--- name: bio-genome-assembly-short-read-assembly source: https://app.decimal.ai/s/bio-genome-assembly-short-read-assembly@2/SKILL.md source_sha256: 9a4e3808eaaf --- # Short-Read Assembly Assemble genomes from Illumina paired-end or single-end reads using SPAdes. ## SPAdes Overview SPAdes (St. Petersburg genome Assembler) uses de Bruijn graph approach with multiple k-mer sizes for robust assembly. ### Installation ```bash conda install -c bioconda spades ``` ## Basic Usage ### Paired-End Assembly ```bash spades.py -1 R1.fastq.gz -2 R2.fastq.gz -o output_dir ``` ### Single-End Assembly ```bash spades.py -s reads.fastq.gz -o output_dir ``` ### With Unpaired Reads ```bash spades.py -1 R1.fastq.gz -2 R2.fastq.gz -s unpaired.fastq.gz -o output_dir ``` ## Assembly Modes ### Isolate Mode (Default for Bacteria) ```bash spades.py --isolate -1 R1.fq.gz -2 R2.fq.gz -o isolate_assembly ``` Best for single-organism isolates with uniform coverage. ### Careful Mode ```bash spades.py --careful -1 R1.fq.gz -2 R2.fq.gz -o careful_assembly ``` Reduces misassemblies at cost of speed. Recommended for small genomes. ### Meta Mode (Metagenomes) ```bash spades.py --meta -1 R1.fq.gz -2 R2.fq.gz -o meta_assembly ``` For mixed microbial communities with varying coverage. ### RNA Mode (Transcriptomes) ```bash spades.py --rna -1 R1.fq.gz -2 R2.fq.gz -o rna_assembly ``` Assembles transcripts from RNA-seq data. ### Plasmid Mode ```bash spades.py --plasmid -1 R1.fq.gz -2 R2.fq.gz -o plasmid_assembly ``` Extracts plasmid sequences from bacterial isolates. ## Key Options | Option | Description | |--------|-------------| | `-o ` | 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 | ## Multiple Libraries ### Paired Libraries with Different Insert Sizes ```bash 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_dir ``` ### With Mate Pairs ```bash spades.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_dir ``` ### With PacBio/Nanopore (Hybrid) ```bash spades.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_assembly ``` ## K-mer Selection ### Auto Selection (Recommended) SPAdes automatically selects appropriate k-mers based on read length. ### Manual K-mer Specification ```bash # 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 output ``` ## Output Files ``` output_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 ``` ### Scaffold FASTA Headers ``` >NODE_1_length_500000_cov_50.5 ``` - `NODE_1` - Contig/scaffold ID - `length_500000` - Sequence length - `cov_50.5` - Average k-mer coverage ## Memory and Performance ### Reduce Memory Usage ```bash # 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 output ``` ### Resume Interrupted Assembly ```bash spades.py --continue -o output_dir ``` ### Skip Error Correction ```bash # If reads already corrected spades.py --only-assembler -1 R1.fq.gz -2 R2.fq.gz -o output ``` ## Complete Workflows ### Bacterial Genome Assembly ```bash #!/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 ``` ### Metagenome Assembly ```bash #!/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" ``` ### Transcriptome Assembly ```bash spades.py \ --rna \ -t 16 \ -1 rnaseq_R1.fq.gz -2 rnaseq_R2.fq.gz \ -o transcriptome_assembly ``` ## Alternative Assemblers | Assembler | Best For | |-----------|----------| | SPAdes | Small genomes, bacteria, fungi | | MEGAHIT | Metagenomes (memory efficient) | | ABySS | Large genomes | | Velvet | Legacy, small genomes | | Trinity | Transcriptomes | ### MEGAHIT (Alternative for Metagenomes) ```bash megahit -1 R1.fq.gz -2 R2.fq.gz -o megahit_output -t 16 ``` ## Troubleshooting ### Out of Memory - Reduce `-m` limit - Use `--meta` mode (more memory efficient) - Try MEGAHIT instead ### Poor Assembly - Check read quality with FastQC - Trim adapters and low-quality bases - Increase coverage if possible - Try `--careful` mode ### Long Runtime - Reduce k-mer values - Use `--only-assembler` if reads pre-corrected - Increase threads ## Related Skills - read-qc - Preprocess reads before assembly - assembly-polishing - Polish assembly with Pilon - assembly-qc - Assess with QUAST/BUSCO - long-read-assembly - Long-read alternatives