LRCstats, a tool for evaluating long reads correction methods | Zendy

Sean La | Zendy; Ehsan Haghshenas | Zendy; Cédric Chauve | Zendy

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LRCstats, a tool for evaluating long reads correction methods

Author(s) -

Sean La,

Ehsan Haghshenas,

Cédric Chauve

Publication year - 2017

Publication title -

bioinformatics

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 3.599

H-Index - 390

eISSN - 1367-4811

pISSN - 1367-4803

DOI - 10.1093/bioinformatics/btx489

Subject(s) - computer science , nanopore sequencing , reference genome , process (computing) , error detection and correction , software , field (mathematics) , data mining , genome , algorithm , programming language , biology , genetics , mathematics , gene , pure mathematics

Third-generation sequencing (TGS) platforms that generate long reads, such as PacBio and Oxford Nanopore technologies, have had a dramatic impact on genomics research. However, despite recent improvements, TGS reads suffer from high-error rates and the development of read correction methods is an active field of research. This motivates the need to develop tools that can evaluate the accuracy of noisy long reads correction tools.

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