Abstract

Bioinformatic pipelines are becoming increasingly complex with the ever-accumulating amount of Next-generation sequencing (NGS) data. Their orchestration is difficult with a simple Bash script, but bioinformatics workflow managers such as Nextflow provide a framework to overcome respective problems. This study used Nextflow to develop a bioinformatic pipeline for detecting expression quantitative trait loci (eQTL) using a DSL2 Nextflow modular syntax, to enable sharing the huge demand for computing power as well as data access limitation across different partners often associated with eQTL studies. Based on the results from a test run with pilot data by measuring the required runtime and computational resources, the new pipeline should be suitable for eQTL studies in large scale analyses.

Details

Title
eQTL-Detect: nextflow-based pipeline for eQTL detection in modular format with sharable and parallelizable scripts
Author
Chitneedi, Praveen Krishna 1   VIAFID ORCID Logo  ; Hadlich, Frieder 1 ; Moreira, Gabriel C M 2 ; Espinosa-Carrasco, Jose 3   VIAFID ORCID Logo  ; Li, Changxi 4 ; Plastow, Graham 4   VIAFID ORCID Logo  ; Fischer, Daniel 5 ; Charlier, Carole 2 ; Rocha, Dominique 6 ; Chamberlain, Amanda J 7 ; Kuehn, Christa 1 

1  Research Institute for Farm Animal Biology (FBN) , Wilhelm-Stahl-Allee 2, 18196 Dummerstorf , Germany 
2  Unit of Animal Genomics, GIGA Institute, University of Liège , 4000 Liège , Belgium 
3  Centre for Genomic Regulation (CRG), The Barcelona Institute of Science and Technology , Dr. Aiguader 88, Barcelona 08003 , Spain 
4  Department of Agricultural, Food and Nutritional Science, University of Alberta , Edmonton T6G 2P5 , Canada 
5  Natural Resources Institute Finland (Luke) , Green Technology, Animal and Plant Genomics and Breeding, FI- 31600 Jokioinen, Finland 
6  Université Paris-Saclay, INRAE, AgroParisTech, GABI , 78350 , Jouy-en-Josas, France 
7  Agriculture Victoria Research, AgriBio, Centre for AgriBiosciences , Bundoora , VIC 3083 , Australia 
Publication year
2024
Publication date
Sep 2024
Publisher
Oxford University Press
e-ISSN
26319268
Source type
Scholarly Journal
Language of publication
English
DOI
https://doi.org/10.1093/nargab/lqae122
ProQuest document ID
3168786532
Copyright
© The Author(s) 2024. Published by Oxford University Press on behalf of NAR Genomics and Bioinformatics. This work is published under https://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.