amplimap: a versatile tool to process and analyze targeted NGS data
File(s)btz582.pdf (332.56 KB)
Published version
Author(s)
Type
Journal Article
Abstract
Summary
amplimap is a command-line tool to automate the processing and analysis of data from targeted next-generation sequencing experiments with PCR-based amplicons or capture-based enrichment systems. From raw sequencing reads, amplimap generates output such as read alignments, annotated variant calls, target coverage statistics and variant allele counts and frequencies for each target base pair. In addition to its focus on user-friendliness and reproducibility, amplimap supports advanced features such as consensus base calling for read families based on unique molecular identifiers and filtering false positive variant calls caused by amplification of off-target loci.
Availability and implementation
amplimap is available as a free Python package under the open-source Apache 2.0 License. Documentation, source code and installation instructions are available at https://github.com/koelling/amplimap.
amplimap is a command-line tool to automate the processing and analysis of data from targeted next-generation sequencing experiments with PCR-based amplicons or capture-based enrichment systems. From raw sequencing reads, amplimap generates output such as read alignments, annotated variant calls, target coverage statistics and variant allele counts and frequencies for each target base pair. In addition to its focus on user-friendliness and reproducibility, amplimap supports advanced features such as consensus base calling for read families based on unique molecular identifiers and filtering false positive variant calls caused by amplification of off-target loci.
Availability and implementation
amplimap is available as a free Python package under the open-source Apache 2.0 License. Documentation, source code and installation instructions are available at https://github.com/koelling/amplimap.
Date Issued
2019-07-26
Date Acceptance
2019-07-22
Citation
Bioinformatics, 2019, 35 (24), pp.5349-5350
ISSN
1367-4803
Publisher
Oxford University Press (OUP)
Start Page
5349
End Page
5350
Journal / Book Title
Bioinformatics
Volume
35
Issue
24
Copyright Statement
© The Author(s) 2019. Published by Oxford University Press.
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.
Identifier
https://academic.oup.com/bioinformatics/advance-article/doi/10.1093/bioinformatics/btz582/5539690
Subjects
01 Mathematical Sciences
06 Biological Sciences
08 Information and Computing Sciences
Bioinformatics
Publication Status
Published
Date Publish Online
2019-07-26