Symposium review: Omics in dairy and animal science-Promise, potential, and pitfalls

Abstract

Sequencing the first genome took 15 yr and $3 billion to complete. Currently, a genome can be sequenced in a day for a few thousand dollars. Comparing the relative abundance of nearly every mRNA transcript and small RNAs from cells and tissues from different experimental conditions has become so easy that it can take longer to transfer the data between computers than to perform the experiment. Nucleotide sequencing techniques have become so sensitive that the greatest concern is not detecting a gene or transcript but rather, falsely identifying one. Better genome sequencing has led to more complete transcriptomic and proteomic databases and, combined with more sensitive instrumentation and separation techniques, is bringing us closer to detecting complete transcriptomes and proteomes. The promise of these powerful omics techniques is to lead us to new and unexpected connections between molecular processes in the context of animal health. This promise cannot be achieved without hypothesis-driven research that connects omics data with animal health experiments. Any researcher who wishes to invest the time and resources in omics experiments should be aware of the common pitfalls and limitations of these techniques so they can avoid these issues and maximize the use of these research tools. Several important questions must be asked: What is the quality of the databases and how they are annotated? Are the annotations based on experimental results or computational predictions? What assumptions are made by the analysis algorithms, and how will this affect the result? Finally, how can the research community use the vast amount of data being generated by omics experiments in ways to achieve the goals of better animal health and production (which is the promise of omics technologies)? Until the observations shown in omics data sets are used to achieve the goals of better animal health and production, the potential of omics technology will not be fully realized.

Keywords: genome-wide association study (GWAS); omics technology; proteomics; sequencing; transcriptomics.