Rapid haploid variant calling and core genome alignment
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.tab core.txt core.vcf
Install Bioconda then:
conda install -c conda-forge -c bioconda -c defaults snippy
Install Homebrew (MacOS) or LinuxBrew (Linux) then:
brew install brewsci/bio/snippy
This will install the latest version direct from Github.
You'll need to add Snippy's
bin directory to your
cd $HOME git clone https://github.com/tseemann/snippy.git $HOME/snippy/bin/snippy --help
Ensure you have the desired version:
Check that all dependencies are installed and working:
- a reference genome in FASTA or GENBANK format (can be in multiple contigs)
- sequence read file(s) in FASTQ or FASTA format (can be .gz compressed) format
- a folder to put the results in
|.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|
|.bed||The variants in BED format|
|.gff||The variants in GFF3 format|
|.bam||The alignments in BAM format. Includes unmapped, multimapping reads. Excludes duplicates.|
|.bam.bai||Index for the .bam file|
|.log||A log file with the commands run and their outputs|
|.aligned.fa||A version of the reference but with
|.consensus.fa||A version of the reference genome with all variants instantiated|
|.consensus.subs.fa||A version of the reference genome with only substitution variants instantiated|
|.raw.vcf||The unfiltered variant calls from Freebayes|
|.filt.vcf||The filtered variant calls from Freebayes|
|.vcf.gz||Compressed .vcf file via BGZIP|
|.vcf.gz.csi||Index for the .vcf.gz via
|.vcf.gz.tbi||Index for the .vcf.gz via TABIX|
|.depth.gz.tbi||Index for the
Columns in the TAB/CSV/HTML formats
|CHROM||The sequence the variant was found in eg. the name after the
|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:
|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)|
|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 caller
The variant calling is done by Freebayes. The key parameters under user control are:
--mincov- the minimum number of reads covering a site to be considered (default=10)
--minfrac- the minimum proportion of those reads which must differ from the reference
--minqual- the minimum VCF variant call "quality" (default=100)
Looking at variants in detail with
If you run Snippy with the
--report option it will automatically run
snippy-vcf_report and generate a
snps.report.txt which has a section
like this for each SNP in
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ >LBB_contig000001:10332 snp A=>T DP=7 Q=66.3052  10301 10311 10321 10331 10341 10351 10361 tcttctccgagaagggaatataatttaaaaaaattcttaaataattcccttccctcccgttataaaaattcttcgcttat ........................................T....................................... ,,,,,, ,,,,,,,,,,,,,,,,,,,,,t,,,,,,,,,,t,,t,,,,,,,,,,,,,,,,g,,,,,,,g,,,,,,,,,t, ,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, .......T..................A............A....... .........................A........A.....T........... .........C.............. .....A.....................C..C........CT.................TA............. ,a,,,,,a,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,t,t,,,g,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, ,,,,,ga,,,,,,,c,,,,,,,t,,,,,,,,,,g,,,,,,t,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, ............T.C..............G...............G...... ,,,,,,,g,,,,,,,,g,,,,,,,,,,, g,,,,,,,,,,,,,,,,,,,,
If you wish to generate this report after you have run Snippy, you can run it directly:
cd snippydir snippy-vcf_report --cpus 8 --auto > snps.report.txt
If you want a HTML version for viewing in a web browser, use the
cd snippydir snippy-vcf_report --html --cpus 16 --auto > snps.report.html
It works by running
samtools tview for each variant, which can be very slow
if you have 1000s of variants. Using
--cpus as high as possible is recommended.
--rgidwill set the Read Group (
RG) ID (
ID) and Sample (
SM) in the BAM and VCF file. If not supplied, it will will use the
--outdirfolder name for both
--mapqualis the minimum mapping quality to accept in variant calling. BWA MEM using
60to mean a read is "uniquely mapped".
--basequalis minimum quality a nucleotide needs to be used in variant calling. We use
13which corresponds to error probability of ~5%. It is a traditional SAMtools value.
--maxsoftis how many bases of an alignment to allow to be soft-clipped before discarding the alignment. This is to encourage global over local alignment, and is passed to the
--minfracare used to apply hard thresholds to the variant calling beyond the existing statistical measure.. The optimal values depend on your sequencing depth and contamination rate. Values of 10 and 0.9 are commonly used.
--targetstakes a BED file and only calls variants in those regions. Not normally needed unless you are only interested in variants in specific locii (eg. AMR genes) but are still performing WGS rather than amplicon sequencing.
--contigsallows you to call SNPs from contigs rather than reads. It shreds the contigs into synthetic reads, as to put the calls on even footing with other read samples in a multi-sample analysis.
