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Probability Model for Boundaries of Short-Read Sequencing

Authors: Florian Schatz; Lars Wienbrandt; Manfred Schimmler;

Probability Model for Boundaries of Short-Read Sequencing

Abstract

The need for sequencing DNA has been growing tremendously over the past few years. Current next-generation sequencing techniques produce huge amounts of data but time and money remain limiting factors for researchers. Given a DNA sample, it is essential to produce a sufficient number of reads to create or recreate a digital representation of the DNA while minimizing the needed resources. This work proposes a theoretical model that yields a set of formulas to calculate amongst others the expected distribution of contig length and estimated N50 value for a low-coverage, short-read sequencing experiment. The formulas can be used as an extension to the well known Lander-Waterman model to model assembly projects. The only input parameters these formulas are based on are the DNA sequence length, the number of reads and the read length. These formulas can provide boundaries (e.g. N50) that can be calculated before a sequencing process in order to reduce or adjust the needed resources for resequencing or de novo assembly and to get enough, but not too much, information or estimate the feasibility of a sequencing project.

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
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