| name | bio-genome-intervals-gtf-gff-handling |
| description | Parses, queries, converts, and extracts from GTF and GFF3 gene-model annotation files - walking the gene/transcript/exon/CDS hierarchy with gffutils (queryable SQLite DB), converting formats and extracting transcript/CDS/protein FASTA with gffread, slurping to dataframes with gtfparse/pyranges, and sanitizing malformed files with AGAT. Covers the 1-based-inclusive vs 0-based BED coordinate conversion (start-1 only), deriving implicit features (introns/UTRs/TSS), phase-not-frame, the stop-codon-in-or-out-of-CDS convention, and the chr1-vs-1 seqid and gene-ID-version mismatches that silently produce all-zero count matrices and dropped joins. Use when extracting features or sequences from an annotation, converting GTF<->GFF3 or GTF->BED, traversing the gene tree, or diagnosing a coordinate/provenance mismatch upstream of counting or DE. |
| tool_type | mixed |
| primary_tool | gffutils |
Version Compatibility
Reference examples tested with: gffutils 0.13+, gffread 0.12+, gtfparse 2.x, pyranges 0.1+ (or 1.0+ - see note), AGAT 1.4+.
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
Two version landmines specific to this skill: (1) gtfparse changed its return type - older releases returned a pandas DataFrame, gtfparse >=2.x returns a polars DataFrame by default; pass result_type='pandas' before chaining pandas idioms (.copy(), boolean masks). (2) pyranges has a major-version API split - pyranges 0.x and the 1.0 rewrite differ in method names and attribute access; check import pyranges; pyranges.__version__ before pasting code. gffutils stores 1-based coordinates while pyranges stores 0-based - their start fields differ by one by design. If code throws ImportError, AttributeError, or TypeError, introspect the installed package and adapt rather than retrying.
GTF/GFF Handling
"Pull these features (or their sequences) out of my annotation, convert it, or find out why my counts are wrong." -> Treat the file as a serialized gene-model tree: walk gene->transcript->exon/CDS, derive implicit features, and reconcile coordinate and namespace conventions before trusting any number.
- CLI:
gffread in.gtf -T -o out.gtf (convert), gffread -w tx.fa -g genome.fa in.gtf (FASTA), agat_convert_sp_gxf2gxf.pl (sanitize)
- Python:
gffutils.create_db(...) then db.children(gene, featuretype='exon') (tree query); gtfparse.read_gtf(..., result_type='pandas') / pyranges.read_gtf(...) (dataframe)
The Single Most Important Modern Insight -- A GTF/GFF3 Is a Serialized Gene-Model Tree, Not a Table of Intervals
Almost every painful bug here comes from the file looking like a CSV while behaving like a tree, or from a coordinate/provenance mismatch the tools never warn about - and every one of these failures is silent: nothing throws, the wrong answer just propagates. Three load-bearing facts the tutorials skip:
-
The coordinate conversion is asymmetric. GTF/GFF3 are 1-based fully inclusive [start, end]; BED (and pyranges-internal) are 0-based half-open [start-1, end). Convert to BED by subtracting 1 from the start only - the end is unchanged (the inclusive 1-based end and the exclusive 0-based end are the same integer). Doing start-1 AND end-1 shifts the feature one base left and is the classic over-correction: invisible in coverage/overlap, catastrophic in CDS translation (a one-base frameshift garbles the protein). gffutils keeps 1-based, pyranges stores 0-based, so their start fields differ by one correctly - never "fix" that discrepancy.
-
The all-zero count matrix. featureCounts/htseq-count match a read to a feature by string equality on the chromosome name, so chr1 != 1 != NC_000001.11 produces a perfectly well-formed matrix of zeros with no error or warning - the only signal is ~0% assigned in the summary. Same bug one altitude up: gene-ID version suffixes (ENSG00000223972.5 vs ENSG00000223972) silently drop rows on an annotation join. Audit every cross-file key (chromosomes between BAM/GTF/FASTA, gene IDs between GTF/count-matrix/annotation) by set intersection, never by eye, before any count or join.
