brentp/spacepile
convert reads from repeated measures of same piece of DNA into spaced matricies for deep learners.
This project helps bioinformaticians and genomics researchers prepare DNA sequencing data for deep learning analysis. It takes raw alignment information (like CIGAR strings) from advanced sequencing methods such as duplex sequencing or PacBio CCS. It then organizes these complex alignments into structured matrices that can be directly fed into deep learning models to improve accuracy in tasks like variant calling or consensus sequencing.
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Use this if you need to transform replicate DNA sequencing reads into a consistent, 'spaced' matrix format suitable for training and evaluating deep learning models for genomic analysis.
Not ideal if you are working with standard, single-read sequencing data or if you need a complete, end-to-end consensus calling solution without integrating deep learning.
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14
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Language
Rust
License
MIT
Category
Last pushed
Apr 21, 2023
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