Snippy - Rapid bacterial variant calling - UK - tue 5 may 2015
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Using Snippy to call variants in bacterial short read datasets via alignment to reference, and then using these alignments to produce core SNP alignments for phylogenomics.
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Snippy - Rapid bacterial variant calling - UK - tue 5 may 2015
- 1. Snippy Torsten Seemann Balti & Bioinformatics - Birmingham, UK - Tue 5 May 2015 Rapid bacterial variant calling & core genome alignments
- 2. Background
- 3. (Far) south east England
- 4. Phyloflagomics UK / Birmingham Australia / Victoria Canada / British Columbia
- 5. A new home Centre for Applied Microbial Genomics
- 6. Microbiological Diagnostic Unit ∷ Oldest public health lab in Australia : established 1897 in Melbourne : large historical isolate collection back to 1950s ∷ National reference laboratory : Salmonella, Listeria, EHEC ∷ WHO regional reference lab : vaccine preventable invasive bacterial pathogens
- 7. New director ∷ Professor Ben Howden : clinician, microbiologist, pathologist : early adopter of genomics and bioinformatics : long term collaborator on MRSA/VRE w/ Tim Stinear ∷ Mandate : modernise service delivery : enhance research output and collaboration : nationally lead the conversion to WGS
- 8. Hardware ∷ Sequencers : NextSeq 500 : 3 x MiSeq : PacBio RS II (arriving 22 May) ∷ Robots : Perkin Elmer (does not have a Twitter account) : Colony picker ∷ Compute : 240 TB, 10 GigE, 3 x 72 core boxes
- 10. Variant calling ∷ Find DNA differences between genomes : variants to explain phenotype : validate your complemented mutant ∷ Two approaches : reference based (read alignment) : reference-free (de novo assembly / k-mer based)
- 11. Types of variants ∷ Substitutions : single nucleotide polymorphism (snp) A➝C : multiple nucleotide polymorphism (mnp) AG➝TC ∷ Indels : insertion (ins) A➝AC : deletion (del) ACCG➝AG ∷ Complex : compound events AC➝T
- 12. My solution
- 13. Snippy ∷ Fast → snappy ∷ Finds variants → SNPs ∷ Australian → Skippy the bush kangaroo
- 14. Input ∷ FASTQ files : paired end, interleaved, or single-end ∷ Reference : FASTA or Genbank ∷ Output folder : self contained bundle of results
- 15. Inside the black box ∷ bwa mem - no clipping needed ∷ samtools - sorted, filtered BAM ∷ freebayes - split / GNU parallel / merge ∷ vcflib/vcftools - VCF filtering ∷ perl - glue
- 16. Outputs ∷ Read alignments : .bam / .bai ∷ Variants : .vcf / .vcf.gz / .vcf.gz.tbi / .gff .bed .tab .csv .html ∷ Consensus : reference with all variants applied to it ∷ Genome alignment : reference with “-” (missing) and “N” low depth
- 17. TAB output CHROM POS TYPE REF ALT EVIDENCE FTYPE STRAND NT_POS AA_POS LOCUS_TAG GENE PRODUCT chr 5958 snp A G G:44 A:0 CDS + 41/600 13/200 ECO_0001 dnaA replication protein DnaA 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
- 18. Phylogenomics
- 20. Phylogenetics 101 ∷ Choose some genes ∷ Sequence each gene from each isolate ∷ Align the protein sequences of each gene ∷ Back-align to nucleotide space ∷ Concatenate all the alignments ∷ Construct a distance matrix (many ways) ∷ Draw a tree (many ways) ∷ Make wild inferences from little data
