Using comparative genomics to drive new discoveries in microbiology. (February 2015)

• Record Type: Journal Article
• Authors: Haft, Daniel H
• Is Part Of: Current opinion in microbiology. Volume 23(2015)
• Page Start: 189
• Page End: 196
• Publication Date: 2015-02
• Subjects: Microbiology -- Periodicals; 579.05
• DOI: 10.1016/j.mib.2014.11.017
• Languages: English
• ISSNs: 1369-5274

Abstract

Highlights: We describe progress in using comparative genomics to make new discoveries. Computational genomics provides for a code-cracking approach to annotation. Patterns of design let biocurators reason by analogy about unknown proteins. Abstract : Bioinformatics looks to many microbiologists like a service industry. In this view, annotation starts with what is known from experiments in the lab, makes reasonable inferences of which genes match other genes in function, builds databases to make all that we know accessible, but creates nothing truly new. Experiments lead, then biocuration and computational biology follow. But the astounding success of genome sequencing is changing the annotation paradigm. Every genome sequenced is an intercepted coded message from the microbial world, and as all cryptographers know, it is easier to decode a thousand messages than a single message. Some biology is best discovered not by phenomenology, but by decoding genome content, forming hypotheses, and doing the first few rounds of validation computationally. Through such reasoning, a role and function may be assigned to a protein with no sequence similarity to any protein yet studied. Experimentation can follow after the discovery to cement and to extend the findings. Unfortunately, this approach remains so unfamiliar to most bench scientists that lab work and comparative genomics typically segregate to different teams working on unconnected projects. This review will discuss several themes in comparative genomics as a discovery method, including highly derived data, use of patterns of design to reason by analogy, and in silico testing of computationally generated hypotheses.