Lessons learnt on the analysis of large sequence data in animal genomics

Biscarini, Filippo ORCID: https://orcid.org/0000-0002-3901-2354, Cozzi, P. and Orozco Ter Wengel, Pablo ORCID: https://orcid.org/0000-0002-7951-4148 2018. Lessons learnt on the analysis of large sequence data in animal genomics. Animal Blood Groups and Biochemical Genetics 49 (3) , pp. 147-158. 10.1111/age.12655

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Abstract

The ’omics revolution has made a large amount of sequence data available to researchers and the industry. This has had a profound impact in the field of bioinformatics, stimulating unprecedented advancements in this discipline. Mostly, this is usually looked at from the perspective of human ’omics, in particular human genomics. Plant and animal genomics, however, have also been deeply influenced by next‐generation sequencing technologies, with several genomics applications now popular among researchers and the breeding industry. Genomics tends to generate huge amounts of data, and genomic sequence data account for an increasing proportion of big data in biological sciences, due largely to decreasing sequencing and genotyping costs and to large‐scale sequencing and resequencing projects. The analysis of big data poses a challenge to scientists, as data gathering currently takes place at a faster pace than does data processing and analysis, and the associated computational burden is increasingly taxing, making even simple manipulation, visualization and transferring of data a cumbersome operation. The time consumed by the processing and analysing of huge data sets may be at the expense of data quality assessment and critical interpretation. Additionally, when analysing lots of data, something is likely to go awry—the software may crash or stop—and it can be very frustrating to track the error. We herein review the most relevant issues related to tackling these challenges and problems, from the perspective of animal genomics, and provide researchers that lack extensive computing experience with guidelines that will help when processing large genomic data sets.

Item Type:	Article
Date Type:	Publication
Status:	Published
Schools:	Medicine Biosciences
Publisher:	Wiley: No OnlineOpen
ISSN:	0268-9146
Date of First Compliant Deposit:	10 April 2018
Date of Acceptance:	11 February 2018
Last Modified:	06 Nov 2023 23:30
URI:	https://orca.cardiff.ac.uk/id/eprint/110601

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