- 21. Phylogenomics 101 ∷ Assemble each genome ∷ Perform whole genome alignment : in nucleotide space, as don’t know what is coding : very computationally expensive : can’t parallelize as with individual genes ∷ Continue as for phylogenetics
- 22. bug1 GATTACCAGCATTAAGG-TTCTCCAATC bug2 GAT---CTGCATTATGGATTCRNCATTC bug3 G-TTACCAGCACTAA-------CCAGTC ∷ Ideally, feed this directly to a tree builder ∷ Properly model gaps, codons and ambiguity ∷ Hard! Whole genome alignment
- 23. Core genome SNPs
- 24. bug1 GATTACCAGCATTAAGG-TTCTCCAATC bug2 GAT---CTGCATTATGGATTCRNCATTC bug3 G-TTACCAGCACTAA-------CCAGTC core | | ||||||||| |||||| Core sites are present in all genomes. Core genome
- 25. bug1 GATTACCAGCATTAAGG-TTCTCCAATC bug2 GAT---CTGCATTATGGATTCRNCATTC bug3 G-TTACCAGCACTAA-------CCAGTC core | | ||||||||| |||||| SNPs | | | | | Core SNPS = polymorphic sites in core genome Core SNPs
- 26. bug1 GATTACCAGCATTAAGG-TTCTCCAATC bug2 GAT---CTGCATTATGGATTCRNCATTC bug3 G-TTACCAGCACTAA-------CCAGTC core | | ||||||||| |||||| SNPs | | | | | SNPs’ | | | | Unambiguous core SNPs
- 27. bug1 GATTACCAGCATTAAGG-TTCTCCAATC bug2 GAT---CTGCATTATGGATTCRNCATTC bug3 G-TTACCAGCACTAA-------CCAGTC SNPs’ | | | | ata ttc ata atg 1 2 3 4 Allele sites
- 28. >bug1 ATAA >bug2 TTTT >bug3 ACAG Alignment ⇢Tree +------ bug3 | ---+--- bug1 | +--------- bug2 --- 1 SNP
- 29. The N±1 problem
- 30. Aligning to reference ∷ Why is whole genome alignment not used? : involves genome (mis)assembly : computationally difficult : expensive to add or remove isolates ∷ Short-cut : choose a single reference : align each isolates reads to the reference : core, by definition, must include the reference
- 31. Read mapping considerations ∷ Choice of reference ∷ Too divergent? : reads may not align well : will get too many core genome SNPs ∷ One solution : Assemble one isolate and use as the reference
- 32. SNPs | | | | | core | | ||||||||| |||||| bug1 GATTACCAGCATTAAGG-TTCTCCAATC bug2 GAT---CTGCATTATGGATTCRNCATTC bug3 G-TTACCAGCACTAA-------CCAGTC core1 ||| ||||||||||| |||||||||| SNPs1 | | || | Remove taxon, different core (1)
- 33. SNPs | | | | | core | | ||||||||| |||||| bug1 GATTACCAGCATTAAGG-TTCTCCAATC bug2 GAT---CTGCATTATGGATTCRNCATTC bug3 G-TTACCAGCACTAA-------CCAGTC core2 | | ||||||||| |||||| SNPs2 | | | | | Remove taxon, different core (2)
- 34. SNPs | | | | | core | | ||||||||| |||||| bug1 GATTACCAGCATTAAGG-TTCTCCAATC bug2 GAT---CTGCATTATGGATTCRNCATTC bug3 G-TTACCAGCACTAA-------CCAGTC core3 | ||||||||||||| |||||| SNPs3 | | Remove taxon, different core (3)
- 35. Core genome alignments ∷ Core SNP alignments : can shift dramatically with taxa content : we are only using globally conserved sites : remember variation still exists outside “core” ∷ Snippy will keep the full alignments : quickly derive subsets on the fly : adding isolates can be done quickly too
- 36. Conclusion
- 37. Snippy summary ∷ The good : Fast, scales to 100 cores : Simple, clean interface and output ∷ The bad : Doesn’t do full consequences yet using snpEff ∷ The ugly? : Written in Perl
- 38. Contact ∷ tseemann.github.io ∷ github.com/tseemann/snippy ∷ @torstenseemann