Studying RNA homology and conservation with Infernal: from single sequences to RNA families

TL;DR

This paper presents a protocol for identifying ncRNA homologs and constructing covariance models using Infernal, enabling systematic characterization of ncRNA families from single sequences to broader classifications.

Contribution

It introduces a step-by-step iterative method for ncRNA homology detection and family construction using covariance models in the Infernal software.

Findings

Discovered a new family of MicA homologs in Xenorhabdus.

Demonstrated the protocol's effectiveness on bacterial small RNA MicA.

Provided a practical guide for ncRNA homology analysis.

Abstract

Emerging high-throughput technologies have led to a deluge of putative non-coding RNA (ncRNA) sequences identified in a wide variety of organisms. Systematic characterization of these transcripts will be a tremendous challenge. Homology detection is critical to making maximal use of functional information gathered about ncRNAs: identifying homologous sequence allows us to transfer information gathered in one organism to another quickly and with a high degree of confidence. ncRNA presents a challenge for homology detection, as the primary sequence is often poorly conserved and de novo secondary structure prediction and search remains difficult. This protocol introduces methods developed by the Rfam database for identifying "families" of homologous ncRNAs starting from single "seed" sequences using manually curated sequence alignments to build powerful statistical models of sequence and structure conservation known as covariance models (CMs), implemented in the Infernal software package. We provide a step-by-step iterative protocol for identifying ncRNA homologs, then constructing an alignment and corresponding CM. We also work through an example for the bacterial small RNA MicA, discovering a previously unreported family of divergent MicA homologs in genus Xenorhabdus in the process.