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A Preprocessor for Shotgun Assembly of Large Genomes
Author(s) -
Michael Roberts,
Brian R. Hunt,
James A. Yorke,
Randall Bolanos,
Arthur L. Delcher
Publication year - 2004
Publication title -
journal of computational biology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.585
H-Index - 95
eISSN - 1557-8666
pISSN - 1066-5277
DOI - 10.1089/cmb.2004.11.734
Subject(s) - shotgun sequencing , sequence assembly , genome , k mer , preprocessor , sequence (biology) , computational biology , computer science , reference genome , hybrid genome assembly , whole genome sequencing , biology , genetics , artificial intelligence , gene , transcriptome , gene expression
The whole-genome shotgun (WGS) assembly technique has been remarkably successful in efforts to determine the sequence of bases that make up a genome. WGS assembly begins with a large collection of short fragments that have been selected at random from a genome. The sequence of bases at each end of the fragment is determined, albeit imprecisely, resulting in a sequence of letters called a "read." Each letter in a read is assigned a quality value, which estimates the probability that a sequencing error occurred in determining that letter. Reads are typically cut off after about 500 letters, where sequencing errors become endemic. We report on a set of procedures that (1) corrects most of the sequencing errors, (2) changes quality values accordingly, and (3) produces a list of "overlaps," i.e., pairs of reads that plausibly come from overlapping parts of the genome. Our procedures, which we call collectively the "UMD Overlapper," can be run iteratively and as a preprocessor for other assemblers. We tested the UMD Overlapper on Celera's Drosophila reads. When we replaced Celera's overlap procedures in the front end of their assembler, it was able to produce a significantly improved genome.

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