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

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.

Calling known variants and identifying new variants while rapidly aligning sequence data

“…Other new variant identification strategies such as the GATK Haplot-ypeCaller or support vector machines (O'Fallon et al, 2013) require extra inputs such as lists of true variants, false-positive variants, or estimates of machine-or allele-specific bias that can improve call rate but often with even longer run times. Researchers in animal genetics often need different computing strategies than those developed in human genetics (Biscarini et al, 2018), primarily because of differing goals and limited budgets. Animal genetics focuses on genomic prediction, low-density genotyping, lower coverage sequencing, and deep pedigrees, whereas human genetics often focuses on disease treatment, higher density genotyping, higher coverage sequencing, unrelated individuals, and discovering genetic origins.…”

Feeding Pre-weaned Calves With Waste Milk Containing Antibiotic Residues Is Related to a Higher Incidence of Diarrhea and Alterations in the Fecal Microbiota

“…Among-groups (BM vs. WM) and pairwise Bray–Curtis dissimilarities were evaluated non-parametrically using the permutational analysis of variance (999 permutations) ( 42 ). Details on the calculation of the mentioned alpha and beta diversity indices can be found in Supplementary File 1 and in Biscarini et al ( 43 ).…”

Dual Analysis of Virus-Host Interactions: The Case of Ostreid herpesvirus 1 and the Cupped Oyster Crassostrea gigas

“…Irrespective of the chosen "omics," the need to manage large amounts of molecular data to correctly infer and validate biological hypotheses imposes a rigorous step-by-step evaluation of the work, including experimental design, sampling protocols, and data processing (eg, statistics, bioinformatics pipelines, visualization, and database matching). 32,33 At the cutting edge of the "omics" integration and multiplatform research, the analysis of host-associated microbiota by metabarcoding of marker genes, shotgun metagenomics, and metabolomics has provided diagnostics and predictive capabilities that can now help elucidate complex interspecies relationships and perturbations, such as symbiotic, commensalistic, opportunistic, and pathogenic. 34,35 Despite the steadily increasing number of transcriptomicsand proteomics-based studies, not many papers refer to the in vivo analysis of host-pathogen interactions.…”

“…Irrespective of the chosen “omics,” the need to manage large amounts of molecular data to correctly infer and validate biological hypotheses imposes a rigorous step-by-step evaluation of the work, including experimental design, sampling protocols, and data processing (eg, statistics, bioinformatics pipelines, visualization, and database matching). 32,33…”