TY - JOUR
T1 - A machine-learning approach to combined evidence validation of genome assemblies
AU - Choi, Jeong-Hyeon
AU - Kim, Sun
AU - Tang, Haixu
AU - Andrews, Justen
AU - Gilbert, Don G.
AU - Colbourne, John K.
N1 - Funding Information:
We are grateful to anonymous reviewers for their valuable comments. This research was supported in part by the Indiana METACyt Initiative of Indiana University, funded in part through a major grant from the Lilly Endowment, Inc. and by NSF Career DBI-0237901. Computer support was provided by an allocation TG-MCB060059N through the TeraGrid Advanced Support, by the University Information Technology Services (UITS) and by The Center for Genomics and Bioinformatics computing group. We thank Richard Repasky (UITS) who helped conceive this project.
PY - 2008/3
Y1 - 2008/3
N2 - Motivation: While it is common to refer to 'the genome sequence' as if it were a single, complete and contiguous DNA string, it is in fact an assembly of millions of small, partially overlapping DNA fragments. Sophisticated computer algorithms (assemblers and scaffolders) merge these DNA fragments into contigs, and place these contigs into sequence scaffolds using the paired-end sequences derived from large-insert DNA libraries. Each step in this automated process is susceptible to producing errors; hence, the resulting draft assembly represents (in practice) only a likely assembly that requires further validation. Knowing which parts of the draft assembly are likely free of errors is critical if researchers are to draw reliable conclusions from the assembled sequence data. Results: We develop a machine-learning method to detect assembly errors in sequence assemblies. Several in silico measures for assembly validation have been proposed by various researchers. Using three benchmarking Drosophila draft genomes, we evaluate these techniques along with some new measures that we propose, including the good-minus-bad coverage (GMB), the good-to-bad-ratio (RGB), the average Z-score (AZ) and the average absolute Z-score (ASZ). Our results show that the GMB measure performs better than the others in both its sensitivity and its specificity for assembly error detection. Nevertheless, no single method performs sufficiently well to reliably detect genomic regions requiring attention for further experimental verification. To utilize the advantages of all these measures, we develop a novel machine learning approach that combines these individual measures to achieve a higher prediction accuracy (i.e. greater than 90%). Our combined evidence approach avoids the difficult and often ad hoc selection of many parameters the individual measures require, and significantly improves the overall precisions on the benchmarking data sets.
AB - Motivation: While it is common to refer to 'the genome sequence' as if it were a single, complete and contiguous DNA string, it is in fact an assembly of millions of small, partially overlapping DNA fragments. Sophisticated computer algorithms (assemblers and scaffolders) merge these DNA fragments into contigs, and place these contigs into sequence scaffolds using the paired-end sequences derived from large-insert DNA libraries. Each step in this automated process is susceptible to producing errors; hence, the resulting draft assembly represents (in practice) only a likely assembly that requires further validation. Knowing which parts of the draft assembly are likely free of errors is critical if researchers are to draw reliable conclusions from the assembled sequence data. Results: We develop a machine-learning method to detect assembly errors in sequence assemblies. Several in silico measures for assembly validation have been proposed by various researchers. Using three benchmarking Drosophila draft genomes, we evaluate these techniques along with some new measures that we propose, including the good-minus-bad coverage (GMB), the good-to-bad-ratio (RGB), the average Z-score (AZ) and the average absolute Z-score (ASZ). Our results show that the GMB measure performs better than the others in both its sensitivity and its specificity for assembly error detection. Nevertheless, no single method performs sufficiently well to reliably detect genomic regions requiring attention for further experimental verification. To utilize the advantages of all these measures, we develop a novel machine learning approach that combines these individual measures to achieve a higher prediction accuracy (i.e. greater than 90%). Our combined evidence approach avoids the difficult and often ad hoc selection of many parameters the individual measures require, and significantly improves the overall precisions on the benchmarking data sets.
UR - http://www.scopus.com/inward/record.url?scp=40749116115&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=40749116115&partnerID=8YFLogxK
U2 - 10.1093/bioinformatics/btm608
DO - 10.1093/bioinformatics/btm608
M3 - Article
C2 - 18204064
AN - SCOPUS:40749116115
SN - 1367-4803
VL - 24
SP - 744
EP - 750
JO - Bioinformatics
JF - Bioinformatics
IS - 6
ER -