Lessons learnt on the analysis of large sequence data in animal genomics |
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| Authors: | F Biscarini P Cozzi P Orozco‐ter Wengel |
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| Affiliation: | 1. CNR‐IBBA, Milan, Italy;2. School of Medicine, Cardiff University, Cardiff, UK;3. Department of Bioinformatics and Biostatistics, PTP Science Park, Lodi, Italy;4. School of Biosciences, Cardiff University, Cardiff, UK |
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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. |
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| Keywords: | animal genetics big data computational biology data analysis genome sequence next‐generation sequencing ’ omics |
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