eDNA_Stream_E01

Scope

• Establish whether squiggle-based taxonomy is feasible on the small computational budget that is available

Dataset:

• NO-MISS bacterial isolates, v10.4.1 chemistry on PromethION flow cells (https://epi2me.nanoporetech.com/nomiss_96bc_p2i_sup_2026/)
  • Downsampling at the pod5 level intended for compute/time reasons -> the full dataset is 293 pod5 / 1.6 TB of raw data

Dependencies:

• dorado >= v 2.0.0
• aws CLI
• conda/mamba (mamba recommended)
• python env with:
  • python 3.14
  • snakemake
  • python-dotenv
• custom-models package: live in a separate repo (git@git.tk-ai.eu:Tom/eDNA_Stream_E01_custom_models.git) included here as the custom_models git submodule; the workflow pip-installs it from that checkout (see workflow/envs/torch_*.yaml)
  • clone with: git clone --recurse-submodules ... (or git submodule update --init on an existing clone)
  • update to a newer version: git submodule update --remote custom_models, then commit the new pinned commit
• .env file with NCBI API key -> slightly speeds up reference fasta download

Running:

cd workflow
snakemake --cores <cores> --resources gpu=<n_gpus> --use-conda

Notes:

• Reads aligned to the FP traps by minimap are excluded for training -> esp. the E. coli reads are highly represented and should not be there, but are stable even when only using stringent alignment