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ITSxpress: Software to rapidly trim internal transcribed spacer sequences with quality scores for amplicon sequencing

software
posted on 2024-04-23, 21:09 authored by ADAM RIVERSADAM RIVERS, Sveinn EinarssonSveinn Einarsson, Kyle C. Weber, Terrence G. Gardner, Shalamar D. Armstrong, Shuang Liu

The ribosomal RNA (rRNA) internal transcribed spacer (ITS) regions are commonly used to identify fungi and other eukaryotic taxa in amplicon sequencing. The highly conserved rRNA regions flanking the ITS need to be trimmed before being used for taxonomic assignment. The Python software package ITSxpress rapidly trims single-end or paired-end sequences in FASTQ format for use in amplicon sequence variant clustering methods like DADA2. This new major release of ITSxpress improves the paired-end merging method, simplifies installation of the QIIME 2 ITSxpress plugin, removes major dependencies, adds use cases, and is compatible with newer compression formats. This paper discusses the modifications to ITSxpress that improve the output and user experience, leading to a major version increase.

Funding

USDA-ARS: 6066-21310-004-000-D

USDA-ARS:6066-21310-005-000-D

History

Data contact name

Rivers, Adam R.

Data contact email

adam.rivers@usda.gov

Publisher

Github

Intended use

Anplicon Sequencing of Eukaryotic ITS

Use limitations

None.

Temporal Extent Start Date

2018-09-06

Frequency

  • asNeeded

Theme

  • Non-geospatial

ISO Topic Category

  • biota
  • environment
  • farming
  • health

National Agricultural Library Thesaurus terms

computer software; internal transcribed spacers; sequence analysis; ribosomal RNA; fungi

OMB Bureau Code

  • 005:18 - Agricultural Research Service

OMB Program Code

  • 005:040 - National Research

ARS National Program Number

  • 301

Pending citation

  • No

Public Access Level

  • Public

Preferred dataset citation

AR Rivers, KC Weber, TG Gardner, S Liu, SD Armstrong. 2018. ITSxpress: software to rapidly trim internally transcribed spacer sequences with quality scores for marker gene analysis. F1000Research 7

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