001, 'bio', 'Promoter Extraction from GenBank (PEG): automatic extraction of eukaryotic promoter sequences in large sets of genes', 'Zhang, T|Zhang, M', 'Cold Spring Harbor Laboratory, PO Box 100, Cold Spring Harbor, NY 11724, USA. zhangt@cshl.org|Cold Spring Harbor Laboratory, PO Box 100, Cold Spring Harbor, NY 11724, USA. zhangt@cshl.org', 'United States|United States', 0, 'Databases, Nucleic Acid|Eukaryotic Cells|Information Storage and Retrieval|Promoter Regions, Genetic|RNA, Messenger|Software|Transcription Initiation Site', 'Journal Article', '10.1093/bioinformatics/17.12.1232', '11751235', 'Promoter Extraction from GenBank (PEG) extracts promoter sequences for large sets of genes using information present in GenBank. For a gene whose promoter sequence is not found, PEG will attempt to extract promoter sequences of the orthologous genes instead.'),
(70101, 2001, 'bio', 'mmsearch: a motif arrangement language and search program', 'Junier, T|Pagni, M|Bucher, P', 'Swiss Institute of Bioinformatics, Switzerland. Thomas.Junier@isrec.unil.ch|Swiss Institute of Bioinformatics, Switzerland. Thomas.Junier@isrec.unil.ch|Swiss Institute of Bioinformatics, Switzerland. Thomas.Junier@isrec.unil.ch', 'Switzerland|Switzerland|Switzerland', 0, 'Programming Languages|Proteins|Receptors, Cytokine|Software|Thioredoxins', 'Journal Article', '10.1093/bioinformatics/17.12.1234', '11751236', 'This paper presents a language for describing arrangements of motifs in biological sequences, and a program that uses the language to find the arrangements in motif match databases. The program does not by itself search for the constituent motifs, and is thus independent of how they are detected, which allows it to use motif match data of various origins.'),
(70102, 2001, 'bio', 'ProPred: prediction of HLA-DR binding sites', 'Singh, H|Raghava, G P', 'Bioinformatics Centre, Institute of Microbial Technology, Sector 39A, Chandigarh-160036, India.|Bioinformatics Centre, Institute of Microbial Technology, Sector 39A, Chandigarh-160036, India.', 'India|India', 609, 'Algorithms|Antigen Presentation|Binding Sites|HLA-DR Antigens|Humans|Peptides|Software', 'Journal Article', '10.1093/bioinformatics/17.12.1236', '11751237', 'ProPred is a graphical web tool for predicting MHC class II binding regions in antigenic protein sequences. The server implement matrix based prediction algorithm, employing amino-acid/position coefficient table deduced from literature. The predicted binders can be visualized either as peaks in graphical interface or as colored residues in HTML interface. This server might be a useful tool in locating the promiscuous binding regions that can bind to several HLA-DR alleles.'),
(70103, 2001, 'bio', 'TMCompare: transmembrane region sequence and structure', 'Togawa, R C|Antoniw, J F|Mullins, J G', 'Bioinformatics Laboratory, Embrapa-Genetic Resources and Biotechnology, Parque Estação Biológico Final Av. W/5 Norte CEP: 70770-900, Caixa Postal: 02372 Brasilia-DF, Brazil. tmcompare@membraneproteins.org|Bioinformatics Laboratory, Embrapa-Genetic Resources and Biotechnology, Parque Estação Biológico Final Av. W/5 Norte CEP: 70770-900, Caixa Postal: 02372 Brasilia-DF, Brazil. tmcompare@membraneproteins.org|Bioinformatics Laboratory, Embrapa-Genetic Resources and Biotechnology, Parque Estação Biológico Final Av. W/5 Norte CEP: 70770-900, Caixa Postal: 02372 Brasilia-DF, Brazil. tmcompare@membraneproteins.org', 'Brazil|Brazil|Brazil', 0, 'Binding Sites|Databases, Factual|Humans|Membrane Proteins|Protein Conformation|Sequence Alignment|Software', 'Journal Article', '10.1093/bioinformatics/17.12.1238', '11751238', 'TMCompare is an alignment and visualization tool for comparison of sequence information for membrane proteins contained in SWISS-PROT entries, with structural information contained in PDB files. The program can be used for: detection of breaks in alpha helical structure of transmembrane regions, examination of differences in coverage between PDB and SWISS-PROT files, examination of annotation differences between PDB files and associated SWISS-PROT files, examination and comparison of assigned PDB alpha helix regions and assigned SWISS-PROT transmembrane regions in linear sequence (one letter code) format, examination of these differences in 3D using the CHIME plugin, allowing, analysis of the alpha and non-alpha content of transmembrane regions.'),
(70104, 2001, 'bio', 'ToolShop: prerelease inspections for protein structure prediction servers', 'Rychlewski, L', 'Bioinformatics Laboratory, BioInfoBank Institute, ul. Limanowskiego 24A, 60-744 Poznan, Poland. leszek@bioinfo.pl', 'Poland', 0, 'Protein Conformation|Proteins|Sensitivity and Specificity|Software', 'Journal Article', '10.1093/bioinformatics/17.12.1240', '11751239', 'The ToolShop server offers a possibility to compare a protein tertiary structure prediction server with other popular servers before releasing it to the public. The comparison is conducted on a set of 203 proteins and the collected models are compared with over 20 other programs using various assessment procedures. The evaluation lasts circa one week.'),
(70105, 2001, 'bio', 'EVA: continuous automatic evaluation of protein structure prediction servers', 'Eyrich, V A|Martí-Renom, M A|Przybylski, D|Madhusudhan, M S|Fiser, A|Pazos, F|Valencia, A|Sali, A|Rost, B', 'Department of Chemistry, Columbia University, 3000 Broadway MC 3136, New York, NY 10027, USA. eva@cubic.bioc.columbia.edu|Department of Chemistry, Columbia University, 3000 Broadway MC 3136, New York, NY 10027, USA. eva@cubic.bioc.columbia.edu|Department of Chemistry, Columbia University, 3000 Broadway MC 3136, New York, NY 10027, USA. eva@cubic.bioc.columbia.edu|Department of Chemistry, Columbia University, 3000 Broadway MC 3136, New York, NY 10027, USA. eva@cubic.bioc.columbia.edu|Department of Chemistry, Columbia University, 3000 Broadway MC 3136, New York, NY 10027, USA. eva@cubic.bioc.columbia.edu|Department of Chemistry, Columbia University, 3000 Broadway MC 3136, New York, NY 10027, USA. eva@cubic.bioc.columbia.edu|Department of Chemistry, Columbia University, 3000 Broadway MC 3136, New York, NY 10027, USA. eva@cubic.bioc.columbia.edu|Department of Chemistry, Columbia University, 3000 Broadway MC 3136, New York, NY 10027, USA. eva@cubic.bioc.columbia.edu|Department of Chemistry, Columbia University, 3000 Broadway MC 3136, New York, NY 10027, USA. eva@cubic.bioc.columbia.edu', 'United States|United States|United States|United States|United States|United States|United States|United States|United States', 47, 'Automation|Internet|Protein Conformation|Proteins|Software', 'Journal Article', '10.1093/bioinformatics/17.12.1242', '11751240', 'Evaluation of protein structure prediction methods is difficult and time-consuming. Here, we describe EVA, a web server for assessing protein structure prediction methods, in an automated, continuous and large-scale fashion. Currently, EVA evaluates the performance of a variety of prediction methods available through the internet. Every week, the sequences of the latest experimentally determined protein structures are sent to prediction servers, results are collected, performance is evaluated, and a summary is published on the web. EVA has so far collected data for more than 3000 protein chains. These results may provide valuable insight to both developers and users of prediction methods.'),
(70106, 2001, 'bio', 'MEGA2: molecular evolutionary genetics analysis software', 'Kumar, S|Tamura, K|Jakobsen, I B|Nei, M', 'Department of Biology, Arizona State University, Tempe, AZ 85287-1501, USA. s.kumar@asu.edu|Department of Biology, Arizona State University, Tempe, AZ 85287-1501, USA. s.kumar@asu.edu|Department of Biology, Arizona State University, Tempe, AZ 85287-1501, USA. s.kumar@asu.edu|Department of Biology, Arizona State University, Tempe, AZ 85287-1501, USA. s.kumar@asu.edu', 'United States|United States|United States|United States', 4479, 'DNA|Data Interpretation, Statistical|Evolution, Molecular|Proteins|Software', 'Journal Article', '10.1093/bioinformatics/17.12.1244', '11751241', 'We have developed a new software package, Molecular Evolutionary Genetics Analysis version 2 (MEGA2), for exploring and analyzing aligned DNA or protein sequences from an evolutionary perspective. MEGA2 vastly extends the capabilities of MEGA version 1 by: (1) facilitating analyses of large datasets, (2) enabling creation and analyses of groups of sequences, (3) enabling specification of domains and genes, (4) expanding the repertoire of statistical methods for molecular evolutionary studies, and (5) adding new modules for visual representation of input data and output results on the Microsoft Windows platform.'),
(70107, 2001, 'bio', 'CONSEL: for assessing the confidence of phylogenetic tree selection', 'Shimodaira, H|Hasegawa, M', 'The Institute of Statistical Mathematics, 4-6-7 Minami-Azabu, Minatoku, Tokyo 106-8569, Japan. shimo@ism.ac.jp|The Institute of Statistical Mathematics, 4-6-7 Minami-Azabu, Minatoku, Tokyo 106-8569, Japan. shimo@ism.ac.jp', 'Japan|Japan', 551, 'Likelihood Functions|Phylogeny|Software', 'Journal Article', '10.1093/bioinformatics/17.12.1246', '11751242', 'CONSEL is a program to assess the confidence of the tree selection by giving the p-values for the trees. The main thrust of the program is to calculate the p-value of the Approximately Unbiased (AU) test using the multi-scale bootstrap technique. This p-value is less biased than the other conventional p-values such as the Bootstrap Probability (BP), the Kishino-Hasegawa (KH) test, the Shimodaira-Hasegawa (SH) test, and the Weighted Shimodaira-Hasegawa (WSH) test. CONSEL calculates all these p-values from the output of the phylogeny program packages such as Molphy, PAML, and PAUP*. Furthermore, CONSEL is applicable to a wide class of problems where the BPs are available.'),
(70108, 2002, 'bio', 'ZTR: a new format for DNA sequence trace data', 'Bonfield, James K|Staden, Rodger', 'MRC Laboratory of Molecular Biology, Hills Road, Cambridge CB2 2QH, UK. jkb@mrc-lmb.cam.ac.uk|MRC Laboratory of Molecular Biology, Hills Road, Cambridge CB2 2QH, UK. jkb@mrc-lmb.cam.ac.uk', 'United Kingdom|United Kingdom', 20, 'Algorithms|Computational Biology|Database Management Systems|Databases, Nucleic Acid|Human Genome Project|Humans|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/18.1.3', '11836205', 'To produce an open and extensible file format for DNA trace data which produces compact files suitable for large-scale storage and efficient use of internet bandwidth.'),
(70109, 2002, 'bio', 'A fast top-down method for constructing reliable radiation hybrid frameworks', 'Bø, Trond H|Jonassen, Inge|Eidhammer, Ingvar|Helgesen, Carsten', 'Department of Informatics, University of Bergen, HIB, N5020 Bergen, Norway. trondb@ii.uib.no|Department of Informatics, University of Bergen, HIB, N5020 Bergen, Norway. trondb@ii.uib.no|Department of Informatics, University of Bergen, HIB, N5020 Bergen, Norway. trondb@ii.uib.no|Department of Informatics, University of Bergen, HIB, N5020 Bergen, Norway. trondb@ii.uib.no', 'Norway|Norway|Norway|Norway', 0, 'Algorithms|Chromosomes, Human|Computational Biology|Databases, Genetic|Genetic Markers|Humans|Radiation Hybrid Mapping', 'Comparative Study', '10.1093/bioinformatics/18.1.11', '11836206', 'Radiation Hybrid Mapping (RHM) is a technique used to order a set of markers on a genome and estimating physical distances between them. RHM provides information on marker placement independent from other methods such as sequencing, and can therefore be used for example in genome sequencing to help ordering contigs. A radiation hybrid framework can be constructed by choosing a set of markers so that the chromosome coverage is good and so that the markers can be ordered with high confidence. Automatically constructing RHM frameworks is a computationally challenging problem.'),
(70110, 2002, 'bio', 'A Bayesian framework for combining gene predictions', 'Pavlovixc4x87, Vladimir|Garg, Ashutosh|Kasif, Simon', 'Bioinformatics Program, Department of Bioengineering, Boston University, Boston, MA 02215, USA. vladimir@bu.edu|Bioinformatics Program, Department of Bioengineering, Boston University, Boston, MA 02215, USA. vladimir@bu.edu|Bioinformatics Program, Department of Bioengineering, Boston University, Boston, MA 02215, USA. vladimir@bu.edu', 'United States|United States|United States', 42, 'Algorithms|Animals|Bayes Theorem|Computational Biology|Drosophila|Expert Systems|Genes, Insect|Genetic Techniques|Genomics|Markov Chains|Software', 'Journal Article', '10.1093/bioinformatics/18.1.19', '11836207', 'Gene identification and gene discovery in new genomic sequences is one of the most timely computational questions addressed by bioinformatics scientists. This computational research has resulted in several systems that have been used successfully in many whole-genome analysis projects. As the number of such systems grows the need for a rigorous way to combine the predictions becomes more essential.'),
(70111, 2002, 'bio', 'PlantSat: a specialized database for plant satellite repeats', 'Macas, Jirí|Mészáros, Tibor|Nouzová, Marcela', 'Institute of Plant Molecular Biology, Laboratory of Molecular Cytogenetics, Branisovská 31, Ceské Budejovice, CZ-37005, Czech Republic. macas@umbr.cas.cz|Institute of Plant Molecular Biology, Laboratory of Molecular Cytogenetics, Branisovská 31, Ceské Budejovice, CZ-37005, Czech Republic. macas@umbr.cas.cz|Institute of Plant Molecular Biology, Laboratory of Molecular Cytogenetics, Branisovská 31, Ceské Budejovice, CZ-37005, Czech Republic. macas@umbr.cas.cz', '-|-|-', 0, 'Computational Biology|DNA, Plant|DNA, Satellite|Databases, Nucleic Acid|Electronic Data Processing|Internet|Plants|Sequence Analysis, DNA|Software|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/18.1.28', '11836208', 'Tandemly organized repetitive sequences (satellite DNA) are widespread in complex eukaryotic genomes. In plants, satellite repeats often represent a substantial part of nuclear DNA but only a little is known about the molecular mechanisms of their amplification and their possible role(s) in genome evolution and function. Unfortunately, addressing these questions via characterization of general sequence properties of known satellite repeats has been hindered by a difficulty in obtaining a complete and unbiased set of sequence data for this analysis. This is mainly due to the presence of multiple entries of homologous sequences and of single entries that contain more than one repeated unit (monomer) in the public databases.'),
(70112, 2002, 'bio', 'Calculating the SNP-effective sample size from an alignment', 'Haubold, Bernhard|Wiehe, Thomas', 'Max-Planck-Institut für Chemische Okologie, Carl-Zeiss-Promenade 10, D-07745 Jena, Germany.|Max-Planck-Institut für Chemische Okologie, Carl-Zeiss-Promenade 10, D-07745 Jena, Germany.', 'Germany|Germany', 3, 'Algorithms|Animals|Computational Biology|Databases, Nucleic Acid|Expressed Sequence Tags|Genome, Human|Humans|Mice|Polymorphism, Single Nucleotide|Sample Size|Sequence Alignment|Software', 'Journal Article', '10.1093/bioinformatics/18.1.36', '11836209', 'The number of Single Nucleotide Polymorphisms (SNPs) detectable in an alignment is a function of the length and the number of the aligned sequences. The latter is called sample size. However, a typical alignment, for instance obtained as a BLAST-search result of a query sequence against an EST database, does not evenly cover the query sequence. Therefore, it is usually not clear what the actual sample size is.'),
(70113, 2002, 'bio', 'Tumor classification by partial least squares using microarray gene expression data', 'Nguyen, Danh V|Rocke, David M', 'Center for Image Processing and Integrated Computing, University of California, Davis, CA 95616, USA. nguyen@wald.ucdavis.edu|Center for Image Processing and Integrated Computing, University of California, Davis, CA 95616, USA. nguyen@wald.ucdavis.edu', 'United States|United States', 616, 'Colonic Neoplasms|Computational Biology|Discriminant Analysis|Female|Gene Expression Profiling|Humans|Least-Squares Analysis|Leukemia|Logistic Models|Lymphoma|Neoplasms|Oligonucleotide Array Sequence Analysis|Ovarian Neoplasms|Principal Component Analysis|Tumor Cells, Cultured', 'Comparative Study', '10.1093/bioinformatics/18.1.39', '11836210', 'One important application of gene expression microarray data is classification of samples into categories, such as the type of tumor. The use of microarrays allows simultaneous monitoring of thousands of genes expressions per sample. This ability to measure gene expression en masse has resulted in data with the number of variables p(genes) far exceeding the number of samples N. Standard statistical methodologies in classification and prediction do not work well or even at all when N < p. Modification of existing statistical methodologies or development of new methodologies is needed for the analysis of microarray data.'),
