title: MaSC: mappability-sensitive cross-correlation for estimating mean fragment length of single-end short-read sequencing data.
abstract: BACKGROUND
  Reliable estimation of the mean fragment length for next-generation short-read sequencing data is an important step in next-generation sequencing analysis pipelines, most notably because of its impact on the accuracy of the enriched regions identified by peak-calling algorithms. Although many peak-calling algorithms include a fragment-length estimation subroutine, the problem has not been adequately solved, as demonstrated by the variability of the estimates returned by different algorithms.
  
  
  RESULTS
  In this article, we investigate the use of strand cross-correlation to estimate mean fragment length of single-end data and show that traditional estimation approaches have mixed reliability. We observe that the mappability of different parts of the genome can introduce an artificial bias into cross-correlation computations, resulting in incorrect fragment-length estimates. We propose a new approach, called mappability-sensitive cross-correlation (MaSC), which removes this bias and allows for accurate and reliable fragment-length estimation. We analyze the computational complexity of this approach, and evaluate its performance on a test suite of NGS datasets, demonstrating its superiority to traditional cross-correlation analysis.
  
  
  BACKGROUND
  An open-source Perl implementation of our approach is available at http://www.perkinslab.ca/Software.html.
authors.affiliation: Regenerative Medicine Program, Ottawa Hospital Research Institute, K1H 8L6, Ottawa, Canada.
authors.forename: Parameswaran
authors.initials: P
authors.lastname: Ramachandran
authors.forename: Gareth A
authors.initials: GA
authors.lastname: Palidwor
authors.forename: Christopher J
authors.initials: CJ
authors.lastname: Porter
authors.forename: Theodore J
authors.initials: TJ
authors.lastname: Perkins
mesh_terms: D000465:Algorithms; D002874:Chromosome Mapping; D003627:Data Interpretation, Statistical; D023281:Genomics; D059014:High-Throughput Nucleotide Sequencing / Q000379:methods*; D006801:Humans; D015203:Reproducibility of Results