A survey of sequence alignment algorithms for next-generation sequencing

By Heng Li, Nils Homer. Briefings Bioinform., 2010

971 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/3c3719a9db32023b381ca909a8759182d880f36f

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Citations in the corpus

Faster and More Accurate Sequence Alignment with SNAP (2011, background): “We discuss the major approaches taken to alignment as well as several recent algorithms, but we refer the reader to [9] for a more comprehensive survey.”
Fast Computation and Applications of Genome Mappability (2012, background): “) have been developed to achieve this goal with suitable efficiency”
The Ensembl gene annotation system (2016, background): “The paired reads provide more informative alignments than single reads because reads that align as a pair have a higher confidence level of being aligned correctly (73).”
ACMG clinical laboratory standards for next-generation sequencing (2013, background): “Several commercially available or open-source tools for read alignment are available that utilize a variety of alignment algorithms and may be more efficient for certain types of data than for others (Li and Homer 2010).”
Computational pan-genomics: status, promises and challenges (2016, methodology): “Most successful approaches use k-mer-based or Burrows–Wheelertransform-based indexes, as reviewed in [124].”
A survey of tools for variant analysis of next-generation genome sequencing data (2013, methodology): “Depending on library preparation and sequencing technology, it is possible to sequence reads that are of a known chromosomal distance [26].”
Rapid and precise alignment of raw reads against redundant databases with KMA (2018, methodology): “The proper choice of alignment method is thus critical for the further investigations [1].”
RobiNA: a user-friendly, integrated software solution for RNA-Seq-based transcriptomics (2012, background): “A range of specialized non-commercial aligners are available to map short sequence reads to a large reference genome or transcriptome [see (7) and (8) for a recent overview on algorithms and tools].”
From RNA-seq reads to differential expression results (2010, neutral): “A detailed explanation of these techniques is beyond the scope of this review, but can be found in [23,26-30].”
SNP Discovery through Next-Generation Sequencing and Its Applications (2012, methodology): “For reviews on NGS software, see Li and Homer [62], Wang et al. [58], and Treangen and Salzberg [63].”