
Genomic Coordinates
FreeConvert and normalize genomic intervals with precision.
Free · Opens the source repo
What Genomic Coordinates does
The Genomic Coordinates skill is designed for bioinformaticians and geneticists who need to manage genomic data across various formats and assemblies. It provides tools to convert genomic intervals between different coordinate conventions, ensuring that data integrity is maintained when moving between formats like BED, GFF/GTF, VCF, and others. This skill addresses the common pitfalls associated with genomic data, such as off-by-one errors and mismatched assembly names, which can lead to incorrect analyses without raising any immediate errors.
At its core, the skill operates on the principle that a genomic coordinate comprises three essential facts: the coordinate number, the convention it adheres to, and the assembly it references. This understanding is crucial for accurate data manipulation. The skill includes scripts for converting between 0-based half-open and 1-based inclusive coordinates, as well as for normalizing variant representations. It also allows users to audit their genomic files against established conventions, helping to identify and rectify issues that could compromise the validity of their analyses.
The Genomic Coordinates skill is particularly useful in scenarios where genomic data must be integrated from multiple sources or when preparing data for downstream analysis. It is ideal for users who frequently encounter different coordinate systems or need to ensure that their data adheres to specific genomic standards. By providing a systematic approach to coordinate conversion and validation, this skill helps prevent subtle errors that could lead to significant issues in genomic studies.
However, users should be aware that while this skill excels at coordinate conversion and validation, it does not perform data analysis or visualization. It is best used as a preprocessing tool to ensure that genomic data is correctly formatted and compatible before further analysis is conducted.
When to use it
Use this skill whenever you need to convert genomic coordinates between different formats or assemblies, or when validating genomic data for analysis.
When not to use it
This skill is not suitable for performing actual data analysis or visualization tasks; it is strictly for coordinate management and validation.
What you can build with it
Converting BED to GFF
When transitioning genomic data from a BED format to GFF for compatibility with a new analysis tool.
Normalizing Variant Data
Before merging multiple VCF files, normalize the variants to ensure consistent representation and avoid silent mismatches.
Auditing Genomic Files
Run an audit on your GTF files to check for common formatting errors before submitting data for publication.
How to install Genomic Coordinates
View source1. Install with the skills CLI
npx skills add k-dense-ai/scientific-agent-skills/genomic-coordinates --agent claude-code2. Or install it manually
Download the skill folder and drop it into ~/.claude/skills/ for all projects, or .claude/skills/ to scope it to one repo. Restart Claude Code so it picks up the new skill.
Anthropic's agentic coding CLI, and the reference implementation of Agent Skills. Drop a skill folder into ~/.claude/skills and Claude Code loads it automatically whenever a task matches the skill's description. Claude Code docs
Inside SKILL.md
Written by k-dense-aiGenomic Coordinates
When to use
Any time a coordinate crosses a boundary: between two file formats, between two tools, between two assemblies, or between the genome and a transcript.
The rule
A coordinate is three facts, not one: the number, the convention it is written in, and the assembly it was measured against. Carry all three or the number is not interpretable.
Coordinate errors are the quietest class of bug in genomics. An off-by-one BED file parses, sorts, and intersects without complaint. A GRCh37 VCF joined against a GRCh38 annotation returns rows. A right-shifted indel simply fails to match its entry in ClinVar, and the result is a variant reported as novel. Nothing raises an error; the answer is just wrong, and it is wrong in a direction that looks plausible.
So: convert with the table, not from memory, and verify against the reference whenever a reference is available.
The two conversions
1-based inclusive -> 0-based half-open : start - 1, end
0-based half-open -> 1-based inclusive : start + 1, end
The end coordinate never moves. If a conversion changed both numbers, it is wrong.
Which format is which
| 0-based, half-open | 1-based, inclusive |
|---|---|
| BED, bedGraph, bigWig, narrowPeak | GFF3, GTF, VCF |
| BAM/CRAM (binary POS) | SAM (text POS) |
| PSL, genePred, refFlat | WIG, Picard interval_list |
| MAF (UCSC multiple alignment) | MAF (TCGA mutation annotation) |
| PyRanges, pybedtools | GRanges/IRanges, samtools & UCSC & Ensembl region strings |
Both "MAF" formats exist, they mean different things, and they disagree. UCSC
serves 0-based files through a 1-based browser box. references/format-conventions.md
has the full table with per-format detail.
cd skills/genomic-coordinates/scripts
python3 convert_coords.py --list # the table
python3 convert_coords.py --from bed --to gff chr1 999 1000
python3 convert_coords.py --from ucsc --to bed "chr7:5,530,601-5,530,625"
python3 convert_coords.py --from granges --to pyranges --input regions.tsv
contig input output length status detail
chr7 chr7:5530601-5530625 5530600-5530625 25 ok
Zero-length BED features (chromStart == chromEnd, a legal insertion point) are
reported as unrepresentable rather than converted to end = start - 1. Exit
code is 1 when any interval is degenerate or invalid.
