Metagenome Assembly

Workflow

1. If MEGAHIT: run megahit with --min-contig-len, --num-cpu-threads, -m memory.
2. If metaSPAdes: run spades.py --meta with -k kmer sizes, -t threads, -m memory.
3. Filter contigs by minimum length.
4. Generate assembly statistics: total contigs, total length, N50, L50, largest contig, GC content.
5. Report assembly summary.

Output Contract

• Contigs FASTA — Assembled contigs filtered by minimum length (<outdir>/contigs_min<N>bp.fasta).
• Assembly statistics — N50, L50, total length, contig count, largest contig, GC percentage (<outdir>/assembly_stats.txt).

Limits

• metaSPAdes requires significantly more memory than MEGAHIT (100-500 GB vs 10-50 GB for human gut metagenomes).
• MEGAHIT is faster and more memory-efficient; recommended as the default for most samples.
• Minimum contig length of 1000 bp is recommended for downstream binning.
• Assembly quality depends heavily on sequencing depth and community complexity.
• MEGAHIT and metaSPAdes (SPAdes) must be installed and available on $PATH.
• This skill does not perform scaffolding; output is contigs only.
• Common failure cases:
  • metaSPAdes running out of memory on complex or deeply sequenced samples.
  • MEGAHIT crashing due to insufficient disk space for intermediate k-mer graph files.
  • Input reads still containing host contamination, inflating assembly size with host contigs.