Identifies non-coding RNAs (tRNA, rRNA, snoRNA, snRNA, riboswitches, sRNAs) using Infernal covariance-model search against Rfam, tRNAscan-SE 2.0 for tRNA, barrnap for rRNA, and ARAGORN for tmRNA, plus the small-RNA-seq boundary for miRNA and the transcript-assembly boundary for lncRNA. Covers the structure-conserved-not-sequence-conserved principle (why BLAST fails), GA-threshold and clan-competition correctness, tRNAscan-SE domain modes and pseudogene flags, rDNA copy-number collapse, and why homology annotation is a recall floor. Use when performing genome-wide ncRNA annotation, choosing the right tool for an RNA class, or interpreting ncRNA counts.
Identifies non-coding RNAs (tRNA, rRNA, snoRNA, snRNA, riboswitches, sRNAs) using Infernal covariance-model search against Rfam, tRNAscan-SE 2.0 for tRNA, barrnap for rRNA, and ARAGORN for tmRNA, plus the small-RNA-seq boundary for miRNA and the transcript-assembly boundary for lncRNA. Covers the structure-conserved-not-sequence-conserved principle (why BLAST fails), GA-threshold and clan-competition correctness, tRNAscan-SE domain modes and pseudogene flags, rDNA copy-number collapse, and why homology annotation is a recall floor. Use when performing genome-wide ncRNA annotation, choosing the right tool for an RNA class, or interpreting ncRNA counts.
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
The Rfam release version drives results (Rfam 15 has ~4,200+ families); record it. The downloaded Rfam.cm ships pre-calibrated, so only cmpress is needed before cmscan. If code throws an error, introspect the installed tool and adapt rather than retrying.
Non-Coding RNA Annotation
"Find non-coding RNAs in my genome" -> Scan an assembly for structured ncRNA families using covariance models (sequence + secondary structure jointly), with specialist detectors for tRNA and the expression boundary for miRNA/lncRNA.
The Single Most Important Modern Insight -- ncRNA Homology Is Structure-Conserved, Not Sequence-Conserved
Protein annotation rides on sequence/ORF signal; structured-ncRNA annotation rides on base-pairing covariation. A G-C pair can become A-U across evolution while the structure is preserved - both positions mutate together (compensatory substitution). To a sequence-only tool these look like two mismatches; to a covariance model (a profile SCFG, the Rfam/Infernal engine) the correlated change is the strongest possible evidence of homology. Three consequences:
BLAST is categorically the wrong tool for structured ncRNA. Two RNase P RNAs may share <60% identity (BLAST sees noise) yet have unmistakable shared structure. Use Infernal covariance models, not BLAST. R-scape (Rivas 2017 Nat Methods 14:45) is the statistical test for whether a structure is actually conserved - it found no significant covariation support for the proposed structures of HOTAIR, SRA, and Xist-RepA.
A homology-based annotation is a recall floor, never a count. A CM can only exist for a family whose structure is conserved across enough divergent sequences to seed a model - so fast-evolving and lineage-specific ncRNAs are invisible by design, and the floor drops further for organisms far from the curation spotlight. Report "at least N conserved-family loci," never "the genome has N ncRNAs."
Whole classes need expression evidence, not genomic search. miRNAs are hypotheses until small-RNA-seq confirms the Dicer processing signature; lncRNAs are not annotatable by homology at all (no conserved structure) - they are transcript catalogs. Use specialist tools where the biology has a sharper signal (tRNAscan-SE, miRDeep2-with-reads).
Infernal cmscan vs full Rfam.cm (GA + clan competition)
structure-aware, family-typed
miRNA
demand small-RNA-seq; miRDeep2
genomic hairpin prediction is unreliable
lncRNA
transcript assembly + coding-potential
not structurally conserved; no CM
Claim a conserved structure
R-scape covariation test (report power)
thermodynamic fold != selected structure
Bacterial AMR/CRISPR arrays
-> prokaryotic-annotation / CRISPRCasFinder
array detection is a separate tool class
Infernal / cmscan (the General ncRNA Engine)
# Rfam ships pre-calibrated; press it once, then scan (cmscan = many models vs one genome)
cmpress Rfam.cm
cmscan -Z <dbsize_Mb> --cut_ga --rfam --nohmmonly \
--tblout out.tblout --fmt 2 --clanin Rfam.clanin \
Rfam.cm genome.fa > out.cmscan
grep -v " = " out.tblout > out.deoverlapped.tblout # drop within-clan overlaps
--cut_ga (gathering threshold): the single most important correctness flag. Each family has a curator-set, per-family bit-score threshold; a fixed E-value would treat a 70-nt tRNA model and a 2,900-nt LSU model identically, which is wrong. Overriding GA to "find more" imports the false positives the curator deliberately excluded.
