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

Authors
Journal
Bioinformatics
1367-4803
Publisher
Oxford University Press
Publication Date
Volume
27
Issue
13
Identifiers
DOI: 10.1093/bioinformatics/btr208
Keywords
  • Ismb/Eccb 2011 Proceedings Papers Committee July 17 To July 19
  • 2011
  • Vienna
  • Austria
  • Original Papers
  • Sequence Analysis
Disciplines
  • Computer Science

Abstract

Motivation: 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. Results: 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. Availability: http://www.cs.toronto.edu/~pashadag. Contact: [email protected]

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