Gap statistics for whole genome shotgun DNA sequencing projects

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Abstract

Motivation

Investigators utilize gap estimates for DNA sequencing projects. Standard theories assume sequences are 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 that relate coverage to a normalized gap measure. Prokaryotic genomes do not correlate to sequence coverage, while eukaryotes show strong correlation if the 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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