Rapid haploid variant calling and core SNP phylogeny
Torsten Seemann (@torstenseemann)
Snippy finds SNPs between a haploid reference genome and your NGS sequence reads. It will find both substitutions (snps) and insertions/deletions (indels). It will use as many CPUs as you can give it on a single computer (tested to 64 cores). It is designed with speed in mind, and produces a consistent set of output files in a single folder. It can then take a set of Snippy results using the same reference and generate a core SNP alignment (and ultimately a phylogenomic tree).
% snippy --cpus 16 --outdir mysnps --ref Listeria.gbk --R1 FDA_R1.fastq.gz --R2 FDA_R2.fastq.gz
<cut>
Walltime used: 3 min, 42 sec
Results folder: mysnps
Done.
% ls mysnps
snps.vcf snps.bed snps.gff snps.csv snps.tab snps.html
snps.bam snps.txt reference/ ...
% head -5 mysnps/snps.tab
CHROM POS TYPE REF ALT EVIDENCE FTYPE STRAND NT_POS AA_POS LOCUS_TAG GENE PRODUCT EFFECT
chr 5958 snp A G G:44 A:0 CDS + 41/600 13/200 ECO_0001 dnaA replication protein DnaA missense_variant c.548A>C p.Lys183Thr
chr 35524 snp G T T:73 G:1 C:1 tRNA -
chr 45722 ins ATT ATTT ATTT:43 ATT:1 CDS - ECO_0045 gyrA DNA gyrase
chr 100541 del CAAA CAA CAA:38 CAAA:1 CDS + ECO_0179 hypothetical protein
plas 619 complex GATC AATA GATC:28 AATA:0
plas 3221 mnp GA CT CT:39 CT:0 CDS + ECO_p012 rep hypothetical protein
% snippy-core --prefix core mysnps1 mysnps2 mysnps3 mysnps4
Loaded 4 SNP tables.
Found 2814 core SNPs from 96615 SNPs.
% ls core.*
core.aln core.tab core.txt
Install HomeBrew (Mac OS X) or LinuxBrew (Linux).
brew tap homebrew/science
brew tap tseemann/homebrew-bioinformatics-linux
brew install snippy
snippy --help
This will install the latest version direct from Github. You'll need to add the bin directory to your PATH.
cd $HOME
git clone https://github.com/tseemann/snippy.git
$HOME/snippy/bin/snippy --help
- a reference genome in FASTA or GENBANK format (can be in multiple contigs)
- sequence read files in FASTQ or FASTA format (can be .gz compressed) format
- a folder to put the results in
| Extension | Description |
|---|---|
| .tab | A simple tab-separated summary of all the variants |
| .csv | A comma-separated version of the .tab file |
| .html | A HTML version of the .tab file |
| .vcf | The final annotated variants in VCF format |
| .vcf.gz | Compressed .vcf file via BGZIP |
| .vcf.gz.tbi | Index for the .vcf.gz via TABIX |
| .bed | The variants in BED format |
| .gff | The variants in GFF3 format |
| .bam | The alignments in BAM format. Note that multi-mapping and unmapped reads are not present. |
| .bam.bai | Index for the .bam file |
| .raw.vcf | The unfiltered variant calls from Freebayes |
| .filt.vcf | The filtered variant calls from Freebayes |
| .log | A log file with the commands run and their outputs |
| .consensus.fa | A version of the reference genome with all variants instantiated |
| .aligned.fa | A version of the reference but with - at position with depth=0 and N for 0 < depth < --mincov (does not have variants) |
| .depth.gz | Output of samtools depth for the .bam file |
| .depth.gz.tbi | Index for the .depth.gz (currently unused) |
| Name | Description |
|---|---|
| CHROM | The sequence the variant was found in eg. the name after the > in the FASTA reference |
| POS | Position in the sequence, counting from 1 |
| TYPE | The variant type: snp msp ins del complex |
| REF | The nucleotide(s) in the reference |
| ALT | The alternate nucleotide(s) supported by the reads |
| EVIDENCE | Frequency counts for REF and ALT |
If you supply a Genbank file as the --reference rather than a FASTA file, Snippy will fill in these extra columns by using the genome annotation to tell you which feature was affected by the variant:
| Name | Description |
|---|---|
| FTYPE | Class of feature affected: CDS tRNA rRNA ... |
| STRAND | Strand the feature was on: + - . |
| NT_POS | Nucleotide position of the variant withinthe feature / Length in nt |
| AA_POS | Residue position / Length in aa (only if FTYPE is CDS) |
| LOCUS_TAG | The /locus_tag of the feature (if it existed) |
| GENE | The /gene tag of the feature (if it existed) |
| PRODUCT | The /product tag of the feature (if it existed) |
| EFFECT | The snpEff annotated consequence of this variant |
| Type | Name | Example |
|---|---|---|
| snp | Single Nucleotide Polymorphism | A => T |
| mnp | Multiple Nuclotide Polymorphism | GC => AT |
| ins | Insertion | ATT => AGTT |
| del | Deletion | ACGG => ACG |
| complex | Combination of snp/mnp | ATTC => GTTA |
The variant calling is done by Freebayes. However, Snippy uses a very simple model for reporting variants, relying on two main options:
--mincovis the minimum number of reads covering the variant position.--minfracis the minimum proportion of those reads which must differ from the reference.
