| name | metaspades |
| description | More sensitive metagenomic assembly via SPAdes meta mode. Use this skill when working with metaspades: installing, choosing parameters, running commands, interpreting outputs, or troubleshooting. Upstream: https://github.com/ablab/spades. For routing across tools, use tool-selection. For end-to-end pipelines, use metagenomics-workflow.
|
| license | MIT |
| category | analysis-tools |
| tags | ["assembly","spades"] |
| upstream | https://github.com/ablab/spades |
metaSPAdes
Upstream: ablab/spades (metaspades.py)
Citation
Nurk, S., Meleshko, D., Korobeynikov, A. & Pevzner, P. A. metaSPAdes: a new versatile metagenomic assembler. Genome Res. 27, 824–834 (2017). https://doi.org/10.1101/gr.213959.116
See also docs/references.md.
Analytical thinking
metaSPAdes often yields longer contigs than MEGAHIT at substantially higher
memory/time cost. Use it when continuity of medium/low-abundance genomes matters
and hardware allows. Hybrid Illumina+long-read modes exist—record which reads
entered assembly vs coverage.
Prerequisites
| Requirement | Minimum | Recommended |
|---|
| RAM | 100 GB+ typical | 500 GB–1 TB large |
| CPU | 16 | 32+ |
| Input | Dehosted PE (± long) | Clean PE from kneaddata |
How to run
metaspades.py -t 32 -m 500 \
-1 s1_1.fastq -2 s1_2.fastq \
-o temp/metaspades
Primary contigs usually at temp/metaspades/contigs.fasta.
Decision tree
Assembly priority?
├─ Speed/memory → megahit
├─ Continuity / hybrid short+long → metaspades
└─ Already have good contigs → skip; go to prodigal / metawrap / basalt
Related skills
megahit · prodigal · basalt · compute via setup
