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…ng, also need to turn that into a real peak file
…ng, also need to turn that into a real peak file
…ng, also need to turn that into a real peak file
…ng, also need to turn that into a real peak file
…s and incoperating the underlying FIRE elements so I can find the natural peak start and end
Add new `mock-fire` CLI subcommand that generates mock BAM files with FIRE elements from a BED file. Each interval becomes a FIRE element, with the 4th column grouping intervals into the same mock read. Bug fixes: - Fix score calculation overflow when quality=255 by capping at 253 and limiting max score to 100 - Fix peak merging to select representative peak by highest score instead of lowest FDR, ensuring the peak_max reflects the position with most FIRE coverage - Add hidden --min-fire-coverage parameter to call-peaks for testing with low-coverage data New files: - src/cli/mock_fire_opts.rs - src/subcommands/mock_fire.rs
added 30 commits
April 22, 2026 10:23
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Summary
This branch adds two major pieces of functionality and ships a co-located Snakemake workflow for model training.
New subcommands
ft call-peaks— end-to-end peak caller implemented in Rust (replaces the previous Python/bash pipeline). Builds a per-chromosome FDR table, identifies local maxima, snaps peak boundaries to the median positions of underlying FIRE elements, merges adjacent peaks, and emits a BED with the expected fire-peak columns (including percent-accessible calls). Code under src/subcommands/call_peaks/ (fdr, peaks, mod — ~1450 lines).ft mock-fire— generates synthetic FIRE-tagged output for peak-calling tests and reproducers. See src/subcommands/mock_fire.rs.ft benchmark(hidden) — profiles the fiberseq iterator to help tune chunk sizes and parallelism. See src/subcommands/benchmark.rs.Extensions and fixes
ft pileup: major refactor to share code with peak calling. Now accepts multiple regions on the CLI or a BED file of regions. Adds default--frac-fibersfiltering. See src/subcommands/pileup.rs and src/cli/pileup_opts.rs.fiber.rsswitched from collecting regions to multi-fetching, reducing peak memory on wide region sets.Train-FIRE workflow
Adds Train-FIRE/ — a self-contained Snakemake pipeline for training FIRE models (positives/negatives construction, feature extraction, XGBoost grid search, mokapot semi-supervised FDR, track-hub generation). Pixi-managed, with a
.tests/minimal smoke config.Test plan
cargo test(includes fibertig regression tests added via fix: pair fa:Z extras with correct annotations on reverse-strand reads #103)cargo clippy -- -D warningsft call-peakson a real fiberseq BAM and diff output against the legacy Python peak callerft pileupwith--regions-bedon a multi-region BED.tests/smoke config runs end-to-end