(70114, 2002, 'bio', 'Linear modes of gene expression determined by independent component analysis', 'Liebermeister, Wolfram', 'Theoretische Biophysik, Institut für Biologie, Humboldt-Universität zu Berlin, Invalidenstrasse 42, 10115 Berlin, Germany. wolfram.liebermeister@rz-hu-berlin.de', 'Germany', 0, 'Algorithms|Cell Cycle|Computational Biology|Gene Expression Profiling|Humans|Linear Models|Lymphoma, B-Cell|Models, Genetic|Saccharomyces cerevisiae', 'Journal Article', '10.1093/bioinformatics/18.1.51', '11836211', 'The expression of genes is controlled by specific combinations of cellular variables. We applied Independent Component Analysis (ICA) to gene expression data, deriving a linear model based on hidden variables, which we term expression modes. The expression of each gene is a linear function of the expression modes, where, according to the ICA model, the linear influences of different modes show a minimal statistical dependence, and their distributions deviate sharply from the normal distribution.'),
(70115, 2002, 'bio', 'Extracting transcriptional events from temporal gene expression patterns during Dictyostelium development', 'Sásik, R|Iranfar, N|Hwa, T|Loomis, W F', 'Department of Physics Division of Biology, University of California at San Diego, La Jolla, CA 92093, USA. sasik@physics.ucsd.edu|Department of Physics Division of Biology, University of California at San Diego, La Jolla, CA 92093, USA. sasik@physics.ucsd.edu|Department of Physics Division of Biology, University of California at San Diego, La Jolla, CA 92093, USA. sasik@physics.ucsd.edu|Department of Physics Division of Biology, University of California at San Diego, La Jolla, CA 92093, USA. sasik@physics.ucsd.edu', 'United States|United States|United States|United States', 0, 'Algorithms|Animals|Computational Biology|Dictyostelium|Gene Expression Profiling|Gene Expression Regulation, Developmental|Genes, Protozoan|Oligonucleotide Array Sequence Analysis|Transcription, Genetic', 'Journal Article', '10.1093/bioinformatics/18.1.61', '11836212', 'The DNA microarray technology can generate a large amount of data describing the time-course of gene expression. These data, when properly interpreted, can yield a great deal of information concerning differential gene expression during development. Much current effort in bioinformatics has been devoted to the analysis of gene expression data, usually via some clustering analysis on the raw data in some abstract high dimensional space. Here, we describe a method where we first process the raw time-course data using a simple biologically based kinetic model of gene expression. This allows us to reduce the vast data to a few vital attributes characterizing each expression profile, e.g. the times of the onset and cessation of the expression of the developmentally regulated genes. These vital attributes can then be trivially clustered by visual inspection to reveal biologically significant effects.'),
(70116, 2002, 'bio', 'Discrete simulation of regulatory homo- and heterodimerization in the apoptosis effector phase', 'Siehs, Christian|Oberbauer, Rainer|Mayer, Gert|Lukas, Arno|Mayer, Bernd', 'Institute for Theoretical Chemistry and Molecular Structural Biology, University of Vienna, Dr Bohrgasse 9, A-1030 Vienna, Austria.|Institute for Theoretical Chemistry and Molecular Structural Biology, University of Vienna, Dr Bohrgasse 9, A-1030 Vienna, Austria.|Institute for Theoretical Chemistry and Molecular Structural Biology, University of Vienna, Dr Bohrgasse 9, A-1030 Vienna, Austria.|Institute for Theoretical Chemistry and Molecular Structural Biology, University of Vienna, Dr Bohrgasse 9, A-1030 Vienna, Austria.|Institute for Theoretical Chemistry and Molecular Structural Biology, University of Vienna, Dr Bohrgasse 9, A-1030 Vienna, Austria.', 'Austria|Austria|Austria|Austria|Austria', 21, 'Apoptosis|Computational Biology|Computer Simulation|Dimerization|Humans|Models, Biological|Proto-Oncogene Proteins c-bcl-2', 'Journal Article', '10.1093/bioinformatics/18.1.67', '11836213', 'Quantitative simulation of molecular reaction networks is among the most promising approaches towards an understanding of complex biochemical pathways. Numerous qualitative as well as quantitative data from diverse experimental settings, in particular from genomics and proteomics, have to be contextually linked to convert static data into dynamic functionality.'),
(70117, 2002, 'bio', 'Tolerating some redundancy significantly speeds up clustering of large protein databases', 'Li, Weizhong|Jaroszewski, Lukasz|Godzik, Adam', 'The Burnham Institute, 10901 N. Torrey Pines Road, La Jolla, CA 92037, USA. liwz@burnham-inst.org|The Burnham Institute, 10901 N. Torrey Pines Road, La Jolla, CA 92037, USA. liwz@burnham-inst.org|The Burnham Institute, 10901 N. Torrey Pines Road, La Jolla, CA 92037, USA. liwz@burnham-inst.org', 'United States|United States|United States', 0, 'Algorithms|Cluster Analysis|Computational Biology|Database Management Systems|Databases, Protein|Software', 'Journal Article', '10.1093/bioinformatics/18.1.77', '11836214', 'Sequence clustering replaces groups of similar sequences in a database with single representatives. Clustering large protein databases like the NCBI Non-Redundant database (NR) using even the best currently available clustering algorithms is very time-consuming and only practical at relatively high sequence identity thresholds. Our previous program, CD-HI, clustered NR at 90% identity in approximately 1 h and at 75% identity in approximately 1 day on a 1 GHz Linux PC (Li et al., Bioinformatics, 17, 282, 2001), however even faster clustering speed is needed because the size of protein databases are rapidly growing and many applications desire a lower attainable thresholds.'),
(70118, 2002, 'bio', 'Improved database searches for orthologous sequences by conditioning on outgroup sequences', 'Cotter, Philip J|Caffrey, Daniel R|Shields, Denis C', 'Department of Clinical Pharmacology, Royal College of Surgeons in Ireland, 123 Stephens Green, Dublin 2, Ireland.|Department of Clinical Pharmacology, Royal College of Surgeons in Ireland, 123 Stephens Green, Dublin 2, Ireland.|Department of Clinical Pharmacology, Royal College of Surgeons in Ireland, 123 Stephens Green, Dublin 2, Ireland.', 'Ireland|Ireland|Ireland', 4, 'Algorithms|Amino Acid Sequence|Animals|Caenorhabditis elegans|Computational Biology|Conserved Sequence|Databases, Genetic|Expressed Sequence Tags|Humans|Molecular Sequence Data|Protein Kinases|Sequence Alignment', 'Journal Article', '10.1093/bioinformatics/18.1.83', '11836215', 'Searches of biological sequence databases are usually focussed on distinguishing significant from random matches. However, the increasing abundance of related sequences on databases present a second challenge: to distinguish the evolutionarily most closely related sequences (often orthologues) from more distantly related homologues. This is particularly important when searching a database of partial sequences, where short orthologous sequences from a non-conserved region will score much more poorly than non-orthologous (outgroup) sequences from a conserved region.'),
(70119, 2002, 'bio', 'Automated ortholog inference from phylogenetic trees and calculation of orthology reliability', 'Storm, Christian E V|Sonnhammer, Erik L L', 'Center for Genomics and Bioinformatics, Karolinska Institutet, S-171 77 Stockholm, Sweden. christian.storm@cbg.ki.se|Center for Genomics and Bioinformatics, Karolinska Institutet, S-171 77 Stockholm, Sweden. christian.storm@cbg.ki.se', 'Sweden|Sweden', 0, 'Algorithms|Animals|Computational Biology|Databases, Protein|Humans|Phylogeny|Proteins|Software|Species Specificity', 'Journal Article', '10.1093/bioinformatics/18.1.92', '11836216', 'Orthologous proteins in different species are likely to have similar biochemical function and biological role. When annotating a newly sequenced genome by sequence homology, the most precise and reliable functional information can thus be derived from orthologs in other species. A standard method of finding orthologs is to compare the sequence tree with the species tree. However, since the topology of phylogenetic tree is not always reliable one might get incorrect assignments.'),
(70120, 2002, 'bio', 'Integrated gene and species phylogenies from unaligned whole genome protein sequences', 'Stuart, Gary W|Moffett, Karen|Baker, Steve', 'Department of Life Sciences, Indiana State University, Terre Haute, IN 47809, USA. G-Stuart@indstate.edu|Department of Life Sciences, Indiana State University, Terre Haute, IN 47809, USA. G-Stuart@indstate.edu|Department of Life Sciences, Indiana State University, Terre Haute, IN 47809, USA. G-Stuart@indstate.edu', 'India|India|India', 0, 'Algorithms|Animals|Computational Biology|Databases, Protein|Genome|Mammals|Mitochondrial Proteins|Phylogeny|Proteins|Sequence Alignment|Software|Species Specificity', 'Journal Article', '10.1093/bioinformatics/18.1.100', '11836217', 'Most molecular phylogenies are based on sequence alignments. Consequently, they fail to account for modes of sequence evolution that involve frequent insertions or deletions. Here we present a method for generating accurate gene and species phylogenies from whole genome sequence that makes use of short character string matches not placed within explicit alignments. In this work, the singular value decomposition of a sparse tetrapeptide frequency matrix is used to represent the proteins of organisms uniquely and precisely as vectors in a high-dimensional space. Vectors of this kind can be used to calculate pairwise distance values based on the angle separating the vectors, and the resulting distance values can be used to generate phylogenetic trees. Protein trees so derived can be examined directly for homologous sequences. Alternatively, vectors defining each of the proteins within an organism can be summed to provide a vector representation of the organism, which is then used to generate species trees.'),
(70121, 2002, 'bio', 'TreeWiz: interactive exploration of huge trees', 'Rost, Ursula|Bornberg-Bauer, Erich', 'Bioinformatics and Computational Biochemistry Group, European Media Laboratory GmbH, Schloss-Wolfsbrunnenweg 33, D-69118, Heidelberg, Germany. Ursula.Rost@eml.villa-bosch.de|Bioinformatics and Computational Biochemistry Group, European Media Laboratory GmbH, Schloss-Wolfsbrunnenweg 33, D-69118, Heidelberg, Germany. Ursula.Rost@eml.villa-bosch.de', 'Germany|Germany', 0, 'Classification|Computational Biology|Databases, Protein|Phylogeny|Software', 'Journal Article', '10.1093/bioinformatics/18.1.109', '11836218', 'The rapidly increasing amount and disparity of biological data requires interpretation at many levels of description. Human judgement and intuition are important because not all data can be automatically and comprehensively analyzed. Visualization of trees and substructures corresponding to certain features are often used to analyze phylogenies or taxonomies. Unfortunately, most existing tools do not cope with the size of current datasets, the required functionality, or both.'),
(70122, 2002, 'bio', 'A viral sampling design for testing the molecular clock and for estimating evolutionary rates and divergence times', 'Seo, Tae-Kun|Thorne, Jeffrey L|Hasegawa, Masami|Kishino, Hirohisa', 'Department of Biosystems Science, The Graduate University for Advanced Studies, Hayama, Kanagawa, 240-0193, Japan. seo@ism.ac.jp|Department of Biosystems Science, The Graduate University for Advanced Studies, Hayama, Kanagawa, 240-0193, Japan. seo@ism.ac.jp|Department of Biosystems Science, The Graduate University for Advanced Studies, Hayama, Kanagawa, 240-0193, Japan. seo@ism.ac.jp|Department of Biosystems Science, The Graduate University for Advanced Studies, Hayama, Kanagawa, 240-0193, Japan. seo@ism.ac.jp', 'Japan|Japan|Japan|Japan', 22, 'Computational Biology|Evolution, Molecular|HIV-1|Humans|Models, Genetic|Models, Statistical|Time Factors|Viruses', 'Journal Article', '10.1093/bioinformatics/18.1.115', '11836219', 'The high pace of viral sequence change means that variation in the times at which sequences are sampled can have a profound effect both on the ability to detect trends over time in evolutionary rates and on the power to reject the Molecular Clock Hypothesis (MCH). Trends in viral evolutionary rates are of particular interest because their detection may allow connections to be established between a patients treatment or condition and the process of evolution. Variation in sequence isolation times also impacts the uncertainty associated with estimates of divergence times and evolutionary rates. Variation in isolation times can be intentionally adjusted to increase the power of hypothesis tests and to reduce the uncertainty of evolutionary parameter estimates, but this fact has received little previous attention.'),
(70123, 2002, 'bio', 'PathFinder: reconstruction and dynamic visualization of metabolic pathways', 'Goesmann, Alexander|Haubrock, Martin|Meyer, Folker|Kalinowski, Jörn|Giegerich, Robert', 'Center for Genome Research, Department of Genetics, Bielefeld University, 33594 Bielefeld, Germany. Alexander.Goesmann@Genetik.Uni-Bielefeld.DE|Center for Genome Research, Department of Genetics, Bielefeld University, 33594 Bielefeld, Germany. Alexander.Goesmann@Genetik.Uni-Bielefeld.DE|Center for Genome Research, Department of Genetics, Bielefeld University, 33594 Bielefeld, Germany. Alexander.Goesmann@Genetik.Uni-Bielefeld.DE|Center for Genome Research, Department of Genetics, Bielefeld University, 33594 Bielefeld, Germany. Alexander.Goesmann@Genetik.Uni-Bielefeld.DE|Center for Genome Research, Department of Genetics, Bielefeld University, 33594 Bielefeld, Germany. Alexander.Goesmann@Genetik.Uni-Bielefeld.DE', 'Germany|Germany|Germany|Germany|Germany', 57, 'Bacillus subtilis|Computational Biology|Database Management Systems|Gene Expression|Genes, Bacterial|Lysine|Metabolism|Software', 'Journal Article', '10.1093/bioinformatics/18.1.124', '11836220', 'Beyond methods for a gene-wise annotation and analysis of sequenced genomes new automated methods for functional analysis on a higher level are needed. The identification of realized metabolic pathways provides valuable information on gene expression and regulation. Detection of incomplete pathways helps to improve a constantly evolving genome annotation or discover alternative biochemical pathways. To utilize automated genome analysis on the level of metabolic pathways new methods for the dynamic representation and visualization of pathways are needed.'),
(70124, 2002, 'bio', 'The Binding Database: data management and interface design', 'Chen, Xi|Lin, Yuhmei|Liu, Ming|Gilson, Michael K', 'Center for Advanced Research in Biotechnology, University of Maryland Biotechnology Institute, 9600 Gudelsky Drive, Rockville, MD 20850, USA.|Center for Advanced Research in Biotechnology, University of Maryland Biotechnology Institute, 9600 Gudelsky Drive, Rockville, MD 20850, USA.|Center for Advanced Research in Biotechnology, University of Maryland Biotechnology Institute, 9600 Gudelsky Drive, Rockville, MD 20850, USA.|Center for Advanced Research in Biotechnology, University of Maryland Biotechnology Institute, 9600 Gudelsky Drive, Rockville, MD 20850, USA.', 'United States|United States|United States|United States', 111, 'Binding Sites|Computational Biology|Database Management Systems|Databases, Factual|Internet|Sequence Alignment|Thermodynamics|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/18.1.130', '11836221', 'The large and growing body of experimental data on biomolecular binding is of enormous value in developing a deeper understanding of molecular biology, in developing new therapeutics, and in various molecular design applications. However, most of these data are found only in the published literature and are therefore difficult to access and use. No existing public database has focused on measured binding affinities and has provided query capabilities that include chemical structure and sequence homology searches.'),
