How to map billions of short reads onto genomes

By Cole Trapnell, Steven L. Salzberg. Nature Biotechnology, 2009

351 citations recorded by the source corpus.

Mapping the vast quantities of short sequence fragments produced by next-generation sequencing platforms is a challenge. What programs are available and how do they work?

Subfields: Molecular Biology

View Paper: https://www.semanticscholar.org/paper/b23ffe4c75753dbb8bb88059ba3d0431ba00cb16

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Citations in the corpus

ANNOVAR: functional annotation of genetic variants from high-throughput sequencing data (2010, background): “There have been dozens of short read alignment software available now with different functionalities (1), as well as several single nucleotide variants (SNV) and copy number variant (CNV) calling algorithms (2).”
ChIP-Seq: advantages and challenges of a maturing technology (2009, background): “Owing to the large number of reads, the use of conventional alignment algorithms can take hundreds or thousands of processor hours; therefore, a new generation of aligners has been develope”
Applications of next generation sequencing in molecular ecology of non-model organisms (2011, background): “If there is already a reference genome (or transcriptome) sequence available, the reads may be mapped directly onto this, without the need for previous assembly (Trapnell and Salzberg, 2009).”
Adaptive seeds tame genomic sequence comparison. (2011, methodology): “In recent years, many methods have been developed for mapping DNA reads to genomes, allowing for small numbers of differences (Trapnell and Salzberg 2009; Li and Homer 2010).”
Tools for mapping high-throughput sequencing data (2012, result): “It complements previous studies that focused mainly on a reduced subset of mappers and/or empirically compared the performance of a small number of mappers (Flicek and Birney, 2009; Li and Homer, 2010; Trapnell and Salzberg, 2009).”
Sequencing technologies — the next generation (2010, methodology): “Genome alignment and assembly After NGS reads have been generated, they are aligned to a known reference sequence or assembled de novo”
Qualimap: evaluating next-generation sequencing alignment data (2012, positive): “With the advent of next-generation sequencing (NGS), a number of novel alignment methods have been developed (see Trapnell and Salzberg, 2009 for a review).”
Computation for ChIP-seq and RNA-seq studies (2009, background): “They can be conceptually subdivided into following fundamental components: (1) a signal profile definition for a ChIP region, (2) a background model, (3) peak call criteria, (4) post call filtering of artifactual peaks, and (5) significance ranking of called peaks.”
Count-based differential expression analysis of RNA sequencing data using R and Bioconductor (2013, background): “The modifications necessary for mapping reads from other platforms are not discussed here”
How many biological replicates are needed in an RNA-seq experiment and which differential expression tool should you use? (2015, neutral): “0.0-beta7 ) (Trapnell and Salzberg 2009) and TopHat2 ( v2.”
Quake: quality-aware detection and correction of sequencing errors (2010, background): “The first step is to align the reads to the reference genome using specialized methods [8] that will only allow a few mismatches between the read and reference, such as up to two mismatches in a recent study [38].”