Tools for mapping high-throughput sequencing data

By Nuno A. Fonseca, Johan Rung, Alvis Brazma, John C. Marioni. Bioinform., 2012

297 citations recorded by the source corpus.

MOTIVATION A ubiquitous and fundamental step in high-throughput sequencing analysis is the alignment (mapping) of the generated reads to a reference sequence. To accomplish this task, numerous software tools have been proposed. Determining the mappers that are most suitable for a specific application is not trivial. RESULTS This survey focuses on classifying mappers through a wide number of characteristics. The goal is to allow practitioners to compare the mappers more easily and find those that are most suitable for their specific problem.

Subfields: Ecology, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/1b7e750595a00e196565e3e26de855e9aaa72954

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Citations in the corpus

Transcriptomics technologies (2017, background): “A list of currently available high-throughput sequence aligners is maintained by the EBI [93][94].”
Count-based differential expression analysis of RNA sequencing data using R and Bioconductor (2013, methodology): “Here R and Bioconductor are adopted to tie together the workflow and provide data structures, and their unique strengths in workflow components are leveraged, including statistical algorithms, visualization”
Computational and analytical challenges in single-cell transcriptomics (2015, background): “In general, most of the methodology developed for bulk RNA-seq, including insights for how to best map the raw sequencing reads, can be reused for scRNA-seq 41–43 .”
Fast and sensitive taxonomic classification for metagenomics with Kaiju (2016, methodology): “For the similar problem of mapping sequencing reads to a reference genome, heuristic methods achieve speed improvements of orders of magnitude by using advanced index structures for fast identiﬁcation and extension of short exact matches (seeds) between query and the reference genome 6 .”