  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.
