Assemble bacterial isolate genomes from Illumina paired-end reads
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 some of its components can be slow and it traditionally did 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, Velvet and Megahit, so you can take advantage of the pre- and post-processing the Shovill provides with those too.
- Estimate genome size and read length from reads (unless
- Reduce FASTQ files to a sensible depth (default
- Trim adapters from reads (with
- Conservatively correct sequencing errors in reads
- Pre-overlap ("stitch") paired-end reads
- Assemble with SPAdes/SKESA/Megahit with modified kmer range and PE + long SE reads
- Correct minor assembly errors by mapping reads back to contigs
- Remove contigs that are too short, too low coverage, or pure homopolymers
- Produce final FASTA with nicer names and parseable annotations
% 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_3 date=20180327 sw=shovill/1.0.1 ATAACGCCCTGCTGGCCCAGGTCATTTTATCCAATCTGGACCTCTCGGCTCGCTTTGAAGAAT GAGCGAATTCGCCGTTCAGTCCGCTGGACTTCGGACTTAAAGCCGCCTAAAACTGCACGAACC ATTGTTCTGAGGGCCTCACTGGATTTTAACATCCTGCTAACGTCAGTTTCCAACGTCCTGTCG
brew install brewsci/bio/shovill shovill --check
conda install -c conda-forge -c bioconda -c defaults shovill shovill --check
Using Bioconda will install all the dependencies for you on MacOS and Linux.
# Docker docker pull staphb/shovill:latest docker run staphb/shovill:latest shovill --help # Singularity singularity build shovill.sif docker://staphb/shovill:latest singularity exec shovill.sif shovill --help
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 (prefer >= 3.14)
- Velvet >= 1.2
- SAMtools >= 1.3 (prefer >= 1.10)
- BWA MEM
- pigz. Pigz should be available with your OS distribution.
- Pilon (Java).
- Trimmomatic (Java)
Note that you will need to make pilon and trimmomatic executables. You can make a simple wrapper for each that just passes the shell arguments.
||The final assembly you should use|
||Full log file for bug reporting|
||List of post-assembly corrections|
||Assembly graph (spades)|
||Assembly graph (megahit)|
||Assembly graph (velvet)|
||Raw assembly (skesa)|
||Raw assembled contigs (spades)|
||Raw assembly (megahit)|
||Raw assembly (velvet)|
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 date=20180327 sw=shovill/0.9 >contig00041 len=339 cov=8.8 corr=0 origname=NODE_41 date=20180327 sw=shovill/0.9
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:
||Length of contig in basepairs|
||Average k-mer coverage as reported by assembler|
||Number of post-assembly corrections (unless
||The original name of the contig (before applying
||YYYYMMDD date when this contig was assembled|
SYNOPSIS De novo assembly pipeline for Illumina paired reads USAGE shovill [options] --outdir DIR --R1 R1.fq.gz --R2 R2.fq.gz 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: 150) --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: 8) --ram n.nn Try to keep RAM usage below this many GB (default: 16) ASSEMBLER --assembler XXX Assembler: skesa velvet megahit spades (default: 'spades') --opts XXX Extra assembler options in quotes eg. spades: "--untrusted-contigs locus.fna" ... (default: '') --kmers XXX K-mers to use <blank=AUTO> (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)
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 ~150x depth, so Shovill will downsample your FASTQ files to this depth. It estimates depth by dividing read yield by genome size.
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
It doesn't need to be a perfect estimate, just in the right ballpark.
This will keep all the intermediate files in
--outdir so you can explore and debug.
By default it will attempt to use all available CPU cores.
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.
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
--nocoor because it has some of that functionality inbuilt and is
If you want to provide some assembler-specific parameters you can use the
parameter. Make sure you quote the parameters so they get passed as a single string
--assembler spades you might use
--opts "--sc --untrusted-contigs similar_genome.fasta" or
A series of kmers are chosen based on the read length distribution. You can override this with this option.
Choosing which stages to use
|Genome size estimation||default||
|Read error correction||default||
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.
shovillaccept single-end reads?
No, but it might one day.
Do you support long reads from Pacbio or Nanopore?
No, this is strictly for Illumina paired-end reads only. Try use Flye. CANU, or Redbean.
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.
Can I assemble metagenomes with Shovill?
No. Please use dedicated tools like Minia 3.x or Megahit. Shovill uses the estimated genome size for many dynamic settings related to read error correction, read subsampling etc.
Please file questions, bugs or ideas to the Issue Tracker
Not published yet.
- Jason Kwong
- Simon Gladman
- Anders Goncalves da Silva