--- name: bio-genome-assembly-metagenome-assembly description: Metagenome assembly from long reads using metaFlye and metaSPAdes with binning strategies. Use when reconstructing genomes from microbial communities, recovering metagenome-assembled genomes (MAGs), or resolving strain-level variation in complex samples. tool_type: cli primary_tool: metaFlye measurable_outcome: Execute skill workflow successfully with valid output within 15 minutes. allowed-tools: - read_file - run_shell_command --- # Metagenome Assembly ## Overview Metagenome assembly reconstructs genomes from mixed microbial communities. Long reads enable recovery of complete circular genomes and resolution of strain-level differences. ## metaFlye (Long Reads) ```bash # ONT metagenome assembly flye --nano-raw reads.fastq.gz \ --meta \ --out-dir flye_meta \ --threads 32 # PacBio HiFi metagenome flye --pacbio-hifi reads.hifi.fastq.gz \ --meta \ --out-dir flye_meta_hifi \ --threads 32 # Key output files: # assembly.fasta - assembled contigs # assembly_graph.gfa - assembly graph # assembly_info.txt - contig statistics ``` ## metaSPAdes (Short Reads) ```bash # Illumina paired-end metagenome metaspades.py -1 R1.fastq.gz -2 R2.fastq.gz \ -o spades_meta \ -t 32 \ -m 500 # With multiple libraries metaspades.py \ --pe1-1 lib1_R1.fq.gz --pe1-2 lib1_R2.fq.gz \ --pe2-1 lib2_R1.fq.gz --pe2-2 lib2_R2.fq.gz \ -o spades_meta -t 32 ``` ## Hybrid Assembly ```bash # Combine short and long reads flye --nano-raw ont_reads.fastq.gz \ --meta \ --out-dir flye_hybrid \ --threads 32 # Polish with short reads pilon --genome flye_hybrid/assembly.fasta \ --frags short_reads.bam \ --output polished \ --threads 16 ``` ## Key Parameters ### metaFlye | Parameter | Description | |-----------|-------------| | --meta | Metagenome mode (handles uneven coverage) | | --min-overlap | Minimum overlap for assembly (default: auto) | | --genome-size | Estimated total size (optional for meta) | | --iterations | Polishing iterations (default: 1) | | --keep-haplotypes | Preserve strain variants | ### metaSPAdes | Parameter | Description | |-----------|-------------| | -m | Memory limit in GB | | --only-assembler | Skip error correction | | -k | K-mer sizes (auto-selected by default) | | --phred-offset | Quality encoding (33 or 64) | ## Binning Workflow ```bash # Step 1: Map reads back to assembly minimap2 -ax map-ont -t 32 assembly.fasta reads.fastq.gz | \ samtools sort -o mapped.bam - # Step 2: Generate depth file jgi_summarize_bam_contig_depths --outputDepth depth.txt mapped.bam # Step 3: Bin with MetaBAT2 metabat2 -i assembly.fasta -a depth.txt -o bins/bin -t 32 # Step 4: Assess bin quality with CheckM2 checkm2 predict --input bins/ --output-directory checkm2_out -x fa --threads 32 ``` ## SemiBin2 (Deep Learning Binning) ```bash # Single-sample binning SemiBin2 single_easy_bin \ -i assembly.fasta \ -b mapped.bam \ -o semibin_out \ --environment global # Multi-sample binning (better for time-series) SemiBin2 multi_easy_bin \ -i assembly.fasta \ -b sample1.bam sample2.bam sample3.bam \ -o semibin_multi ``` ## Quality Assessment ```bash # Assembly stats seqkit stats assembly.fasta # CheckM2 for bin completeness checkm2 predict -i bins/ -o checkm2_out -x fa -t 32 # GTDB-Tk for taxonomic classification gtdbtk classify_wf --genome_dir bins/ --out_dir gtdbtk_out --cpus 32 # QUAST for assembly metrics metaquast.py -o metaquast_out assembly.fasta -t 32 ``` ## Circular Genome Detection ```bash # Flye marks circular contigs in assembly_info.txt grep "Y" flye_meta/assembly_info.txt | cut -f1 > circular_contigs.txt # Extract circular contigs seqkit grep -f circular_contigs.txt assembly.fasta > circular_genomes.fasta ``` ## Python Pipeline ```python import subprocess from pathlib import Path import pandas as pd def run_metaflye(reads, output_dir, read_type='nano-raw', threads=32): cmd = ['flye', f'--{read_type}', reads, '--meta', '--out-dir', output_dir, '--threads', str(threads)] subprocess.run(cmd, check=True) return Path(output_dir) / 'assembly.fasta' def run_binning(assembly, bam, output_dir, threads=32): depth_file = Path(output_dir) / 'depth.txt' subprocess.run(['jgi_summarize_bam_contig_depths', '--outputDepth', str(depth_file), bam], check=True) bins_dir = Path(output_dir) / 'bins' bins_dir.mkdir(exist_ok=True) subprocess.run(['metabat2', '-i', assembly, '-a', str(depth_file), '-o', str(bins_dir / 'bin'), '-t', str(threads)], check=True) return bins_dir def assess_bins(bins_dir, output_dir, threads=32): subprocess.run(['checkm2', 'predict', '--input', str(bins_dir), '--output-directory', output_dir, '-x', 'fa', '--threads', str(threads)], check=True) results = pd.read_csv(Path(output_dir) / 'quality_report.tsv', sep='\t') high_quality = results[(results['Completeness'] > 90) & (results['Contamination'] < 5)] return high_quality # Example workflow assembly = run_metaflye('ont_reads.fq.gz', 'flye_out') bins = run_binning(str(assembly), 'mapped.bam', 'binning_out') hq_bins = assess_bins(bins, 'checkm2_out') print(f'High-quality MAGs: {len(hq_bins)}') ``` ## Expected Outputs | Metric | Good Assembly | |--------|---------------| | N50 | >50 kb | | Largest contig | >1 Mb | | HQ MAGs (>90% complete, <5% contam) | Varies by sample | | Circular genomes | Sample dependent | ## Troubleshooting | Issue | Solution | |-------|----------| | Few long contigs | Increase read depth or length | | High chimeric rate | Use --keep-haplotypes in Flye | | Poor binning | Add more samples for differential coverage | | Missing taxa | Check read QC; consider targeted enrichment | ## Related Skills - genome-assembly/contamination-detection - CheckM2/GUNC - metagenomics/taxonomic-profiling - Kraken2/Bracken - metagenomics/functional-profiling - HUMAnN - long-read-sequencing/read-qc - Input quality control