bio-genome-assembly-short-read-assembly

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---

name: bio-genome-assembly-short-read-assembly

description: De novo genome assembly from Illumina short reads using SPAdes. Covers bacterial, fungal, and small eukaryotic genome assembly, as well as metagenome and transcriptome assembly modes. Use when assembling genomes from Illumina reads.

tool_type: cli

primary_tool: SPAdes

measurable_outcome: Execute skill workflow successfully with valid output within 15 minutes.

allowed-tools:

• read_file
• run_shell_command

---

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

conda install -c bioconda spades

Basic Usage

Paired-End Assembly

spades.py -1 R1.fastq.gz -2 R2.fastq.gz -o output_dir

Single-End Assembly

spades.py -s reads.fastq.gz -o output_dir

With Unpaired Reads

spades.py -1 R1.fastq.gz -2 R2.fastq.gz -s unpaired.fastq.gz -o output_dir

Assembly Modes

Isolate Mode (Default for Bacteria)

spades.py --isolate -1 R1.fq.gz -2 R2.fq.gz -o isolate_assembly

Best for single-organism isolates with uniform coverage.

Careful Mode

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)

spades.py --meta -1 R1.fq.gz -2 R2.fq.gz -o meta_assembly

For mixed microbial communities with varying coverage.

RNA Mode (Transcriptomes)

spades.py --rna -1 R1.fq.gz -2 R2.fq.gz -o rna_assembly

Assembles transcripts from RNA-seq data.

Plasmid Mode

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 <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 |

Multiple Libraries

Paired Libraries with Different Insert Sizes

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

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)

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

# 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

# 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

spades.py --continue -o output_dir

Skip Error Correction

# If reads already corrected
spades.py --only-assembler -1 R1.fq.gz -2 R2.fq.gz -o output

Complete Workflows

Bacterial Genome Assembly

#!/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

#!/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

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)

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

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