---

name: bio-rna-structure-ncrna-search description: Searches for non-coding RNA homologs and classifies RNA families with Infernal covariance models against Rfam, scoring sequence AND secondary-structure conservation jointly. Use when deciding whether a covariance model is the right tool versus BLAST/nhmmer (structured ncRNA versus lncRNA or mature miRNA); choosing the Rfam gathering threshold over a flat E-value; resolving clan overlaps; building and calibrating a custom CM from a structure-annotated alignment; or preferring a family-specialized tool (tRNAscan-SE, barrnap) over a generic Rfam scan. tool_type: cli primary_tool: Infernal

Version Compatibility

Reference examples tested with: Infernal 1.1.4+, BioPython 1.83+, pandas 2.2+

Before using code patterns, verify installed versions match. If versions differ:

• CLI: <tool> --version then <tool> --help to confirm flags
• Python: pip show <package> then help(module.function) to check signatures

If code throws ImportError, AttributeError, or TypeError, introspect the installed package and adapt the example to match the actual API rather than retrying.

ncRNA Search

"Search my sequences for known non-coding RNA families" -> Score candidates against Rfam covariance models that capture both sequence and consensus secondary structure, or build a custom model for a novel family.

• CLI: cmscan for querying sequences against the Rfam CM database
• CLI: cmsearch for one CM against a sequence database
• CLI: cmbuild + cmcalibrate + cmpress for a custom covariance model

The governing principle: a covariance model is only worth it for STRUCTURED RNA

A covariance model (CM) is a profile stochastic context-free grammar that models a family's consensus secondary structure AND primary sequence jointly. Its power source is covariation: a base pair is scored by a joint distribution over pair states, so a compensatory double mutation (C-G to G-C, or a G-U wobble) that PRESERVES the pair scores well even though both positions changed -- a BLAST or profile-HMM sees two mismatches and loses the signal. This is why Infernal detects remote homologs of structured RNAs (tRNA, rRNA, riboswitches, SRP, RNase P, ribozymes, snoRNA) past the sequence twilight zone.

The decisive corollary: a CM offers NO advantage when there is little conserved secondary structure to exploit. Many lncRNAs (R-scape finds no significant covariation in HOTAIR/Xist/SRA), mature miRNAs (~22 nt, no base-pairing in the mature strand), and primary-sequence-only motifs gain nothing from a CM -- it is slow and calibration-heavy, and a profile-HMM (nhmmer) or BLASTN is the correct, faster tool. A CM built from a structure-FREE alignment (no real #=GC SS_cons pairs) collapses to an HMM and buys nothing but runtime.

Query / target property	Right tool	Why
Structured ncRNA, remote homology (tRNA, rRNA, riboswitch, ribozyme, SRP, snoRNA)	Infernal CM (Rfam)	covariation recovers pairs across sequence divergence
Structured RNA with a family-specialized tool	the specialist (table below)	tuned models + biology logic beat a generic scan
Close homolog, high identity, any RNA	BLASTN / nhmmer	sequence signal suffices, 100-1000x faster
lncRNA, mature miRNA, sequence-only motif	nhmmer / BLASTN	no conserved structure to exploit -> CM is overhead
Novel structured RNA, no Rfam family	build a custom CM, AFTER validating structure with R-scape	a CM is only as good as its SS_cons

Infernal toolchain

• cmbuild model.cm aln.sto -- build a CM from a structure-annotated Stockholm alignment; REQUIRES a #=GC SS_cons line.
• cmcalibrate model.cm -- fit E-value statistics; SLOW (minutes to hours) but MANDATORY before any E-value is meaningful. Without it, search still runs but only bit-score thresholding is valid.
• cmpress model.cm -- build the binary index cmscan requires (cmsearch does not need it).
• cmsearch CM seqdb -- one CM vs a sequence database (e.g. one family vs a genome).
• cmscan CMdb seqs -- query sequences vs a CM database (e.g. all of Rfam.cm); this is the genome/transcript ncRNA-annotation layout.
• cmalign CM seqs -- align/fold hits back to the CM consensus to recover each hit's secondary structure.

Rfam.cm ships PRE-CALIBRATED: run cmpress on it, but do NOT cmcalibrate it. Calibration is only for locally built custom models, and must be re-run after every rebuild.

E-values depend on database size; gathering thresholds do not

A CM E-value scales linearly with the searched database size (Z): the SAME hit gets a different E-value depending on what was searched. cmsearch Z defaults to the target sequence-DB size counted on both strands (total residues x2); cmscan Z defaults to (query length x2 x number of models). So a cmscan E-value and a cmsearch E-value for the same locus are NOT comparable, and a flat -E 1e-5 is exactly what curated thresholds replace. Fix Z explicitly with -Z <Mb> for reproducible cross-run comparison.