(70125, 2002, 'bio', 'Concept and prototype of protein-ligand docking simulator with force feedback technology', 'Nagata, Hiroshi|Mizushima, Hiroshi|Tanaka, Hiroshi', 'National Cancer Center Research Institute, 5-1-1, Tsukiji, Chuo-ku, Tokyo 104-0045, Japan. hi-nagata@kddilabs.co.jp|National Cancer Center Research Institute, 5-1-1, Tsukiji, Chuo-ku, Tokyo 104-0045, Japan. hi-nagata@kddilabs.co.jp|National Cancer Center Research Institute, 5-1-1, Tsukiji, Chuo-ku, Tokyo 104-0045, Japan. hi-nagata@kddilabs.co.jp', 'Japan|Japan|Japan', 38, 'Computational Biology|Computer Simulation|Drug Design|Ligands|Models, Molecular|Proteins|Static Electricity|Thermodynamics|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/18.1.140', '11836222', 'A novel concept for a protein-ligand docking simulator using Virtual Reality (VR) technologies, in particular the tactile sense technology, was designed and a prototype was developed. Most conventional docking simulators are based on numerical differential calculations of the total energy between a protein and a ligand. However, the basic concept of our method differs from that of conventional simulators. Our design utilizes the force between a ligand and a protein instead of the total energy. The most characteristic function of the system is its ability to enable the user to touch and sense the electrostatic potential field of a protein molecule. The user can scan the surface of a protein using a globular probe, which is given an electrostatic charge, and is controlled by a force feedback device. The electrostatic force between the protein and the probe is calculated in real time and immediately fed back into the force feedback device. The user can easily search interactively for positions where the probe is strongly attracted to the force field. Such positions can be regarded as candidate sites where functional groups of ligands corresponding to the probe can bind to the target protein. Certain limitations remain, for example, only twenty protein atoms can be used to generate the electrostatic field. Furthermore, the system can only use globular probes, preventing drug molecules or small chemical groups from being simulated. These limitations are the result of our insufficient computer resources. However, our prototype system has the potential to become a novel application method as well as being applicable to conventional VR technologies, especially to force feedback technologies.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(70126, 2002, 'bio', 'Classifying G-protein coupled receptors with support vector machines', 'Karchin, Rachel|Karplus, Kevin|Haussler, David', 'Department of Computer Science, University of California, Santa Cruz, CA 95064, USA. rachelk@soe.ucsc.edu|Department of Computer Science, University of California, Santa Cruz, CA 95064, USA. rachelk@soe.ucsc.edu|Department of Computer Science, University of California, Santa Cruz, CA 95064, USA. rachelk@soe.ucsc.edu', 'United States|United States|United States', 221, 'Algorithms|Amino Acid Sequence|Computational Biology|Databases, Protein|GTP-Binding Proteins|Humans|Internet|Molecular Sequence Data|Receptors, Cell Surface|Sequence Alignment', 'Comparative Study', '10.1093/bioinformatics/18.1.147', '11836223', 'The enormous amount of protein sequence data uncovered by genome research has increased the demand for computer software that can automate the recognition of new proteins. We discuss the relative merits of various automated methods for recognizing G-Protein Coupled Receptors (GPCRs), a superfamily of cell membrane proteins. GPCRs are found in a wide range of organisms and are central to a cellular signalling network that regulates many basic physiological processes. They are the focus of a significant amount of current pharmaceutical research because they play a key role in many diseases. However, their tertiary structures remain largely unsolved. The methods described in this paper use only primary sequence information to make their predictions. We compare a simple nearest neighbor approach (BLAST), methods based on multiple alignments generated by a statistical profile Hidden Markov Model (HMM), and methods, including Support Vector Machines (SVMs), that transform protein sequences into fixed-length feature vectors.'),
(70127, 2002, 'bio', 'Machine learning of functional class from phenotype data', 'Clare, Amanda|King, Ross D', 'Department of Computer Science, University of Wales, Aberystwyth SY23 3DB, UK. ajc@aber.ac.uk|Department of Computer Science, University of Wales, Aberystwyth SY23 3DB, UK. ajc@aber.ac.uk', 'United Kingdom|United Kingdom', 48, 'Artificial Intelligence|Computational Biology|Databases, Genetic|Mutation|Open Reading Frames|Phenotype|Saccharomyces cerevisiae', 'Journal Article', '10.1093/bioinformatics/18.1.160', '11836224', 'Mutant phenotype growth experiments are an important novel source of functional genomics data which have received little attention in bioinformatics. We applied supervised machine learning to the problem of using phenotype data to predict the functional class of Open Reading Frames (ORFs) in Saccaromyces cerevisiae. Three sources of data were used: TRansposon-Insertion Phenotypes, Localization and Expression in Saccharomyces (TRIPLES), European Functional Analysis Network (EUROFAN) and Munich Information Center for Protein Sequences (MIPS). The analysis of the data presented a number of challenges to machine learning: multi-class labels, a large number of sparsely populated classes, the need to learn a set of accurate rules (not a complete classification), and a very large amount of missing values. We modified the algorithm C4.5 to deal with these problems.'),
(70128, 2002, 'bio', 'Prediction of 3D neighbours of molecular surface patches in proteins by artificial neural networks', 'Dietmann, S|Frömmel, C', 'Medical Faculty of the Humboldt University, (Charité) Institute of Biochemistry, Monbijoustr. 2A, Berlin D-10117, Germany. dietmann@ebi.ac.uk|Medical Faculty of the Humboldt University, (Charité) Institute of Biochemistry, Monbijoustr. 2A, Berlin D-10117, Germany. dietmann@ebi.ac.uk', 'Germany|Germany', 6, 'Computational Biology|Hemoglobins|Humans|Models, Molecular|Myoglobin|Neural Networks (Computer)|Protein Conformation|Protein Structure, Secondary|Protein Subunits|Proteins|Surface Properties', 'Comparative Study', '10.1093/bioinformatics/18.1.167', '11836225', 'Molecular Surface Patches (MSPs) of proteins are responsible for selective interactions between internal parts of one protein molecule or between protein and other molecules. The prediction of the neighbours of a distinct Secondary Structural Element (SSE) would be an important step for protein structure prediction.'),
(70129, 2002, 'bio', 'SACS--self-maintaining database of antibody crystal structure information', 'Allcorn, Lee C|Martin, Andrew C R', 'School of Animal and Microbial Sciences, University of Reading, Whiteknights, PO Box 228, Reading RG6 6AJ, UK.|School of Animal and Microbial Sciences, University of Reading, Whiteknights, PO Box 228, Reading RG6 6AJ, UK.', 'United Kingdom|United Kingdom', 49, 'Antibodies|Computational Biology|Crystallography, X-Ray|Databases, Protein|Internet', 'Comparative Study', '10.1093/bioinformatics/18.1.175', '11836226', 'The rapid increase in the number of structures in the Protein Databank (PDB) makes it difficult to find all structures in a given protein class. Automatically-maintained web-based summaries are one solution to this problem.'),
(70130, 2002, 'bio', 'Association of nucleotide patterns with gene function classes: application to human 3 untranslated sequences', 'Conklin, Darrell|Jonassen, Inge|Aasland, Rein|Taylor, William R', 'ZymoGenetics Inc., 1201 Eastlake Avenue East, Seattle, WA 98102, USA. conklin@zgi.com|ZymoGenetics Inc., 1201 Eastlake Avenue East, Seattle, WA 98102, USA. conklin@zgi.com|ZymoGenetics Inc., 1201 Eastlake Avenue East, Seattle, WA 98102, USA. conklin@zgi.com|ZymoGenetics Inc., 1201 Eastlake Avenue East, Seattle, WA 98102, USA. conklin@zgi.com', 'United States|United States|United States|United States', 0, '3 Untranslated Regions|Algorithms|Base Composition|Cluster Analysis|Computational Biology|Cytokines|Databases, Nucleic Acid|Gene Expression Regulation|Humans|Pattern Recognition, Automated', 'Journal Article', '10.1093/bioinformatics/18.1.182', '11836227', 'Gene expression is dependent on two main types of signals, one involving transcription factors which initiates gene transcription, and another which regulates the translation of a nascent mRNA. These post-transcriptional events play an important yet incompletely understood role in regulating gene expression and cellular behavior. Many of the identified cis acting elements for translational regulation occur within the 3untranslated region (3 UTR), and some have been observed to occur with surprising regularity within certain protein function classes.'),
(70131, 2002, 'bio', 'Physics-based gene identification: proof of concept for Plasmodium falciparum', 'Yeramian, Edouard|Bonnefoy, Serge|Langsley, Gordon', 'Genopole, Centre de Bioinformatique, FRE CNRS 2364, URA CNRS 1960, Institut Pasteur, 75724 Paris Cedex 15, France.|Genopole, Centre de Bioinformatique, FRE CNRS 2364, URA CNRS 1960, Institut Pasteur, 75724 Paris Cedex 15, France.|Genopole, Centre de Bioinformatique, FRE CNRS 2364, URA CNRS 1960, Institut Pasteur, 75724 Paris Cedex 15, France.', 'France|France|France', 22, 'Algorithms|Animals|Biophysical Phenomena|Biophysics|Computational Biology|DNA, Protozoan|Genes, Protozoan|Genetic Techniques|Genome, Protozoan|Genomics|Plasmodium falciparum', 'Journal Article', '10.1093/bioinformatics/18.1.190', '11836228', 'The ab initio prediction of new genes in eukaryotic genomes represents a difficult task, notably for the identification of complex split genes. A Physics-Based Gene Identification (PBGI) method was formulated recently (Yeramian, Gene, 255, 139-150, 151-168, 2000a,b) to address this problem, taking as a model the Plasmodium falciparum genome. Here, the predictive power of this method is put under experimental test for this genome. The presented results demonstrate the usefulness of the PBGI as a gene-identification tool for P. falciparum, notably for the discovery of new genes with no homology to known genes. Perspectives opened by this new method for other eukaryotic genomes are also mentioned.'),
(70132, 2002, 'bio', 'Trev: a DNA trace editor and viewer', 'Bonfield, James K|Beal, Kathryn F|Betts, Matthew J|Staden, Rodger', 'MRC Laboratory of Molecular Biology, Hills Road, Cambridge CB2 2QH, UK. jkb@mrc-lmb.cam.ac.jp|MRC Laboratory of Molecular Biology, Hills Road, Cambridge CB2 2QH, UK. jkb@mrc-lmb.cam.ac.jp|MRC Laboratory of Molecular Biology, Hills Road, Cambridge CB2 2QH, UK. jkb@mrc-lmb.cam.ac.jp|MRC Laboratory of Molecular Biology, Hills Road, Cambridge CB2 2QH, UK. jkb@mrc-lmb.cam.ac.jp', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom', 10, 'Computational Biology|Computer Graphics|Data Display|Electronic Data Processing|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/18.1.194', '11836229', 'Trev is a DNA trace editor and viewer, which is available free for UNIX and Microsoft Windows platforms. It can read all the commonly used file formats, including the new, compact ZTR files.'),
(70133, 2002, 'bio', 'Locating probable genes using Fourier transform approach', 'Issac, Biju|Singh, Harpreet|Kaur, Harpreet|Raghava, G P S', 'Institute of Microbial Technology, Chandigarh-160036, India.|Institute of Microbial Technology, Chandigarh-160036, India.|Institute of Microbial Technology, Chandigarh-160036, India.|Institute of Microbial Technology, Chandigarh-160036, India.', 'India|India|India|India', 0, 'Algorithms|Chromosomes, Bacterial|Computational Biology|Databases, Genetic|Fourier Analysis|Genes, Bacterial|Genetic Techniques|Internet|Software|Vibrio cholerae', 'Journal Article', '10.1093/bioinformatics/18.1.196', '11836230', 'FTG is a web server for analyzing nucleotide sequences to predict the genes using Fourier transform techniques. This server implements the existing Fourier transform algorithms for gene prediction and allows the rapid visualization of analysis by output in GIF format.'),
(70134, 2002, 'bio', 'Dragon Promoter Finder: recognition of vertebrate RNA polymerase II promoters', 'Bajic, Vladimir B|Seah, Seng Hong|Chong, Allen|Zhang, Guanglan|Koh, Judice L Y|Brusic, Vladimir', 'BIC-KRDL, Kent Ridge Digital Laboratories, 21, Heng Mui Keng Terrace, Singapore 119613. bajicv@krdl.org.sg|BIC-KRDL, Kent Ridge Digital Laboratories, 21, Heng Mui Keng Terrace, Singapore 119613. bajicv@krdl.org.sg|BIC-KRDL, Kent Ridge Digital Laboratories, 21, Heng Mui Keng Terrace, Singapore 119613. bajicv@krdl.org.sg|BIC-KRDL, Kent Ridge Digital Laboratories, 21, Heng Mui Keng Terrace, Singapore 119613. bajicv@krdl.org.sg|BIC-KRDL, Kent Ridge Digital Laboratories, 21, Heng Mui Keng Terrace, Singapore 119613. bajicv@krdl.org.sg|BIC-KRDL, Kent Ridge Digital Laboratories, 21, Heng Mui Keng Terrace, Singapore 119613. bajicv@krdl.org.sg', 'Singapore|Singapore|Singapore|Singapore|Singapore|Singapore', 76, 'Algorithms|Computational Biology|Databases, Nucleic Acid|Exons|Humans|Introns|Neural Networks (Computer)|Promoter Regions, Genetic|RNA Polymerase II|Software', 'Comparative Study', '10.1093/bioinformatics/18.1.198', '11836231', 'Dragon Promoter Finder (DPF) locates RNA polymerase II promoters in DNA sequences of vertebrates by predicting Transcription Start Site (TSS) positions. DPFs algorithm uses sensors for three functional regions (promoters, exons and introns) and an Artificial Neural Network (ANN). Results on a large and diverse evaluation set indicate that DPF exhibits a superior predicting ability for TSS location compared to three other promoter-finding programs.'),
(70135, 2002, 'bio', 'LigBase: a database of families of aligned ligand binding sites in known protein sequences and structures', 'Stuart, Ashley C|Ilyin, Valentin A|Sali, Andrej', 'Laboratories of Molecular Biophysics, Pels Family Center for Biochemistry and Structural Biology, The Rockefeller University, 1230 York Avenue, New York, NY 10021, USA. ash@guitar.rockefeller.edu|Laboratories of Molecular Biophysics, Pels Family Center for Biochemistry and Structural Biology, The Rockefeller University, 1230 York Avenue, New York, NY 10021, USA. ash@guitar.rockefeller.edu|Laboratories of Molecular Biophysics, Pels Family Center for Biochemistry and Structural Biology, The Rockefeller University, 1230 York Avenue, New York, NY 10021, USA. ash@guitar.rockefeller.edu', 'United States|United States|United States', 63, 'Binding Sites|Computational Biology|Databases, Protein|Internet|Ligands|Proteins|Sequence Alignment', 'Journal Article', '10.1093/bioinformatics/18.1.200', '11836232', 'A database comprising all ligand-binding sites of known structure aligned with all related protein sequences and structures is described. Currently, the database contains approximately 50000 ligand-binding sites for small molecules found in the Protein Data Bank (PDB). The structure-structure alignments are obtained by the Combinatorial Extension (CE) program (Shindyalov and Bourne, Protein Eng., 11, 739-747, 1998) and sequence-structure alignments are extracted from the ModBase database of comparative protein structure models for all known protein sequences (Sanchez et al., Nucleic Acids Res., 28, 250-253, 2000). It is possible to search for binding sites in LigBase by a variety of criteria. LigBase reports summarize ligand data including relevant structural information from the PDB file, such as ligand type and size, and contain links to all related protein sequences in the TrEMBL database. Residues in the binding sites are graphically depicted for comparison with other structurally defined family members. LigBase provides a resource for the analysis of families of related binding sites.'),
(70136, 2002, 'bio', 'Using biplots to interpret gene expression patterns in plants', 'Chapman, Scott|Schenk, Peer|Kazan, Kemal|Manners, John', 'CSIRO Plant Industry, Long Pocket Laboratories, 120 Meiers Rd, Indooroopilly 4068, Australia. scott.chapman@csiro.au|CSIRO Plant Industry, Long Pocket Laboratories, 120 Meiers Rd, Indooroopilly 4068, Australia. scott.chapman@csiro.au|CSIRO Plant Industry, Long Pocket Laboratories, 120 Meiers Rd, Indooroopilly 4068, Australia. scott.chapman@csiro.au|CSIRO Plant Industry, Long Pocket Laboratories, 120 Meiers Rd, Indooroopilly 4068, Australia. scott.chapman@csiro.au', 'Australia|Australia|Australia|Australia', 51, 'Computational Biology|Gene Expression Profiling|Genes, Plant|Internet|Multigene Family|Oligonucleotide Array Sequence Analysis|Plants|Principal Component Analysis', 'Journal Article', '10.1093/bioinformatics/18.1.202', '11836233', 'Plant biologists in fields of ecology, evolution, genetics and breeding frequently use multivariate methods. This paper illustrates Principal Component Analysis (PCA) and Gabriels biplot as applied to microarray expression data from plant pathology experiments.'),
