danaSeq¶

Metagenomic and amplicon analysis pipelines for Oxford Nanopore and Illumina sequencing data. Six independent Nextflow DSL2 pipelines cover real-time read classification, long-read and short-read assembly, metatranscriptomic RNA-seq, 16S/18S/ITS amplicon denoising, and downstream MAG analysis including binning, annotation, taxonomy, and metabolic profiling. Assembly pipelines produce a FASTA + depth table that feeds directly into mag_analysis; illumina_rna consumes those same assembly/MAG FASTAs as mapping references.

Architecture¶

danaSeq/
├── nanopore_live/          Real-time analysis during sequencing
│   │                       9 modules, 14 processes -> DuckDB
│
├── nanopore_assembly/      Long-read assembly + mapping + depth
│   │                       3 modules: preprocess, assembly, mapping
│   │                       Flye/metaMDBG/myloasm -> minimap2 -> CoverM
│   │                       Output: assembly.fasta + depths.txt + BAMs
│
├── illumina_assembly/      Multi-assembler consensus + mapping + depth
│   │                       7 modules: preprocess, error_correct, normalize,
│   │                       merge_reads, assembly, dedupe, mapping
│   │                       Output: assembly.fasta + depths.txt + BAMs
│
├── illumina_amplicon/      Amplicon (16S/18S/ITS) denoising to ASVs
│   │                       11 modules: primers, demultiplex, denoise, merge,
│   │                       taxonomy, phylogeny, renormalize, metadata,
│   │                       cluster, network, shiny
│   │                       cutadapt -> papa2 (DADA2) -> MAFFT/FastTree
│   ├── viz/                Interactive Svelte dashboard (ASV explorer)
│   └── bin/                Pipeline scripts
│
├── illumina_rna/           Metatranscriptomic RNA-seq vs references
│   │                       5 modules: preprocess, rrna, alignment, quantify, summarize
│   │                       SortMeRNA -> BBmap -> featureCounts
│   │                       Output: gene_counts.tsv per reference + viz JSONs
│   ├── viz/                Interactive Svelte dashboard (gene-expression heatmap)
│
└── mag_analysis/           Technology-agnostic downstream analysis
    │                       10 modules: binning, annotation, taxonomy, rrna,
    │                       metabolism, mge, eukaryotic, gene_depths,
    │                       phylogeny, viz
    ├── viz/                Interactive Svelte dashboard
    └── bin/                Pipeline scripts

Pipelines¶

Pipeline	Purpose	Key tools
nanopore_live	Real-time analysis during sequencing	Kraken2, Prokka/Bakta, HMMER3, DuckDB
nanopore_assembly	Long-read assembly + mapping + depth	Flye, metaMDBG, myloasm, minimap2, CoverM
illumina_assembly	Multi-assembler consensus assembly	Tadpole, Megahit, SPAdes, metaSPAdes, BBMap
illumina_rna	Metatranscriptomic RNA-seq vs references	SortMeRNA, BBmap, subread/featureCounts
mag_analysis	Technology-agnostic downstream analysis	7-binner consensus, DAS Tool, Binette, 50+ processes

Getting Started¶

1. Clone the repository¶

git clone https://github.com/rec3141/danaSeq.git
cd danaSeq

2. Choose a runtime¶

Method	Best for	Setup
Conda	Local/laptop development	`cd <pipeline> && ./install.sh`
Docker	Reproducible runs, CI	`docker pull ghcr.io/rec3141/danaseq-mag-analysis:latest`
Apptainer	HPC clusters	`./run-*.sh --apptainer` (auto-pulls SIF)

3. Download databases¶

# Interactive menu (shows sizes and descriptions)
./download-databases.sh

# Human reference (~4 GB, required by both assembly pipelines)
./download-databases.sh --human

# MAG analysis databases
./download-databases.sh --genomad --checkv --checkm2 --kaiju

Quality Standards¶

MAGs are classified per MIMAG (Bowers et al. 2017):

Quality	Completeness	Contamination	Additional
High	>90%	<5%	23S, 16S, 5S rRNA + tRNAs
Medium	>50%	<10%	--
Low	<50%	<10%	--

Resource Requirements¶

Configuration	CPUs	RAM	Storage
Minimum (no Kraken)	16	32 GB	500 GB
Recommended	32	128 GB	1 TB
Full analysis	32	256 GB	2 TB

License¶

MIT. See LICENSE.