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