Title
Automated gene identification in large-scale genomic sequences.
Abstract
Computational methods for gene identification in genomic sequences typically have two phases: coding region recognition and gene parsing, While there are a number of effective methods for recognizing coding regions (exons), parsing the recognized exons into proper gene structures, to a large extent, remains an unsolved problem, We have developed a computer program which can automatically parse the recognized exons into gene models that are most consistent with the available Expressed Sequence Tags (ESTs) and a set of biological heuristics, derived empirically, The gene modeling algorithm used in this program provides a general framework for applying EST information so the modeling accuracy improves as the amount of available EST information increases, Based on preliminary tests on a number of large DNA sequences, using the dbEST database, we have observed that the algorithm can (1) accurately model complicated multiple gene structures, including embedded genes, (2) identify falsely-recognized exons and locate missed exons by the initial exon recognition phase, and (3) make more accurate exon boundary predictions, if the necessary EST information is available, We have extended this EST-based gene modeling algorithm to model genes on unfinished DNA contigs at the end of the shotgun sequencing, This extended version can automatically determine the orientations and the relative order of the DNA contigs (with gaps between them) using the available ESTs as reference models, before the gene modeling phase.
Year
DOI
Venue
1997
10.1089/cmb.1997.4.325
JOURNAL OF COMPUTATIONAL BIOLOGY
Keywords
Field
DocType
multiple gene structure prediction,expressed sequence tags,sequence comparison and analysis,pattern recognition,dynamic programming
Gene,Expressed sequence tag,Biology,Exon,Coding region,Heuristics,DNA sequencing,Parsing,Bioinformatics
Journal
Volume
Issue
ISSN
4.0
3
1066-5277
Citations 
PageRank 
References 
12
1.99
1
Authors
2
Name
Order
Citations
PageRank
Y Xu19346.16
Edward C. Uberbacher222186.43