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Scientific-ToolingScientific-Tooling/structured-intelligence/skills/metagenome-assembly/SKILL.md

metagenome-assembly

Skill4 starsChanged 12 days agoMITView on GitHub

De novo assembly of metagenomic contigs from short reads using MEGAHIT or metaSPAdes.

The file

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2 rules. About 0 are common, 0 are distinctive, and 2 are very specific to this project.

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  1. Very specific:

    If MEGAHIT: run megahit with --min-contig-len, --num-cpu-threads, -m memory.

    line 29
  2. Very specific:

    If metaSPAdes: run spades.py --meta with -k kmer sizes, -t threads, -m memory.

    line 30

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Files
SKILL.md

Skill: metagenome-assembly

Use When

  • The user wants to assemble metagenomic reads into contigs for downstream analysis.
  • The user needs contigs for downstream binning and gene prediction.
  • The user wants to compare assemblers (MEGAHIT vs metaSPAdes).
  • The user has sufficient compute resources for de novo metagenomic assembly.

Inputs

  • Required:
    • Host-depleted FASTQ file(s) (.fastq, .fq, .fastq.gz, .fq.gz).
  • Optional:
    • --assembler STR — Assembler to use: megahit or metaspades (default: megahit).
    • --threads N — Number of threads (default: 4).
    • --memory N — Memory limit in GB (default: 16).
    • --min-length N — Minimum contig length in bp (default: 1000).
    • --kmer-sizes STR — Comma-separated k-mer sizes for metaSPAdes (e.g., 21,33,55,77).
    • --outdir DIR — Output directory (default: assembly_results).

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.

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