Bio Genome Annotation Ncrna Annotation

Version Compatibility

Reference examples tested with: Infernal 1.1.4+, Rfam 15+ (CM library), tRNAscan-SE 2.0.12+, barrnap 0.9+, ARAGORN 1.2+, 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

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.

• CLI: cmscan --cut_ga --rfam --nohmmonly --fmt 2 --clanin Rfam.clanin Rfam.cm genome.fa (Infernal), tRNAscan-SE -B genome.fa

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:

1. 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.
2. 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."
3. 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).

Tool Taxonomy

RNAmmer is the legacy rRNA tool (license-encumbered, HMMER2) - use barrnap instead unless reproducing old annotations.

Decision Tree by Scenario

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").

tRNAscan-SE 2.0

tRNAscan-SE -B -o trna.out -f trna.ss -m trna.stats --gff trna.gff3 genome.fa   # bacterial

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.

barrnap (rRNA)

barrnap --kingdom bac genome.fa > rrna.gff3   # bac | arc | euk | mito

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

def parse_infernal_tbl(tbl_file):
    rows = []
    with open(tbl_file) as f:
        for line in f:
            if line.startswith('#'):
                continue
            p = line.split()
            if len(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.