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Whole Exome Sequencing for Variant Discovery and Prioritisation

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Whole Exome Sequencing for Variant Discovery and Prioritisation. First, a recap. What have we learned?. NGS platforms – short and long reads What the data looks like How to QC data General procedures in processing data - PowerPoint PPT Presentation

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Whole Exome Sequencing for Variant Discovery and PrioritisationFirst, a recap. What have we learned?NGS platforms short and long reads

What the data looks like

How to QC data

General procedures in processing data

How to find biological signal in data - RNA-Seq lectures + practical (in progress)

Theres a LOT more, but its not necessarily more complex or very different!

2013: ~ 800 papers2014: ~ 1200 papersForero DA, 2012Exomes: Publication Trends Total: 925 (Oct 2012)NGS Variation Discovery Workflow (resequencing based)

Variant Discovery Application: DiseaseAn equivalent of the genome would amount almost 2000 books, containing 1.5 million letters each (average books with 200 pages)!

This information is contained in any single cell of the body.Monogenic DiseasesSingle mutation

How do we find it in all those books?

A bioinformatics challenge

NGS sequencers can only read small portions

So, the library is fragments of pages of the books!Mendelian Disease Gene Discovery7

Gilissen, Genome Biol 2011Mendelian Disease Gene Discovery8Gilissen, Genome Biol 2011

Opportunities and ChallengesEnabling technologies: NGS machines, open-source algorithms, capture reagents, lowering cost, big sample collections

Exomes more cost effective: Sequence patient DNA and filter common SNPs; compare parents child trios; compare paired normal cancer

Challenges:Still cant interpret many Mendelian disordersRare variants need large samples sizesExome might miss region (e.g. novel non-coding genes)

9Shendure, Genome Biol 2011Why exome sequencing?WGS still too costly & added value of intergenic mutations is low

WES: targeted sequencing of coding regions (~1% of human genome)

Mendelian disorders disrupt protein-coding sequences (mostly)

Large fraction of rare non-synonymous variants in human genome are predicted to be deleterious

Splice sites also enriched for highly functional variation

The exome represents a highly enriched subset of the genome in which to search for variants with large effect sizes

A representation of the relationship between the size of the mutational target and the frequency of disease for disorders caused by de novo mutationsGilissen, Genom Biol 2011

Majewski, J Med Genet 2011

Bamshad, Nat Rev Genet 2011Maximizing chances of finding disease-causing rare variants using exome sequencingExample: Comparative SequencingSomatic mutation detection between normal / cancer pairs

More mutation yield and better causal gene identification than Mendelian disorders14

Meyerson et al, Nat Rev Genet 2010

Pierce, Am J Hum Genet 2010Perrault syndrome (HSD17B4)BUT Exome Analysis for single patient can be informativeExome sequencing procedure

Read MappingMapping hundreds of millions of reads the reference genome is CPU and RAM intensive, and slow

Read quality decreases with length (small single nucleotide mismatches or indels real or artifact?)

Very few mappers appropriately deal with indels

Mapping output: SAM (BAM) or BED

17Mapped Data: SAM specificationGeneric sequence alignment format

Describes alignment of reads to a reference

Flexible - stores all the alignment information

Simple enough to be easily generated or converted from other existing alignment formats

Keeps track of chromosome position, alignment quality and alignment features (extended cigar)

Includes mate pair / paired end information

Original FASTQ data can be reproduced from SAM (and BAM)SAM FIELDS

BAM formatBinary version of SAM - more compactMakes downstream analysis independent from the mapping programAllows most of operations on alignment to work on a stream without loading the whole alignment into memoryAllows the file to be indexed by genomic position to efficiently retrieve all reads aligning to a locusVCF formatEmerging standard for storing variant dataOriginally designed for SNPs and short INDELs, it also works for structural variationsConsists of header and data sectionsThe data section is TAB delimited with each line consisting of at least 8 mandatory fieldsVCF FIELDS

Variant filtering

Variant PrioritizationHeuristic filtering to identify novel genes for Mendelian disorders25

Stitziel et al, Genome Biol 2011More than just SNVs and short indels

Structural VariationBreakDancerChen et al, Nat Meth 2009

Only looks at anomalous read pairs

Copy Number Variation DetectionChange in read coverage28

Example WES-based variant discovery workflowMap the reads to a reference genomeindex the reference genomeMap (BWA, BOWTIE, NOVOAOLIGN, ETC)Sort BAM fileRemove PCR duplicatesRealign around indels (optional)Call variantsRecalibrate quality scores (optional)Filter variants Basic variant annotationBiological interpretation only starts here

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