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.

对第一个基因组进行测序需要15年和30亿美元才能完成。目前，一个基因组可以在一天之内完成几千美元的测序。比较来自不同实验条件的细胞和组织中几乎每种mRNA转录本和小RNA的相对丰度已经变得如此容易，以至于在计算机之间传输数据要比进行实验花费更长的时间。核苷酸测序技术已经变得如此敏感，以至于最大的关注不是检测基因或转录本，而是错误地鉴定一个基因或转录本。更好的基因组测序带来了更完整的转录组学和蛋白质组学数据库，并且结合更灵敏的仪器和分离技术，使我们更接近检测完整的转录组和蛋白质组学。这些强大的组学技术的承诺是，在动物健康的背景下，将我们引导到分子过程之间的新的和出乎意料的联系。没有假设驱动的研究将组学数据与动物健康实验联系起来，就无法实现这一诺言。任何希望将时间和资源投入到组学实验中的研究人员都应意识到这些技术的常见陷阱和局限性，以便他们避免这些问题并最大限度地利用这些研究工具。必须提出几个重要的问题：数据库的质量是什么？如何对其进行注释？注释是基于实验结果还是基于计算预测？分析算法做出了哪些假设，这将如何影响结果？最后，研究团体如何利用组学实验产生的大量数据来实现更好的动物健康和生产的目标（这是组学技术的承诺）？除非使用组学数据集中显示的观察结果来实现更好的动物健康和生产的目标，否则就不会完全实现组学技术的潜力。