By default Snippy uses --mincov 10 --minfrac 0.9 which is reasonable for most cases, but for very high coverage data you may get mixed populations such as (REF:310 ALT:28). Snippy may use a more statistical approach in future versions like Nesoni does.
If you call SNPs for multiple isolates from the same reference, you can produce an alignment of "core SNPs" which can be used to build a high-resolution phylogeny (ignoring possible recombination). A "core site" is a genomic position that is present in all the samples. A core site can have the same nucleotide in every sample ("monomorphic") or some samples can be different ("polymorphic" or "variant"). If we ignore the complications of "ins", "del" variant types, and just use variant sites, these are the "core SNP genome".
- a set of Snippy folders which used the same
--refsequence.
| Extension | Description |
|---|---|
| .aln | A core SNP alignment in the --aformat format (default FASTA) |
| .full.aln | A whole genome SNP alignment (includes invariant sites) |
| .tab | Tab-separated columnar list of core SNP sites with alleles and annotations |
| .txt | Tab-separated columnar list of alignment/core-size statistics |
Sometimes one of your samples is only available as contigs, without
corresponding FASTQ reads. You can still use these contigs with Snippy
to find variants against a reference. It does this by shredding the contigs
into 250 bp single-end reads at 2 × --mincov uniform coverage.
To use this feature, instead of providing --R1 and --R2 you use the
--ctgs option with the contigs file:
% ls
ref.gbk mutant.fasta
% snippy --outdir mut1 --ref ref.gbk --ctgs mut1.fasta
Shredding mut1.fasta into pseudo-reads.
Identified 257 variants.
% snippy --outdir mut2 --ref ref.gbk --ctgs mut2.fasta
Shredding mut2.fasta into pseudo-reads.
Identified 413 variants.
% snippy-core mut1 mut2
Found 129 core SNPs from 541 variant sites.
% ls
core.aln core.full.aln ...
This output folder is completely compatible with snippy-core so you can
mix FASTQ and contig based snippy output folders to produce alignments.
The de novo assembly process attempts to reconstruct the reads into the original DNA sequences they were derived from. These reconstructed sequences are called contigs or scaffolds. For various reasons, small errors can be introduced into the assembled contigs which are not supported by the original reads used in the assembly process.
A common strategy is to align the reads back to the contigs to check for discrepancies. These errors appear as variants (SNPs and indels). If we can reverse these variants than we can "correct" the contigs to match the evidence provided by the original reads. Obviously this strategy can go wrong if one is not careful about how the read alignment is performed and which variants are accepted.
Snippy is able to help with this contig correction process. In fact, it produces a
snps.consensus.fa FASTA file which is the ref.fa input file provided but with the
discovered variants in snps.vcf applied!
However, Snippy is not perfect and sometimes finds questionable variants. Typically
you would make a copy of snps.vcf (let's call it corrections.vcf) and remove those
lines corresponding to variants we don't trust. For example, when correcting Roche 454
and PacBio SMRT contigs, we primarily expect to find A/T homopolymer errors and hence
expect to see ins more than snp type variants.
In this case you need to run the correcting process manually using these steps:
% cd snippy-outdir
% cp snps.vcf corrections.vcf
% $EDITOR corrections.vcf
% bgzip -c corrections.vcf > corrections.vcf.gz
% tabix -p vcf corrections.vcf.gz
% vcf-consensus corrections.vcf.gz < ref.fa > corrected.fa
You may wish to iterate this process by using corrected.fa as a new --ref for
a repeated run of Snippy. Sometimes correcting one error allows BWA to align things
it couldn't before, and new errors are uncovered.
Snippy may not be the best way to correct assemblies - you should consider dedicated tools such as PILON or iCorn2, or adjust the Quiver parameters (for Pacbio data).
Sometimes you are interested in the reads which did not align to the reference genome. These reads represent DNA that was novel to your sample which is potentially interesting. A standard strategy is to de novo assemble the unmapped reads to discover these novel DNA elements, which often comprise mobile genetic elements such as plasmids.
By default, Snippy does not keep the unmapped reads, not even in the BAM file.
If you wish to keep them, use the --unmapped option and the unaligned reads will
be saved to a compressed FASTQ file:
% snippy --outdir out --unmapped ....
% ls out/
snps.unmapped.fastq.gz ....
The name Snippy is a combination of SNP (pronounced "snip") , snappy (meaning "quick") and Skippy the Bush Kangaroo (to represent its Australian origin)
Snippy is free software, released under the GPL (version 3).
Please submit suggestions and bug reports here: https://github.com/tseemann/snippy/issues
- Perl >= 5.6
- Perl Modules: Time::Piece, File::Slurp, Bioperl >= 1.6
- bwa mem >= 0.7.12
- samtools >= 1.3
- GNU parallel > 2013xxxx
- freebayes >= 1.1
- freebayes sripts (freebayes-parallel, fasta_generate_regions.py)
- vcflib (vcfstreamsort, vcfuniq, vcffirstheader)
- vcftools (vcf-consensus)
- snpEff >= 4.3
For Linux (compiled on Centos 7) and Mac OS X (compiled on Sierra Brew) all the binaries, JARs and scripts are included.