Core SNP phylogeny
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
To simplify running a set of isolate sequences (reads or contigs)
against the same reference, you can use the
This script requires a tab separated input file as follows, and
can handle paired-end reads, single-end reads, and assembled contigs.
# input.tab = ID R1 [R2] Isolate1 /path/to/R1.fq.gz /path/to/R2.fq.gz Isolate1b /path/to/R1.fastq.gz /path/to/R2.fastq.gz Isolate1c /path/to/R1.fa /path/to/R2.fa # single end reads supported too Isolate2 /path/to/SE.fq.gz Isolate2b /path/to/iontorrent.fastq # or already assembled contigs if you don't have reads Isolate3 /path/to/contigs.fa Isolate3b /path/to/reference.fna.gz
Then one would run this to generate the output script.
The first parameter should be the
The remaining parameters should be any remaining
snippy parameters. The
ID will be used for
% snippy-multi input.tab --ref Reference.gbk --cpus 16 > runme.sh % less runme.sh # check the script makes sense % sh ./runme.sh # leave it running over lunch
It will also run
snippy-core at the end to generate the
core genome SNP alignment files
|.aln||A core SNP alignment in the
|.full.aln||A whole genome SNP alignment (includes invariant sites)|
|.tab||Tab-separated columnar list of core SNP sites with alleles but NO annotations|
|.vcf||Multi-sample VCF file with genotype
|.txt||Tab-separated columnar list of alignment/core-size statistics|
|.ref.fa||FASTA version/copy of the
|.self_mask.bed||BED file generated if
core.full.aln an alphabet soup?
core.full.aln file is a FASTA formatted mutliple sequence alignment file.
It has one sequence for the reference, and one for each sample participating in
the core genome calculation. Each sequence has the same length as the reference
||Same as the reference|
||Different from the reference|
||Zero coverage in this sample or a deletion relative to the reference|
||Low coverage in this sample (based on
||Masked region of reference (from
||Heterozygous or poor quality genotype (has
You can remove all the "weird" characters and replace them with
N using the included
snippy-clean_full_aln. This is useful when you need to pass it to a tree-building
or recombination-removal tool:
% snippy-clean_full_aln core.full.aln > clean.full.aln % run_gubbins.py -p gubbins clean.full.aln % snp-sites -c gubbins.filtered_polymorphic_sites.fasta > clean.core.aln % FastTree -gtr -nt clean.core.aln > clean.core.tree
- If you want to mask certain regions of the genome, you can provide a BED file
--maskparameter. Any SNPs in those regions will be excluded. This is common for genomes like M.tuberculosis where pesky repetitive PE/PPE/PGRS genes cause false positives, or masking phage regions. A
--maskbed file for M.tb is provided with Snippy in the
etc/Mtb_NC_000962.3_mask.bedfolder. It is derived from the XLSX file from https://gph.niid.go.jp/tgs-tb/
- If you use the
snippy --cleanupoption the reference files will be deleted. This means
snippy-corecan not "auto-find" the reference. In this case you simply use
snippy-core --reference REFto provide the reference in FASTA format.
Increasing speed when too many reads
Sometimes you will have far more sequencing depth that you need to call SNPs. A common problem is a whole MiSeq flowcell for a single bacterial isolate, where 25 million reads results in genome depth as high as 2000x. This makes Snippy far slower than it needs to be, as most SNPs will be recovered with 50-100x depth. If you know you have 10 times as much data as you need, Snippy can randomly sub-sample your FASTQ data:
# have 1000x depth, only need 100x so sample at 10% snippy --subsample 0.1 ... <snip> Sub-sampling reads at rate 0.1 <snip>
Only calling SNPs in particular regions
If you are looking for specific SNPs, say AMR releated ones in particular genes in your reference genome, you can save much time by only calling variants there. Just put the regions of interest into a BED file:
snippy --targets sites.bed ...
Finding SNPs between contigs
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
--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.
Correcting assembly errors
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
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 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
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 2).
Please submit suggestions and bug reports to the Issue Tracker
- perl >= 5.18
- bioperl >= 1.7
- bwa mem >= 0.7.12
- minimap2 >= 2.0
- samtools >= 1.7
- bcftools >= 1.7
- bedtools >= 2.0
- GNU parallel >= 2013xxxx
- freebayes >= 1.1 (freebayes, freebayes-parallel, fasta_generate_regions.py)
- vcflib >= 1.0 (vcfstreamsort, vcfuniq, vcffirstheader)
- vt >= 0.5
- snpEff >= 4.3
- samclip >= 0.2
- seqtk >= 1.2
- snp-sites >= 2.0
- any2fasta >= 0.4
- wgsim >= 1.8 (for testing only -
For Linux (compiled on Ubuntu 16.04 LTS) and macOS (compiled on High Sierra Brew) some of the binaries, JARs and scripts are included.