-
phase is not frame, and the stop codon is a 3-bp ghost. Phase (column 8) is the strand-aware count of bases to trim from the segment's transcriptional 5' end to reach the next codon (0/1/2) - recompute it on any CDS edit (AGAT/gffread do; hand-editing coordinates without fixing phase frameshifts the translation). GTF (Ensembl/GENCODE) excludes the stop codon from CDS; GenBank/GFF3 often include it - so a CDS length off by exactly 3 nt (or a protein +/-1 stop) between two sources is a convention mismatch, not a bug.
Tool Taxonomy
| Tool | Role | Mechanism | When |
|---|
| gffutils | Queryable gene-tree DB (Python) | builds a SQLite DB; children/parents/region traverse the hierarchy; keeps 1-based coords | walk gene->transcript->exon/CDS, derive introns, query by ID/coordinate |
| gffread | Converter + sequence extractor (CLI) | fast C++, genome-aware; one binary | GTF<->GFF3, extract transcript/CDS/protein FASTA, region filter |
| pyranges | Vectorized interval engine (Python) | PyRanges/pandas-like; stores 0-based half-open | overlap joins, set ops, dataframe-native interval work |
| gtfparse | GTF -> dataframe (Python) | one call explodes column 9 into attribute columns | quick column/filter work; NOT hierarchy-aware (flat table) |
| AGAT | GFF/GTF sanitizer (Perl CLI) | reconstructs the full tree; adds missing features, fixes IDs/phase, deflates attributes | a malformed/non-standard file - run FIRST, before parsing |
Decision Tree by Scenario
| Scenario | Recommended | Why |
|---|
| Walk the gene/transcript/exon hierarchy, derive introns | gffutils create_db + children/parents | the hierarchy is the point; flat parsers lose it |
| Convert GTF<->GFF3 or extract transcript/CDS/protein FASTA | gffread (-T, -w/-x/-y -g) | genome-aware, knows the stop-codon convention |
| Quick column/filter on a clean modern GTF | gtfparse (result_type='pandas') | one-call dataframe; verify return type first |
| Overlap/set ops, large in-memory interval joins | pyranges | vectorized; route arithmetic -> interval-arithmetic |
File malformed: no ##gff-version, missing gene/exon lines, dup IDs, mixed conventions | AGAT agat_convert_sp_gxf2gxf.pl first | sanitize once vs writing a brittle parser around it |
| GTF -> BED for bedtools | start-1, end unchanged (-> bed-file-basics) | the off-by-one boundary is where it bites |
| Counts came out all-zero or DE join dropped rows | intersect seqid / gene-ID namespaces | string-equality match; no error is emitted |
| Counting reads per gene/feature | -> rna-quantification/featurecounts-counting | the seqid/strand landmines live there; set -s from chemistry |
| Judge whether the annotation itself is sound | -> genome-annotation/annotation-qc | this skill operates on the file, not its quality |
Walk the Gene Tree and Derive Introns (gffutils)
Goal: Traverse gene -> transcript -> exon and reconstruct features (introns) that the file does not store explicitly.
Approach: Build a SQLite DB once (disabling gene/transcript inference when those lines already exist, for a ~100x speedup), then query children ordered by position and synthesize introns from the exon gaps.
import gffutils
db = gffutils.create_db('annotation.gtf', 'annotation.db', force=True,
disable_infer_genes=True, disable_infer_transcripts=True,
merge_strategy='create_unique')
gene = db['ENSG00000141510']
for tx in db.children(gene, featuretype=['mRNA', 'transcript'], order_by='start'):
exons = list(db.children(tx, featuretype='exon', order_by='start'))
introns = list(db.interfeatures(exons, new_featuretype='intron'))
print(tx.id, len(exons), 'exons', len(introns), 'introns')
A modern GTF without disable_infer_* triggers the slow inference/merge machinery; an older minimal GTF lacking gene/transcript lines needs inference ON so gffutils reconstructs the envelopes. Match the flag to the file. introns, UTRs (exon - CDS), and TSS are derived, not stored - never infer biological absence from a missing feature line.
Convert Formats and Extract Sequences (gffread)
gffread is genome-aware and respects the stop-codon convention, so it is the safe path for sequence extraction (naive coordinate math is not).
gffread annotation.gff3 -T -o annotation.gtf
gffread -w transcripts.fa -g genome.fa annotation.gtf
gffread -x cds.fa -g genome.fa annotation.gtf
gffread -y proteins.fa -g genome.fa annotation.gtf
gffread annotation.gtf -C -o coding.gtf
-g needs the genome FASTA (gffread auto-creates the .fai). -w/-x/-y splice the segments per transcript, so they handle multi-exon models correctly - do not concatenate exon FASTAs by hand.
