Shovill

Faster SPAdes (or better SKESA/Megahit/Velvet) assembly of Illumina reads

Introduction

The SPAdes genome assembler has become the de facto standard de novo genome assembler for Illumina whole genome sequencing data of bacteria and other small microbes. SPAdes was a major improvement over previous assemblers like Velvet, but it can be very slow to run and does not handle overlapping paired-end reads well.

Shovill is a pipeline which uses SPAdes at its core, but alters the steps before and after the primary assembly step to get similar results in less time.

Shovill also supports other assemblers like SKESA and Megahit, so you can take advantage of the pre- and post-processing the Shovill provides with those too.

Main steps

1. Estimate genome size and read length from reads (unless --gsize provided)
2. Reduce FASTQ files to a sensible depth (default --depth 100)
3. Trim adapters from reads (with --trim only)
4. Conservatively correct sequencing errors in reads
5. Pre-overlap ("stitch") paired-end reads
6. Assemble with SPAdes/SKESA/Megahit with modified kmer range and PE + long SE reads
7. Correct minor assembly errors by mapping reads back to contigs
8. Remove contigs that are too short, too low coverage, or pure homopolymers
9. Produce final FASTA with nicer names and parseable annotations

Quick Start

% shovill --outdir out --R1 test/R1.fq.gz --R2 test/R2.fq.gz

<snip>
Final assembly in: test/contigs.fa
It contains 17 (min=150) contigs totalling 169611 bp.
Done.

% ls out

contigs.fa   contigs.gfa   shovill.corrections  
shovill.log  spades.fasta

% head -n 4 out/contigs.fa

>contig00001 len=52653 cov=32.7 corr=1 origname=NODE_1_length_52642_cov_32.67243_pilon
ATAACGCCCTGCTGGCCCAGGTCATTTTATCCAATCTGGACCTCTCGGCTCGCTTTGAAGAAT
GAGCGAATTCGCCGTTCAGTCCGCTGGACTTCGGACTTAAAGCCGCCTAAAACTGCACGAACC
ATTGTTCTGAGGGCCTCACTGGATTTTAACATCCTGCTAACGTCAGTTTCCAACGTCCTGTCG

Installation

Homebrew

brew install brewsci/bio/shovill
shovill --check

Using Homebrew will install all the dependencies for you: Linux or MacOS

Conda

conda -c bioconda install shovill
shovill --check

Big thanks to @slugger70 who tirelessly handles Bioconda packaging for all my tools.

Docker

Use the Bioboxes Shovill container.

Source

git clone https://github.com/tseemann/shovill.git
./shovill/bin/shovill --help
./shovill/bin/shovill --check

You will need to install all the dependencies manually:

• SPAdes >= 3.11
• SKESA
• MEGAHIT
• Velvet >= 1.2
• Lighter
• FLASH
• SAMtools >= 1.3
• BWA MEM
• MASH >= 2.0
• seqtk
• pigz
• Pilon (Java)
• Trimmomatic (Java)

Output files

Filename	Description
contigs.fa	The final assembly you should use
shovill.log	Full log file for bug reporting
shovill.corrections	List of post-assembly corrections
contigs.gfa	Assembly graph (spades)
contigs.fastg	Assembly graph (megahit)
contigs.LastGraph	Assembly graph (velvet)
skesa.fasta	Raw assembly (skesa)
spades.fasta	Raw assembled contigs (spades)
megahit.fasta	Raw assembly (megahit)
velvet.fasta	Raw assembly (velvet)

contigs.fa

This is most important output file - the final, corrected assembly. It contains entries like this:

>contig00001 len=263154 cov=8.9 corr=1 origname=NODE_1_length_263154_cov_8.86703_pilon
>contig00041 len=339 cov=8.8 corr=0 origname=NODE_41_length_339_cov_8.77027_pilon

The sequence IDs are named as per the --namefmt option, and the comment field is a series of space-separated name=value pairs with the following meanings:

Pair	Meaning
len	Length of contig in basepairs
cov	Average k-mer coverage as reported by assembler
corr	Number of post-assembly corrections (unless --nocorr used)
origname	The original name of the contig (before applying --namefmt)

