Linked Life Data

16108706

Source:http://linkedlifedata.com/resource/pubmed/id/16108706

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
6
pubmed:dateCreated
2005-8-19
pubmed:abstractText
The Gibbs sampling method has been widely used for sequence analysis after it was successfully applied to the problem of identifying regulatory motif sequences upstream of genes. Since then, numerous variants of the original idea have emerged: however, in all cases the application has been to finding short motifs in collections of short sequences (typically less than 100 nucleotides long). In this paper, we introduce a Gibbs sampling approach for identifying genes in multiple large genomic sequences up to hundreds of kilobases long. This approach leverages the evolutionary relationships between the sequences to improve the gene predictions, without explicitly aligning the sequences. We have applied our method to the analysis of genomic sequence from 14 genomic regions, totaling roughly 1.8 Mb of sequence in each organism. We show that our approach compares favorably with existing ab initio approaches to gene finding, including pairwise comparison based gene prediction methods which make explicit use of alignments. Furthermore, excellent performance can be obtained with as little as four organisms, and the method overcomes a number of difficulties of previous comparison based gene finding approaches: it is robust with respect to genomic rearrangements, can work with draft sequence, and is fast (linear in the number and length of the sequences). It can also be seamlessly integrated with Gibbs sampling motif detection methods.
pubmed:grant
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:chemical
pubmed:status
MEDLINE
pubmed:issn
1066-5277
pubmed:author
pubmed:issnType
Print
pubmed:volume
12
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
599-608
pubmed:dateRevised
2007-11-14
pubmed:meshHeading
pubmed:articleTitle
Large multiple organism gene finding by collapsed Gibbs sampling.
pubmed:affiliation
Department of Computer Science, University of California at Berkeley, Berkeley, CA 94720.
pubmed:publicationType
Journal Article, Comparative Study, Research Support, U.S. Gov't, P.H.S., Research Support, N.I.H., Extramural