NCBI's Virus Discovery Hackathon: Engaging Research Communities to Identify Cloud Infrastructure Requirements

Ryan Connor, Rodney Brister, Jan P. Buchmann, Ward Deboutte, Rob Edwards, Joan Martí-Carreras, Mike Tisza, Vadim Zalunin, Juan Andrade-Martínez, Adrian Cantu, Michael D'Amour, Alexandre Efremov, Lydia Fleischmann, Laura Forero-Junco, Sanzhima Garmaeva, Melissa Giluso, Cody Glickman, Margaret Henderson, Benjamin Kellman, David KristensenCarl Leubsdorf, Kyle Levi, Shane Levi, Suman Pakala, Vikas Peddu, Alise Ponsero, Eldred Ribeiro, Farrah Roy, Lindsay Rutter, Surya Saha, Migun Shakya, Ryan Shean, Matthew Miller, Benjamin Tully, Christopher Turkington, Ken Youens-Clark, Bert Vanmechelen, Ben Busby

Research output: Contribution to journalArticlepeer-review

Abstract

A wealth of viral data sits untapped in publicly available metagenomic data sets when it might be extracted to create a usable index for the virological research community. We hypothesized that work of this complexity and scale could be done in a hackathon setting. Ten teams comprised of over 40 participants from six countries, assembled to create a crowd-sourced set of analysis and processing pipelines for a complex biological data set in a three-day event on the San Diego State University campus starting 9 January 2019. Prior to the hackathon, 141,676 metagenomic data sets from the National Center for Biotechnology Information (NCBI) Sequence Read Archive (SRA) were pre-assembled into contiguous assemblies (contigs) by NCBI staff. During the hackathon, a subset consisting of 2953 SRA data sets (approximately 55 million contigs) was selected, which were further filtered for a minimal length of 1 kb. This resulted in 4.2 million (Mio) contigs, which were aligned using BLAST against all known virus genomes, phylogenetically clustered and assigned metadata. Out of the 4.2 Mio contigs, 360,000 contigs were labeled with domains and an additional subset containing 4400 contigs was screened for virus or virus-like genes. The work yielded valuable insights into both SRA data and the cloud infrastructure required to support such efforts, revealing analysis bottlenecks and possible workarounds thereof. Mainly: (i) Conservative assemblies of SRA data improves initial analysis steps; (ii) existing bioinformatic software with weak multithreading/multicore support can be elevated by wrapper scripts to use all cores within a computing node; (iii) redesigning existing bioinformatic algorithms for a cloud infrastructure to facilitate its use for a wider audience; and (iv) a cloud infrastructure allows a diverse group of researchers to collaborate effectively. The scientific findings will be extended during a follow-up event. Here, we present the applied workflows, initial results, and lessons learned from the hackathon.

Original languageEnglish
Article number714
Number of pages18
JournalGenes
Volume10
Issue number9
DOIs
Publication statusPublished - Sep 2019
Externally publishedYes

Bibliographical note

© 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).

Keywords

  • cloud computing
  • hackathon
  • infrastructure
  • metagenomic
  • SRA
  • STRIDES
  • viruses

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