Using comparative genomics to drive new discoveries in microbiology

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.

The healthy human microbiome

“…Population-scale studies of the microbiome can themselves be used to mitigate this situation partially, in that microbial gene families that are prevalent and abundant but not yet well-understood can be prioritized for characterization. Likewise microbial communities provide a new source of guilt-by-association information that can be used computationally to generate predictions of gene function [ 127 , 128 ]. Nevertheless, returning to the field’s microbiological roots may ultimately prove most important in this area: the best biochemical characterizations still derive from culture-based physiology, microbial metabolism, co-culture and interactions, and controlled laboratory environments coupled with high-throughput molecular assays [ 15 , 129 , 130 ].…”

A Manual Curation Strategy to Improve Genome Annotation: Application to a Set of Haloarchael Genomes

“…Decision-making is an important aspect of manual curation, which seems resistant to automation. As different protein families evolve at a different rate and differ in their tendency to adopt new functions, there is no general cutoff which can be applied [ 35 ]. Even in a manual curation effort, this step is a potential source of annotation errors.…”

“…Advanced bioinformatic methods have a tremendous potential to advance biological knowledge (see e.g., [ 35 ]). Preferably, bioinformatic predictions should be backed up by experimental analyses, as has been done for archaeosortase [ 48 , 49 ].…”

Antimicrobial Peptides, Polymorphic Toxins, and Self-Nonself Recognition Systems in Archaea: an Untapped Armory for Intermicrobial Conflicts

“…S2 in the supplemental material). This motif has not been described previously but, like many other terminal motifs, might represent a protein targeting signal (19,20). Additionally, these loci include genes for two essential regulators of the tail assembly, the CDC48-like AAAϩ ATPase (arCOG01308) and the tail terminator protein (ar-COG11409, DUF4255 family) (15).…”