(70137, 2002, 'bio', 'CIT: identification of differentially expressed clusters of genes from microarray data', 'Rhodes, Daniel R|Miller, Jeremy C|Haab, Brian B|Furge, Kyle A', 'Laboratory of DNA and Protein Microarray Technology, Van Andel Research Institute, Grand Rapids, MI 49053, USA.|Laboratory of DNA and Protein Microarray Technology, Van Andel Research Institute, Grand Rapids, MI 49053, USA.|Laboratory of DNA and Protein Microarray Technology, Van Andel Research Institute, Grand Rapids, MI 49053, USA.|Laboratory of DNA and Protein Microarray Technology, Van Andel Research Institute, Grand Rapids, MI 49053, USA.', 'United States|United States|United States|United States', 28, 'Algorithms|Computational Biology|Gene Expression Profiling|Humans|Melanoma|Multigene Family|Oligonucleotide Array Sequence Analysis|Software|Tumor Cells, Cultured', 'Journal Article', '10.1093/bioinformatics/18.1.205', '11836234', 'Cluster Identification Tool (CIT) is a microarray analysis program that identifies differentially expressed genes. Following division of experimental samples based on a parameter of interest, CIT uses a statistical discrimination metric and permutation analysis to identify clusters of genes or individual genes that best differentiate between the experimental groups. CIT integrates with the freely available CLUSTER and TREEVIEW programs to form a more complete microarray analysis package.'),
(70138, 2002, 'bio', 'Genesis: cluster analysis of microarray data', 'Sturn, Alexander|Quackenbush, John|Trajanoski, Zlatko', 'Institute of Biomedical Engineering, Graz University of Technology, Krenngasse 37, 8010 Graz, Austria.|Institute of Biomedical Engineering, Graz University of Technology, Krenngasse 37, 8010 Graz, Austria.|Institute of Biomedical Engineering, Graz University of Technology, Krenngasse 37, 8010 Graz, Austria.', 'Austria|Austria|Austria', 0, 'Algorithms|Cluster Analysis|Computational Biology|Gene Expression Profiling|Oligonucleotide Array Sequence Analysis|Principal Component Analysis|Programming Languages|Promoter Regions, Genetic|Software|Transcription, Genetic', 'Journal Article', '10.1093/bioinformatics/18.1.207', '11836235', 'A versatile, platform independent and easy to use Java suite for large-scale gene expression analysis was developed. Genesis integrates various tools for microarray data analysis such as filters, normalization and visualization tools, distance measures as well as common clustering algorithms including hierarchical clustering, self-organizing maps, k-means, principal component analysis, and support vector machines. The results of the clustering are transparent across all implemented methods and enable the analysis of the outcome of different algorithms and parameters. Additionally, mapping of gene expression data onto chromosomal sequences was implemented to enhance promoter analysis and investigation of transcriptional control mechanisms.'),
(70139, 2002, 'bio', 'PCSB--a program collection for structural biology and biophysical chemistry', 'Hofmann, A|Wlodawer, A', 'Macromolecular Crystallography Laboratory, National Cancer Institute at Frederick, National Institutes of Health, Frederick, MD 21702, USA. hofmanna@ncicrf.gov|Macromolecular Crystallography Laboratory, National Cancer Institute at Frederick, National Institutes of Health, Frederick, MD 21702, USA. hofmanna@ncicrf.gov', 'United States|United States', 28, 'Algorithms|Chemistry, Physical|Computational Biology|Databases, Protein|Molecular Biology|Programming Languages|Software', 'Journal Article', '10.1093/bioinformatics/18.1.209', '11836236', 'We present the first package of Java classes specifically aimed at the handling of structural and biophysical problems. To enable object-oriented programming a basis of fundamental Java classes is required which deals with basic operations of vectors, matrices, amino acid sequences, crystal symmetries and PDB files. Five classes, which carry out these basic operations, were constructed and bundled together with several utility functions in the PCSB package. Furthermore, to demonstrate their applicability and to obtain programs handling common tasks in structural laboratories, we present the first six applications of PCSB. All applications are portable to different platforms and require only the Java Runtime Environment to be installed on the system. available as PDF file.'),
(70140, 2002, 'bio', 'DICHROWEB: an interactive website for the analysis of protein secondary structure from circular dichroism spectra', 'Lobley, A|Whitmore, L|Wallace, B A', 'Department of Crystallography, Birkbeck College, University of London, London WC1E 7HX, UK.|Department of Crystallography, Birkbeck College, University of London, London WC1E 7HX, UK.|Department of Crystallography, Birkbeck College, University of London, London WC1E 7HX, UK.', 'United Kingdom|United Kingdom|United Kingdom', 582, 'Algorithms|Circular Dichroism|Computational Biology|Databases, Protein|Internet|Protein Structure, Secondary', 'Journal Article', '10.1093/bioinformatics/18.1.211', '11836237', 'A user-friendly website for the analysis of protein secondary structures from Circular Dichroism (CD) and Synchrotron Radiation Circular Dichroism (SRCD) spectra has been created.'),
(70141, 2002, 'bio', 'Geno3D: automatic comparative molecular modelling of protein', 'Combet, Christophe|Jambon, Martin|Deléage, Gilbert|Geourjon, Christophe', 'Institut de Biologie et Chimie des Protéines, IBCP-CNRS UMR 5086, Pôle Bioinformatique Lyonnais, Bioinformatics and structural NMR, 7, Passage du Vercors, 69 367 Lyon Cedex 07, France.|Institut de Biologie et Chimie des Protéines, IBCP-CNRS UMR 5086, Pôle Bioinformatique Lyonnais, Bioinformatics and structural NMR, 7, Passage du Vercors, 69 367 Lyon Cedex 07, France.|Institut de Biologie et Chimie des Protéines, IBCP-CNRS UMR 5086, Pôle Bioinformatique Lyonnais, Bioinformatics and structural NMR, 7, Passage du Vercors, 69 367 Lyon Cedex 07, France.|Institut de Biologie et Chimie des Protéines, IBCP-CNRS UMR 5086, Pôle Bioinformatique Lyonnais, Bioinformatics and structural NMR, 7, Passage du Vercors, 69 367 Lyon Cedex 07, France.', 'France|France|France|France', 98, 'Algorithms|Computational Biology|Computer Simulation|Internet|Models, Molecular|Proteins|Sequence Alignment|Sequence Homology, Amino Acid', 'Comparative Study', '10.1093/bioinformatics/18.1.213', '11836238', 'Geno3D (http://geno3d-pbil.ibcp.fr) is an automatic web server for protein molecular modelling. Starting with a query protein sequence, the server performs the homology modelling in six successive steps: (i) identify homologous proteins with known 3D structures by using PSI-BLAST, (ii) provide the user all potential templates through a very convenient user interface for target selection, (iii) perform the alignment of both query and subject sequences, (iv) extract geometrical restraints (dihedral angles and distances) for corresponding atoms between the query and the template, (v) perform the 3D construction of the protein by using a distance geometry approach and (vi) finally send the results by e-mail to the user.'),
(70142, 2002, 'bio', 'Symmetry observations in long nucleotide sequences: a commentary on the Discovery Note of Qi and Cuticchia', 'Forsdyke, D R', 'Department of Biochemistry, Queens University, Kingston, Ontario, Canada K7L 3N6.', 'Canada', 15, 'Base Sequence|Codon|Computational Biology|DNA|Genomics|Sequence Alignment|Sequence Analysis, DNA', 'Letter', '10.1093/bioinformatics/18.1.215', '11836239', 'The relative quantities of bases in DNA were determined chemically many years before sequencing technologies permitted direct counting of bases. Apparently unaware of the rich literature on the topic, bioinformaticists are today rediscovering the wheels of Chargaff, Wyatt and other biochemists. It follows from Chargaffs second parity rule (%A = %T, %G = %C for single stranded DNA) that the symmetries observed for the two pairs of complementary mononucleotide bases, should also apply to the eight pairs of complementary dinucleotide bases, the thirty-two pairs of complementary trinucleotide bases, etc. This was made explicit by Prabhu in 1993 in a study of complete genomes and long genome segments from a wide range of taxa, and was rediscovered by Qi and Cuticchia in 2001 in a study of complete genomes. It follows from Chargaffs GC-rule (%GC tends to be uniform and species specific) that, within a species, oligonucleotides of the same GC% will be at approximately equal quantities in single stranded DNA. Thus, for example, while quantities of CAT and ATG (reverse complements) will be closely correlated because of both of the above Chargaff rules, CAT and GTA (forward complements) will show some correlation only because of the latter rule. The need for complete genomic sequences in bioinformatic analyses may have been somewhat overplayed.'),
(70143, 2002, 'bio', 'Beyond the central dogma', 'Henikoff, Steven', 'Department of Biochemistry, Queens University, Kingston, Ontario, Canada K7L 3N6.', 'Canada', 13, 'Computational Biology|DNA|Gene Expression|Gene Silencing|Models, Genetic|Retroelements', 'Editorial', '10.1093/bioinformatics/18.2.223', '11847069', '-'),
(70144, 2002, 'bio', 'A Bayesian approach to discriminate between alternative DNA sequence segmentations', 'Husmeier, Dirk|Wright, Frank', 'Biomathematics and Statistics Scotland, SCRI, Invergowrie, Dundee DD2 5DA, UK. dirk@bioss.ac.uk|Biomathematics and Statistics Scotland, SCRI, Invergowrie, Dundee DD2 5DA, UK. dirk@bioss.ac.uk', 'United Kingdom|United Kingdom', 2, 'Algorithms|Bayes Theorem|Computational Biology|DNA|Databases, Nucleic Acid|Dengue Virus|Hepatitis B virus|Models, Genetic|Mosaicism|Neisseria|Sequence Alignment|Software', 'Journal Article', '10.1093/bioinformatics/18.2.226', '11847070', 'As a result of recombination or rate variation, a DNA sequence alignment may have a mosaic structure, where different segments correspond to different evolutionary histories. While several methods have been developed to predict DNA mosaic structures, they do not properly address the question of whether the predicted segmentation itself is statistically significant, or whether it is significantly better than an alternative mosaic structure predicted with another method. The objective of the present article is to devise an approximate Bayesian hypothesis test to discriminate between alternative candidate mosaic structures.'),
(70145, 2002, 'bio', 'A data-mining approach to spacer oligonucleotide typing of Mycobacterium tuberculosis', 'Sebban, M|Mokrousov, I|Rastogi, N|Sola, C', 'French West Indies and Guiana University, TRIVIA, Department of Mathematics and Computer Science, Campus Fouillole, 97159 Pointe-à-Pitre Cedex, Guadeloupe.|French West Indies and Guiana University, TRIVIA, Department of Mathematics and Computer Science, Campus Fouillole, 97159 Pointe-à-Pitre Cedex, Guadeloupe.|French West Indies and Guiana University, TRIVIA, Department of Mathematics and Computer Science, Campus Fouillole, 97159 Pointe-à-Pitre Cedex, Guadeloupe.|French West Indies and Guiana University, TRIVIA, Department of Mathematics and Computer Science, Campus Fouillole, 97159 Pointe-à-Pitre Cedex, Guadeloupe.', 'Guadeloupe|Guadeloupe|Guadeloupe|Guadeloupe', 0, 'Algorithms|Bacterial Typing Techniques|Computational Biology|DNA, Bacterial|DNA, Intergenic|Data Interpretation, Statistical|Decision Trees|Models, Genetic|Mycobacterium tuberculosis', 'Journal Article', '10.1093/bioinformatics/18.2.235', '11847071', 'The Direct Repeat (DR) locus of Mycobacterium tuberculosis is a suitable model to study (i) molecular epidemiology and (ii) the evolutionary genetics of tuberculosis. This is achieved by a DNA analysis technique (genotyping), called sp acer oligo nucleotide typing (spoligotyping ). In this paper, we investigated data analysis methods to discover intelligible knowledge rules from spoligotyping, that has not yet been applied on such representation. This processing was achieved by applying the C4.5 induction algorithm and knowledge rules were produced. Finally, a Prototype Selection (PS) procedure was applied to eliminate noisy data. This both simplified decision rules, as well as the number of spacers to be tested to solve classification tasks. In the second part of this paper, the contribution of 25 new additional spacers and the knowledge rules inferred were studied from a machine learning point of view. From a statistical point of view, the correlations between spacers were analyzed and suggested that both negative and positive ones may be related to potential structural constraints within the DR locus that may shape its evolution directly or indirectly.'),
(70146, 2002, 'bio', 'Identification of characteristic oligonucleotides in the bacterial 16S ribosomal RNA sequence dataset', 'Zhang, Zhengdong|Willson, Richard C|Fox, George E', 'Department of Biology and Biochemistry, University of Houston, Houston, TX 77204-5001, USA. zzhang@bayou.uh.edu|Department of Biology and Biochemistry, University of Houston, Houston, TX 77204-5001, USA. zzhang@bayou.uh.edu|Department of Biology and Biochemistry, University of Houston, Houston, TX 77204-5001, USA. zzhang@bayou.uh.edu', 'United States|United States|United States', 15, 'Algorithms|Bacteria|Computational Biology|Databases, Nucleic Acid|Oligoribonucleotides|Phylogeny|RNA, Bacterial|RNA, Ribosomal, 16S', 'Journal Article', '10.1093/bioinformatics/18.2.244', '11847072', 'The phylogenetic structure of the bacterial world has been intensively studied by comparing sequences of 16S ribosomal RNA (16S rRNA). This database of sequences is now widely used to design probes for the detection of specific bacteria or groups of bacteria one at a time. The success of such methods reflects the fact that there are local sequence segments that are highly characteristic of particular organisms or groups of organisms. It is not clear, however, the extent to which such signature sequences exist in the 16S rRNA dataset. A better understanding of the numbers and distribution of highly informative oligonucleotide sequences may facilitate the design of hybridization arrays that can characterize the phylogenetic position of an unknown organism or serve as the basis for the development of novel approaches for use in bacterial identification.'),
(70147, 2002, 'bio', 'Adjustments and measures of differential expression for microarray data', 'Tsodikov, A|Szabo, A|Jones, D', 'Huntsman Cancer Institute and Department of Oncological Sciences, University of Utah, 2000 Circle of Hope, Salt Lake City, UT 84112-5550, USA. alexander.tsodikov@hci.utah.edu|Huntsman Cancer Institute and Department of Oncological Sciences, University of Utah, 2000 Circle of Hope, Salt Lake City, UT 84112-5550, USA. alexander.tsodikov@hci.utah.edu|Huntsman Cancer Institute and Department of Oncological Sciences, University of Utah, 2000 Circle of Hope, Salt Lake City, UT 84112-5550, USA. alexander.tsodikov@hci.utah.edu', 'United States|United States|United States', 47, 'Colonic Neoplasms|Computational Biology|Computer Simulation|DNA, Neoplasm|Data Interpretation, Statistical|Gene Expression Profiling|Humans|Models, Genetic|Models, Statistical|Oligonucleotide Array Sequence Analysis|Software|Stochastic Processes|Tumor Cells, Cultured', 'Journal Article', '10.1093/bioinformatics/18.2.251', '11847073', 'Existing analyses of microarray data often incorporate an obscure data normalization procedure applied prior to data analysis. For example, ratios of microarray channels intensities are normalized to have common mean over the set of genes. We made an attempt to understand the meaning of such procedures from the modeling point of view, and to formulate the model assumptions that underlie them. Given a considerable diversity of data adjustment procedures, the question of their performance, comparison and ranking for various microarray experiments was of interest.'),
