Usage

Usage

hashbrown [-l LONG_FOLDER] [-s SHORT_FOLDER] -o OUTPUT_FOLDER [options]

Give long reads (-l), short reads (-s), or both; the pipeline follows the inputs:

Inputs	Pipeline
-l	long-read assembly (Flye or Shasta) + Medaka polishing + QC
-l + -s	the same, then short-read polishing (Polypolish → pyPOLCA)
-s	short-read assembly (SKESA or SPAdes) + QC

Inputs

• Long reads: one fastq file per sample (gzipped or not, found recursively). The sample name is everything before the first underscore of the file name — ecoli_pass.fastq.gz is sample ecoli. A file named unclassified* (Nanopore barcoding leftovers) is dropped.
• Short reads: _R1/_R2 in the file name mark a pair; a file without either is a single-end sample (short-read-only pipeline; the polishers need pairs, so in hybrid mode such files are skipped).
• Hybrid: the short-read sample names must match the long-read ones (same text before the first underscore). Unmatched short-read files are reported and skipped.

Options

Option	Default	Meaning
-a, --assembler	flye with -l, skesa with -s alone	flye, shasta (long reads), skesa, spades (short reads only)
--trim-long	off	trim long-read adapters with Porechop before assembling
--filter-long	off	drop the worst 5% of the long reads (and reads < 500 bp) with Filtlong
--trim-short	off	trim the short reads with fastp (quality ≥ 10 sliding window, length ≥ 64)
--size	auto	genome size for Flye; also caps the filtered reads at the best 100x
--min-size	Shasta 3000 / Flye auto	minimum read length (Shasta) or minimum overlap (Flye)
--long-polisher	medaka	medaka or none
--model	r1041_e82_400bps_bacterial_methylation	Medaka model. The default is ONT's bacterial model (R10.4.1, basecaller-independent), the benchmark winner; --list-models prints the alternatives
--short-polisher	polypolish+pypolca	polypolish+pypolca, polypolish, pypolca or none, applied in order
--ion-torrent	off	the short reads are Ion Torrent data: fastp skips adapter trimming, SPAdes uses its Ion Torrent mode. Polishing a Nanopore assembly with them is warned against (shared homopolymer errors)
--annotate	auto	report annotation: auto (Bakta when installed), bakta, pgap (Docker, ~1 h/genome) or none
-t, --threads	all	total threads
-p, --parallel	2	samples processed at once (each gets threads / parallel)
-m, --memory	85% of RAM	memory in GB (SKESA, SPAdes)

Pick the Medaka model that matches your flowcell and basecaller: hashbrown --list-models.

Optional QC modules

Every run ends with an HTML report (8_report/, or 4_report/ without long reads). The core of it — stage funnels, coverage, figures — needs nothing extra. The deeper QC runs only when its tool is installed (any missing one is reported as "skipped" with the install hint, never an error):

Tool	Adds to the report	Install
pycoQC-ng	per-stage read QC pages, both platforms	pip install pycoqc-ng
WeasyPrint	the PDF twin of the report	conda install weasyprint
dnaapler	final assemblies reoriented to dnaA/rep	conda install dnaapler
Merqury	consensus QV per assembly stage (short-read k-mers)	conda install merqury
mash + NCBI datasets + fastANI (or skani)	species confirmation: closest genome and ANI	conda install mash ncbi-datasets-cli fastani
CheckM2	completeness / contamination	conda install checkm2
BUSCO	single-copy ortholog completeness (auto-lineage)	conda install busco
barrnap, tRNAscan-SE	rRNA / tRNA sanity counts	conda install barrnap trnascan-se
mlst	7-gene MLST scheme and sequence type	conda install mlst
MOB-suite	plasmid reconstruction, replicon/relaxase typing, mobility	conda install mob_suite
AMRFinderPlus	resistance genes and point mutations	conda install ncbi-amrfinderplus
abricate	virulence factors (VFDB) and plasmid replicons (PlasmidFinder)	conda install abricate
Bakta	genome annotation (default when installed; --annotate none to skip)	conda install bakta
PGAP	submission-grade NCBI annotation via Docker (--annotate pgap, ~1 h/genome)	ncbi/pgap
ConFindr	intra-species contamination from the short reads	conda install confindr
GenomeScope2	read-based genome size/heterozygosity estimate (needs merqury's meryl)	conda install genomescope2

The blob plot (per-contig GC vs coverage, colored by the closest RefSeq genome) needs no extra tool: it uses mash, seqkit and NCBI datasets, and draws uncolored when the mash database or internet is absent.

Some modules need a local database first (default ~/.hashbrown/db, override with $HASHBROWN_DB; AMRFinderPlus and MOB-suite manage their own locations):

hashbrown db mash       # RefSeq genome sketches for the species ID (~800 MB)
hashbrown db checkm2    # CheckM2 DIAMOND database (~3 GB)
hashbrown db bakta      # Bakta light database (~2 GB)
hashbrown db amrfinder  # AMRFinderPlus database
hashbrown db mob        # MOB-suite database
hashbrown db confindr   # ConFindr rMLST core databases (full coverage needs a free PubMLST account)

Install the heavy ones in their own conda env if they ever conflict with HashBrown's, or just to keep the solve fast — the pipeline only needs them on the PATH. CheckM2 and BUSCO pull large dependency stacks, so this is the recommended way to add them:

conda create -n hashbrown-qc -c conda-forge -c bioconda checkm2 busco
# expose them to HashBrown runs (conda entry points carry their own env in the shebang):
ln -s "$(conda info --base)/envs/hashbrown-qc/bin/"{checkm2,busco} "$CONDA_PREFIX/bin/"

Resuming

Each major step leaves a done_* flag file in the output folder. Rerunning the same command skips the steps already done and continues; the QC always reruns (it is cheap and depends on everything before it). To redo a step, delete its flag (and its output folder, e.g. done_polishing_short and 6_polished_short/) and rerun.

Performance

• -p/--parallel trades memory for speed: each of the p samples runs with threads / p threads. Flye and SPAdes can use tens of GB per sample — keep -p 2 (or 1) unless the machine is large.
• Medaka always runs one sample at a time with all the threads: parallel Medaka instances crash.
• The QC maps reads once per report; with many samples it is the longest part after assembly.