Importing statistical measures into Artemis enhances gene identification in the <it>Leishmania </it>genome project
<p>Abstract</p> <p>Background</p> <p>Seattle Biomedical Research Institute (SBRI) as part of the <it>Leishmania </it>Genome Network (LGN) is sequencing chromosomes of the trypanosomatid protozoan species <it>Leishmania major</it>. At SBRI, chromo...
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| Format: | Article |
| Language: | English |
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BMC
2003-06-01
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| Series: | BMC Bioinformatics |
| Online Access: | http://www.biomedcentral.com/1471-2105/4/23 |
| _version_ | 1828728913700847616 |
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| author | McDonagh Paul D Worthey EA Aggarwal Gautam Myler Peter J |
| author_facet | McDonagh Paul D Worthey EA Aggarwal Gautam Myler Peter J |
| author_sort | McDonagh Paul D |
| collection | DOAJ |
| description | <p>Abstract</p> <p>Background</p> <p>Seattle Biomedical Research Institute (SBRI) as part of the <it>Leishmania </it>Genome Network (LGN) is sequencing chromosomes of the trypanosomatid protozoan species <it>Leishmania major</it>. At SBRI, chromosomal sequence is annotated using a combination of trained and untrained non-consensus gene-prediction algorithms with A<smcaps>RTEMIS</smcaps>, an annotation platform with rich and user-friendly interfaces.</p> <p>Results</p> <p>Here we describe a methodology used to import results from three different protein-coding gene-prediction algorithms (G<smcaps>LIMMER</smcaps>, T<smcaps>ESTCODE</smcaps> and G<smcaps>ENESCAN</smcaps>) into the A<smcaps>RTEMIS</smcaps> sequence viewer and annotation tool. Comparison of these methods, along with the C<smcaps>ODON</smcaps>U<smcaps>SAGE</smcaps> algorithm built into A<smcaps>RTEMIS</smcaps>, shows the importance of combining methods to more accurately annotate the <it>L. major </it>genomic sequence.</p> <p>Conclusion</p> <p>An improvised and powerful tool for gene prediction has been developed by importing data from widely-used algorithms into an existing annotation platform. This approach is especially fruitful in the <it>Leishmania </it>genome project where there is large proportion of novel genes requiring manual annotation.</p> |
| first_indexed | 2024-04-12T14:23:52Z |
| format | Article |
| id | doaj.art-23f2be939aa94d31afae9483b191870f |
| institution | Directory Open Access Journal |
| issn | 1471-2105 |
| language | English |
| last_indexed | 2024-04-12T14:23:52Z |
| publishDate | 2003-06-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Bioinformatics |
| spelling | doaj.art-23f2be939aa94d31afae9483b191870f2022-12-22T03:29:30ZengBMCBMC Bioinformatics1471-21052003-06-01412310.1186/1471-2105-4-23Importing statistical measures into Artemis enhances gene identification in the <it>Leishmania </it>genome projectMcDonagh Paul DWorthey EAAggarwal GautamMyler Peter J<p>Abstract</p> <p>Background</p> <p>Seattle Biomedical Research Institute (SBRI) as part of the <it>Leishmania </it>Genome Network (LGN) is sequencing chromosomes of the trypanosomatid protozoan species <it>Leishmania major</it>. At SBRI, chromosomal sequence is annotated using a combination of trained and untrained non-consensus gene-prediction algorithms with A<smcaps>RTEMIS</smcaps>, an annotation platform with rich and user-friendly interfaces.</p> <p>Results</p> <p>Here we describe a methodology used to import results from three different protein-coding gene-prediction algorithms (G<smcaps>LIMMER</smcaps>, T<smcaps>ESTCODE</smcaps> and G<smcaps>ENESCAN</smcaps>) into the A<smcaps>RTEMIS</smcaps> sequence viewer and annotation tool. Comparison of these methods, along with the C<smcaps>ODON</smcaps>U<smcaps>SAGE</smcaps> algorithm built into A<smcaps>RTEMIS</smcaps>, shows the importance of combining methods to more accurately annotate the <it>L. major </it>genomic sequence.</p> <p>Conclusion</p> <p>An improvised and powerful tool for gene prediction has been developed by importing data from widely-used algorithms into an existing annotation platform. This approach is especially fruitful in the <it>Leishmania </it>genome project where there is large proportion of novel genes requiring manual annotation.</p>http://www.biomedcentral.com/1471-2105/4/23 |
| spellingShingle | McDonagh Paul D Worthey EA Aggarwal Gautam Myler Peter J Importing statistical measures into Artemis enhances gene identification in the <it>Leishmania </it>genome project BMC Bioinformatics |
| title | Importing statistical measures into Artemis enhances gene identification in the <it>Leishmania </it>genome project |
| title_full | Importing statistical measures into Artemis enhances gene identification in the <it>Leishmania </it>genome project |
| title_fullStr | Importing statistical measures into Artemis enhances gene identification in the <it>Leishmania </it>genome project |
| title_full_unstemmed | Importing statistical measures into Artemis enhances gene identification in the <it>Leishmania </it>genome project |
| title_short | Importing statistical measures into Artemis enhances gene identification in the <it>Leishmania </it>genome project |
| title_sort | importing statistical measures into artemis enhances gene identification in the it leishmania it genome project |
| url | http://www.biomedcentral.com/1471-2105/4/23 |
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