Building multiple BAM-derived models in one pass

If you need both a fragment length model and a GC bias model from the same BAM, eidolon gen-bam-models walks the BAM once and feeds both observers from the single pass — half the I/O of running the two per-tool commands back-to-back.

$ eidolon gen-bam-models -c gen_bam_models_config.yml

Copy template_config/gen_bam_models.yml and fill in the sections for whichever models you want:

bam_file: /path/to/aligned.bam
min_mapq: 0   # walker-level filter; FragLengthObserver enforces MAPQ > 10 internally

frag_length:
  output_file: /path/to/frag_length_model.json.gz
  overwrite_output: false
  min_reads: 2

gc_bias:
  reference: /path/to/reference.fa
  output_file: /path/to/gc_bias_model.json.gz
  overwrite_output: false
  bed_file: .
  window_size: 100
  window_stride: 100
  min_windows_per_bin: 10

Both sections are optional but at least one must be present. The output models are interchangeable with what gen-frag-length-model and gen-gc-bias-model would produce on their own, so the resulting files plug into gen-reads exactly the same way.

One filter for the shared walk. The top-level min_mapq is the only MAPQ knob exposed to the unified walker, and it gates both observers. The standalone tools differ: gen-frag-length-model hard-codes its own MAPQ > 10 cutoff (via BamWalkFilter::for_frag_length()), while gen-gc-bias-model honors the min_mapq you pass it. If you want the unified runner’s frag-length output to match the standalone command byte-for-byte, set min_mapq: 10; if you want the GC bias output to match a standalone run with min_mapq: 20, set min_mapq: 20. When the two policies need to differ, run gen-bam-models twice — one config per output — and the per-observer BAM iteration savings still beat running the standalone commands.