z-logo
open-access-imgOpen Access
SVseq: an approach for detecting exact breakpoints of deletions with low-coverage sequence data
Author(s) -
Jin Zhang,
Yufeng Wu
Publication year - 2011
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/btr563
Subject(s) - breakpoint , indel , sequence (biology) , false positive paradox , computer science , computational biology , algorithm , data mining , biology , genetics , artificial intelligence , chromosome , gene , genotype , single nucleotide polymorphism
Structural variation (SV), such as deletion, is an important type of genetic variation and may be associated with diseases. While there are many existing methods for detecting SVs, finding deletions is still challenging with low-coverage short sequence reads. Existing deletion finding methods for sequence reads either use the so-called split reads mapping for detecting deletions with exact breakpoints, or rely on discordant insert sizes to estimate approximate positions of deletions. Neither is completely satisfactory with low-coverage sequence reads.

The content you want is available to Zendy users.

Already have an account? Click here to sign in.
Having issues? You can contact us here
Accelerating Research

Address

John Eccles House
Robert Robinson Avenue,
Oxford Science Park, Oxford
OX4 4GP, United Kingdom