Convert GTF to BED with the Right Coordinate Shift
Goal: Emit a BED of a chosen feature type for bedtools, without the off-by-one frameshift.
Approach: Parse to a pandas frame, filter to the feature type, subtract 1 from the start only, leave the end untouched.
import gtfparse
df = gtfparse.read_gtf('annotation.gtf', result_type='pandas')
genes = df[df['feature'] == 'gene'].copy()
genes['start'] = genes['start'] - 1
bed = genes[['seqname', 'start', 'end', 'gene_id', 'score', 'strand']]
bed.to_csv('genes.bed', sep='\t', header=False, index=False)
For TSS/promoter derivation (strand-aware: + strand TSS = start, - strand TSS = end), route to proximity-operations - the promoter window is an imposed definition, not an annotated feature.
Sanitize a Malformed File First (AGAT)
When a file lacks ##gff-version 3, has non-Sequence-Ontology types, is missing gene/exon/UTR lines, has duplicate IDs, or mixes conventions, sanitize it once rather than coding around it:
agat_convert_sp_gxf2gxf.pl -g messy.gff3 -o clean.gff3
agat_convert_sp_gff2gtf.pl -g clean.gff3 -o clean.gtf
AGAT makes decisions (which convention to standardize to, how to derive missing features) - usually a feature, but when a source's exact encoding must be preserved (e.g. auditing a submission), inspect what it changed rather than trusting blindly.
Per-Method Failure Modes
Over-correcting the coordinate conversion
Trigger: subtracting 1 from both start and end when converting to BED. Mechanism: only the start representation differs; the inclusive 1-based end equals the exclusive 0-based end. Symptom: every feature shifted one base left; invisible in coverage, frameshifts CDS translation. Fix: start-1, end unchanged.
Comparing coordinates across gffutils and pyranges
Trigger: asserting equality on start fields from both libraries in one script. Mechanism: gffutils keeps 1-based, pyranges stores 0-based. Symptom: an off-by-one that looks like a bug; "fixing" it introduces a real error. Fix: confirm each library's convention; expect the difference.
All-zero count matrix (seqid mismatch)
Trigger: BAM aligned to chr1, GTF annotated with 1. Mechanism: counters match reads to features by chromosome-name string equality. Symptom: well-formed matrix of zeros, no error; ~0% assigned in the summary. Fix: intersect the BAM @SQ/idxstats chromosome set with the GTF column-1 set programmatically; remap one namespace, re-confirm.
Dropped rows on a gene-ID join
Trigger: count matrix keyed ENSG... joined to annotation keyed ENSG....5. Mechanism: exact string match on a versioned vs unversioned ID. Symptom: join returns a dataframe but rows vanish / annotation is NA. Fix: strip .\d+$ on both sides for matching; keep the version in the stored annotation for provenance.
CDS length off by exactly 3
Trigger: comparing CDS/protein length across two sources, or translating after a convention-flipping conversion. Mechanism: GTF excludes the stop codon from CDS; GenBank/GFF3 often include it. Symptom: length differs by 3 nt / 1 aa; protein does/does not end in *. Fix: suspect the convention before debugging code; extract CDS with gffread/AGAT, which know it.
Editing CDS coordinates without recomputing phase
Trigger: trimming/merging/lifting CDS coordinates, leaving column 8 as-is. Mechanism: phase is a static integer; the chain of per-segment phases depends on cumulative coding length. Symptom: downstream translation (gffread -y, table2asn, EMBL) frameshifts or rejects. Fix: treat a CDS edit + phase recompute as one atomic operation; let AGAT/gffread recompute.
gffutils pathologically slow on a modern GTF
Trigger: create_db on a GENCODE/Ensembl GTF without the infer flags. Mechanism: gffutils infers gene/transcript envelopes and runs the merge machinery. Symptom: create_db hangs for many minutes. Fix: disable_infer_genes=True, disable_infer_transcripts=True when those lines already exist (~100x faster).
