| name | hdma |
| description | Use for any task involving the HDMA (Human Development Multiomic Atlas) repository — using its data (fragments, count matrices, Seurat/ArchR/BPCells objects, ChromBPNet models, contribution scores, bigwigs, motifs, motif instances, caCREs, ABC loops) or its code (preprocessing, global analysis, ChromBPNet training/interpretation, enhancer/ABC analysis, variant scoring, figure reproduction). Triggers on downloading data from Zenodo, finding files for a given cell type or organ, loading an object, reprocessing raw SRA data, loading tracks in a genome browser, running or adapting a pipeline step, finding which script made a figure, or setting up R/conda envs. This is a router skill — read the relevant file under references/ before acting, because the repo has non-obvious conventions (data lives on Zenodo not in-repo; cluster files are named by Cluster_ChromBPNet; scripts read a gitignored ROOT_DIR.txt). |
HDMA
The Human Development Multiomic Atlas (HDMA) is a single-cell multiome
(RNA + ATAC) atlas of human fetal tissues with per-cell-type ChromBPNet models,
a motif lexicon, and enhancer/variant analyses. This repository is a
reproducibility companion to the paper — it enhances the Methods; it is not a
turnkey pipeline. Genome build throughout is hg38 / GENCODE release 42.
This skill is a router. Read the relevant reference file before acting —
the repo's own docs (DATA.md, README.md, code/03-chrombpnet/README.md,
MOTIFS.html) are authoritative; this skill points you to the right part of them
and flags footguns that cause silent mistakes.
Cross-cutting concepts (read first)
- Code-only repo; data on Zenodo. A fresh clone contains code + rendered
HTMLs only. All data and analysis products live in the Zenodo community
https://zenodo.org/communities/hdma; outputs/data are gitignored.
- Cluster naming has two schemes.
cluster_id / RNA_Clusters (Table S2,
"c"-prefixed) vs. Cluster_ChromBPNet (Table S2 column 19, e.g.
Adrenal_c0, Muscle_c6). The latter names almost every ChromBPNet / motif /
bigwig / training-region file — use it when locating those.
- Supplementary tables are the keys. Table S14 (data type → URL/DOI/record
ID), Table S2 (cell metadata / cluster map), Table S6 (motif lexicon
annotations). All three ship in the repo's
tables/ directory
(tables/table_s14.tsv, table_s2.tsv, table_s6.tsv; column dictionaries in
tables/README.md).
- Authoritative docs live in the repo:
DATA.md / DATA.html, README.md,
code/03-chrombpnet/README.md, MOTIFS.html.
Task → reference
| If the task is… | Read |
|---|
| download/find/load any deposited data; understand a data format; reprocess raw SRA data; load tracks in a genome browser | references/data.md |
| run or adapt a pipeline step; set up R/conda envs; find the script behind a figure; use utils/helpers; understand repo layout & path config | references/code.md |
Conventions used throughout
- Genome build hg38 / GENCODE 42; fragment coordinates are 0-based.
- Assumes a Slurm HPC on Linux (CentOS 7.9); R 4.1.2; one conda env per
Python analysis (env name embedded in each Slurm submission script).
- Outputs, figures, motifs, and data directories are gitignored — inputs come
from Zenodo.
