bio-genome-assembly-short-read-assembly — independently scanned and version-tracked by SaferSkills.
SaferSkills independently audited bio-genome-assembly-short-read-assembly (Agent Skill) and scored it 100/100 (green). The audit ran 55 deterministic rules across Security, Supply Chain, Maintenance, Transparency, and Community; it found 0 high-severity and 0 lower-severity findings. The full rule-by-rule trace and per-finding evidence are below. Free, methodology-open.
Findings & checks · 0 flagged
Every scanned point with the score it earned and what moved between them.
First recorded scan — no prior version to compare against.
The primary manifest — the file an agent reads to learn what this artifact does.
Reference examples tested with: FastQC 0.12+, MEGAHIT 1.2+, SPAdes 3.15+
Before using code patterns, verify installed versions match. If versions differ:
<tool> --version then <tool> --help to confirm flagsIf code throws ImportError, AttributeError, or TypeError, introspect the installed package and adapt the example to match the actual API rather than retrying.
"Assemble a genome from Illumina reads" → Build a de novo assembly from short paired-end reads using de Bruijn graph algorithms with multiple k-mer sizes.
spades.py -1 R1.fq.gz -2 R2.fq.gz -o outputSPAdes (St. Petersburg genome Assembler) uses de Bruijn graph approach with multiple k-mer sizes for robust assembly.
conda install -c bioconda spadesspades.py -1 R1.fastq.gz -2 R2.fastq.gz -o output_dirspades.py -s reads.fastq.gz -o output_dirspades.py -1 R1.fastq.gz -2 R2.fastq.gz -s unpaired.fastq.gz -o output_dirspades.py --isolate -1 R1.fq.gz -2 R2.fq.gz -o isolate_assemblyBest for single-organism isolates with uniform coverage.
spades.py --careful -1 R1.fq.gz -2 R2.fq.gz -o careful_assemblyReduces misassemblies at cost of speed. Recommended for small genomes.
spades.py --meta -1 R1.fq.gz -2 R2.fq.gz -o meta_assemblyFor mixed microbial communities with varying coverage.
spades.py --rna -1 R1.fq.gz -2 R2.fq.gz -o rna_assemblyAssembles transcripts from RNA-seq data.
spades.py --plasmid -1 R1.fq.gz -2 R2.fq.gz -o plasmid_assemblyExtracts plasmid sequences from bacterial isolates.
| Option | Description |
|---|---|
-o <dir> | Output directory |
-t <#> | Number of threads (default: 16) |
-m <#> | Memory limit in GB (default: 250) |
-k <#,#,...> | K-mer sizes (auto by default) |
--careful | Reduce misassemblies |
--isolate | Isolate mode for uniform coverage |
--meta | Metagenome mode |
--rna | RNA-seq assembly |
--cov-cutoff <#> | Coverage cutoff (default: off) |
--only-assembler | Skip error correction |
--continue | Resume interrupted run |
spades.py \
--pe1-1 short_R1.fq.gz --pe1-2 short_R2.fq.gz \
--pe2-1 long_R1.fq.gz --pe2-2 long_R2.fq.gz \
-o output_dirspades.py \
--pe1-1 paired_R1.fq.gz --pe1-2 paired_R2.fq.gz \
--mp1-1 mate_R1.fq.gz --mp1-2 mate_R2.fq.gz \
-o output_dirspades.py \
-1 illumina_R1.fq.gz -2 illumina_R2.fq.gz \
--pacbio pacbio.fq.gz \
-o hybrid_assembly
# Or with Nanopore
spades.py \
-1 illumina_R1.fq.gz -2 illumina_R2.fq.gz \
--nanopore nanopore.fq.gz \
-o hybrid_assemblySPAdes automatically selects appropriate k-mers based on read length.
# For 150bp reads
spades.py -k 21,33,55,77 -1 R1.fq.gz -2 R2.fq.gz -o output
# For 250bp reads
spades.py -k 21,33,55,77,99,127 -1 R1.fq.gz -2 R2.fq.gz -o outputoutput_dir/
├── scaffolds.fasta # Final scaffolds (use this)
├── contigs.fasta # Contigs before scaffolding
├── assembly_graph.gfa # Assembly graph
├── spades.log # Log file
├── params.txt # Parameters used
└── K*/ # Intermediate k-mer assemblies>NODE_1_length_500000_cov_50.5NODE_1 - Contig/scaffold IDlength_500000 - Sequence lengthcov_50.5 - Average k-mer coverage# Limit memory to 32GB
spades.py -m 32 -1 R1.fq.gz -2 R2.fq.gz -o output
# Use fewer threads
spades.py -t 8 -1 R1.fq.gz -2 R2.fq.gz -o outputspades.py --continue -o output_dir# If reads already corrected
spades.py --only-assembler -1 R1.fq.gz -2 R2.fq.gz -o outputGoal: Run end-to-end assembly pipelines for specific use cases.
Approach: Combine SPAdes in the appropriate mode with basic statistics reporting.
#!/bin/bash
set -euo pipefail
R1=$1
R2=$2
OUTDIR=$3
THREADS=${4:-16}
echo "=== Bacterial Genome Assembly ==="
# Run SPAdes in isolate mode
spades.py \
--isolate \
--careful \
-t $THREADS \
-1 $R1 -2 $R2 \
-o $OUTDIR
# Basic stats
echo "Assembly statistics:"
grep -c "^>" ${OUTDIR}/scaffolds.fasta
seqkit stats ${OUTDIR}/scaffolds.fasta#!/bin/bash
set -euo pipefail
R1=$1
R2=$2
OUTDIR=$3
spades.py \
--meta \
-t 32 \
-m 200 \
-1 $R1 -2 $R2 \
-o $OUTDIR
echo "Metagenome assembly complete: ${OUTDIR}/scaffolds.fasta"spades.py \
--rna \
-t 16 \
-1 rnaseq_R1.fq.gz -2 rnaseq_R2.fq.gz \
-o transcriptome_assembly| Assembler | Best For |
|---|---|
| SPAdes | Small genomes, bacteria, fungi |
| MEGAHIT | Metagenomes (memory efficient) |
| ABySS | Large genomes |
| Velvet | Legacy, small genomes |
| Trinity | Transcriptomes |
megahit -1 R1.fq.gz -2 R2.fq.gz -o megahit_output -t 16-m limit--meta mode (more memory efficient)--careful mode--only-assembler if reads pre-corrected~30 seconds. Free. No account. Every finding cites a rule and a line of evidence.