Quantitative Thresholds
| Convention / threshold | Source | Rationale |
|---|
| GTF/GFF3 1-based inclusive; convert to BED with start-1, end unchanged | UCSC/SO format specs | the inclusive 1-based end == the exclusive 0-based end; over-correcting both ends frameshifts CDS |
| CDS length differs by exactly 3 nt between sources | GTF vs GenBank/GFF3 stop-codon convention | GTF (Ensembl/GENCODE) excludes the stop from CDS; GenBank/GFF3 often include it |
disable_infer_* -> ~100x create_db speedup | gffutils docs | inference/merge machinery is skipped when gene/transcript lines already exist |
| seqid intersection required before counting | featureCounts/htseq string-equality match | non-overlapping chromosome names -> all-zero matrix with no error |
Strip .\d+$ from gene IDs on both sides before a join | Ensembl/GENCODE/RefSeq versioned accessions | version suffix tracks model revision; mismatch drops rows silently |
featureCounts default -s 0 (unstranded) vs htseq-count -s yes (stranded) | tool defaults (Liao 2014; Anders 2015) | switching tools changes the counting model; set -s from library chemistry, not the default |
Common Errors
| Error / symptom | Cause | Solution |
|---|
| All genes count zero | seqid mismatch (chr1 vs 1 vs NC_...) | intersect BAM and GTF chromosome sets; remap one namespace |
Counts low and flip when -s changes | wrong strandedness (featureCounts vs htseq defaults differ) | set -s from the library prep chemistry; verify assignment rate |
| Join drops rows / NA annotation | gene-ID version suffix (ENSG....5 vs ENSG...) | strip .\d+$ on both sides for matching |
| Biotype filter returns empty | attribute key differs by source: GENCODE uses gene_type, Ensembl/RefSeq use gene_biotype | check the actual key (it travels with the chr1-vs-1 provenance split); query the present key |
| Translated protein is garbage | over-corrected coordinate (start-1 AND end-1) | subtract 1 from start only |
| CDS/protein off by 3 nt / 1 aa | stop-codon-in-or-out-of-CDS convention | extract with gffread/AGAT; do not debug coordinate math |
| gtfparse pandas idioms raise AttributeError | gtfparse >=2.x returns polars | pass result_type='pandas' |
| pyranges AttributeError | 0.x vs 1.0 API mismatch | check pyranges.__version__; use matching method names |
| gffutils create_db hangs | infer machinery on a modern GTF | set disable_infer_genes=True, disable_infer_transcripts=True |
gffread -w/-x/-y errors | missing or unindexed genome FASTA | pass -g genome.fa (gffread creates the .fai) |
References
- Pertea G, Pertea M. 2020. GFF Utilities: GffRead and GffCompare. F1000Research 9:304.
- Stovner EB, Saetrom P. 2020. PyRanges: efficient comparison of genomic intervals in Python. Bioinformatics 36:918-919.
- Dale R. gffutils: GFF and GTF file manipulation and interconversion. Software, https://github.com/daler/gffutils (no journal publication).
- Dainat J. AGAT: Another Gff Analysis Toolkit to handle annotations in any GTF/GFF format. Zenodo. doi:10.5281/zenodo.3552717.
- Rubinsteyn A, et al. gtfparse: parsing tools for GTF (gene transfer format) files. Software, https://github.com/openvax/gtfparse (no journal publication).
- Liao Y, Smyth GK, Shi W. 2014. featureCounts: an efficient general purpose program for assigning sequence reads to genomic features. Bioinformatics 30:923-930.
- Anders S, Pyl PT, Huber W. 2015. HTSeq - a Python framework to work with high-throughput sequencing data. Bioinformatics 31:166-169.
Related Skills
- bed-file-basics - BED format and the coordinate conversion this skill feeds into
- interval-arithmetic - Set operations on the features extracted here
- proximity-operations - Strand-aware TSS/promoter derivation from extracted features
- rna-quantification/featurecounts-counting - Consumes the GTF/GFF features; the seqid/strand landmines live there
- genome-annotation/functional-annotation - Downstream of feature/sequence extraction from the annotation
- genome-annotation/annotation-qc - Judges whether the annotation this skill parses is sound
- differential-expression/de-results - Map gene coordinates back to DE results
