Bio Genome Assembly Short Read Assembly

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

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

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