A machine-learning approach to combined evidence validation of genome assemblies Export

@article{choi:machinelearning, abstract = {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. Availability: http://people.cgb.indiana.edu/jeochoi/gav/ Contact: jeochoi@indiana.edu Supplementary information: Supplementary data are available at Bioinformatics online. 10.1093/bioinformatics/btm608}, author = {Choi, Jeong-Hyeon and Kim, Sun and Tang, Haixu and Andrews, Justen and Gilbert, Don G. and Colbourne, John K.}, citeulike-article-id = {2529436}, citeulike-linkout-0 = {http://dx.doi.org/10.1093/bioinformatics/btm608}, citeulike-linkout-1 = {http://bioinformatics.oxfordjournals.org/content/24/6/744.abstract}, citeulike-linkout-2 = {http://bioinformatics.oxfordjournals.org/content/24/6/744.full.pdf}, citeulike-linkout-3 = {http://bioinformatics.oxfordjournals.org/cgi/content/abstract/24/6/744}, citeulike-linkout-4 = {http://view.ncbi.nlm.nih.gov/pubmed/18204064}, citeulike-linkout-5 = {http://www.hubmed.org/display.cgi?uids=18204064}, day = {15}, doi = {10.1093/bioinformatics/btm608}, journal = {Bioinformatics}, keywords = {assembly, learning, machine, thesis, validation}, month = {March}, number = {6}, pages = {744--750}, posted-at = {2008-04-25 16:38:14}, priority = {0}, title = {A machine-learning approach to combined evidence validation of genome assemblies}, url = {http://dx.doi.org/10.1093/bioinformatics/btm608}, volume = {24}, year = {2008} }

TY - JOUR ID - choi:machinelearning L3 - citeulike-article-id:2529436 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. Availability: http://people.cgb.indiana.edu/jeochoi/gav/ Contact: jeochoi@indiana.edu Supplementary information: Supplementary data are available at Bioinformatics online. 10.1093/bioinformatics/btm608 IS - 6 JF - Bioinformatics EP - 750 TI - A machine-learning approach to combined evidence validation of genome assemblies VL - 24 SP - 744 KW - assembly KW - learning KW - machine KW - thesis KW - validation AU - Choi, Jeong-Hyeon AU - Kim, Sun AU - Tang, Haixu AU - Andrews, Justen AU - Gilbert, Don AU - Colbourne, John PY - 2008/03/15/ UR - http://dx.doi.org/10.1093/bioinformatics/btm608 ER -