| Preprocess Data: |
| ================ |
| This pipeline preprocesses Pfam-A.seed (multiple sequence alignments and |
| phylogenetic trees from Pfam v36.0) into training inputs for a pairHMM-based |
| model. The pipeline runs in five stages: |
|
|
| 1. Initial cleaning -- filters MSAs: removes short/invalid peptides, |
| duplicate sequences (within and across families), |
| and prunes phylogenetic trees to match cleaned MSAs. |
| 2. Split & cherry-pick -- partitions Pfam families into train splits and an |
| out-of-distribution (OOD) validation set (selected |
| by alignment width and gap fraction), then extracts |
| "cherries" (leaf pairs) from each tree. |
| 3. Feature generation -- encodes aligned and unaligned sequences as numpy |
| arrays, paired with tree and alignment metadata. |
| 4. Count precomputation -- uses JAX to batch-compute substitution, insertion, |
| deletion, and transition counts needed for pairHMM |
| inputs (this step can be slow on large datasets). |
| 5. Concatenation & teardown -- concatenates per-part arrays into per-split |
| .npy files and .tsv metadata, moves all |
| outputs into DATA/ and intermediates.tar.gz. |
|
|
|
|
| Outputs: |
| -------- |
|
|
| After the pipeline completes, two top-level directories are created: |
|
|
| DATA/ |
| *_aligned_mats.npy -- alignments |
| *_seqs_unaligned.npy -- unaligned sequences |
| *_AAcounts.npy -- amino acid emission counts |
| *_AAcounts_subsOnly.npy -- emission counts from substitution columns only |
| *_subCounts.npy -- substitution counts |
| *_insCounts.npy -- insertion counts |
| *_delCounts.npy -- deletion counts |
| *_transCounts.npy -- M/I/D/S/E transition counts |
| *_metadata.tsv -- per-sample metadata |
| *_longest_alignment.txt -- length of the longest alignment in the split |
| *_longest_seqs.txt -- length of the longest unaligned seq in split |
| DATA/info/pfams_in_* -- list of Pfam families in each split |
|
|
| intermediates.tar.gz -- intermediate files (cherry folders, removal |
| logs, duplicate reports) compressed for audit |
|
|
|
|
| Requirements: |
| ------------- |
|
|
| Databases / external tools: |
| - Pfam v36.0 seed file: ftp.ebi.ac.uk/pub/databases/Pfam/releases/Pfam36.0/ |
| - FastTree 2.1.11 (No SSE3 build): used to impute missing phylogenetic trees |
|
|
| Python packages: |
| - Python 3.9.18 |
| - JAX 0.4.28 -- batch computation of pairHMM transition/emission counts |
| - Biopython 1.81 -- parsing and pruning phylogenetic trees |
|
|
|
|
| Arguement for clean_data.py: |
| ---------------------------- |
|
|
| Required: |
| -pfam_seed_file Path to the Pfam-A.seed file (or example seed file) |
| -tree_dir Directory containing per-family .tree files |
|
|
| Optional: |
| -num_splits Number of training splits (default: 10) |
| -topk1_valid Number of widest Pfam families held out for OOD valid |
| (default: 3; set to 0 to skip) |
| -topk2_valid Number of gappiest Pfam families held out for OOD valid |
| (default: 8; set to 0 to skip) |
| -rand_key Random seed for split assignment (default: 6) |
| -metadata_header Header string added to output stats file (default: metadata) |
| -alphabet_size Amino acid alphabet size (default: 20) |
| -max_len Maximum sequence length for padding (default: 5000) |
| -batch_size Batch size for count precomputation (default: 1000) |
|
|
|
|
| Quickstart: |
| ----------- |
|
|
| Unzip EXAMPLE_INPUTS.zip, then run: |
|
|
| python clean_data.py \ |
| -pfam_seed_file EXAMPLE_INPUTS/EXAMPLE_Pfam-A.seed \ |
| -tree_dir EXAMPLE_INPUTS/trees/ \ |
| -num_splits 2 \ |
| -topk1_valid 0 \ |
| -topk2_valid 0 |
|
|
| The example uses -topk1_valid 0 and -topk2_valid 0 because the example dataset |
| is too small to hold out families for OOD validation. |
|
|
