Raddatz Barbara B, Spitzbarth Ingo, Matheis Katja A, Kalkuhl Arno, Deschl Ulrich, Baumgärtner Wolfgang, Ulrich Reiner
1 Department of Pathology, University of Veterinary Medicine Hannover, Hannover, Germany.
2 Center of Systems Neuroscience, Hannover, Germany.
Vet Pathol. 2017 Sep;54(5):734-755. doi: 10.1177/0300985817709887. Epub 2017 Jun 23.
High-throughput, genome-wide transcriptome analysis is now commonly used in all fields of life science research and is on the cusp of medical and veterinary diagnostic application. Transcriptomic methods such as microarrays and next-generation sequencing generate enormous amounts of data. The pathogenetic expertise acquired from understanding of general pathology provides veterinary pathologists with a profound background, which is essential in translating transcriptomic data into meaningful biological knowledge, thereby leading to a better understanding of underlying disease mechanisms. The scientific literature concerning high-throughput data-mining techniques usually addresses mathematicians or computer scientists as the target audience. In contrast, the present review provides the reader with a clear and systematic basis from a veterinary pathologist's perspective. Therefore, the aims are (1) to introduce the reader to the necessary methodological background; (2) to introduce the sequential steps commonly performed in a microarray analysis including quality control, annotation, normalization, selection of differentially expressed genes, clustering, gene ontology and pathway analysis, analysis of manually selected genes, and biomarker discovery; and (3) to provide references to publically available and user-friendly software suites. In summary, the data analysis methods presented within this review will enable veterinary pathologists to analyze high-throughput transcriptome data obtained from their own experiments, supplemental data that accompany scientific publications, or public repositories in order to obtain a more in-depth insight into underlying disease mechanisms.
高通量全基因组转录组分析如今在生命科学研究的各个领域普遍应用，且正处于医学和兽医诊断应用的前沿。诸如微阵列和新一代测序等转录组学方法会产生海量数据。通过理解一般病理学所获得的致病专业知识为兽医病理学家提供了深厚的背景，这对于将转录组数据转化为有意义的生物学知识至关重要，从而有助于更好地理解潜在的疾病机制。关于高通量数据挖掘技术的科学文献通常以数学家或计算机科学家为目标受众。相比之下，本综述从兽医病理学家的角度为读者提供了清晰且系统的基础。因此，目的在于：（1）向读者介绍必要的方法学背景；（2）介绍微阵列分析中通常执行的连续步骤，包括质量控制、注释、标准化、差异表达基因的选择、聚类、基因本体和通路分析、手动选择基因的分析以及生物标志物发现；（3）提供可公开获取且用户友好的软件套件的参考文献。总之，本综述中介绍的数据分析方法将使兽医病理学家能够分析从他们自己的实验、科学出版物附带的补充数据或公共存储库中获得的高通量转录组数据，以便更深入地洞察潜在的疾病机制。
