Scalable and efficient DNA sequencing analysis on different compute infrastructures aiding variant discovery

Author:

Hanssen Friederike1234ORCID,Garcia Maxime U567ORCID,Folkersen Lasse8ORCID,Pedersen Anders Sune9ORCID,Lescai Francesco10ORCID,Jodoin Susanne13ORCID,Miller Edmund11ORCID,Seybold Matthias1ORCID,Wacker Oskar13ORCID,Smith Nicholas12ORCID,Gabernet Gisela113ORCID,Nahnsen Sven123414ORCID

Affiliation:

1. Quantitative Biology Center, Eberhard-Karls University of Tübingen , Otfried-Müller Str. 37, Tübingen 72076, Baden-Württemberg, Germany

2. Department of Computer Science, Eberhard-Karls University of Tübingen , 72076 Baden-Württemberg, Germany

3. M3 Research Center, University Hospital , Otfried-Müller Str. 37, Tübingen 72076, Baden-Württemberg, Germany

4. Cluster of Excellence iFIT (EXC 2180) ‘Image-Guided and Functionally Instructed Tumor Therapies’, Eberhard-Karls University of Tübingen , Tübingen 72076, Baden-Württemberg, Germany

5. Seqera Labs , Carrer de Marià Aguilò, 28, Barcelona 08005, Spain

6. Barntumörbanken, Department of Oncology-Pathology, Karolinska Institutet , BioClinicum, Visionsgatan 4, Solna 17164, Sweden

7. National Genomics Infrastructure, SciLifeLab , SciLifeLab, Tomtebodavägen 23, Solna 17165, Sweden

8. Nucleus Genomics , 584 Broadway, New York, 10012 NY, USA

9. National Genome Center Denmark , Ørestads Boulevard 5, Copenhagen 2300, Denmark

10. Department of Biology and Biotechnology ”L. Spallanzani”, University of Pavia , via Ferrata, 9, Pavia, 27100 PV, Italy

11. Department of Biological Sciences and Center for Systems Biology, University of Texas at Dallas , 800 W Campbell Rd, Richardson, TX 75080, USA

12. Department of Informatics, Technical University of Munich , Boltzmannstr. 3, Garching, 85748 Bavaria, Germany

13. Department of Pathology, Yale School of Medicine , 300 George, New Haven, CT 06510, USA

14. Institute for Bioinformatics and Medical Informatics (IBMI), Eberhard-Karls University of Tübingen , Tübingen 72076, Baden-Württemberg, Germany

Abstract

Abstract DNA variation analysis has become indispensable in many aspects of modern biomedicine, most prominently in the comparison of normal and tumor samples. Thousands of samples are collected in local sequencing efforts and public databases requiring highly scalable, portable, and automated workflows for streamlined processing. Here, we present nf-core/sarek 3, a well-established, comprehensive variant calling and annotation pipeline for germline and somatic samples. It is suitable for any genome with a known reference. We present a full rewrite of the original pipeline showing a significant reduction of storage requirements by using the CRAM format and runtime by increasing intra-sample parallelization. Both are leading to a 70% cost reduction in commercial clouds enabling users to do large-scale and cross-platform data analysis while keeping costs and CO2 emissions low. The code is available at https://nf-co.re/sarek.

Funder

Deutsche Forschungsgemeinschaft

Publisher

Oxford University Press (OUP)

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