Variant visualisation and quality control
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1 Variant visualisation and quality control You really should be making plots! 25/06/14 Paul Theodor Pyl 1
2 Classical Sequencing Example DNA.BAM.VCF Aligner Variant Caller A single sample sequencing run 25/06/14 Paul Theodor Pyl 2
3 Comparative Sequencing Example Case DNA.BAM.VCF Comparisons Control DNA.BAM.VCF A comparative genomics example 25/06/14 Paul Theodor Pyl 3
4 Scary Sequencing Example DNA (many Samples).BAM.VCF What to do with 1000.BAM files (~200GB each).bam.bam.bam.bam.bam.bam.bam.bam.bam.bam.bam.bam 25/06/14 Paul Theodor Pyl 4
5 How are we dealing with this? 25/06/14 Paul Theodor Pyl 5
6 SNV Calling Pretty well established for diploid monoclonal populations (i.e. non-cancer human sample); e.g. GATK; samtools Can be more problematic in interesting samples, e.g. cancer: Math might make unreasonable assumptions (copy number, clonality, etc. ) Specialised tools exists: e.g. MuTect 25/06/14 Paul Theodor Pyl 6
7 Example VCF #CHROM POS ID REF ALT QUAL FILTER INFO Format Control Tumour rs C A 65 PASS GMAF=0.02 AD:DP:GT 0:42:0/0 24:38:0/ G C 60 PASS GMAF=0.3 AD:DP:GT 0:18:0/0 5:14:0/1 25/06/14 Paul Theodor Pyl 7
8 Visualisation is Key 25/06/14 Paul Theodor Pyl 8
9 What the VCF file will tell you 25/06/14 Paul Theodor Pyl 9
10 What the VCF file won t tell you 25/06/14 Paul Theodor Pyl 10
11 What the VCF file won t tell you 25/06/14 Paul Theodor Pyl 11
12 Example VCF revisited #CHROM POS ID REF ALT QUAL FILTER INFO Format Control Tumour rs C A 65 PASS GMAF=0.02 AD:DP:GT 0:42:0/0 24:38:0/ G C 60 PASS GMAF=0.3 AD:DP:GT 0:18:0/0 5:14:0/1 25/06/14 Paul Theodor Pyl 12
13 Example: CDC27 25/06/14 Paul Theodor Pyl 13
14 25/06/14 Paul Theodor Pyl 14
15 Half-time Summary Visualisation gives context A list of positons (i.e. VCF file) is likely to miss out on some of that Good to know: Regions that are always hard (e.g. CDC27) Regions that show specific artifacts from library prep / sequencer (if the samples are all processed the same) 25/06/14 Paul Theodor Pyl 15
16 Important post-processing Steps After alignment: Remove duplicates (Alignments) InDel realignment (GATK) ATTAC- - ACAC TTACACAC GATTACTTACAC ATTAC- - ACAC TTAC- - ACAC GATTACTTACAC ATTACACAC TTACACAC GATTACTTACAC 25/06/14 Paul Theodor Pyl 16
17 Important post-processing Steps (Variants) Ensembl Variant Effect Predictor R package: ensemblvep (or use command line tool) Location / overlapping genes etc. GMAF (1000genomes or HapMap) SIFT / PolyPhen Scores Annotate with available data e.g. mismatch rates in other samples of the same cohort Local mismatch rate within a sample (e.g. genomic distance to the next 10 mismatches) 25/06/14 Paul Theodor Pyl 17
18 Visualisation Tools Genome Browser, e.g. IGV Programmatic access? (IGV can be scripted) h5vc R/Bioconductor package Processing BAM files into nucleotide tallies Analysing and visualising on those Shamelessly advertising my own software J 25/06/14 Paul Theodor Pyl 18
![Nucleotide Tallies Table of (mis)matches, coverages, deletions, insertions, softclips, Genomic Position A C G T A C G T A C G T [ ] +](/docs-images/74/70927791/images/19-0.jpg "+ G T - - A C G T A C G T A C G T [ ] + + G T - - C G S 1 S 2 C G [ ] + G - Sample One Sample Two Coverage 25/06/14 Paul Theodor Pyl")
19 Nucleotide Tallies Table of (mis)matches, coverages, deletions, insertions, softclips, Genomic Position A C G T A C G T A C G T [ ] + + G T - - A C G T A C G T A C G T [ ] + + G T - - C G S 1 S 2 C G [ ] + G - Sample One Sample Two Coverage 25/06/14 Paul Theodor Pyl 19
