SNIKT: sequence-independent adapter identification and removal in long-read shotgun sequencing data

Piyush Ranjan; Christopher A Brown; John R Erb-Downward; Robert P Dickson

doi:10.1093/bioinformatics/btac389

SNIKT: sequence-independent adapter identification and removal in long-read shotgun sequencing data

Bioinformatics. 2022 Aug 2;38(15):3830-3832. doi: 10.1093/bioinformatics/btac389.

Authors

Piyush Ranjan¹, Christopher A Brown^{1

2}, John R Erb-Downward¹, Robert P Dickson^{1

3

4}

Affiliations

¹ Division of Pulmonary and Critical Care Medicine, Department of Internal Medicine, University of Michigan Medical School, Ann Arbor, MI 48109, USA.
² Institute for Research on Innovation and Science, Institute for Social Research, University of Michigan, Ann Arbor, MI 48109, USA.
³ Department of Microbiology and Immunology, University of Michigan Medical School, Ann Arbor, MI 48109, USA.
⁴ Weil Institute for Critical Care Research & Innovation, Ann Arbor, MI 48109, USA.

Abstract

Summary: Here, we introduce SNIKT, a command-line tool for sequence-independent visual confirmation and input-assisted removal of adapter contamination in whole-genome shotgun or metagenomic shotgun long-read sequencing DNA or RNA data.

Availability and implementation: SNIKT is implemented in R and is compatible with Unix-like platforms. The source code, along with documentation, is freely available under an MIT license at https://github.com/piyuranjan/SNIKT.

Supplementary information: Supplementary data are available at Bioinformatics online.

SNIKT: sequence-independent adapter identification and removal in long-read shotgun sequencing data

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Abstract

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