Hayes C Nelson, Nakahara Hikaru, Ono Atsushi, Tsuge Masataka, Oka Shiro
Department of Gastroenterology, Graduate School of Biomedical & Health Sciences, Hiroshima University, Hiroshima 734-8551, Japan.
Department of Clinical and Molecular Genetics, Hiroshima University, Hiroshima 734-8551, Japan.
Genes (Basel). 2024 Nov 29;15(12):1551. doi: 10.3390/genes15121551.
Bioinformatics is a rapidly evolving field charged with cataloging, disseminating, and analyzing biological data. Bioinformatics started with genomics, but while genomics focuses more narrowly on the genes comprising a genome, bioinformatics now encompasses a much broader range of omics technologies. Overcoming barriers of scale and effort that plagued earlier sequencing methods, bioinformatics adopted an ambitious strategy involving high-throughput and highly automated assays. However, as the list of omics technologies continues to grow, the field of bioinformatics has changed in two fundamental ways. Despite enormous success in expanding our understanding of the biological world, the failure of bulk methods to account for biologically important variability among cells of the same or different type has led to a major shift toward single-cell and spatially resolved omics methods, which attempt to disentangle the conflicting signals contained in heterogeneous samples by examining individual cells or cell clusters. The second major shift has been the attempt to integrate two or more different classes of omics data in a single multimodal analysis to identify patterns that bridge biological layers. For example, unraveling the cause of disease may reveal a metabolite deficiency caused by the failure of an enzyme to be phosphorylated because a gene is not expressed due to aberrant methylation as a result of a rare germline variant. : There is a fine line between superficial understanding and analysis paralysis, but like a detective novel, multi-omics increasingly provides the clues we need, if only we are able to see them.
生物信息学是一个快速发展的领域，负责对生物数据进行编目、传播和分析。生物信息学始于基因组学，但基因组学更专注于构成基因组的基因，而生物信息学现在涵盖了范围更广的组学技术。生物信息学克服了困扰早期测序方法的规模和工作量障碍，采用了一种雄心勃勃的策略，涉及高通量和高度自动化的检测。然而，随着组学技术的不断增加，生物信息学领域在两个基本方面发生了变化。尽管在扩展我们对生物世界的理解方面取得了巨大成功，但批量方法未能解释同一类型或不同类型细胞之间生物学上重要的变异性，这导致了向单细胞和空间分辨组学方法的重大转变，这些方法试图通过检查单个细胞或细胞簇来解开异质样本中包含的冲突信号。第二个重大转变是试图在单一的多模态分析中整合两种或更多不同类别的组学数据，以识别跨越生物层次的模式。例如，揭示疾病原因可能会发现一种代谢物缺乏，这是由于一种酶未能磷酸化导致的，而这种酶未能磷酸化是因为一个基因由于罕见的种系变异导致异常甲基化而未表达。在表面理解和分析瘫痪之间存在一条细线，但就像一部侦探小说一样，多组学越来越多地提供我们需要的线索，只要我们能够看到它们。