Variants are not intervals
A VCF POS for an indel is the anchor base — the base before the event,
itself unchanged. And the same change can be written many ways:
chr1:7:CAC:C, chr1:3:CAC:C and chr1:2:GCA:G are one deletion. Joining,
deduplicating, or looking up variants before normalising loses real matches
silently, and it loses them preferentially in repeats, where indels concentrate.
Normalise — trim to parsimony, then left-align against the reference — before any comparison:
python3 normalize_variant.py --fasta ref.fa chr1 7 CAC C
python3 normalize_variant.py --fasta ref.fa --split --input cohort.vcf
python3 normalize_variant.py --fasta ref.fa --compare chr1:7:CAC:C chr1:2:GCA:G
input normalized type pos_shift ref_check changed
chr1:7:CAC:C chr1:2:GCA:G deletion 5 ok yes
Every record's REF is checked against the FASTA first. A MISMATCH means the
variants and the reference are different assemblies — stop and run
check_contigs.py rather than adjusting coordinates. Multi-allelic records must
be split with --split before normalising, never after.
HGVS shifts indels the opposite way, 3'-most along the transcript. For a
minus-strand gene that is the opposite genomic direction from VCF's
left-alignment. Details and the full procedure: references/variant-representation.md.
Check the assembly before trusting a join
python3 check_contigs.py --identify unknown.fa.fai
python3 check_contigs.py variants.vcf annotation.gtf --genome GRCh38.fa.fai
file kind contigs naming assembly detail
ref.fa.fai sizes 25 plain GRCh37 24/24 primary chromosome lengths match;
chrM is 16569 bp, i.e. GRCh37/38 (rCRS MT)
The script reads .fai, .chrom.sizes, VCF headers, SAM headers, FASTA, BED,
and GTF/GFF, identifies the assembly from primary-chromosome lengths, and reports
every reason a join between two files would go wrong: naming mismatch, length
conflict, coordinates past a contig end, contigs present in one file only. Exit
code 1 on any incompatibility.
GRCh37 and hg19 differ only in the mitochondrion — 16,569 bp (rCRS) versus
16,571 bp. Nuclear coordinates are identical, so a mixed pipeline runs fine and
only the mtDNA results are wrong. check_contigs.py reports which one it found.
Builds, naming schemes, ALT contigs, and liftover pitfalls:
references/reference-builds.md.
Audit a file against its own format
python3 audit_intervals.py peaks.bed
python3 audit_intervals.py gencode.gtf --genome hg38.chrom.sizes
python3 audit_intervals.py cohort.vcf --genome GRCh38.fa.fai
Looks for the evidence that a coordinate mistake leaves behind:
| Finding | What it proves |
|---|---|
start_below_one in GFF/GTF | 0-based data in a 1-based file; everything is one base left |
many_zero_length in BED | 1-based single-base features written into a 0-based file |
past_contig_end | wrong assembly, or an off-by-one at the contig edge |
mixed_contig_naming | any join will silently match one subset |
first_block_offset | BED12 blockStarts written as absolute coordinates |
not_parsimonious | untrimmed alleles; normalise before joining |
bad_alt_allele | Ensembl/VEP - notation in a VCF, which has no anchor base |
Exit code 1 on any fatal finding, so it works as a CI gate on a data directory.
Transcript, CDS, and protein positions
c.742 and chr17:7,674,220 are both "position", and neither converts to the
other by arithmetic. Transcript coordinates count spliced bases in transcription
order — decreasing genomic coordinate on the minus strand — and c.1 is the A
of the initiator ATG, not the start of the transcript.
The rules that get mis-remembered: there is no c.0; 5' UTR positions are
negative and 3' UTR positions take a *; GFF phase is the bases to remove to
reach the next codon, not start % 3; and a c. description is meaningless
without a versioned transcript accession, because the same variant numbers
differently in each transcript. references/transcript-coordinates.md has the
conversion procedure and the boundary cases.
Do the conversion with a tool that holds the transcript model — VEP,
bcftools csq, Mutalyzer, the hgvs package — not by hand.
Reporting results
State the assembly next to the coordinates, every time.
chr7:5,530,601-5,530,625 is not a location; chr7:5,530,601-5,530,625 (GRCh38)
is. Say which convention a coordinate column is in, in the column header or the
file's documentation. When a conversion produced a result, say which direction it
went.
References
references/format-conventions.md— every format's convention, with per-format detail, BED12 block rules, region-string syntax, and tool behaviour.references/variant-representation.md— VCF allele conventions, the normalisation algorithm, equivalence checking, multi-allelic splitting, and how HGVS disagrees with VCF.references/reference-builds.md— build signatures, GRCh37 vs hg19, ALT contigs, naming schemes, and liftover failure modes.references/transcript-coordinates.md— genomic ↔ transcript ↔ CDS ↔ protein, HGVS numbering, phase, and transcript choice.
Frequently asked questions about Genomic Coordinates
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