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fastCDS

Map protein-domain amino-acid coordinates to their underlying genomic CDS/UTR/intron structure, using any GENCODE, Ensembl, or NCBI RefSeq GTF.

For each input query - a protein_id or a transcript_id, optionally with an aa range - fastCDS answers two related questions:

1. Mapping - which exact genomic bases code this domain?
2. Structure - how is the whole transcript organised into 5'UTR / CDS / 3'UTR / intron, and where does the domain fall on it?

The workflow

Three steps: get an index (build it from a GTF with index, or fetch a pre-built one), map your queries onto it, then plot.

flowchart LR
    GTF[GTF annotation] -->|fastCDS index| IDX[(index<br/>human.idx)]
    ZEN[Zenodo] -->|fastCDS fetch| IDX
    BED[query BED<br/>protein + aa range] --> MAP
    IDX --> MAP[fastCDS map]
    MAP --> TSV[isoform_structure.tsv]
    MAP --> B12[domain_blocks.bed<br/>BED12 track for IGV/UCSC]
    TSV -->|fastCDS plot| OUT{--out extension}
    OUT -->|.pdf / .png / .svg| STATIC[static figure]
    OUT -->|.html| INTER[interactive viewer<br/>engine: js default, or plotly]

Loading

The same four commands, in order:

fastCDS index gencode.v49.primary_assembly.annotation.gtf  # 1a build an index 
fastCDS fetch human --out human.idx     # 1b get a pre-built index from Zenodo
fastCDS map   --index human.idx \           # 2. map queries   -> see Mapping
                --bed queries.bed --out-dir results --output all
fastCDS plot  --isoform results/isoform_structure.tsv \   # 3. plot -> see Plotting
                --input-id TP53_DBD --out tp53.pdf

Command	Does	Page
index	Build a binary index from a GTF.	Building an index
fetch	Download a pre-built index from Zenodo.	Building an index
map	Map protein/domain queries to genomic structure.	Mapping
plot	Render a static (PDF/PNG) or interactive (HTML) figure.	Plotting

The same workflow from Python:

import fastCDS as fc

idx = fc.fetch_index("human")
mapper = fc.Mapper(index=idx)
result = mapper.map_batch([
    {"protein_id": "ENSP00000269305", "aa_start": 102, "aa_end": 292, "domain_id": "TP53_DBD"},
])
result.summary          # DataFrame, one row per query
fc.plot(result, input_id="TP53_DBD", out="tp53.pdf")

New here? Start with Installation, then walk the sidebar top to bottom. The Tutorials and Notebooks page has a copy-paste run from zero to a figure.