NanoPack2: population-scale evaluation of long-read sequencing data

Abstract Summary Increases in the cohort size in long-read sequencing projects necessitate more efficient software for quality assessment and processing of sequencing data from Oxford Nanopore Technologies and Pacific Biosciences. Here, we describe novel tools for summarizing experiments, filtering...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:Bioinformatics (Oxford, England) Jg. 39; H. 5
Hauptverfasser: De Coster, Wouter, Rademakers, Rosa
Format: Journal Article
Sprache:Englisch
Veröffentlicht: England Oxford University Press 04.05.2023
Schlagworte:
ISSN:1367-4811, 1367-4803, 1367-4811
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Beschreibung
Zusammenfassung:Abstract Summary Increases in the cohort size in long-read sequencing projects necessitate more efficient software for quality assessment and processing of sequencing data from Oxford Nanopore Technologies and Pacific Biosciences. Here, we describe novel tools for summarizing experiments, filtering datasets, visualizing phased alignments results, and updates to the NanoPack software suite. Availability and implementation The cramino, chopper, kyber, and phasius tools are written in Rust and available as executable binaries without requiring installation or managing dependencies. Binaries build on musl are available for broad compatibility. NanoPlot and NanoComp are written in Python3. Links to the separate tools and their documentation can be found at https://github.com/wdecoster/nanopack. All tools are compatible with Linux, Mac OS, and the MS Windows Subsystem for Linux and are released under the MIT license. The repositories include test data, and the tools are continuously tested using GitHub Actions and can be installed with the conda dependency manager.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 23
ISSN:1367-4811
1367-4803
1367-4811
DOI:10.1093/bioinformatics/btad311