title: Error correction of high-throughput sequencing datasets with non-uniform coverage.
abstract: BACKGROUND
  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.
  
  
  BACKGROUND
  http://www.cs.toronto.edu/~pashadag.
  
  
  BACKGROUND
  pmedvedev@cs.ucsd.edu.
authors.affiliation: Department of Computer Science and Engineering, University of California, San Diego, CA, USA. pmedvedev@cs.ucsd.edu
authors.forename: Paul
authors.initials: P
authors.lastname: Medvedev
authors.forename: Eric
authors.initials: E
authors.lastname: Scott
authors.forename: Boyko
authors.initials: B
authors.lastname: Kakaradov
authors.forename: Pavel
authors.initials: P
authors.lastname: Pevzner
mesh_terms: D000465:Algorithms; D004926:Escherichia coli / Q000235:genetics; D059014:High-Throughput Nucleotide Sequencing / Q000379:methods*; D015233:Models, Statistical; D059010:Single-Cell Analysis / Q000379:methods*