(70148, 2002, 'bio', 'Probabilistic Boolean Networks: a rule-based uncertainty model for gene regulatory networks', 'Shmulevich, Ilya|Dougherty, Edward R|Kim, Seungchan|Zhang, Wei', 'Cancer Genomics Laboratory, University of Texas M.D. Anderson Cancer Center, 1515 Holcombe Blvd, Box 85, Houston, TX 77030, USA. is@ieee.org|Cancer Genomics Laboratory, University of Texas M.D. Anderson Cancer Center, 1515 Holcombe Blvd, Box 85, Houston, TX 77030, USA. is@ieee.org|Cancer Genomics Laboratory, University of Texas M.D. Anderson Cancer Center, 1515 Holcombe Blvd, Box 85, Houston, TX 77030, USA. is@ieee.org|Cancer Genomics Laboratory, University of Texas M.D. Anderson Cancer Center, 1515 Holcombe Blvd, Box 85, Houston, TX 77030, USA. is@ieee.org', 'United States|United States|United States|United States', 0, 'Cell Cycle|Computational Biology|Gene Expression Regulation|Markov Chains|Models, Genetic|Models, Statistical', 'Journal Article', '10.1093/bioinformatics/18.2.261', '11847074', 'Our goal is to construct a model for genetic regulatory networks such that the model class: (i) incorporates rule-based dependencies between genes, (ii) allows the systematic study of global network dynamics, (iii) is able to cope with uncertainty, both in the data and the model selection, and (iv) permits the quantification of the relative influence and sensitivity of genes in their interactions with other genes.'),
(70149, 2002, 'bio', 'Mixture modelling of gene expression data from microarray experiments', 'Ghosh, Debashis|Chinnaiyan, Arul M', 'Department of Biostatistics, School of Public Health, University of Michigan, 1420 Washington Heights, Room M4057, Ann Arbor, MI 48109-2029, USA. ghoshd@umich.edu|Department of Biostatistics, School of Public Health, University of Michigan, 1420 Washington Heights, Room M4057, Ann Arbor, MI 48109-2029, USA. ghoshd@umich.edu', 'United States|United States', 145, 'Algorithms|Cluster Analysis|Computational Biology|Databases, Genetic|Gene Expression Profiling|Humans|Male|Melanoma|Models, Genetic|Models, Statistical|Oligonucleotide Array Sequence Analysis|Prostatic Neoplasms|Skin Neoplasms', 'Journal Article', '10.1093/bioinformatics/18.2.275', '11847075', 'Hierarchical clustering is one of the major analytical tools for gene expression data from microarray experiments. A major problem in the interpretation of the output from these procedures is assessing the reliability of the clustering results. We address this issue by developing a mixture model-based approach for the analysis of microarray data. Within this framework, we present novel algorithms for clustering genes and samples. One of the byproducts of our method is a probabilistic measure for the number of true clusters in the data.'),
(70150, 2002, 'bio', 'Inference of a genetic network by a combined approach of cluster analysis and graphical Gaussian modeling', 'Toh, Hiroyuki|Horimoto, Katsuhisa', 'Department of Bioinformatics, Biomolecular Engineering Research Institute 6-2-3, Furuedai, Suita, Osaka 565-0874, Japan. toh@beri.co.jp|Department of Bioinformatics, Biomolecular Engineering Research Institute 6-2-3, Furuedai, Suita, Osaka 565-0874, Japan. toh@beri.co.jp', 'Japan|Japan', 148, 'Bayes Theorem|Cluster Analysis|Computational Biology|Gene Expression Profiling|Models, Genetic|Models, Statistical|Oligonucleotide Array Sequence Analysis|Saccharomyces cerevisiae|Software', 'Journal Article', '10.1093/bioinformatics/18.2.287', '11847076', 'Recent advances in DNA microarray technologies have made it possible to measure the expression levels of thousands of genes simultaneously under different conditions. The data obtained by microarray analyses are called expression profile data. One type of important information underlying the expression profile data is the genetic network, that is, the regulatory network among genes. Graphical Gaussian Modeling (GGM) is a widely utilized method to infer or test relationships among a plural of variables.'),
(70151, 2002, 'bio', 'Extensive feature detection of N-terminal protein sorting signals', 'Bannai, Hideo|Tamada, Yoshinori|Maruyama, Osamu|Nakai, Kenta|Miyano, Satoru', 'Human Genome Center, Institute of Medical Science, University of Tokyo, 4-6-1 Shirokanedai, Minato-ku, Tokyo, 108-8639, Japan. bannai@ims.u-tokyo.ac.jp|Human Genome Center, Institute of Medical Science, University of Tokyo, 4-6-1 Shirokanedai, Minato-ku, Tokyo, 108-8639, Japan. bannai@ims.u-tokyo.ac.jp|Human Genome Center, Institute of Medical Science, University of Tokyo, 4-6-1 Shirokanedai, Minato-ku, Tokyo, 108-8639, Japan. bannai@ims.u-tokyo.ac.jp|Human Genome Center, Institute of Medical Science, University of Tokyo, 4-6-1 Shirokanedai, Minato-ku, Tokyo, 108-8639, Japan. bannai@ims.u-tokyo.ac.jp|Human Genome Center, Institute of Medical Science, University of Tokyo, 4-6-1 Shirokanedai, Minato-ku, Tokyo, 108-8639, Japan. bannai@ims.u-tokyo.ac.jp', 'Japan|Japan|Japan|Japan|Japan', 0, 'Amino Acid Sequence|Computational Biology|Neural Networks (Computer)|Protein Sorting Signals|Proteins|Software|Subcellular Fractions', 'Journal Article', '10.1093/bioinformatics/18.2.298', '11847077', 'The prediction of localization sites of various proteins is an important and challenging problem in the field of molecular biology. TargetP, by Emanuelsson et al. (J. Mol. Biol., 300, 1005-1016, 2000) is a neural network based system which is currently the best predictor in the literature for N-terminal sorting signals. One drawback of neural networks, however, is that it is generally difficult to understand and interpret how and why they make such predictions. In this paper, we aim to generate simple and interpretable rules as predictors, and still achieve a practical prediction accuracy. We adopt an approach which consists of an extensive search for simple rules and various attributes which is partially guided by human intuition.'),
(70152, 2002, 'bio', 'Predicting reliable regions in protein sequence alignments', 'Cline, Melissa|Hughey, Richard|Karplus, Kevin', 'Center for Biomolecular Science and Engineering, Jack Baskin School of Engineering, University of California, Santa Cruz, CA 95064, USA. cline@soe.ucsc.edu|Center for Biomolecular Science and Engineering, Jack Baskin School of Engineering, University of California, Santa Cruz, CA 95064, USA. cline@soe.ucsc.edu|Center for Biomolecular Science and Engineering, Jack Baskin School of Engineering, University of California, Santa Cruz, CA 95064, USA. cline@soe.ucsc.edu', 'United States|United States|United States', 67, 'Algorithms|Computational Biology|Neural Networks (Computer)|Proteins|Sequence Alignment|Software', 'Journal Article', '10.1093/bioinformatics/18.2.306', '11847078', 'Protein sequence alignments have a myriad of applications in bioinformatics, including secondary and tertiary structure prediction, homology modeling, and phylogeny. Unfortunately, all alignment methods make mistakes, and mistakes in alignments often yield mistakes in their application. Thus, a method to identify and remove suspect alignment positions could benefit many areas in protein sequence analysis.'),
(70153, 2002, 'bio', 'Models@Home: distributed computing in bioinformatics using a screensaver based approach', 'Krieger, Elmar|Vriend, Gert', 'CMBI, Center for Molecular and Biomolecular Informatics, Toernooiveld 1, NL-6525 ED Nijmegen, The Netherlands. elmar.krieger@cmbi.kum.nl|CMBI, Center for Molecular and Biomolecular Informatics, Toernooiveld 1, NL-6525 ED Nijmegen, The Netherlands. elmar.krieger@cmbi.kum.nl', 'Netherlands|Netherlands', 99, 'Computational Biology|Computer Communication Networks|Computer Simulation|Computer Systems|Databases, Protein|Proteins|Software|Thermodynamics', 'Journal Article', '10.1093/bioinformatics/18.2.315', '11847079', 'Due to the steadily growing computational demands in bioinformatics and related scientific disciplines, one is forced to make optimal use of the available resources. A straightforward solution is to build a network of idle computers and let each of them work on a small piece of a scientific challenge, as done by Seti@Home (http://setiathome.berkeley.edu), the worlds largest distributed computing project.'),
(70154, 2002, 'bio', 'A cluster validity framework for genome expression data', 'Azuaje, F', 'Department of Computer Science, Trinity College, Dublin 2, Ireland. Francisco.Azuaje@Cval.html', 'Ireland', 71, 'Cluster Analysis|Computational Biology|Data Interpretation, Statistical|Gene Expression Profiling|Genome, Human|Humans|Leukemia|Reproducibility of Results|Software', 'Journal Article', '10.1093/bioinformatics/18.2.319', '11847080', 'This paper presents a method for the assessment of expression cluster validity.'),
(70155, 2002, 'bio', 'Avoiding cross hybridization by choosing nonredundant targets on cDNA arrays', 'Nielsen, H B|Knudsen, S', 'Center for Biological Sequence Analysis, BioCentrum-DTU, Technical University of Denmark, Building 208, 2800 Lyngby, Denmark. hbjorn@cbs.dtu.dk|Center for Biological Sequence Analysis, BioCentrum-DTU, Technical University of Denmark, Building 208, 2800 Lyngby, Denmark. hbjorn@cbs.dtu.dk', 'Denmark|Denmark', 0, 'Animals|Computational Biology|DNA Primers|Databases, Genetic|Humans|Oligonucleotide Array Sequence Analysis|Polymerase Chain Reaction|Software', 'Journal Article', '10.1093/bioinformatics/18.2.321', '11847081', 'PROBEWIZ designs PCR primers for amplifying probes for cDNA arrays. The probes are designed to have minimal homology to other expressed sequences from a given organism. The primer selection is based on user-defined penalties for homology, primer quality, and proximity to the 3 end.'),
(70156, 2002, 'bio', 'DRAGON View: information visualization for annotated microarray data', 'Bouton, Christopher M L S|Pevsner, Jonathan', 'Department of Neuroscience, Johns Hopkins School of Medicine, Baltimore, MD 21205, USA.cbouton@jhmi.edu|Department of Neuroscience, Johns Hopkins School of Medicine, Baltimore, MD 21205, USA.cbouton@jhmi.edu', 'United States|United States', 27, 'Computational Biology|Computer Graphics|Databases, Genetic|Gene Expression Profiling|Internet|Oligonucleotide Array Sequence Analysis|Software', 'Journal Article', '10.1093/bioinformatics/18.2.323', '11847082', 'The DRAGON View information visualization tools aid in the comprehensive analysis of large-scale gene expression data that has been annotated with biologically relevant information through the generation of three types of complementary graphical outputs.'),
(70157, 2002, 'bio', 'Tclass: tumor classification system based on gene expression profile', 'Wuju, Li|Momiao, Xiong', 'Beijing Institute of Basic Medical Sciences, PO Box 130(3), Beijing 100850, Peoples Republic of China. wujuli@china.com|Beijing Institute of Basic Medical Sciences, PO Box 130(3), Beijing 100850, Peoples Republic of China. wujuli@china.com', 'China|China', 0, 'Colonic Neoplasms|Computational Biology|Gene Expression Profiling|Neoplasms|Oligonucleotide Array Sequence Analysis|Software', 'Journal Article', '10.1093/bioinformatics/18.2.325', '11847083', 'A method that incorporates feature selection into Fishers linear discriminant analysis for gene expression based tumor classification and a corresponding program Tclass were developed. The proposed method was applied to a public gene expression data set for colon cancer that consists of 22 normal and 40 tumor colon tissue samples to evaluate its performance for classification. Preliminary results demonstrated that using only a subset of genes ranging from 3 to 10 can achieve high classification accuracy.'),
(70158, 2002, 'bio', 'CGO: utilizing and integrating gene expression microarray data in clinical research and data management', 'Bumm, Klaus|Zheng, Mingzhong|Bailey, Clyde|Zhan, Fenghuang|Chiriva-Internati, M|Eddlemon, Paul|Terry, Julian|Barlogie, Bart|Shaughnessy, John D', 'Donna D. and Donald M. Lambert Laboratory of Myeloma Genetics and Myeloma & Transplantation Research Center, University of Arkansas for Medical Sciences, Little Rock, AR 72205, USA.|Donna D. and Donald M. Lambert Laboratory of Myeloma Genetics and Myeloma & Transplantation Research Center, University of Arkansas for Medical Sciences, Little Rock, AR 72205, USA.|Donna D. and Donald M. Lambert Laboratory of Myeloma Genetics and Myeloma & Transplantation Research Center, University of Arkansas for Medical Sciences, Little Rock, AR 72205, USA.|Donna D. and Donald M. Lambert Laboratory of Myeloma Genetics and Myeloma & Transplantation Research Center, University of Arkansas for Medical Sciences, Little Rock, AR 72205, USA.|Donna D. and Donald M. Lambert Laboratory of Myeloma Genetics and Myeloma & Transplantation Research Center, University of Arkansas for Medical Sciences, Little Rock, AR 72205, USA.|Donna D. and Donald M. Lambert Laboratory of Myeloma Genetics and Myeloma & Transplantation Research Center, University of Arkansas for Medical Sciences, Little Rock, AR 72205, USA.|Donna D. and Donald M. Lambert Laboratory of Myeloma Genetics and Myeloma & Transplantation Research Center, University of Arkansas for Medical Sciences, Little Rock, AR 72205, USA.|Donna D. and Donald M. Lambert Laboratory of Myeloma Genetics and Myeloma & Transplantation Research Center, University of Arkansas for Medical Sciences, Little Rock, AR 72205, USA.|Donna D. and Donald M. Lambert Laboratory of Myeloma Genetics and Myeloma & Transplantation Research Center, University of Arkansas for Medical Sciences, Little Rock, AR 72205, USA.', 'United States|United States|United States|United States|United States|United States|United States|United States|United States', 3, 'Computational Biology|Database Management Systems|Gene Expression Profiling|Humans|Oligonucleotide Array Sequence Analysis|Software', 'Journal Article', '10.1093/bioinformatics/18.2.327', '11847084', 'Clinical GeneOrganizer (CGO) is a novel windows-based archiving, organization and data mining software for the integration of gene expression profiling in clinical medicine. The program implements various user-friendly tools and extracts data for further statistical analysis. This software was written for Affymetrix GeneChip *.txt files, but can also be used for any other microarray-derived data. The MS-SQL server version acts as a data mart and links microarray data with clinical parameters of any other existing database and therefore represents a valuable tool for combining gene expression analysis and clinical disease characteristics.'),
(70159, 2002, 'bio', 'ISEApeaks: an Excel platform for GeneScan and Immunoscope data retrieval, management and analysis', 'Collette, A|Six, A', 'Immunochimie Analytique, CNRS URA 1961, Université Pierre et Marie Curie and Institut Pasteur, 25-28 rue du Dr Roux, 75724 Paris Cedex 15, France. collette@pasteur.fr|Immunochimie Analytique, CNRS URA 1961, Université Pierre et Marie Curie and Institut Pasteur, 25-28 rue du Dr Roux, 75724 Paris Cedex 15, France. collette@pasteur.fr', 'France|France', 31, 'Computational Biology|Database Management Systems|Databases, Nucleic Acid|Humans|Receptors, Immunologic|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/18.2.329', '11847085', 'ISEApeaks retrieves and handles DNA sequencer raw data (peak area and nucleotidic length). This Macintosh package efficiently interfaces GeneScan and Immunoscope softwares to Excel by extracting data from scattered files, organizing data and gathering it into a unique peak database for all samples.'),
(70160, 2002, 'bio', 'INCLUSive: integrated clustering, upstream sequence retrieval and motif sampling', 'Thijs, Gert|Moreau, Yves|De Smet, Frank|Mathys, Janick|Lescot, Magali|Rombauts, Stephane|Rouze, Pierre|De Moor, Bart|Marchal, Kathleen', 'ESAT_ SISTA/COSIC, KULeuven, Kasteelpark Arenberg 10, 3001 Leuven-Heverlee, Belgium. Gert.Thijs@esat.kuleuven.ac.be|ESAT_ SISTA/COSIC, KULeuven, Kasteelpark Arenberg 10, 3001 Leuven-Heverlee, Belgium. Gert.Thijs@esat.kuleuven.ac.be|ESAT_ SISTA/COSIC, KULeuven, Kasteelpark Arenberg 10, 3001 Leuven-Heverlee, Belgium. Gert.Thijs@esat.kuleuven.ac.be|ESAT_ SISTA/COSIC, KULeuven, Kasteelpark Arenberg 10, 3001 Leuven-Heverlee, Belgium. Gert.Thijs@esat.kuleuven.ac.be|ESAT_ SISTA/COSIC, KULeuven, Kasteelpark Arenberg 10, 3001 Leuven-Heverlee, Belgium. Gert.Thijs@esat.kuleuven.ac.be|ESAT_ SISTA/COSIC, KULeuven, Kasteelpark Arenberg 10, 3001 Leuven-Heverlee, Belgium. Gert.Thijs@esat.kuleuven.ac.be|ESAT_ SISTA/COSIC, KULeuven, Kasteelpark Arenberg 10, 3001 Leuven-Heverlee, Belgium. Gert.Thijs@esat.kuleuven.ac.be|ESAT_ SISTA/COSIC, KULeuven, Kasteelpark Arenberg 10, 3001 Leuven-Heverlee, Belgium. Gert.Thijs@esat.kuleuven.ac.be|ESAT_ SISTA/COSIC, KULeuven, Kasteelpark Arenberg 10, 3001 Leuven-Heverlee, Belgium. Gert.Thijs@esat.kuleuven.ac.be', 'Belgium|Belgium|Belgium|Belgium|Belgium|Belgium|Belgium|Belgium|Belgium', 61, 'Algorithms|Cluster Analysis|Computational Biology|Databases, Genetic|Gene Expression Profiling|Multigene Family|Oligonucleotide Array Sequence Analysis|Software', 'Journal Article', '10.1093/bioinformatics/18.2.331', '11847086', 'INCLUSive allows automatic multistep analysis of microarray data (clustering and motif finding). The clustering algorithm (adaptive quality-based clustering) groups together genes with highly similar expression profiles. The upstream sequences of the genes belonging to a cluster are automatically retrieved from GenBank and can be fed directly into Motif Sampler, a Gibbs sampling algorithm that retrieves statistically over-represented motifs in sets of sequences, in this case upstream regions of co-expressed genes.'),
