YAHA: Fast and flexible long-read alignment with optimal breakpoint detection

Citations of this article
Mendeley users who have this article in their library.


Motivation: With improved short-read assembly algorithms and the recent development of long-read sequencers, split mapping will soon be the preferred method for structural variant (SV) detection. Yet, current alignment tools are not well suited for this.Results: We present YAHA, a fast and flexible hash-based aligner. YAHA is as fast and accurate as BWA-SW at finding the single best alignment per query and is dramatically faster and more sensitive than both SSAHA2 and MegaBLAST at finding all possible alignments. Unlike other aligners that report all, or one, alignment per query, or that use simple heuristics to select alignments, YAHA uses a directed acyclic graph to find the optimal set of alignments that cover a query using a biologically relevant breakpoint penalty. YAHA can also report multiple mappings per defined segment of the query. We show that YAHA detects more breakpoints in less time than BWA-SW across all SV classes, and especially excels at complex SVs comprising multiple breakpoints. © The Author(s) 2012. Published by Oxford University Press.




Faust, G. G., & Hall, I. M. (2012). YAHA: Fast and flexible long-read alignment with optimal breakpoint detection. Bioinformatics, 28(19), 2417–2424. https://doi.org/10.1093/bioinformatics/bts456

Register to see more suggestions

Mendeley helps you to discover research relevant for your work.

Already have an account?

Save time finding and organizing research with Mendeley

Sign up for free