Bit scores are DB-size-INDEPENDENT, which is precisely why Rfam stores its curated cutoffs as bit scores:

Flag	Threshold (bit-score cutoff stored in the CM)	When
--cut_ga	GA (gathering): the curated family-membership cutoff	the correct DEFAULT for Rfam annotation
--cut_tc	TC (trusted): lowest score of any known true positive	most conservative
--cut_nc	NC (noise): highest score of a known false positive	most permissive, exploratory
-E / --incE	flat E-value (reporting / inclusion)	custom CM, or a non-Rfam DB with no curated GA
-T / --incT	flat bit score	custom CM with no calibration, or DB-size-robust cut

--cut_ga beats a flat E-value for Rfam because each family has a different signal-to-noise profile (a 70 nt tRNA vs a 2900 nt rRNA vs a short riboswitch); one flat cutoff over- or under-calls per family, and reintroduces the DB-size dependence GA was designed to avoid.

The canonical Rfam annotation command

# One-time setup
wget https://ftp.ebi.ac.uk/pub/databases/Rfam/CURRENT/Rfam.cm.gz && gunzip Rfam.cm.gz
wget https://ftp.ebi.ac.uk/pub/databases/Rfam/CURRENT/Rfam.clanin
cmpress Rfam.cm   # NOT cmcalibrate -- Rfam.cm is pre-calibrated

# Annotate a genome. -Z = 2 x genome size in Mb keeps E-values reproducible; --rfam is the
# large-DB strict filter; --nohmmonly forces CM mode so GA cutoffs stay valid for every model.
cmscan -Z 100 --cut_ga --rfam --nohmmonly --fmt 2 --clanin Rfam.clanin \
    --tblout genome.tblout Rfam.cm genome.fa > genome.cmscan

# Clan deoverlapping: --fmt 2 adds the 'olp' column; the documented Rfam filter drops hits
# marked '=' (dominated by a higher-scoring clanmate), keeping '^' (best of an overlap) and '*' (no overlap).
grep -v ' = ' genome.tblout > genome.deoverlapped.tblout

--clanin with --fmt 2 plus the grep -v ' = ' post-filter is the documented deoverlap path; --oclan is a valid in-tool alternative but not what the modern Rfam pipeline uses, so do not treat it as mandatory.

Reading --fmt 2 output (the column shift that breaks fmt-1 parsers)

--fmt 2 prepends an idx column and inserts a clan name column versus the default --fmt 1, so every downstream field index shifts -- a parser written for fmt 1 silently reads the wrong columns. Verified 0-based fmt-2 cmscan indices: idx 0, target/family name 1, accession 2, query/seq name 3, query accession 4, clan 5, mdl type 6, mdl_from 7, mdl_to 8, seq_from 9, seq_to 10, strand 11, trunc 12, pass 13, gc 14, bias 15, score 16, E-value 17, inc 18, olp 19. In cmscan the model name is column 1 and the sequence name is column 3; in cmsearch they are reversed -- a parser must branch on which tool produced the file.

Interpretive columns: trunc is no, 5', 3', or 5'&3' for hits running off a contig end (often REAL incomplete genes worth keeping, not a discard flag; --anytrunc allows truncation at any internal position (E-values become less accurate), --notrunc disables it entirely); bias is the composition correction already subtracted (a large bias flags a low-complexity hit); mdl_from/mdl_to are consensus coordinates, so a small model span is a partial match even when trunc is no.

Recovering the structure of a hit (the CM payoff over BLAST)

A significant CM hit yields not just a family label but an implied secondary structure -- the distinctive payoff a BLAST hit cannot give. Fold the hit sequences back to the model with cmalign to map the consensus structure onto them.

# Pull the family CM from Rfam, then align hits to recover their consensus structure
cmfetch Rfam.cm RF00005 > tRNA.cm
cmalign --outformat Pfam tRNA.cm hits.fa > hits.sto   # Stockholm with #=GC SS_cons per hit

The #=GC SS_cons line in the output is the structural hypothesis for each hit -- the prior to feed to probing (structure-probing) or to validate with covariation (covariation-analysis).

A hit is a family assignment, not a functional call

A significant CM hit means the locus has sequence + structure consistent with the family; it does NOT prove the RNA is expressed, processed, or functional. Pseudogenes (tRNA-derived SINEs, rRNA pseudogenes) score well. tRNAscan-SE's high-confidence-set logic exists precisely to separate likely-functional tRNAs from numerous genomic pseudogenes. Frame a CM hit as a family assignment plus a structural hypothesis; confirm function with orthogonal evidence (expression, conservation, synteny, probing).