(70161, 2002, 'bio', 'PROMO: detection of known transcription regulatory elements using species-tailored searches', 'Messeguer, Xavier|Escudero, Ruth|Farré, Domènec|Núñez, Oscar|Martínez, Javier|Albà, M Mar', 'Algorithmics and Genetics Group, Software Department, Universitat Politècnica de Catalunya, C/Jordi Girona Salgado, 1-3, 08034 Barcelona, Spain.|Algorithmics and Genetics Group, Software Department, Universitat Politècnica de Catalunya, C/Jordi Girona Salgado, 1-3, 08034 Barcelona, Spain.|Algorithmics and Genetics Group, Software Department, Universitat Politècnica de Catalunya, C/Jordi Girona Salgado, 1-3, 08034 Barcelona, Spain.|Algorithmics and Genetics Group, Software Department, Universitat Politècnica de Catalunya, C/Jordi Girona Salgado, 1-3, 08034 Barcelona, Spain.|Algorithmics and Genetics Group, Software Department, Universitat Politècnica de Catalunya, C/Jordi Girona Salgado, 1-3, 08034 Barcelona, Spain.|Algorithmics and Genetics Group, Software Department, Universitat Politècnica de Catalunya, C/Jordi Girona Salgado, 1-3, 08034 Barcelona, Spain.', 'Spain|Spain|Spain|Spain|Spain|Spain', 692, 'Animals|Binding Sites|Computational Biology|DNA|Humans|Software|Species Specificity|Transcription Factors', 'Journal Article', '10.1093/bioinformatics/18.2.333', '11847087', 'We have developed a set of tools to construct positional weight matrices from known transcription factor binding sites in a species or taxon-specific manner, and to search for matches in DNA sequences.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(70162, 2002, 'bio', 'GenomePixelizer--a visualization program for comparative genomics within and between species', 'Kozik, A|Kochetkova, E|Michelmore, R', 'Department of Vegetable Crops, University of California, Davis, CA 95616, USA. akozik@atcg.org|Department of Vegetable Crops, University of California, Davis, CA 95616, USA. akozik@atcg.org|Department of Vegetable Crops, University of California, Davis, CA 95616, USA. akozik@atcg.org', 'United States|United States|United States', 49, 'Computational Biology|Gene Duplication|Genomics|Software|Species Specificity', 'Comparative Study', '10.1093/bioinformatics/18.2.335', '11847088', 'GenomePixelizer is a visualization tool that generates custom images of the physical or genetic positions of specified sets of genes in whole genomes or parts of genomes. Multiple sets of genes can be shown simultaneously with user-defined characteristics displayed. It allows the analysis of duplication events within and between species based on sequence similarities. The program is written in Tcl/Tk and works on any platform that supports the Tcl/Tk toolkit. GenomePixelizer generates HTML ImageMap tags for each gene in the image allowing links to databases. Images can be saved and presented on web pages.'),
(70163, 2002, 'bio', 'Generating samples under a Wright-Fisher neutral model of genetic variation', 'Hudson, Richard R', 'Department of Ecology and Evolution, University of Chicago, 1101 E. 57th Street, Chicago, IL 60637, USA. rr-hudson@uchicago.edu', 'United States', 1616, 'Computational Biology|Gene Conversion|Genetic Variation|Genetics, Population|Models, Genetic|Monte Carlo Method|Recombination, Genetic|Software', 'Journal Article', '10.1093/bioinformatics/18.2.337', '11847089', 'A Monte Carlo computer program is available to generate samples drawn from a population evolving according to a Wright-Fisher neutral model. The program assumes an infinite-sites model of mutation, and allows recombination, gene conversion, symmetric migration among subpopulations, and a variety of demographic histories. The samples produced can be used to investigate the sampling properties of any sample statistic under these neutral models.'),
(70164, 2002, 'bio', 'QTL Express: mapping quantitative trait loci in simple and complex pedigrees', 'Seaton, George|Haley, Chris S|Knott, Sara A|Kearsey, Mike|Visscher, Peter M', 'Institute of Cell, Animal and Population Biology, University of Edinburgh, West Mains Road, Edinburgh EH9 3JT, UK. george.seaton@ed.ac.uk|Institute of Cell, Animal and Population Biology, University of Edinburgh, West Mains Road, Edinburgh EH9 3JT, UK. george.seaton@ed.ac.uk|Institute of Cell, Animal and Population Biology, University of Edinburgh, West Mains Road, Edinburgh EH9 3JT, UK. george.seaton@ed.ac.uk|Institute of Cell, Animal and Population Biology, University of Edinburgh, West Mains Road, Edinburgh EH9 3JT, UK. george.seaton@ed.ac.uk|Institute of Cell, Animal and Population Biology, University of Edinburgh, West Mains Road, Edinburgh EH9 3JT, UK. george.seaton@ed.ac.uk', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom', 390, 'Chromosome Mapping|Computational Biology|Genetics, Population|Internet|Linear Models|Pedigree|Quantitative Trait, Heritable|Software', 'Journal Article', '10.1093/bioinformatics/18.2.339', '11847090', 'QTL Express is the first application for Quantitative Trait Locus (QTL) mapping in outbred populations with a web-based user interface. User input of three files containing a marker map, trait data and marker genotypes allows mapping of single or multiple QTL by the regression approach, with the option to perform permutation or bootstrap tests.'),
(70165, 2002, 'bio', 'PATRI-paternity inference using genetic data', 'Signorovitch, J|Nielsen, R', 'Department of Biometrics, Cornell University, Ithaca, NY 14853-7801, USA. jes48@cornell.edu|Department of Biometrics, Cornell University, Ithaca, NY 14853-7801, USA. jes48@cornell.edu', 'United States|United States', 21, 'Animals|Computational Biology|Female|Genetic Techniques|Genetics, Population|Genotype|Humans|Likelihood Functions|Male|Paternity|Software', 'Journal Article', '10.1093/bioinformatics/18.2.341', '11847091', 'PATRI is a new application for paternity analysis using genetic data that accounts for the sampling fraction of potential fathers.'),
(70166, 2002, 'bio', 'Translation initiation start prediction in human cDNAs with high accuracy', 'Hatzigeorgiou, Artemis G', 'Metagen GmbH, Ihnestr.63, 14195 Berlin-Dahlem, Germany. agh@pcbi.upenn.edu', 'Germany', 57, 'Algorithms|Computational Biology|DNA, Complementary|Humans|Models, Genetic|Neural Networks (Computer)|Peptide Chain Initiation, Translational|RNA, Messenger|Software', 'Journal Article', '10.1093/bioinformatics/18.2.343', '11847092', 'Correct identification of the Translation Initiation Start (TIS) in cDNA sequences is an important issue for genome annotation. The aim of this work is to improve upon current methods and provide a performance guaranteed prediction.'),
(70167, 2002, 'bio', 'Exploring the pathway structure of metabolism: decomposition into subnetworks and application to Mycoplasma pneumoniae', 'Schuster, S|Pfeiffer, T|Moldenhauer, F|Koch, I|Dandekar, T', 'Max Delbrück Center for Molecular Medicine, Department of Bioinformatics, D-13092 Berlin-Buch, Germany. stschust@mdc-berlin.de|Max Delbrück Center for Molecular Medicine, Department of Bioinformatics, D-13092 Berlin-Buch, Germany. stschust@mdc-berlin.de|Max Delbrück Center for Molecular Medicine, Department of Bioinformatics, D-13092 Berlin-Buch, Germany. stschust@mdc-berlin.de|Max Delbrück Center for Molecular Medicine, Department of Bioinformatics, D-13092 Berlin-Buch, Germany. stschust@mdc-berlin.de|Max Delbrück Center for Molecular Medicine, Department of Bioinformatics, D-13092 Berlin-Buch, Germany. stschust@mdc-berlin.de', 'Germany|Germany|Germany|Germany|Germany', 160, 'Algorithms|Arginine|Computational Biology|Metabolism|Models, Biological|Mycoplasma pneumoniae|Nucleotides|Software', 'Journal Article', '10.1093/bioinformatics/18.2.351', '11847093', 'Reconstructing and analyzing the metabolic map of microorganisms is an important challenge in bioinformatics. Pathway analysis of large metabolic networks meets with the problem of combinatorial explosion of pathways. Therefore, appropriate algorithms for an automated decomposition of these networks into smaller subsystems are needed.'),
(70168, 2002, 'bio', 'Structure motif discovery and mining the PDB', 'Jonassen, Inge|Eidhammer, Ingvar|Conklin, Darrell|Taylor, William R', 'Department of Informatics, University of Bergen, HIB, N5020 Bergen, Norway. Inge.Jonassen@ii.uib.no|Department of Informatics, University of Bergen, HIB, N5020 Bergen, Norway. Inge.Jonassen@ii.uib.no|Department of Informatics, University of Bergen, HIB, N5020 Bergen, Norway. Inge.Jonassen@ii.uib.no|Department of Informatics, University of Bergen, HIB, N5020 Bergen, Norway. Inge.Jonassen@ii.uib.no', 'Norway|Norway|Norway|Norway', 31, 'Algorithms|Amino Acid Motifs|Computational Biology|Cystine|Databases, Protein|Molecular Structure|Protein Structure, Secondary|Proteins|Software', 'Journal Article', '10.1093/bioinformatics/18.2.362', '11847094', 'Many of the most interesting functional and evolutionary relationships among proteins are so ancient that they cannot be reliably detected through sequence analysis and are apparent only through a comparison of the tertiary structures. The conserved features can often be described as structural motifs consisting of a few single residues or Secondary Structure (SS) elements. Confidence in such motifs is greatly boosted when they are found in more than a pair of proteins.'),
(70169, 2002, 'bio', 'The EBI SRS server--recent developments', 'Zdobnov, Evgeni M|Lopez, Rodrigo|Apweiler, Rolf|Etzold, Thure', 'EMBL Outstation, The European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridgeshire CB10 1SD, UK. Evugeni.Zdobnov@EBI.ac.uk|EMBL Outstation, The European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridgeshire CB10 1SD, UK. Evugeni.Zdobnov@EBI.ac.uk|EMBL Outstation, The European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridgeshire CB10 1SD, UK. Evugeni.Zdobnov@EBI.ac.uk|EMBL Outstation, The European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridgeshire CB10 1SD, UK. Evugeni.Zdobnov@EBI.ac.uk', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom', 79, 'Computational Biology|Computer Storage Devices|Data Interpretation, Statistical|Database Management Systems|Databases, Genetic|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/18.2.368', '11847095', 'The current data explosion is intractable without advanced data management systems. The numerous data sets become really useful when they are interconnected under a uniform interface--representing the domain knowledge. The SRS has become an integration system for both data retrieval and applications for data analysis. It provides capabilities to search multiple databases by shared attributes and to query across databases fast and efficiently.'),
(70170, 2002, 'bio', 'Interactive InterPro-based comparisons of proteins in whole genomes', 'Kanapin, A|Apweiler, R|Biswas, M|Fleischmann, W|Karavidopoulou, Y|Kersey, P|Kriventseva, E V|Mittard, V|Mulder, N|Oinn, T|Phan, I|Servant, F|Zdobnov, E', 'EMBL Outstation, The European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridgeshire CB10 1SD, UK. alex@ebi.ac.uk|EMBL Outstation, The European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridgeshire CB10 1SD, UK. alex@ebi.ac.uk|EMBL Outstation, The European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridgeshire CB10 1SD, UK. alex@ebi.ac.uk|EMBL Outstation, The European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridgeshire CB10 1SD, UK. alex@ebi.ac.uk|EMBL Outstation, The European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridgeshire CB10 1SD, UK. alex@ebi.ac.uk|EMBL Outstation, The European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridgeshire CB10 1SD, UK. alex@ebi.ac.uk|EMBL Outstation, The European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridgeshire CB10 1SD, UK. alex@ebi.ac.uk|EMBL Outstation, The European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridgeshire CB10 1SD, UK. alex@ebi.ac.uk|EMBL Outstation, The European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridgeshire CB10 1SD, UK. alex@ebi.ac.uk|EMBL Outstation, The European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridgeshire CB10 1SD, UK. alex@ebi.ac.uk|EMBL Outstation, The European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridgeshire CB10 1SD, UK. alex@ebi.ac.uk|EMBL Outstation, The European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridgeshire CB10 1SD, UK. alex@ebi.ac.uk|EMBL Outstation, The European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridgeshire CB10 1SD, UK. alex@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', 10, 'Computational Biology|Databases, Protein|Genome|Proteome|Software', 'Comparative Study', '10.1093/bioinformatics/18.2.374', '11847096', 'The SWISS-PROT group at the EBI has developed the Proteome Analysis Database utilizing existing resources and providing comprehensive and integrated comparative analysis of the predicted protein coding sequences of the complete genomes of bacteria, archaea and eukaryotes. The Proteome Analysis Database is accompanied by a program that has been designed to carry out interactive InterPro proteome comparisons for any one proteome against any other one or more of the proteomes in the database.'),
(70171, 2002, 'bio', 'Bioinformatics and the theoretical foundations of molecular biology', 'Ouzounis, Christos', 'EMBL Outstation, The European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridgeshire CB10 1SD, UK. alex@ebi.ac.uk', 'United Kingdom', 0, 'Computational Biology', 'Editorial', '10.1093/bioinformatics/18.3.377', '11934735', '-'),
(70172, 2002, 'bio', 'Separation of nearly identical repeats in shotgun assemblies using defined nucleotide positions, DNPs', 'Tammi, Martti T|Arner, Erik|Britton, Tom|Andersson, Björn', 'Department of Genetics and Pathology, Rudbeck Laboratory, Uppsala University, Uppsala, Sweden.|Department of Genetics and Pathology, Rudbeck Laboratory, Uppsala University, Uppsala, Sweden.|Department of Genetics and Pathology, Rudbeck Laboratory, Uppsala University, Uppsala, Sweden.|Department of Genetics and Pathology, Rudbeck Laboratory, Uppsala University, Uppsala, Sweden.', 'Sweden|Sweden|Sweden|Sweden', 13, 'Algorithms|Base Sequence|Cluster Analysis|Computational Biology|Computer Simulation|Deoxyribonucleoproteins|Feasibility Studies|Models, Genetic|Models, Statistical|Molecular Sequence Data|Repetitive Sequences, Nucleic Acid|Sensitivity and Specificity|Sequence Alignment|Sequence Analysis, DNA', 'Comparative Study', '10.1093/bioinformatics/18.3.379', '11934736', 'An increasingly important problem in genome sequencing is the failure of the commonly used shotgun assembly programs to correctly assemble repetitive sequences. The assembly of non-repetitive regions or regions containing repeats considerably shorter than the average read length is in practice easy to solve, while longer repeats have been a difficult problem. We here present a statistical method to separate arbitrarily long, almost identical repeats, which makes it possible to correctly assemble complex repetitive sequence regions. The differences between repeat units may be as low as 1% and the sequencing error may be up to ten times higher. The method is based on the realization that a comparison of only a part of all overlapping sequences at a time in a data set does not generate enough information for a conclusive analysis. Our method uses optimal multi-alignments consisting of all the overlaps of each read. This makes it possible to determine defined nucleotide positions, DNPs, which constitute the differences between the repeat units. Differences between repeats are distinguished from sequencing errors using statistical methods, where the probabilities of obtaining certain combinations of candidate DNPs are calculated using the information from the multi-alignments. The use of DNPs and combinations of DNPs will allow for optimal and rapid assemblies of repeated regions. This method can solve repeats that differ in only two positions in a read length, which is the theoretical limit for repeat separation. We predict that this method will be highly useful in shotgun sequencing in the future.'),
