Introduction

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.

Publications

  1. Error correction of high-throughput sequencing datasets with non-uniform coverage.
    Cite this
    Medvedev P, Scott E, Kakaradov B, Pevzner P, 2011-07-01 - Bioinformatics (Oxford, England)

Credits

  1. Paul Medvedev
    Developer

    Department of Computer Science and Engineering, University of California, United States of America

  2. Eric Scott
    Developer

  3. Boyko Kakaradov
    Developer

  4. Pavel Pevzner
    Investigator

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Summary
AccessionBT006539
Tool TypeApplication
Category
PlatformsLinux/Unix
Technologies
User InterfaceTerminal Command Line
Download Count0
Submitted ByPavel Pevzner