Sequencing smart:De novo sequencing and assembly approaches for a non-model mammal

Etherington, Graham J., Heavens, Darren, Baker, David, Lister, Ashleigh, McNelly, Rose, Garcia, Gonzalo, Clavijo, Bernardo, Macaulay, Iain, Haerty, Wilfried ORCID: https://orcid.org/0000-0003-0111-191X and Di Palma, Federica (2020) Sequencing smart:De novo sequencing and assembly approaches for a non-model mammal. GigaScience, 9 (5). ISSN 2047-217X

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Abstract

Background: Whilst much sequencing effort has focused on key mammalian model organisms such as mouse and human, little is known about the relationship between genome sequencing techniques for non-model mammals and genome assembly quality. This is especially relevant to non-model mammals, where the samples to be sequenced are often degraded and of low quality. A key aspect when planning a genome project is the choice of sequencing data to generate. This decision is driven by several factors, including the biological questions being asked, the quality of DNA available, and the availability of funds. Cutting-edge sequencing technologies now make it possible to achieve highly contiguous, chromosome-level genome assemblies, but rely on high-quality high molecular weight DNA. However, funding is often insufficient for many independent research groups to use these techniques. Here we use a range of different genomic technologies generated from a roadkill European polecat (Mustela putorius) to assess various assembly techniques on this low-quality sample. We evaluated different approaches for de novo assemblies and discuss their value in relation to biological analyses. Results: Generally, assemblies containing more data types achieved better scores in our ranking system. However, when accounting for misassemblies, this was not always the case for Bionano and low-coverage 10x Genomics (for scaffolding only). We also find that the extra cost associated with combining multiple data types is not necessarily associated with better genome assemblies. Conclusions: The high degree of variability between each de novo assembly method (assessed from the 7 key metrics) highlights the importance of carefully devising the sequencing strategy to be able to carry out the desired analysis. Adding more data to genome assemblies does not always result in better assemblies, so it is important to understand the nuances of genomic data integration explained here, in order to obtain cost-effective value for money when sequencing genomes.

Item Type: Article
Additional Information: Funding Information: This work was strategically funded by the BBSRC Core Strategic ProgrammeGrant BBS/E/T/000PR9817 at the Earlham Institute (EI). High-throughput sequencing and library construction was delivered via the BBSRC National Capability in Genomics (BB/CCG1720/1) by members of the Genomic Pipelines Group. This research was supported in part by the NBI Computing infrastructure for Science (CiS) group through the use of the EI High Performance Computing facilities. Funding Information: The submission of sequencing data was brokered by the COPO platform [58], funded by the BBSRC (BB/L024055/1), and supported by CyVerse UK, part of the Earlham Institute National Capability in e-Infrastructure. All datasets supporting the results of this article are available in the ENA repository under umbrella project accession No. PRJEB34131. Optical maps, annotations, and other results are available from the GigaScience GigaDB repository [59], and protocols are available from protocols.io [37]. Publisher Copyright: © 2020 The Author(s) 2020. Published by Oxford University Press.
Uncontrolled Keywords: assembly,bionano,chromium,illumina,non-model organism,polecat,sequencing,vertebrate,health informatics,computer science applications ,/dk/atira/pure/subjectarea/asjc/2700/2718
Faculty \ School: Faculty of Science > School of Biological Sciences
Faculty of Medicine and Health Sciences > Norwich Medical School
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Depositing User: LivePure Connector
Date Deposited: 15 Sep 2022 14:30
Last Modified: 20 Oct 2022 19:33
URI: https://ueaeprints.uea.ac.uk/id/eprint/88316
DOI: 10.1093/gigascience/giaa045

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