(70173, 2002, 'bio', 'Assessing the significance of consistently mis-regulated genes in cancer associated gene expression matrices', 'Wahde, Mattias|Klus, Gregory T|Bittner, Michael L|Chen, Yidong|Szallasi, Zoltan', 'Division of Mechatronics, Chalmers University of Technology, Göteborg, Sweden. mwahde@me.chalmers.se|Division of Mechatronics, Chalmers University of Technology, Göteborg, Sweden. mwahde@me.chalmers.se|Division of Mechatronics, Chalmers University of Technology, Göteborg, Sweden. mwahde@me.chalmers.se|Division of Mechatronics, Chalmers University of Technology, Göteborg, Sweden. mwahde@me.chalmers.se|Division of Mechatronics, Chalmers University of Technology, Göteborg, Sweden. mwahde@me.chalmers.se', 'Sweden|Sweden|Sweden|Sweden|Sweden', 0, 'Algorithms|Breast Neoplasms|Gene Expression|Gene Expression Profiling|Gene Expression Regulation, Neoplastic|Humans|Models, Genetic|Models, Statistical|Sample Size|Sensitivity and Specificity', 'Journal Article', '10.1093/bioinformatics/18.3.389', '11934737', 'The simplest level of statistical analysis of cancer associated gene expression matrices is aimed at finding consistently up- or down-regulated genes within a given set of tumor samples. Considering the high level of gene expression diversity detected in cancer, one needs to assess the probability that the consistent mis-regulation of a given gene is due to chance. Furthermore, it is important to determine the required sample number that will ensure the meaningful statistical analysis of massively parallel gene expression measurements.'),
(70174, 2002, 'bio', 'An integrated approach utilizing artificial neural networks and SELDI mass spectrometry for the classification of human tumours and rapid identification of potential biomarkers', 'Ball, G|Mian, S|Holding, F|Allibone, R O|Lowe, J|Ali, S|Li, G|McCardle, S|Ellis, I O|Creaser, C|Rees, R C', 'Department of Life Sciences, Nottingham Trent University, Clifton Lane, Clifton, Nottingham NG11 8NS, UK. graham.balls@ntu.ac.uk|Department of Life Sciences, Nottingham Trent University, Clifton Lane, Clifton, Nottingham NG11 8NS, UK. graham.balls@ntu.ac.uk|Department of Life Sciences, Nottingham Trent University, Clifton Lane, Clifton, Nottingham NG11 8NS, UK. graham.balls@ntu.ac.uk|Department of Life Sciences, Nottingham Trent University, Clifton Lane, Clifton, Nottingham NG11 8NS, UK. graham.balls@ntu.ac.uk|Department of Life Sciences, Nottingham Trent University, Clifton Lane, Clifton, Nottingham NG11 8NS, UK. graham.balls@ntu.ac.uk|Department of Life Sciences, Nottingham Trent University, Clifton Lane, Clifton, Nottingham NG11 8NS, UK. graham.balls@ntu.ac.uk|Department of Life Sciences, Nottingham Trent University, Clifton Lane, Clifton, Nottingham NG11 8NS, UK. graham.balls@ntu.ac.uk|Department of Life Sciences, Nottingham Trent University, Clifton Lane, Clifton, Nottingham NG11 8NS, UK. graham.balls@ntu.ac.uk|Department of Life Sciences, Nottingham Trent University, Clifton Lane, Clifton, Nottingham NG11 8NS, UK. graham.balls@ntu.ac.uk|Department of Life Sciences, Nottingham Trent University, Clifton Lane, Clifton, Nottingham NG11 8NS, UK. graham.balls@ntu.ac.uk|Department of Life Sciences, Nottingham Trent University, Clifton Lane, Clifton, Nottingham NG11 8NS, UK. graham.balls@ntu.ac.uk', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom', 205, 'Astrocytoma|Biomarkers, Tumor|Central Nervous System Neoplasms|Feasibility Studies|Humans|Models, Neurological|Neural Networks (Computer)|Sensitivity and Specificity|Spectrometry, Mass, Matrix-Assisted Laser Desorption-Ionization', 'Comparative Study', '10.1093/bioinformatics/18.3.395', '11934738', 'MALDI mass spectrometry is able to elicit macromolecular expression data from cellular material and when used in conjunction with Ciphergen protein chip technology (also referred to as SELDI-Surface Enhanced Laser Desorption/Ionization), it permits a semi-high throughput approach to be taken with respect to sample processing and data acquisition. Due to the large array of data that is generated from a single analysis (8-10000 variables using a mass range of 2-15 kDa-this paper) it is essential to implement the use of algorithms that can detect expression patterns from such large volumes of data correlating to a given biological/pathological phenotype from multiple samples. If successful, the methodology could be extrapolated to larger data sets to enable the identification of validated biomarkers correlating strongly to disease progression. This would not only serve to enable tumours to be classified according to their molecular expression profile but could also focus attention upon a relatively small number of molecules that might warrant further biochemical/molecular characterization to assess their suitability as potential therapeutic targets.'),
(70175, 2002, 'bio', 'Analysis of matched mRNA measurements from two different microarray technologies', 'Kuo, Winston Patrick|Jenssen, Tor-Kristian|Butte, Atul J|Ohno-Machado, Lucila|Kohane, Isaac S', 'Childrens Hospital Informatics Program and Division of Endocrinology, Department of Medicine, Childrens Hospital, Brigham and Womens Hospital, Harvard Medical School, Boston, MA 02115, USA. wpkuo@mit.edu|Childrens Hospital Informatics Program and Division of Endocrinology, Department of Medicine, Childrens Hospital, Brigham and Womens Hospital, Harvard Medical School, Boston, MA 02115, USA. wpkuo@mit.edu|Childrens Hospital Informatics Program and Division of Endocrinology, Department of Medicine, Childrens Hospital, Brigham and Womens Hospital, Harvard Medical School, Boston, MA 02115, USA. wpkuo@mit.edu|Childrens Hospital Informatics Program and Division of Endocrinology, Department of Medicine, Childrens Hospital, Brigham and Womens Hospital, Harvard Medical School, Boston, MA 02115, USA. wpkuo@mit.edu|Childrens Hospital Informatics Program and Division of Endocrinology, Department of Medicine, Childrens Hospital, Brigham and Womens Hospital, Harvard Medical School, Boston, MA 02115, USA. wpkuo@mit.edu', 'United States|United States|United States|United States|United States', 0, 'Cluster Analysis|Databases, Nucleic Acid|Gene Expression|Gene Expression Regulation, Neoplastic|Humans|National Library of Medicine (U.S.)|Oligonucleotide Array Sequence Analysis|RNA, Messenger|Reproducibility of Results|Sensitivity and Specificity|Tumor Cells, Cultured|United States', 'Comparative Study', '10.1093/bioinformatics/18.3.405', '11934739', '[corrected] The existence of several technologies for measuring gene expression makes the question of cross-technology agreement of measurements an important issue. Cross-platform utilization of data from different technologies has the potential to reduce the need to duplicate experiments but requires corresponding measurements to be comparable.'),
(70176, 2002, 'bio', 'A mixture model-based approach to the clustering of microarray expression data', 'McLachlan, G J|Bean, R W|Peel, D', 'Department of Mathematics, University of Queensland, Brisbane, Queensland 4072, Australia.|Department of Mathematics, University of Queensland, Brisbane, Queensland 4072, Australia.|Department of Mathematics, University of Queensland, Brisbane, Queensland 4072, Australia.', 'Australia|Australia|Australia', 0, 'Algorithms|Cluster Analysis|Colonic Neoplasms|Data Interpretation, Statistical|Databases, Genetic|Feasibility Studies|Gene Expression|Gene Expression Regulation, Neoplastic|Humans|Leukemia, Myeloid, Acute|Models, Statistical|Oligonucleotide Array Sequence Analysis|Precursor Cell Lymphoblastic Leukemia-Lymphoma|Sensitivity and Specificity|Stochastic Processes', 'Journal Article', '10.1093/bioinformatics/18.3.413', '11934740', 'This paper introduces the software EMMIX-GENE that has been developed for the specific purpose of a model-based approach to the clustering of microarray expression data, in particular, of tissue samples on a very large number of genes. The latter is a nonstandard problem in parametric cluster analysis because the dimension of the feature space (the number of genes) is typically much greater than the number of tissues. A feasible approach is provided by first selecting a subset of the genes relevant for the clustering of the tissue samples by fitting mixtures of t distributions to rank the genes in order of increasing size of the likelihood ratio statistic for the test of one versus two components in the mixture model. The imposition of a threshold on the likelihood ratio statistic used in conjunction with a threshold on the size of a cluster allows the selection of a relevant set of genes. However, even this reduced set of genes will usually be too large for a normal mixture model to be fitted directly to the tissues, and so the use of mixtures of factor analyzers is exploited to reduce effectively the dimension of the feature space of genes.'),
(70177, 2002, 'bio', 'Microarray data warehouse allowing for inclusion of experiment annotations in statistical analysis', 'Fellenberg, Kurt|Hauser, Nicole C|Brors, Benedikt|Hoheisel, Jörg D|Vingron, Martin', 'Department of Theoretical Bioinformatics, German Cancer Research Center, PO Box 101949, D-69009 Heidelberg, Germany. k.fellenberg@dkfz.de|Department of Theoretical Bioinformatics, German Cancer Research Center, PO Box 101949, D-69009 Heidelberg, Germany. k.fellenberg@dkfz.de|Department of Theoretical Bioinformatics, German Cancer Research Center, PO Box 101949, D-69009 Heidelberg, Germany. k.fellenberg@dkfz.de|Department of Theoretical Bioinformatics, German Cancer Research Center, PO Box 101949, D-69009 Heidelberg, Germany. k.fellenberg@dkfz.de|Department of Theoretical Bioinformatics, German Cancer Research Center, PO Box 101949, D-69009 Heidelberg, Germany. k.fellenberg@dkfz.de', 'Germany|Germany|Germany|Germany|Germany', 51, 'Algorithms|Data Interpretation, Statistical|Database Management Systems|Databases, Factual|Databases, Genetic|Gene Expression|Humans|Information Storage and Retrieval|Oligonucleotide Array Sequence Analysis|Quality Control|Software Design', 'Journal Article', '10.1093/bioinformatics/18.3.423', '11934741', 'Microarray technology provides access to expression levels of thousands of genes at once, producing large amounts of data. These datasets are valuable only if they are annotated by sufficiently detailed experiment descriptions. However, in many databases a substantial number of these annotations is in free-text format and not readily accessible to computer-aided analysis.'),
(70178, 2002, 'bio', 'JenPep: a database of quantitative functional peptide data for immunology', 'Blythe, Martin J|Doytchinova, Irini A|Flower, Darren R', 'Edward Jenner Institute for Vaccine Research, Compton, Berkshire RG0 7NN, UK.|Edward Jenner Institute for Vaccine Research, Compton, Berkshire RG0 7NN, UK.|Edward Jenner Institute for Vaccine Research, Compton, Berkshire RG0 7NN, UK.', 'United Kingdom|United Kingdom|United Kingdom', 121, 'ATP-Binding Cassette Transporters|Databases, Protein|Epitopes, T-Lymphocyte|Humans|Internet|Major Histocompatibility Complex|Peptides|Protein Binding|Receptors, Antigen, T-Cell|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/18.3.434', '11934742', 'The compilation of quantitative binding data underlies attempts to derive tools for the accurate prediction of epitopes in cellular immunology and is part of our concerted goal to develop practical computational vaccinology.'),
(70179, 2002, 'bio', 'PatternHunter: faster and more sensitive homology search', 'Ma, Bin|Tromp, John|Li, Ming', 'Computer Science Department, University of Western Ontario, London N6A 5B8, Canada Bioinformatics Solutions Inc., 145 Columbia Street West, Waterloo, Ont. N2L 3L2, Canada.|Computer Science Department, University of Western Ontario, London N6A 5B8, Canada Bioinformatics Solutions Inc., 145 Columbia Street West, Waterloo, Ont. N2L 3L2, Canada.|Computer Science Department, University of Western Ontario, London N6A 5B8, Canada Bioinformatics Solutions Inc., 145 Columbia Street West, Waterloo, Ont. N2L 3L2, Canada.', 'Canada|Canada|Canada', 0, 'Algorithms|Base Sequence|DNA|Databases, Nucleic Acid|Genome|Models, Statistical|Molecular Sequence Data|National Library of Medicine (U.S.)|Quality Control|Sensitivity and Specificity|Sequence Alignment|Sequence Homology|Software|Time Factors|United States', 'Comparative Study', '10.1093/bioinformatics/18.3.440', '11934743', 'Genomics and proteomics studies routinely depend on homology searches based on the strategy of finding short seed matches which are then extended. The exploding genomic data growth presents a dilemma for DNA homology search techniques: increasing seed size decreases sensitivity whereas decreasing seed size slows down computation.'),
(70180, 2002, 'bio', 'D-ASSIRC: distributed program for finding sequence similarities in genomes', 'Vincens, Pierre|Badel-Chagnon, Anne|André, Cécile|Hazout, Serge', 'Département de Biologie (FR 36), Ecole Normale Supérieure, 46 rue dUlm, 75230 Paris Cedex 05, France. vincens@biologie.ens.fr|Département de Biologie (FR 36), Ecole Normale Supérieure, 46 rue dUlm, 75230 Paris Cedex 05, France. vincens@biologie.ens.fr|Département de Biologie (FR 36), Ecole Normale Supérieure, 46 rue dUlm, 75230 Paris Cedex 05, France. vincens@biologie.ens.fr|Département de Biologie (FR 36), Ecole Normale Supérieure, 46 rue dUlm, 75230 Paris Cedex 05, France. vincens@biologie.ens.fr', 'France|France|France|France', 0, 'Algorithms|Base Sequence|Computing Methodologies|DNA|Databases, Nucleic Acid|Genome|Molecular Sequence Data|National Library of Medicine (U.S.)|Quality Control|Saccharomyces cerevisiae|Sensitivity and Specificity|Sequence Alignment|Sequence Homology|Software|Time Factors|United States', 'Comparative Study', '10.1093/bioinformatics/18.3.446', '11934744', 'Locating the regions of similarity in a genome requires the availability of appropriate tools such as Accelerated Search for SImilar Regions in Chromosomes (ASSIRC, Vincens et al., Bioinformatics, 14, 715-725, 1998). The aim of this paper is to present different strategies for improving this program by distributing the operations and data to multiple processing units and to assess the efficiency of the different implementations in terms of running time as a function of the number of processing units.'),
(70181, 2002, 'bio', 'Multiple sequence alignment using partial order graphs', 'Lee, Christopher|Grasso, Catherine|Sharlow, Mark F', 'Department of Chemistry and Biochemistry, University of California, Los Angeles, Los Angeles, CA 90095-1570, USA. leec@mbi.ucla.edu|Department of Chemistry and Biochemistry, University of California, Los Angeles, Los Angeles, CA 90095-1570, USA. leec@mbi.ucla.edu|Department of Chemistry and Biochemistry, University of California, Los Angeles, Los Angeles, CA 90095-1570, USA. leec@mbi.ucla.edu', 'United States|United States|United States', 525, 'Algorithms|Base Sequence|DNA|Databases, Genetic|Expressed Sequence Tags|Glucose-1-Phosphate Adenylyltransferase|Humans|Models, Statistical|Molecular Sequence Data|Nucleotidyltransferases|Plant Proteins|Quality Control|RNA, Messenger|Sensitivity and Specificity|Sequence Alignment|Sequence Homology|Software|Time Factors', 'Comparative Study', '10.1093/bioinformatics/18.3.452', '11934745', 'Progressive Multiple Sequence Alignment (MSA) methods depend on reducing an MSA to a linear profile for each alignment step. However, this leads to loss of information needed for accurate alignment, and gap scoring artifacts.'),
(70182, 2002, 'bio', 'The complexity of comparing reaction systems', 'Ettinger, Mark', 'Los Alamos National Laboratory, Los Alamos, NM 87545, USA. ettinger@lanl.gov', 'United States', 0, 'Algorithms|Databases, Factual|Feasibility Studies|Glycolysis|Humans|Models, Chemical|Models, Statistical|Molecular Conformation|Pyrimidines', 'Comparative Study', '10.1093/bioinformatics/18.3.465', '11934746', 'As more genomic data becomes available there is increased attention on understanding the mechanisms encoded in the genome. New XML dialects like CellML and Systems Biology Markup Language (SBML) are being developed to describe biological networks of all types. In the absence of detailed kinetic information for these networks, stoichiometric data is an especially valuable source of information. Network databases are the next logical step beyond storing purely genomic information. Just as comparison of entries in genomic databases has been a vital algorithmic problem through the course of the sequencing project, comparison of networks in network databases will be a crucial problem as we seek to integrate higher-order network knowledge.'),