--nohmmonly forces full CM (structure-aware) scoring so scores are GA-comparable.
-Z <dbsize_Mb> = total_residues x 2 / 1e6 (both strands), making E-values run-comparable.
--fmt 2 --clanin Rfam.clanin + the grep -v " = " deoverlap step is mandatory, not a nicety: clans group related families (the tRNA models, SSU/LSU rRNA), so one locus hits several models and the raw table double-counts (a 16S locus becomes "several rRNA genes").
Modes: -E eukaryotic (default), -B bacterial, -A archaeal, -G general (mixed/metagenome), -M mammal/-M vert mitochondrial, -O organellar (disables pseudogene checking). Domain choice is not cosmetic - the wrong mode mis-scores and miscalls isotypes; there is no auto-detect. Report the high-confidence set, not raw hits (raw counts include pseudogenes/SINEs and can be 2-10x inflated in eukaryotes). The pseudogene flag is reliable in eukaryotic nuclear genomes but false-positive-prone in organelles/odd mito-tRNAs (truncated arms read as "decayed") - hence -O/-D.
Reports partial rRNA at contig edges as (partial). rDNA copy number from an assembly is essentially always wrong - near-identical rRNA arrays collapse in short-read assemblies, so the annotated count is a floor (off by orders of magnitude in eukaryotes); use long reads or read depth for true copy number.
Parsing and Combining ncRNA Calls with Python
Goal: Merge Infernal and tRNAscan-SE into one ncRNA annotation, preferring the specialist for tRNA.
Approach: Parse the deoverlapped Infernal table, drop its tRNA rows (tRNAscan-SE is the authority for tRNA), and combine with the tRNAscan-SE high-confidence set; keep evidence provenance per class.
import pandas as pd
defparse_infernal_tbl(tbl_file):
rows = []
withopen(tbl_file) as f:
for line in f:
if line.startswith('#'):
continue
p = line.split()
iflen(p) < 18:
continue
rows.append({'rfam_name': p[1], 'seqid': p[3], 'strand': p[11],
'score': float(p[16]), 'evalue': float(p[17])})
df = pd.DataFrame(rows)
return df[~df['rfam_name'].str.contains('tRNA', case=False, na=False)] # tRNAscan-SE owns tRNA
The miRNA Disaster and lncRNA Non-Annotatability
Most computationally predicted miRNAs are false positives, and much of miRBase is contaminated. Any genome folds into astronomically many hairpins; foldability is not evidence of a miRNA. The discriminating signal - a precise homogeneous 5' end, a detectable star strand, a ~22 nt mode - is visible only in small-RNA-seq read pileups (miRDeep2 scores this geometry). Fromm 2015 found <1/3 of human and ~16% of metazoan miRBase entries are bona fide; closely related species differing by >1,000 miRNAs in miRBase is annotation noise, not biology. Animal-trained tools mis-call plant miRNAs (DCL1 biogenesis, 21/24 nt) - use plant-specific tools. Report homology-only hits as "miRNA candidates," never genes.
lncRNAs are not annotatable by homology in principle - no conserved structure (R-scape), poor sequence conservation. "lncRNA annotation" is transcript cataloguing (assemble RNA-seq, subtract coding potential), inheriting every transcript-assembly pathology, so catalog size tracks sequencing depth and filter policy. The same human genome "has" ~16,000 to >100,000 lncRNAs depending only on the annotation source (RefSeq vs GENCODE vs NONCODE) - these encode different answers to the transcription-vs-selection function debate (Graur 2013), not different biology. Never quote a bare lncRNA count without source + version.
Per-Method Failure Modes
BLAST for structured ncRNA
Trigger: using BLAST to find ncRNA homologs. Mechanism: BLAST scores sequence identity, blind to covariation. Symptom: misses structure-conserved homologs; ranks pseudogenes above real homologs. Fix: Infernal/Rfam covariance models.