20 Genomics Analyses on Tallies Having the data as a matrix: Easy subsetting (e.g. selecting all controls) Easy building of summary statistics applying functions to the matrix E.g. summarise control samples Many Analyses, especially variant calling and visualisation, can be performed on tallies (we don t need the BAM s for it) 25/06/14 Paul Theodor Pyl 20
![Comparisons a[,c(2,4),] Population level statistics apply(a, c(1,3), sum) Background](/docs-images/74/70927791/images/21-2.jpg "of mismatches in control samples apply(a[,3:4,], c(1,3), mean) 25/06/14 Paul Theodor")
21 Genomics Analyses on Tallies Control 1 Control 2 Case 1 Case 2 Genomic Position A C G T A C G T A C G T A A C G C G T T A C G T A C G T [ ] G T G T G T Pairwise Comparisons a[,c(2,4),] Population level statistics apply(a, c(1,3), sum) Background of mismatches in control samples apply(a[,3:4,], c(1,3), mean) 25/06/14 Paul Theodor Pyl 21
22 What is HDF5 Hierarchical Data Format Efficient storage of numerical data Two kinds of objects Groups Folders Datasets Files.BAM HDF5 150GB 6.3 GB 25/06/14 Paul Theodor Pyl 22
23 HDF5 A brief overview Introduced in 1987 National Center for Supercomputing Maintained by the HDFGroup Production Use NASA Imaging The Lord of the Rings 25/06/14 Paul Theodor Pyl 23
24 What to store in our HDF5 file 4 data-sets per Chromosome Counts 4D : [bases x samples x strands x positions] Coverages 3D : [samples x strands x positions] Deletions 3D : [samples x strands x positions] Reference 1D : [positions] 25/06/14 Paul Theodor Pyl 24
25 The h5vc package Available in R/Bioconductor Functionality: creating / interacting with HDF5 tally files Variant calling Data exploration Plotting... BIOINFORMATICS 2014, pages 1 3 APPLICATIONS NOTE doi: /bioinformatics/btu026 Genome analysis Advance Access publication January 21, 2014 h5vc: scalable nucleotide tallies with HDF5 Paul Theodor Pyl*, Julian Gehring, Bernd Fischer and Wolfgang Huber* EMBL Heidelberg, Genome Biology Unit, Meyerhofstr. 1, Heidelberg, Germany Associate Editor: John Hancock Bioinformatics Advance Access published February 5, /06/14 Paul Theodor Pyl 25
26 Applying Functions Block-wise variantcalls <- h5dapply( filename = "example.tally.hfs5", group = "/ExampleStudy/16", blocksize = , names = c("counts", "Coverages"), dims = c(4, 3), range = c( , ), FUN = callvariants, sampledata = sampledata ) Block #1 Block #n A C G T A C G T A C G T [ ] + + G T - - Sample One Genomic Position 25/06/14 Paul Theodor Pyl 26
27 Tutorial Tomorrow Example Workflow Creating Tally Files Variant Calling Visualisation and Quality Control Creating Reports 25/06/14 Paul Theodor Pyl 27
28 My Current Workflow Alignment (e.g. gsnap) Postprocessing (GATK) Remove PCR duplicates InDel realignment Tallying (h5vc) Variant Calling (e.g. h5vc) Ensembl VEP ReportingTools (Interactive HTML tables) 25/06/14 Paul Theodor Pyl 28
29 Final Summary (comparative) variant calling is not completely solved yet Ø We need to do some quality control Plotting variants can be helpful Tables of variants risk missing important context We should try to formalise the intuitions we use for visual inspection (we re working on it) HDF5-based nucleotide tallies allow for analysis and visualisation of SNVs in context 25/06/14 Paul Theodor Pyl 29
30 Acknowledgement EMBL and the Huber Lab Wolfgang Bernd Fischer Simon Anders Julian Gehring 25/06/14 Paul Theodor Pyl 30
31 Tutorial this afternoon labs/ ExampleData.zip Tutorial.Rmd Tutorial.R Tutorial.pdf Get newest version of h5vc: source(" bioclite( h5vc") 25/06/14 Paul Theodor Pyl 31
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