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Vulcan: Improved long-read mapping and structural variant calling via dual-mode alignment
Author(s) -
Yilei Fu,
Medhat Mahmoud,
Viginesh Vaibhav Muraliraman,
Fritz J. Sedlazeck,
Todd J. Treangen
Publication year - 2021
Publication title -
gigascience
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.947
H-Index - 54
ISSN - 2047-217X
DOI - 10.1093/gigascience/giab063
Subject(s) - computer science , pipeline (software) , heuristics , precision and recall , context (archaeology) , nanopore sequencing , artificial intelligence , reference genome , genome , data mining , genetics , biology , paleontology , gene , programming language , operating system
Long-read sequencing has enabled unprecedented surveys of structural variation across the entire human genome. To maximize the potential of long-read sequencing in this context, novel mapping methods have emerged that have primarily focused on either speed or accuracy. Various heuristics and scoring schemas have been implemented in widely used read mappers (minimap2 and NGMLR) to optimize for speed or accuracy, which have variable performance across different genomic regions and for specific structural variants. Our hypothesis is that constraining read mapping to the use of a single gap penalty across distinct mutational hot spots reduces read alignment accuracy and impedes structural variant detection.

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