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      The Genome Sequence of Caenorhabditis briggsae: A Platform for Comparative Genomics

      , 1 , 2 , 9 , 3 , 4 , 5 , 1 , 3 , 6 , 6 , 7 , 6 , 13 , 1 , 8 , 14 , 3 , 3 , 6 , 1 , 3 , 9 , 6 , 6 , 3 , 3 , 10 , 3 , 3 , 6 , 11 , 6 , 6 , 3 , 10 , 6 , 3 , 12 , 5 , 6 , 3 , 6 , 3 , 9

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          The soil nematodes Caenorhabditis briggsae and Caenorhabditis elegans diverged from a common ancestor roughly 100 million years ago and yet are almost indistinguishable by eye. They have the same chromosome number and genome sizes, and they occupy the same ecological niche. To explore the basis for this striking conservation of structure and function, we have sequenced the C. briggsae genome to a high-quality draft stage and compared it to the finished C. elegans sequence. We predict approximately 19,500 protein-coding genes in the C. briggsae genome, roughly the same as in C. elegans. Of these, 12,200 have clear C. elegans orthologs, a further 6,500 have one or more clearly detectable C. elegans homologs, and approximately 800 C. briggsae genes have no detectable matches in C. elegans. Almost all of the noncoding RNAs (ncRNAs) known are shared between the two species. The two genomes exhibit extensive colinearity, and the rate of divergence appears to be higher in the chromosomal arms than in the centers. Operons, a distinctive feature of C. elegans, are highly conserved in C. briggsae, with the arrangement of genes being preserved in 96% of cases. The difference in size between the C. briggsae (estimated at approximately 104 Mbp) and C. elegans (100.3 Mbp) genomes is almost entirely due to repetitive sequence, which accounts for 22.4% of the C. briggsae genome in contrast to 16.5% of the C. elegans genome. Few, if any, repeat families are shared, suggesting that most were acquired after the two species diverged or are undergoing rapid evolution. Coclustering the C. elegans and C. briggsae proteins reveals 2,169 protein families of two or more members. Most of these are shared between the two species, but some appear to be expanding or contracting, and there seem to be as many as several hundred novel C. briggsae gene families. The C. briggsae draft sequence will greatly improve the annotation of the C. elegans genome. Based on similarity to C. briggsae, we found strong evidence for 1,300 new C. elegans genes. In addition, comparisons of the two genomes will help to understand the evolutionary forces that mold nematode genomes.


          With the Caenorhabditis briggsae genome now in hand, C. elegans biologists have a powerful new research tool to refine their knowledge of gene function in C. elegans and to study the path of genome evolution

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          Most cited references 121

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          Gapped BLAST and PSI-BLAST: a new generation of protein database search programs.

           S Altschul (1997)
          The BLAST programs are widely used tools for searching protein and DNA databases for sequence similarities. For protein comparisons, a variety of definitional, algorithmic and statistical refinements described here permits the execution time of the BLAST programs to be decreased substantially while enhancing their sensitivity to weak similarities. A new criterion for triggering the extension of word hits, combined with a new heuristic for generating gapped alignments, yields a gapped BLAST program that runs at approximately three times the speed of the original. In addition, a method is introduced for automatically combining statistically significant alignments produced by BLAST into a position-specific score matrix, and searching the database using this matrix. The resulting Position-Specific Iterated BLAST (PSI-BLAST) program runs at approximately the same speed per iteration as gapped BLAST, but in many cases is much more sensitive to weak but biologically relevant sequence similarities. PSI-BLAST is used to uncover several new and interesting members of the BRCT superfamily.
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            CLUSTAL W: improving the sensitivity of progressive multiple sequence alignment through sequence weighting, position-specific gap penalties and weight matrix choice.

            The sensitivity of the commonly used progressive multiple sequence alignment method has been greatly improved for the alignment of divergent protein sequences. Firstly, individual weights are assigned to each sequence in a partial alignment in order to down-weight near-duplicate sequences and up-weight the most divergent ones. Secondly, amino acid substitution matrices are varied at different alignment stages according to the divergence of the sequences to be aligned. Thirdly, residue-specific gap penalties and locally reduced gap penalties in hydrophilic regions encourage new gaps in potential loop regions rather than regular secondary structure. Fourthly, positions in early alignments where gaps have been opened receive locally reduced gap penalties to encourage the opening up of new gaps at these positions. These modifications are incorporated into a new program, CLUSTAL W which is freely available.
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              Initial sequencing and analysis of the human genome.

               James Galagan (2001)
              The human genome holds an extraordinary trove of information about human development, physiology, medicine and evolution. Here we report the results of an international collaboration to produce and make freely available a draft sequence of the human genome. We also present an initial analysis of the data, describing some of the insights that can be gleaned from the sequence.

                Author and article information

                PLoS Biol
                PLoS Biology
                Public Library of Science (San Francisco, USA )
                November 2003
                17 November 2003
                : 1
                : 2
                1simpleCold Spring Harbor Laboratory, Cold Spring Harbor New YorkUnited States of America
                2simpleDepartment of Genetics, Washington University School of Medicine St. Louis, MissouriUnited States of America
                3simpleGenome Sequencing Center, Washington University School of Medicine St. Louis, MissouriUnited States of America
                4simpleBiochemistry and Molecular Genetics, University of Colorado Denver, ColoradoUnited States of America
                5simpleDepartment of Computer Science and Engineering, Washington University St. Louis, MissouriUnited States of America
                6simpleWellcome Trust Sanger Institute HinxtonUnited Kingdom
                7simpleDepartment of Genetics, Trinity College DublinIreland
                8simpleNew York University School of Medicine, New York New YorkUnited States of America
                9simpleDepartment of Genome Sciences, University of Washington Seattle, WashingtonUnited States of America
                10simpleGenome Sciences Centre, British Columbia Cancer Agency VancouverCanada
                11simpleNational Institutes of Health, Bethesda MarylandUnited States of America
                12simpleDepartment of Molecular Genetics and Microbiology, Duke University Durham, North CarolinaUnited States of America
                13simpleMedical Research Council Laboratory of Molecular Biology CambridgeUnited Kingdom
                14simpleDepartment of Biology, New York University New York, New YorkUnited States of America
                Copyright: © 2003 Stein et al. This is an open-access article distributed under the terms of the Public Library of Science Open-Access License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited
                Research Article
                Bioinformatics/Computational Biology
                Genetics/Genomics/Gene Therapy

                Life sciences


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