Abstract
BackgroundDecreasing sequencing costs and development of new protocols for characterizing global methylation, gene expression patterns and regulatory regions have stimulated the generation of large livestock datasets. Here, we discuss experiences in the analysis of whole-genome and transcriptome sequence data.MethodsWe analyzed whole-genome sequence (WGS) data from 132 individuals from five canid species (Canis familiaris, C. latrans, C. dingo, C. aureus and C. lupus) and 61 breeds, three bison (Bison bison), 64 water buffalo (Bubalus bubalis) and 297 bovines from 17 breeds. By individual, data vary in extent of reference genome depth of coverage from 4.9X to 64.0X. We have also analyzed RNA-seq data for 580 samples representing 159 Bos taurus and Rattus norvegicus animals and 98 tissues. By aligning reads to a reference assembly and calling variants, we assessed effects of average depth of coverage on the actual coverage and on the number of called variants. We examined the identity of unmapped reads by assembling them and querying produced contigs against the non-redundant nucleic acids database. By imputing high-density single nucleotide polymorphism data on 4010 US registered Angus animals to WGS using Run4 of the 1000 Bull Genomes Project and assessing the accuracy of imputation, we identified misassembled reference sequence regions.ResultsWe estimate that a 24X depth of coverage is required to achieve 99.5 % coverage of the reference assembly and identify 95 % of the variants within an individual’s genome. Genomes sequenced to low average coverage (e.g., <10X) may fail to cover 10 % of the reference genome and identify <75 % of variants. About 10 % of genomic DNA or transcriptome sequence reads fail to align to the reference assembly. These reads include loci missing from the reference assembly and misassembled genes and interesting symbionts, commensal and pathogenic organisms.ConclusionsAssembly errors and a lack of annotation of functional elements significantly limit the utility of the current draft livestock reference assemblies. The Functional Annotation of Animal Genomes initiative seeks to annotate functional elements, while a 70X Pac-Bio assembly for cow is underway and may result in a significantly improved reference assembly.Electronic supplementary materialThe online version of this article (doi:10.1186/s12711-016-0237-6) contains supplementary material, which is available to authorized users.
Highlights
Decreasing sequencing costs and development of new protocols for characterizing global methylation, gene expression patterns and regulatory regions have stimulated the generation of large livestock datasets
While this figure contains data for samples that had been sequenced from only a single library preparation and using different chemistry versions on both Illumina Genome Analyzer and HiSeq2000/2500 instruments, it is clear that samples that were sequenced to an average depth of
We found contigs representing M. bovis and Mycoplasma wenyonii in one animal challenged with M. haemolytica and Mycoplasma hyopneumoniae in one animal challenged with bovine viral diarrhea virus (BVDV)
Summary
Decreasing sequencing costs and development of new protocols for characterizing global methylation, gene expression patterns and regulatory regions have stimulated the generation of large livestock datasets. What was less obvious to us at the time was the fact that the causal mutation was almost certainly included in the generated data, our ability to detect it was by no means guaranteed. Our experience of such analyses at the time was limited to the discovery of variants that we had conducted for the design of the Illumina BovineSNP50 assay and this involved alignment of the sequences against a reference genome and calling variants. We discuss some of our experiences in tackling these issues in the analysis of RNA-seq and wholegenome sequence data
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