accmix 0.2.1

RAD-seq EM mixture with logistic prior: scan, annotate, model, diff

_{Under active development — interfaces may evolve}

accmix: Accessibility Mixture Model

CLI toolkit to (1) scan PWMs genome-wide, (2) compute accessibility-derived site scores s_l, (3) annotate with TSS/conservation/TPM, and (4) fit and evaluate a Gaussian EM with a logistic prior.

Installation

Install from github repository

pip install -e .

Install from pypi

pip install accmix

Documentation

Full user and API documentation is available at:

• https://yangli04.github.io/RAD-seq_EM_model/

Data layout

Example inputs are referenced under data/...:

• data/fasta/test.fa – small test genome FASTA.
• data/pwms/M00124_example.txt – example PWM for scanning.
• data/clipseq/ELAVL1_HeLa.bed – example CLIP-seq peaks.

CLI overview

The main entry point is accmix with subcommands:

1. Scan PWM and GC

accmix scan \
  -f data/fasta/test.fa \
  -p data/pwms/M00124_example.txt \
  -o results/M00124_example

Outputs (for example PWM):

• results/M00124_example_topA.tsv.gz
• results/M00124_example_botB.tsv.gz

2. Annotate accessibility (compute s_l)

accmix annotate-acc \
  -n data/AS/ANC1C.hisat3n_table.bed6 \
  -f data/AS/ANC1xC.hisat3n_table.bed6 \
  -t results/M00124_example_topA.tsv.gz \
  -o results/M00124_example_sl.parquet

Important options:

• -M / --M – inner flank size (default: 50).
• -N / --N – outer flank size (default: 500). Outer flank length is N - M.

3. Annotate TSS / conservation / TPM

accmix annotate-tss \
  -i results/M00124_example_sl.parquet \
  -o results/M00124_example_annotated.parquet \
  -r data/evaluation/RNAseq_HeLa_TPM.parquet \
  -c data/evaluation/phastCons100way.bed.gz \
  -p data/evaluation/phastCons100way.parquet \
  -y data/evaluation/phyloP100way.parquet \
  -R data/fasta/test.fa

4. Fit EM model (Gaussian + logistic prior)

accmix model \
  -i results/M00124_example_annotated.parquet \
  -o results/RBP_Motif \
  -r ExampleRBP \
  -m M00124

Outputs:

• results/RBP_Motif.XXXXXX.model.parquet – input data with prior_p and posterior_r.
• results/RBP_Motif.XXXXXX.model.json – fitted model parameters.

Notes

• Dependencies: polars, pyranges, metagene, numpy, scipy, pyarrow, numba, tqdm, typer[all], scikit-learn, matplotlib, seaborn, pandas.
• The CLI options mirror the underlying scripts; run accmix <command> --help for full details.

All files under data/ can be downloaded from:

https://(university of chicago's short name, with 8 letters).box.com/s/(remove following first nine letters and final 6 letters)meipiannitsmywbgimvl47w9nynq66hur3c8dnirvzhende

If you use this package, please cite:

Li Y., accmix: Accessibility Mixture Model, GitHub repository, https://github.com/yangli04/RAD-seq_EM_model