Using quality scores and longer reads improves accuracy of Solexa read mapping

By Andrew D. Smith, Zhenyu Xuan, Michael Q. Zhang. BMC Bioinformatics, 2008

370 citations recorded by the source corpus.

BackgroundSecond-generation sequencing has the potential to revolutionize genomics and impact all areas of biomedical science. New technologies will make re-sequencing widely available for such applications as identifying genome variations or interrogating the oligonucleotide content of a large sample (e.g. ChIP-sequencing). The increase in speed, sensitivity and availability of sequencing technology brings demand for advances in computational technology to perform associated analysis tasks. The Solexa/Illumina 1G sequencer can produce tens of millions of reads, ranging in length from ~25–50 nt, in a single experiment. Accurately mapping the reads back to a reference genome is a critical task in almost all applications. Two sources of information that are often ignored when mapping reads from the Solexa technology are the 3' ends of longer reads, which contain a much higher frequency of sequencing errors, and the base-call quality scores.ResultsTo investigate whether these sources of information can be used to improve accuracy when mapping reads, we developed the RMAP tool, which can map reads having a wide range of lengths and allows base-call quality scores to determine which positions in each read are more important when mapping. We applied RMAP to analyze data re-sequenced from two human BAC regions for varying read lengths, and varying criteria for use of quality scores. RMAP is freely available for downloading at http://rulai.cshl.edu/rmap/.ConclusionOur results indicate that significant gains in Solexa read mapping performance can be achieved by considering the information in 3' ends of longer reads, and appropriately using the base-call quality scores. The RMAP tool we have developed will enable researchers to effectively exploit this information in targeted re-sequencing projects.

Subfields: Genetics, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/98ac5bd7e57db6a9b63e81f333a32cc38f6257be

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Citations in the corpus

A survey of sequence alignment algorithms for next-generation sequencing (2010, methodology): “RMAP [20, 21], which is based on the Baeza–”
BSMAP: whole genome bisulfite sequence MAPping program (2009, neutral): “Because of these advantages, the seeding algorithm has been incorporated into most short read mapping software, including SOAP [5], ELAND, MAQ [6], and RMAP [7].”
Utilization of next-generation sequencing platforms in plant genomics and genetic variant discovery (2010, methodology): “A variety of tools that are beyond the scope of this paper have been developed for alignment and de novo assembly of short reads (for examples, see Warren et al. 2007; Dohm et al. 2007; Jeck et al. 2007; De Bona et al. 2008; Chaisson and Pevzner 2008; Li et al. 2008a; Smith et al. 2008; Zerbino and ”
Ultrafast and memory-efficient alignment of short DNA sequences to the human genome (2009, methodology): “Maq and SOAP take the same basic algorithmic approach as other recent read mapping tools such as RMAP [10], ZOOM [11], and SHRiMP [12].”
Fast and accurate long-read alignment with Burrows–Wheeler transform (2010, background): “Availability: http://bio-bwa.sourceforge.net Contact: rd@sanger.ac.uk Received on September 19, 2009; revised on November 24, 2009; accepted on December 16, 2009”
Computational methods for transcriptome annotation and quantification using RNA-seq (2011, background): “Seed methods 31–38 such as mapping and assembly with quality (MAQ) 33 and Stampy 35 find matches for short subsequences, termed ‘seeds’, assuming that at least each approach and their application to RNA-seq analysis.”
Assembly Algorithms for Next-Generation Sequencing Data (2010, neutral): “Published software includes SOAP [68,69], MAQ [70], Bowtie [71], RMAP [72], CloudBurst [73], SHRiMP [74], RazerS [75], PerM [76], segemehl [77], GenomeMapper [78], and BOAT [79].”
BS-Seeker2: a versatile aligning pipeline for bisulfite sequencing data (2013, methodology): “It supports both local and gapped alignment by integrating Bowtie2 [14], Bowtie [6], SOAP [15] and RMAP [16].”