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

Bioinformatics
Jeong-Hyeon ChoiJohn K Colbourne

Abstract

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. 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 GM...Continue Reading

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Citations

Jun 2, 2009·Briefings in Bioinformatics·Mihai Pop
Mar 23, 2010·Genome Research·Stephen MeaderGerton Lunter
Mar 12, 2010·Genome Biology·David R Kelley, Steven L Salzberg
Sep 17, 2014·Trends in Plant Science·Chuang MaXiangfeng Wang
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Jan 30, 2013·Nature Reviews. Genetics·Niranjan Nagarajan, Mihai Pop
Aug 24, 2018·Briefings in Bioinformatics·Kleber Padovani de SouzaRonnie Alves

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