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Bioinformatics Advance Access published online on February 12, 2004
Bioinformatics, doi:10.1093/bioinformatics/bth120
Bioinformatics © Oxford University Press 2004; all rights reserved
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Received May 19, 2003
Revised December 12, 2003
Accepted January 5, 2004

Article

Gap statistics for whole genome shotgun DNA sequencing projects

Michael C. Wendl 1* Shiaw-Pyng Yang 1

1 Genome Sequencing Center, Washington University School of Medicine, Box 8501, 4444 Forest Park Blvd., Saint Louis, MO 63108, USA

* To whom correspondence should be addressed. E-mail: mwendl{at}wustl.edu.


  Abstract

Motivation: Investigators utilize gap estimates for DNA sequencing projects. Standard theories assume sequence is independently and identically distributed, leading to appreciable under-prediction of gaps.

Results: Using a statistical scaling factor and data from 20 representative whole genome shotgun projects, we construct regression equations which relate coverage to a normalized gap measure. Prokaryotic genomes do not correlate to sequence coverage, while eukaryotes show strong correlation if chaff is ignored. Gaps decrease at an exponential rate of only about one-third of that predicted via theory alone. Case studies suggest that departure from theory can largely be attributed to assembly difficulties for repeat-rich genomes, but bias and coverage anomalies are also important when repeats are sparse. Such factors cannot be readily characterized a priori, suggesting upper limits on the accuracy of gap prediction. We also find that diminishing coverage probability discussed in other studies is a theoretical artifact that does not arise for the typical project.


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