Specialized tool beats a generic Rfam scan (when one exists)

RNA class	Use this, not a generic Rfam scan	Why
tRNA	tRNAscan-SE 2.0	tRNA-specific isotype/anticodon models, pseudogene filtering, high-confidence set
rRNA (5S/16S/18S/23S/28S)	barrnap or RNAmmer	per-kingdom HMM models tuned for rRNA; fast
C/D-box snoRNA	snoscan	models the guide-target duplex + box C/D a generic CM cannot
H/ACA + C/D snoRNA (ab initio)	snoReport 2.0	RNA-fold + SVM on box/structure features
miRNA	miRBase lookup / miRDeep2	CMs are poor on mature miRNA; precursors need read support

Use the generic Rfam cmscan for broad "what ncRNA families are in here" sweeps and for classes without a dedicated tool.

Building a custom CM well

# The Stockholm alignment MUST carry a #=GC SS_cons line in WUSS notation: <> or () = nested pairs,
# [] and {} = additional pseudoknot layers, . = unpaired (a structure-free alignment yields only an
# HMM-equivalent model). Validate the SS_cons covariation with R-scape FIRST (covariation-analysis).
cmbuild -n MYFAM myfam.cm alignment.sto
cmcalibrate --cpu 8 myfam.cm     # required for E-values; re-run after every rebuild
cmpress myfam.cm
cmsearch --cpu 8 -T 30 --tblout hits.tbl myfam.cm target.fa > hits.out

If cmcalibrate is skipped, thresholding MUST use bit score (-T <bits>), not E-value. The iterative search-align-rebuild loop (cmsearch -A new_hits.sto) grows a family but risks homology overextension and model drift -- gate each round on score and retained covariation, and recalibrate after every rebuild.

Common Errors

Symptom	Cause	Fix
Parsed score/evalue are nonsense numbers	parsing --fmt 2 output with fmt-1 indices	use fmt-2 indices (score 16, E-value 17), or run --fmt 1
Error: failed to open ... .i1m (cmscan)	Rfam.cm not pressed	cmpress Rfam.cm
E-values look meaningful on a custom CM but are not	cmcalibrate was skipped	calibrate, or threshold on bit score -T
Recalibrating Rfam.cm takes hours	Rfam.cm is already calibrated	press it, never calibrate it
Same hit, different E-value across runs	E-value scales with database size Z	use --cut_ga, or fix -Z <Mb>
Redundant overlapping hits from related families	clan overlap not resolved	--fmt 2 --clanin then grep -v ' = '
A CM search on a lncRNA finds nothing useful	no conserved structure to exploit	use nhmmer/BLASTN instead of a CM
tRNA search misses or over-calls genes	generic Rfam tRNA model lacks tRNA logic	use tRNAscan-SE 2.0

Related Skills

• secondary-structure-prediction - Predict structure for novel ncRNA candidates with no Rfam hit
• covariation-analysis - Validate a custom CM's SS_cons with R-scape before building
• structure-probing - Experimental reactivities to corroborate a CM's consensus structure
• genome-annotation/ncrna-annotation - Genome-wide ncRNA annotation pipelines
• alignment/msa-statistics - Evaluate alignment quality before CM building
• database-access/entrez-fetch - Fetch Rfam/RNAcentral records

References

• Eddy SR, Durbin R. 1994. RNA sequence analysis using covariance models. Nucleic Acids Res 22(11):2079-2088. doi:10.1093/nar/22.11.2079
• Nawrocki EP, Eddy SR. 2013. Infernal 1.1: 100-fold faster RNA homology searches. Bioinformatics 29(22):2933-2935. doi:10.1093/bioinformatics/btt509
• Griffiths-Jones S, Bateman A, Marshall M, Khanna A, Eddy SR. 2003. Rfam: an RNA family database. Nucleic Acids Res 31(1):439-441. doi:10.1093/nar/gkg006
• Kalvari I, Nawrocki EP, Ontiveros-Palacios N, Argasinska J, Lamkiewicz K, Marz M, Griffiths-Jones S, Toffano-Nioche C, Gautheret D, Weinberg Z, Rivas E, Eddy SR, Finn RD, Bateman A, Petrov AI. 2021. Rfam 14: expanded coverage of metagenomic, viral and microRNA families. Nucleic Acids Res 49(D1):D192-D200. doi:10.1093/nar/gkaa1047
• Chan PP, Lin BY, Mak AJ, Lowe TM. 2021. tRNAscan-SE 2.0: improved detection and functional classification of transfer RNA genes. Nucleic Acids Res 49(16):9077-9096. doi:10.1093/nar/gkab688
• Lagesen K, Hallin P, Rodland EA, Staerfeldt HH, Rognes T, Ussery DW. 2007. RNAmmer: consistent and rapid annotation of ribosomal RNA genes. Nucleic Acids Res 35(9):3100-3108. doi:10.1093/nar/gkm160
• Lowe TM, Eddy SR. 1999. A computational screen for methylation guide snoRNAs in yeast. Science 283(5405):1168-1171. doi:10.1126/science.283.5405.1168
• Rivas E, Clements J, Eddy SR. 2017. A statistical test for conserved RNA structure shows lack of evidence for structure in lncRNAs. Nat Methods 14(1):45-48. doi:10.1038/nmeth.4066