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nanoTimeSort bins Oxford Nanopore reads by cumulative sequencing time intervals, using the read start time embedded in every FASTQ header by MinKNOW, Guppy and Dorado.

Pages

• Installation — requirements and install options
• Usage — CLI reference, output naming, supported FASTQ headers
• Tutorial — a complete worked example, from input data to binned output
• How it works — the three-stage pipeline and why v1.0 is ~100× faster
• FAQ — common questions (header compatibility, cumulative bins, multi-member gzip)

What is it for?

A Nanopore run produces reads continuously over hours or days. Splitting the run into cumulative time slices lets you re-analyze "what I would have had after 1 h, 2 h, 3 h…" without re-running the sequencer. Typical uses:

• Time-to-answer studies — how much sequencing time is needed to detect a pathogen, type a strain, or reach a target depth?
• Assembly saturation — at what point do more reads stop improving the assembly?
• Real-time pipeline benchmarking — replay a run as realistic incremental datasets.
• Run QC — spot a pore-death or air-bubble event by comparing per-interval yields (the read and bp counts are in the file names).

Quick start

pip install nanotimesort
nanotimesort -f fastq_pass/ -o binned/ -i 1h -p my_sample

See the Tutorial for a full walk-through.