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| pubmed-article:16076884 | lifeskim:mentions | umls-concept:C0017337 | lld:lifeskim |
| pubmed-article:16076884 | lifeskim:mentions | umls-concept:C0449416 | lld:lifeskim |
| pubmed-article:16076884 | lifeskim:mentions | umls-concept:C0439064 | lld:lifeskim |
| pubmed-article:16076884 | lifeskim:mentions | umls-concept:C0681842 | lld:lifeskim |
| pubmed-article:16076884 | lifeskim:mentions | umls-concept:C1158478 | lld:lifeskim |
| pubmed-article:16076884 | lifeskim:mentions | umls-concept:C1705422 | lld:lifeskim |
| pubmed-article:16076884 | lifeskim:mentions | umls-concept:C0332120 | lld:lifeskim |
| pubmed-article:16076884 | pubmed:issue | 18 | lld:pubmed |
| pubmed-article:16076884 | pubmed:dateCreated | 2005-9-1 | lld:pubmed |
| pubmed-article:16076884 | pubmed:abstractText | Computational gene finding systems play an important role in finding new human genes, although no systems are yet accurate enough to predict all or even most protein-coding regions perfectly. Ab initio programs can be augmented by evidence such as expression data or protein sequence homology, which improves their performance. The amount of such evidence continues to grow, but computational methods continue to have difficulty predicting genes when the evidence is conflicting or incomplete. Genome annotation pipelines collect a variety of types of evidence about gene structure and synthesize the results, which can then be refined further through manual, expert curation of gene models. | lld:pubmed |
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| pubmed-article:16076884 | pubmed:language | eng | lld:pubmed |
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| pubmed-article:16076884 | pubmed:citationSubset | IM | lld:pubmed |
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| pubmed-article:16076884 | pubmed:status | MEDLINE | lld:pubmed |
| pubmed-article:16076884 | pubmed:month | Sep | lld:pubmed |
| pubmed-article:16076884 | pubmed:issn | 1367-4803 | lld:pubmed |
| pubmed-article:16076884 | pubmed:author | pubmed-author:SalzbergSteve... | lld:pubmed |
| pubmed-article:16076884 | pubmed:author | pubmed-author:AllenJonathan... | lld:pubmed |
| pubmed-article:16076884 | pubmed:issnType | Print | lld:pubmed |
| pubmed-article:16076884 | pubmed:day | 15 | lld:pubmed |
| pubmed-article:16076884 | pubmed:volume | 21 | lld:pubmed |
| pubmed-article:16076884 | pubmed:owner | NLM | lld:pubmed |
| pubmed-article:16076884 | pubmed:authorsComplete | Y | lld:pubmed |
| pubmed-article:16076884 | pubmed:pagination | 3596-603 | lld:pubmed |
| pubmed-article:16076884 | pubmed:dateRevised | 2007-11-14 | lld:pubmed |
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| pubmed-article:16076884 | pubmed:year | 2005 | lld:pubmed |
| pubmed-article:16076884 | pubmed:articleTitle | JIGSAW: integration of multiple sources of evidence for gene prediction. | lld:pubmed |
| pubmed-article:16076884 | pubmed:affiliation | Center for Bioinformatics and Computational Biology, University of Maryland Institute for Advanced Computer Studies, College Park, MD 20742, USA. jeallen@umiacs.umd.edu | lld:pubmed |
| pubmed-article:16076884 | pubmed:publicationType | Journal Article | lld:pubmed |
| pubmed-article:16076884 | pubmed:publicationType | Research Support, U.S. Gov't, P.H.S. | lld:pubmed |
| pubmed-article:16076884 | pubmed:publicationType | Research Support, N.I.H., Extramural | lld:pubmed |
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