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Motivation: During an Oxford Nanopore run, MinKNOW writes basecalled reads to disk as a growing series of small FASTQ files. On ships and at field stations, analysis must run on the sequencing workstation, keep up with the run, and end with a loss-free move of the data to archival storage. Results: nanopore_live is the real-time component of the dānaSeq suite, a Nextflow DSL2 workflow that processes a MinKNOW output directory one file at a time, either once (batch mode) or continuously as new files appear (watch mode). Damaged files are salvaged and reads are filtered by length and a Filtlong-style quality score. Optional steps add Kraken2 classification, per-read GTDB sendsketch queries, tetranucleotide profiles, minimap2 mapping to reference genomes, Bakta or Prokka annotation, and HMMER or DIAMOND searches of protein databases such as pollutant-gene catalogues. Results are organised by flow cell and barcode and loaded into one DuckDB database per barcode, which feeds an interactive dashboard that is refreshed every 10 minutes in watch mode and can be published to microscape.app. An optional launcher mode detects the end of the run, waits for the workflow to finish and archives reads and outputs, merging per-barcode chunks only after size, gzip and checksum checks. The pipeline runs on a sequencing workstation or within a cluster allocation, using conda environments or a Docker image. Availability and implementation: dānaSeq is released under the MIT license at https://github.com/rec3141/danaSeq (this description corresponds to release v1.3.0, https://github.com/rec3141/danaSeq/releases/tag/v1.3.0); documentation at https://rec3141.github.io/danaSeq/.
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R. Eric Collins (2026) studied this question.
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