Missing GA / clan competition
Trigger:cmscan without --cut_ga/--nohmmonly, or without --clanin/--fmt 2 deoverlap. Mechanism: wrong thresholds; HMM-only scores not GA-comparable; within-clan overlaps uncollapsed. Symptom: inflated/redundant family calls (one locus counted several times). Fix: the full canonical command + grep -v " = ".
Wrong tRNAscan-SE domain mode
Trigger:-E on a bacterium, or default on a metagenome. Mechanism: domain CMs differ; no auto-detect. Symptom: mis-scored/miscalled isotypes. Fix:-B/-A/-G per source; report the high-confidence set.
Counting raw tRNA/rRNA hits
Trigger: reporting raw tRNAscan-SE hits or genomic rRNA as copy number. Mechanism: SINEs/pseudogenes/NUMTs inflate tRNA; rDNA arrays collapse. Symptom: 150+ bacterial "tRNAs"; "5 copies of 18S". Fix: high-confidence set; treat copy numbers as floors; flag NUMT-type organellar tRNAs in the nucleus.
miRNA/lncRNA from genome alone
Trigger: calling miRNAs from hairpins or lncRNAs from CM search. Mechanism: no genomic signal suffices. Symptom: false-positive "genes". Fix: small-RNA-seq (miRNA); transcript assembly + coding-potential (lncRNA); label homology-only as candidates.
Quantitative Thresholds
Threshold
Source
Rationale
--cut_ga per-family GA threshold
Rfam curation
family-specific noise floor; never override genome-wide
-Z = residues x 2 / 1e6
Infernal
run-comparable E-values (both strands)
Bacterial tRNA ~28-90 (E. coli ~89; symbionts ~28-35)
GtRNAdb
150+ implies fragments/pseudogenes
Eukaryotic tRNA ~170-570 (amplified)
tRNA literature
raw hits far higher (SINEs/pseudogenes); use high-confidence set
Bacterial rRNA operons 1-15 (E. coli ~7)
copy-number norm
annotated count is a floor (rDNA collapse)
miRNA needs small-RNA-seq Dicer signature
Fromm 2015
foldability is not a miRNA; >1/3 miRBase is artifact
R-scape covariation + power before claiming structure
Rivas 2017/2020
thermodynamic fold != selected structure
Common Errors
Error / symptom
Cause
Solution
cmscan slow
full Rfam scan
--rfam preset; split genome; parallelize
Redundant overlapping calls
no clan competition
--fmt 2 --clanin; grep -v " = "
Missing expected ncRNAs
stale Rfam / cmpress not run
check Rfam version; verify .i1{f,i,m,p} files
Too many tRNA "pseudogenes"
normal in eukaryotes; false-positive in organelles
Ontiveros-Palacios N, et al. 2025. Rfam 15: RNA families database in 2025. Nucleic Acids Res 53:D258-D267.
Chan PP, et al. 2021. tRNAscan-SE 2.0: improved detection and functional classification of transfer RNA genes. Nucleic Acids Res 49:9077-9096.
Lowe TM, Eddy SR. 1997. tRNAscan-SE: a program for improved detection of transfer RNA genes in genomic sequence. Nucleic Acids Res 25:955-964.
Laslett D, Canback B. 2004. ARAGORN, a program to detect tRNA genes and tmRNA genes in nucleotide sequences. Nucleic Acids Res 32:11-16.
Lagesen K, et al. 2007. RNAmmer: consistent and rapid annotation of ribosomal RNA genes. Nucleic Acids Res 35:3100-3108.
Friedländer MR, et al. 2012. miRDeep2 accurately identifies known and hundreds of novel microRNA genes in seven animal clades. Nucleic Acids Res 40:37-52.
Lowe TM, Eddy SR. 1999. A computational screen for methylation guide snoRNAs in yeast (snoscan). Science 283:1168-1171.
Fromm B, et al. 2015. A uniform system for the annotation of vertebrate microRNA genes and the evolution of the human microRNAome (MirGeneDB). Annu Rev Genet 49:213-242.
Rivas E, Clements J, Eddy SR. 2017. A statistical test for conserved RNA structure shows lack of evidence for structure in lncRNAs (R-scape). Nat Methods 14:45-48.
Graur D, et al. 2013. On the immortality of television sets: "function" in the human genome according to the evolution-free gospel of ENCODE. Genome Biol Evol 5:578-590.