Advanced options

GENERAL
  --help          This help
  --version       Print version and exit
  --check         Check dependencies are installed
INPUT
  --R1 XXX        Read 1 FASTQ (default: '')
  --R2 XXX        Read 2 FASTQ (default: '')
  --depth N       Sub-sample --R1/--R2 to this depth. Disable with --depth 0 (default: 100)
  --gsize XXX     Estimated genome size eg. 3.2M <blank=AUTODETECT> (default: '')
OUTPUT
  --outdir XXX    Output folder (default: '')
  --force         Force overwite of existing output folder (default: OFF)
  --minlen N      Minimum contig length <0=AUTO> (default: 0)
  --mincov n.nn   Minimum contig coverage <0=AUTO> (default: 2)
  --namefmt XXX   Format of contig FASTA IDs in 'printf' style (default: 'contig%05d')
  --keepfiles     Keep intermediate files (default: OFF)
RESOURCES
  --tmpdir XXX    Fast temporary directory (default: '/tmp/tseemann')
  --cpus N        Number of CPUs to use (0=ALL) (default: 0)
  --ram n.nn      Try to keep RAM usage below this many GB (default: 8)
ASSEMBLER
  --assembler XXX Assembler: spades skesa megahit velvet (default: 'skesa')
  --kmers XXX     K-mers to use <blank=AUTO> (default: '')
  --opts XXX      Extra assembler options eg. spades: --plasmid --sc ... (default: '')
MODULES
  --trim          Enable adaptor trimming (default: OFF)
  --noreadcorr    Disable read error correction (default: OFF)
  --nostitch      Disable read stitching (default: OFF)
  --nocorr        Disable post-assembly correction (default: OFF)

--depth

Giving an assembler too much data is a bad thing. There comes a point where you are no longer adding new information (as the genome is a fixed size), and only adding more noise (sequencing errors). Most assemblers seem to be happy with ~100x depth, so Shovill will downsample your FASTQ files to this depth. It estimates depth by dividing read yield by genome size.

--gsize

The genome size is needed to estimate depth and for the read error correction stage. If you don't provide --gsize, it will be estimated via k-mer frequencies using mash. It doesn't need to be a perfect estimate, just in the right ballpark.

--keepfiles

This will keep all the intermediate files in --outdir so you can explore and debug.

--cpus

By default it will attempt to use all available CPU cores.

--ram

Shovill will do its best to keep memory usage below this value, but it is not guaranteed. If you are on a HPC cluster, you should make sure you tell your job submission engine a value higher than this.

--assembler

By default it will use SPAdes, but you can also choose Megahit or SKESA. These are much faster than SPAdes, but give lesser assemblies. If you use SKESA you can probably use --noreadcorr and --nocoor because it has some of that functionality inbuilt and is conservative.

--opts

If you want to provide some assembler-specific parameters you can use the --opts parameter. Make sure you quote the parameters so they get passed as a single string eg. For --assembler spades you might use --opts "--sc --untrusted-contigs similar_genome.fasta" or --opts '--sc'.

--kmers

A series of kmers are chosen based on the read length distribution. You can override this with this option.

Choosing which stages to use

Stage	Enable	Disable
Genome size estimation	default	--gsize XX
Read subsampling	--depth N	--depth 0
Read trimming	--trim	default
Read error correction	default	--noreadcorr
Read stitching/overlap	default	--nostitch
Contig correction	default	--nocorr

Environment variables recognised

These env-vars will be used as defaults instead of the built-in defaults. You can use the normal command line option to override them still.

Variable	Option	Default
$SHOVILL_CPUS	--cpus	1
$SHOVILL_RAM	--ram	4
$SHOVILL_ASSEMBLER	--assembler	spades
$TMPDIR	--tmpdir	/tmp

FAQ

• Does shovill accept single-end reads?

  No, but it might one day.

• Do you support long reads from Pacbio or Nanopore?

  No, this is strictly an Illumina based pipeline.

• Why does Shovill crash?

  Shovill has a lot of dependencies. If any dependencies are not installed correctly it will die. Spades also doesn't handle --cpus > 16 very well - try giving more RAM.

Feedback

Please file questions, bugs or ideas to the Issue Tracker

License

GPLv3

Citation

Not published yet.

Authors

• Torsten Seemann (with Jason Kwong, Simon Gladman, Anders Goncalves da Silva)