(70183, 2002, 'bio', 'STOCKS: STOChastic Kinetic Simulations of biochemical systems with Gillespie algorithm', 'Kierzek, Andrzej M', 'Institute of Biochemistry and Biophysics, Polish Academy of Sciences, Pawinskiego 5a, 02-106 Warszawa, Poland. andrezejk@ibb.waw.pl', 'Poland', 98, 'Algorithms|Computer Simulation|Databases, Factual|Escherichia coli|Gene Expression Regulation|Kinetics|Models, Chemical|Models, Statistical|Monte Carlo Method|Protein Biosynthesis|Proteins|Software|Stochastic Processes|Transcription, Genetic', 'Comparative Study', '10.1093/bioinformatics/18.3.470', '11934747', 'The availability of a huge amount of molecular data concerning various biochemical reactions provoked numerous attempts to study the dynamics of cellular processes by means of kinetic models and computer simulations. Biochemical processes frequently involve small numbers of molecules (e.g. a few molecules of a transcriptional regulator binding to one molecule of a DNA regulatory region). Such reactions are subject to significant stochastic fluctuations. Monte Carlo methods must be employed to study the functional consequences of the fluctuations and simulate processes that cannot be modelled by continuous fluxes of matter. This provides the motivation to develop software dedicated to Monte Carlo simulations of cellular processes with the rigorously proven Gillespie algorithm.'),
(70184, 2002, 'bio', 'Long-range control of expression in yeast', 'Mannila, Heikki|Patrikainen, Anne|Seppänen, Jouni K|Kere, Juha', 'Laboratory for Computer and Information Science, Helsinki University of Technology, PO Box 5400, 02015 HUT, Finland.|Laboratory for Computer and Information Science, Helsinki University of Technology, PO Box 5400, 02015 HUT, Finland.|Laboratory for Computer and Information Science, Helsinki University of Technology, PO Box 5400, 02015 HUT, Finland.|Laboratory for Computer and Information Science, Helsinki University of Technology, PO Box 5400, 02015 HUT, Finland.', 'Finland|Finland|Finland|Finland', 6, 'Databases, Nucleic Acid|Gene Expression Regulation|Genome|Internet|Open Reading Frames|Saccharomyces cerevisiae|Sensitivity and Specificity|Statistics as Topic', 'Journal Article', '10.1093/bioinformatics/18.3.482', '11934748', '-'),
(70185, 2002, 'bio', 'Assembly of fingerprint contigs: parallelized FPC', 'Ness, S R|Terpstra, W|Krzywinski, M|Marra, M A|Jones, S J M', 'Genome Sequence Centre, British Columbia Cancer Agency, 600 West 10th Avenue, Vancouver, BC V5Z 4E6, Canada.|Genome Sequence Centre, British Columbia Cancer Agency, 600 West 10th Avenue, Vancouver, BC V5Z 4E6, Canada.|Genome Sequence Centre, British Columbia Cancer Agency, 600 West 10th Avenue, Vancouver, BC V5Z 4E6, Canada.|Genome Sequence Centre, British Columbia Cancer Agency, 600 West 10th Avenue, Vancouver, BC V5Z 4E6, Canada.|Genome Sequence Centre, British Columbia Cancer Agency, 600 West 10th Avenue, Vancouver, BC V5Z 4E6, Canada.', 'Canada|Canada|Canada|Canada|Canada', 0, 'Algorithms|Animals|Base Sequence|Cloning, Molecular|Computing Methodologies|Contig Mapping|DNA Fingerprinting|Databases, Genetic|Humans|Internet|Mice|Molecular Sequence Data|Restriction Mapping|Sensitivity and Specificity|Sequence Tagged Sites|Software|Time Factors', 'Journal Article', '10.1093/bioinformatics/18.3.484', '11934749', 'One of the more common uses of the program FingerPrint Contigs (FPC) is to assemble random restriction digest fingerprints of overlapping genomic clones into contigs. To improve the rate of assembling contigs from large fingerprint databases we have adapted FPC so that it can be run in parallel on multiple processors and servers. The current version of parallelized FPC has been used in our laboratory to assemble mammalian BAC fingerprint databases, each containing more than 300000 BAC fingerprints.'),
(70186, 2002, 'bio', 'OligoArray: genome-scale oligonucleotide design for microarrays', 'Rouillard, Jean-Marie|Herbert, Christopher J|Zuker, Michael', 'Department of Chemical Engineering, University of Michigan, Ann Arbor, MI 48109-2136, USA. jean-marie.rouillard@cgm.cnrs-gif.fr|Department of Chemical Engineering, University of Michigan, Ann Arbor, MI 48109-2136, USA. jean-marie.rouillard@cgm.cnrs-gif.fr|Department of Chemical Engineering, University of Michigan, Ann Arbor, MI 48109-2136, USA. jean-marie.rouillard@cgm.cnrs-gif.fr', 'United States|United States|United States', 0, 'Computer-Aided Design|Databases, Genetic|Drug Design|Equipment Design|Genome|National Institutes of Health (U.S.)|Oligonucleotide Array Sequence Analysis|Oligonucleotides|Saccharomyces cerevisiae|Sensitivity and Specificity|Software|United States', 'Journal Article', '10.1093/bioinformatics/18.3.486', '11934750', 'OligoArray is a program that computes gene specific and secondary structure free oligonucleotides for genome-scale oligonucleotide microarray construction or other applications.'),
(70187, 2002, 'bio', 'ProbeMatchDB--a web database for finding equivalent probes across microarray platforms and species', 'Wang, Pinglang|Ding, Fei|Chiang, Hsienyuan|Thompson, Robert C|Watson, Stanley J|Meng, Fan', 'Department of Psychiatry and Mental Health Research Institute, University of Michigan Medical School, Ann Arbor, MI 48109, USA.|Department of Psychiatry and Mental Health Research Institute, University of Michigan Medical School, Ann Arbor, MI 48109, USA.|Department of Psychiatry and Mental Health Research Institute, University of Michigan Medical School, Ann Arbor, MI 48109, USA.|Department of Psychiatry and Mental Health Research Institute, University of Michigan Medical School, Ann Arbor, MI 48109, USA.|Department of Psychiatry and Mental Health Research Institute, University of Michigan Medical School, Ann Arbor, MI 48109, USA.|Department of Psychiatry and Mental Health Research Institute, University of Michigan Medical School, Ann Arbor, MI 48109, USA.', 'United States|United States|United States|United States|United States|United States', 10, 'Animals|DNA Probes|Database Management Systems|Databases, Nucleic Acid|Expressed Sequence Tags|Gene Expression|Humans|Internet|Mice|Molecular Sequence Data|Oligonucleotide Array Sequence Analysis|Rats|Software|Species Specificity', 'Journal Article', '10.1093/bioinformatics/18.3.488', '11934751', 'ProbeMatchDB is a web-based database designed to facilitate the search of EST/cDNA sequences or STS markers that can be used to represent the same gene across different microarray platforms and species. It can be used for finding equivalent EST clones in the Research Genetics sequence verified clone set based on results from Affymetirx GeneChips. It will also help to identify probes representing orthologous genes across human, mouse and rat on different microarray platforms.'),
(70188, 2002, 'bio', 'GeneANOVA--gene expression analysis of variance', 'Didier, G|Brézellec, P|Remy, E|Hénaut, A', 'Laboratoire Génome et Informatique, Tour Evry 2, 523 place des terrasses de lAgora, 91034 Evry, France. didier@genopole.cnrs.fr|Laboratoire Génome et Informatique, Tour Evry 2, 523 place des terrasses de lAgora, 91034 Evry, France. didier@genopole.cnrs.fr|Laboratoire Génome et Informatique, Tour Evry 2, 523 place des terrasses de lAgora, 91034 Evry, France. didier@genopole.cnrs.fr|Laboratoire Génome et Informatique, Tour Evry 2, 523 place des terrasses de lAgora, 91034 Evry, France. didier@genopole.cnrs.fr', 'France|France|France|France', 28, 'Analysis of Variance|Databases, Genetic|Gene Expression|Genetic Variation|Oligonucleotide Array Sequence Analysis|Software|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/18.3.490', '11934752', 'GeneANOVA is an ANOVA-based software devoted to the analysis of gene expression data.'),
(70189, 2002, 'bio', 'GRIMM: genome rearrangements web server', 'Tesler, Glenn', 'Department of Computer Science and Engineering, University of California, San Diego, CA 92093-0114, USA.', 'United States', 218, 'Algorithms|Animals|Chromosomes|Databases, Nucleic Acid|Evolution, Molecular|Gene Order|Gene Rearrangement|Genome|Humans|Internet|Software|Species Specificity', 'Journal Article', '10.1093/bioinformatics/18.3.492', '11934753', 'Genome Rearrangements In Man and Mouse (GRIMM) is a tool for analyzing rearrangements of gene orders in pairs of unichromosomal and multichromosomal genomes, with either signed or unsigned gene data. Although there are several programs for analyzing rearrangements in unichromosomal genomes, this is the first to analyze rearrangements in multichromosomal genomes. GRIMM also provides a new algorithm for analyzing comparative maps for which gene directions are unknown.'),
(70190, 2002, 'bio', 'MaxBench: evaluation of sequence and structure comparison methods', 'Leplae, Raphael|Hubbard, Tim J P', 'Sanger Centre, Wellcome Trust Genome Campus, Hinxton, Cambridgeshire CB10 1SA, UK. lp1@sanger.ac.uk|Sanger Centre, Wellcome Trust Genome Campus, Hinxton, Cambridgeshire CB10 1SA, UK. lp1@sanger.ac.uk', 'United Kingdom|United Kingdom', 0, 'Algorithms|Database Management Systems|Databases, Protein|Evaluation Studies as Topic|Molecular Sequence Data|National Library of Medicine (U.S.)|Protein Conformation|Proteins|Quality Control|Sensitivity and Specificity|Sequence Analysis, Protein|Software|United States', 'Journal Article', '10.1093/bioinformatics/18.3.494', '11934754', 'MaxBench is a web-based system available for evaluating the results of sequence and structure comparison methods, based on the SCOP protein domain classification. The system makes it easy for developers to both compare the overall performance of their methods to standard algorithms and investigate the results of individual comparisons.'),
(70191, 2002, 'bio', 'CASA: a server for the critical assessment of protein sequence alignment accuracy', 'Kahsay, Robel Y|Wang, Guoli|Dongre, Nataraj|Gao, Guang|Dunbrack, Roland L', 'Delaware Biotechnology Institute and Department of Electrical and Computer Engineering, University of Delaware, 15 Innovation Way, Newark, DE 19711, USA.|Delaware Biotechnology Institute and Department of Electrical and Computer Engineering, University of Delaware, 15 Innovation Way, Newark, DE 19711, USA.|Delaware Biotechnology Institute and Department of Electrical and Computer Engineering, University of Delaware, 15 Innovation Way, Newark, DE 19711, USA.|Delaware Biotechnology Institute and Department of Electrical and Computer Engineering, University of Delaware, 15 Innovation Way, Newark, DE 19711, USA.|Delaware Biotechnology Institute and Department of Electrical and Computer Engineering, University of Delaware, 15 Innovation Way, Newark, DE 19711, USA.', 'United States|United States|United States|United States|United States', 4, 'Algorithms|Calibration|Computing Methodologies|Databases, Protein|Evaluation Studies as Topic|Internet|National Library of Medicine (U.S.)|Proteins|Sequence Alignment|Sequence Analysis, Protein|Software|United States', 'Comparative Study', '10.1093/bioinformatics/18.3.496', '11934755', 'A public server for evaluating the accuracy of protein sequence alignment methods is presented. CASA is an implementation of the alignment accuracy benchmark presented by Sauder et al. (Proteins, 40, 6-22, 2000). The benchmark currently contains 39321 pairwise protein structure alignments produced with the CE program from SCOP domain definitions. The server produces graphical and tabular comparisons of the accuracy of a users input sequence alignments with other commonly used programs, such as BLAST, PSI-BLAST, Clustal W, and SAM-T99.'),
(70192, 2002, 'bio', 'BetaTPred: prediction of beta-TURNS in a protein using statistical algorithms', 'Kaur, Harpreet|Raghava, G P S', 'Institute of Microbial Technology, Sector-39A, Chandigarh, India.|Institute of Microbial Technology, Sector-39A, Chandigarh, India.', 'India|India', 21, 'Algorithms|Amino Acid Sequence|Computing Methodologies|Internet|Models, Molecular|Models, Statistical|Molecular Sequence Data|Protein Structure, Secondary|Proteins|Software', 'Journal Article', '10.1093/bioinformatics/18.3.498', '11934756', 'beta-turns play an important role from a structural and functional point of view. beta-turns are the most common type of non-repetitive structures in proteins and comprise on average, 25% of the residues. In the past numerous methods have been developed to predict beta-turns in a protein. Most of these prediction methods are based on statistical approaches. In order to utilize the full potential of these methods, there is a need to develop a web server.'),
(70193, 2002, 'bio', 'DIVERGE: phylogeny-based analysis for functional-structural divergence of a protein family', 'Gu, Xun|Vander Velden, Kent', 'Department of Zoology and Genetics, Program of Bioinformatics and Computational Biology, Iowa State University, IA 50011, USA. xgu@iastate.edu|Department of Zoology and Genetics, Program of Bioinformatics and Computational Biology, Iowa State University, IA 50011, USA. xgu@iastate.edu', 'United States|United States', 0, 'Amino Acid Sequence|Cluster Analysis|Evolution, Molecular|Genetic Variation|Models, Genetic|Models, Statistical|Molecular Sequence Data|Multigene Family|Phylogeny|Proteins|Sequence Analysis, Protein|Sequence Homology, Amino Acid|Software', 'Journal Article', '10.1093/bioinformatics/18.3.500', '11934757', 'DetectIng Variability in Evolutionary Rates among GEnes (DIVERGE) is a software system to study functional divergence of a protein family by detecting site-specific change in evolutionary rate using a multiple alignment of amino acid sequences for a given phylogenetic tree. The program first conducts a statistical test for site-specific rate shifts along the tree, and predicting candidate amino acid residues responsible for functional divergence based on posterior analysis. These results can then be mapped on the 3D protein structure if available.'),
(70194, 2002, 'bio', 'TREE-PUZZLE: maximum likelihood phylogenetic analysis using quartets and parallel computing', 'Schmidt, Heiko A|Strimmer, Korbinian|Vingron, Martin|von Haeseler, Arndt', 'Max-Planck-Institut für Molekulare Genetik, Ihnestr. 73, D-14195 Berlin, Germany. hschmidt@eva.mpg.de|Max-Planck-Institut für Molekulare Genetik, Ihnestr. 73, D-14195 Berlin, Germany. hschmidt@eva.mpg.de|Max-Planck-Institut für Molekulare Genetik, Ihnestr. 73, D-14195 Berlin, Germany. hschmidt@eva.mpg.de|Max-Planck-Institut für Molekulare Genetik, Ihnestr. 73, D-14195 Berlin, Germany. hschmidt@eva.mpg.de', 'Germany|Germany|Germany|Germany', 0, 'Algorithms|Computing Methodologies|Database Management Systems|Databases, Genetic|Internet|Likelihood Functions|Mathematical Computing|Molecular Sequence Data|Phylogeny|Sequence Analysis|Software', 'Journal Article', '10.1093/bioinformatics/18.3.502', '11934758', 'TREE-PUZZLE is a program package for quartet-based maximum-likelihood phylogenetic analysis (formerly PUZZLE, Strimmer and von Haeseler, Mol. Biol. Evol., 13, 964-969, 1996) that provides methods for reconstruction, comparison, and testing of trees and models on DNA as well as protein sequences. To reduce waiting time for larger datasets the tree reconstruction part of the software has been parallelized using message passing that runs on clusters of workstations as well as parallel computers.'),
(70195, 2002, 'bio', 'From sequence to biology: the impact on bioinformatics', 'Fuchs, Rainer', 'Max-Planck-Institut für Molekulare Genetik, Ihnestr. 73, D-14195 Berlin, Germany. hschmidt@eva.mpg.de', 'Germany', 12, 'Computational Biology|Genome, Human|Humans|Sequence Analysis|Workforce', 'Editorial', '10.1093/bioinformatics/18.4.505', '12016047', '-'),
(70196, 2002, 'bio', 'Compensation for nucleotide bias in a genome by representation as a discrete channel with noise', 'Schreiber, Mark|Brown, Chris', 'Department of Biochemistry, University of Otago, PO Box 56, Dunedin, New Zealand.|Department of Biochemistry, University of Otago, PO Box 56, Dunedin, New Zealand.', 'New Zealand|New Zealand', 0, 'Algorithms|Amino Acid Motifs|Borrelia burgdorferi|Codon|Databases, Nucleic Acid|Escherichia coli|Genome, Bacterial|Information Storage and Retrieval|Models, Genetic|Reproducibility of Results|Sensitivity and Specificity|Sequence Alignment|Sequence Analysis, DNA|Stochastic Processes', 'Journal Article', '10.1093/bioinformatics/18.4.507', '12016048', 'Calculation of the information content of motifs in genomes highly biased in nucleotide composition is likely to lead to overestimates of the amount of useful information in the motif. Calculating relative information can compensate for biases, however the resulting information content is the amount seen by an observer and not by a macromolecule binding to the motif. The latter is needed to calculate the discriminatory power of the motif and to compare motifs between species.');
