Surveillance of aquatic invasive species using shotgun metagenomics — rather than PCR-based metabarcoding — is bottlenecked by the lack of a curated, versioned panel of reference genomes for the species managers actually need to detect. AISGDB is a working space for building that panel: methodology, inventory, and pipeline integration in one place.
Companion pipeline: danaSeq's nanopore_live, whose --mapping_refs module consumes any reference directory conforming to the AISGDB schema.
Where the published literature stands on shotgun metagenomic detection of AIS, with a per-paper synopsis of the references that matter and a candid assessment of why no operational panel exists today.
Funder-facing proposal to construct, validate, and publicly release a curated whole-genome reference panel keyed to the NOAA GLANSIS watchlist. Four aims, twelve months, deferred budget/PI/program fields. LaTeX source ships alongside the HTML.
All 370 species in the NOAA GLANSIS Great Lakes watchlist, tiered against the NCBI Datasets v2 + Entrez APIs as assembled WGS / unassembled WGS (SRA) / transcriptome / markers only / nothing. Each species name links to its NCBI Taxonomy Browser page. Filterable + sortable; underlying script + machine-readable outputs (TSV, JSON) ship in the repo.
The 101 species on Canada's federal Aquatic Invasive Species Regulations Schedule (Part 2 prohibited + Part 3 species-at-risk), tiered the same way as GLANSIS, with species names linked to NCBI Taxonomy. The federal regulation is the master Canadian list — provincial regulations like Manitoba MR 173/2015 are mirrors of it. Outputs: TSV · JSON.
The inventory script is fully reproducible and re-runnable as new assemblies land:
# pull the GLANSIS Darwin Core archive
curl -sS -L -o /tmp/glansis.zip "https://nas.er.usgs.gov/ipt/archive.do?r=nas_glansis"
mkdir -p /tmp/glansis && cd /tmp/glansis && unzip -o /tmp/glansis.zip
# re-query NCBI (cache + retry-safe)
python3 glansis_inventory.py --occurrence /tmp/glansis/occurrence.txt