ResSeq: Enhancing Short-Read Sequencing Alignment by Rescuing Error-Containing Reads

Weixing Feng, Peichao Sang, Deyuan Lian, Yansheng Dong, Fengfei Song, Meng Li, Bo He, Fenglin Cao, Yunlong Liu

Research output: Contribution to journalArticle

Abstract

Next-generation short-read sequencing is widely utilized in genomic studies. Biological applications require an alignment step to map sequencing reads to the reference genome, before acquiring expected genomic information. This requirement makes alignment accuracy a key factor for effective biological interpretation. Normally, when accounting for measurement errors and single nucleotide polymorphisms, short read mappings with a few mismatches are generally considered acceptable. However, to further improve the efficiency of short-read sequencing alignment, we propose a method to retrieve additional reliably aligned reads (reads with more than a pre-defined number of mismatches), using a Bayesian-based approach. In this method, we first retrieve the sequence context around the mismatched nucleotides within the already aligned reads; these loci contain the genomic features where sequencing errors occur. Then, using the derived pattern, we evaluate the remaining (typically discarded) reads with more than the allowed number of mismatches, and calculate a score that represents the probability that a specific alignment is correct. This strategy allows the extraction of more reliably aligned reads, therefore improving alignment sensitivity. Implementation: The source code of our tool, ResSeq, can be downloaded from: https://github.com/hrbeubiocenter/Resseq.

Original languageEnglish (US)
Article number6942207
Pages (from-to)795-798
Number of pages4
JournalIEEE/ACM Transactions on Computational Biology and Bioinformatics
Volume12
Issue number4
DOIs
StatePublished - Jul 1 2015

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Keywords

  • Alignment
  • Error Analysis
  • Sequencing
  • Short-Read

ASJC Scopus subject areas

  • Biotechnology
  • Genetics
  • Applied Mathematics

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