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Large Multiple Organism Gene Finding by Collapsed Gibbs Sampling

Chatterji, Sourav and Pachter, Lior (2005) Large Multiple Organism Gene Finding by Collapsed Gibbs Sampling. Journal of Computational Biology, 12 (6). pp. 599-608. ISSN 1066-5277. doi:10.1089/cmb.2005.12.599.

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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.

Item Type:Article
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URLURL TypeDescription
Pachter, Lior0000-0002-9164-6231
Additional Information:© 2005 Mary Ann Liebert, Inc. Thanks to Simon Cawley for helpful discussions and comments. This work was partially funded with a grant from the NIH (R01: HG2362-1).
Funding AgencyGrant Number
NIHR01 HG2362-1
Issue or Number:6
Record Number:CaltechAUTHORS:20170308-125857130
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Official Citation:Sourav Chatterji and Lior Pachter. Journal of Computational Biology. August 2005, 12(6): 599-608. doi:10.1089/cmb.2005.12.599.
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:74903
Deposited By: George Porter
Deposited On:08 Mar 2017 21:20
Last Modified:15 Nov 2021 16:29

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