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Error correction of high-throughput sequencing datasets with non-uniform coverage.

ABSTRACT: The continuing improvements to high-throughput sequencing (HTS) platforms have begun to unfold a myriad of new applications. As a result, error correction of sequencing reads remains an important problem. Though several tools do an excellent job of correcting datasets where the reads are sampled close to uniformly, the problem of correcting reads coming from drastically non-uniform datasets, such as those from single-cell sequencing, remains open.In this article, we develop the method Hammer for error correction without any uniformity assumptions. Hammer is based on a combination of a Hamming graph and a simple probabilistic model for sequencing errors. It is a simple and adaptable algorithm that improves on other tools on non-uniform single-cell data, while achieving comparable results on normal multi-cell data.http://www.cs.toronto.edu/~pashadag.pmedvedev@cs.ucsd.edu.

SUBMITTER: Medvedev P

PROVIDER: S-EPMC3117386 | biostudies-literature | 2011 Jul

REPOSITORIES: biostudies-literature

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Error correction of high-throughput sequencing datasets with non-uniform coverage.

Medvedev Paul P Scott Eric E Kakaradov Boyko B Pevzner Pavel P

Bioinformatics (Oxford, England) 20110701 13

<h4>Motivation</h4>The continuing improvements to high-throughput sequencing (HTS) platforms have begun to unfold a myriad of new applications. As a result, error correction of sequencing reads remains an important problem. Though several tools do an excellent job of correcting datasets where the reads are sampled close to uniformly, the problem of correcting reads coming from drastically non-uniform datasets, such as those from single-cell sequencing, remains open.<h4>Results</h4>In this articl ...[more]

PMID: 21685062

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