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View Code? Open in Web Editor NEWA very simple hidden Markov model Segmentation and Genome Annotation Algorithm (SAGA) implementation for studying the effects of different parameters.
A very simple hidden Markov model Segmentation and Genome Annotation Algorithm (SAGA) implementation for studying the effects of different parameters.
Currently, using own ugly passing around big fat lists of (processed, cleaned, pruned, etc.) tracks as NumPy arrays through a few functions. Clean up this code by inheriting PyTorch's DataLoader class, which handles random sampling and speeds everything up with multiprocessing too.
๐ก: Would go well with switch hmmlearn --> pomegranate
Right now, parse_bigWigs.py
finds all the bigWigs in a directory written into the script as a const variable (by @WeiXie03).
Implement: genomedata-style command line interface with
-t
: explicit track name and path specification-s
: chromosome sizes filepython parse_bigWigs.py -s <chromosome sizes file> -t <track name> <track file path> -t <track name> <track file path> -t ...
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