Hospital Universitari Vall d’HebronInstitut de Recerca - VHIR

Institut d’Investigació Sanitària de l’Instituto de Salud Carlos III (ISCIII)

Bioinformàtica per la Recerca Biomèdica

http://ueb.vhir.org/2014BRB

Ferran Briansóferran.brianso@vhir.org

15/05/2014

INTRODUCTION TO NGSVARIANT CALLING ANALYSIS

1. NGS WORKFLOW OVERVIEW

2. WET LAB STEPS

3. IMPORTANT SEQUENCING CONCEPTS

4. NGS ANALYSIS WORKFLOW

1. Primary analysis: de-multiplexing, QC

2. Secondary analysis: read mapping and variant calling

3. Tertiary analysis: annotation, filtering...

5. VISUALIZATION

6. COMMON PIPELINES AND FORMATS

7. CONCLUSIONS

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PRESENTATION OUTLINE

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NGS WORKFLOW OVERVIEW1

3Extracted from Dr Kassahn's publicly shared slides (2013)

LIBRARY PREPARATION2

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Select targetHybridization-based cature or PCR

Add adaptersContain binding sequencesBarcodesPrimer sequences

Amplify material

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Select targetHybridization-based cature or PCR

Add adaptersContain binding sequencesBarcodesPrimer sequences

Amplify material

A) Fragment DNA

B) End-repair

C) A-tailing, adapter ligation and PCR

D) Final library contains• sample insert• indices (barcodes)• flowcell binding sequences• primer binding sequences

LIBRARY PREPARATION2

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Select targetHybridization-based cature or PCR

Add adaptersContain binding sequencesBarcodesPrimer sequences

Amplify material

LIBRARY PREPARATION2

TEMPLATE PREPARATION

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Attachment of librarye.g. To Illumina Flowcell

Amplification of library moleculese.g. Brigde amplification

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BRIDGE AMPLIFICATION

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SEQUENCING

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Sequencing-by-Synthesis

Detection by:• Illumina – fluorescence• Ion Torrent – pH• ROCHE 454 – PO4 and light

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SEQUENCING-BY-SYNTHESIS (ILLUMINA)

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IMPORTANT SEQUENCING CONCEPTS1

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Barcoding/Indexing: allows multiplexing of different samples

Single-end vs paired-end sequencing

Coverage: avg. number reads per target

Quality scores (Qscore): log-scales!

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NGS DATA ANALYSIS WORKFLOW4

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DE-MULTIPLEXING (BARCODE SPLITTING)

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FASTQ FORMAT

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see en.wikipedia.org/wiki/FASTQ_format

SEQUENCE QUALITY: fastQC

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http://www.bioinformatics.babraham.ac.uk/projects/fastqc/

Details of the output https://docs.google.com/document/pub?id=16GwPmwYW7o_r-ZUgCu8-oSBBY1gC97TfTTinGDk98Ws

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NGS DATA ANALYSIS WORKFLOW4

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READ MAPPING (BASIC ALIGNMENT)4

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Comparison against reference genome(! not assembly !)

Many aligners(short reads, longer reads, RNAseq...)Examples: BWA, Bowtie

SAM/BAM files

BURROWS-WHEELER ALIGNMENT TOOL (BWA)

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Popular tool for genomic sequence data (not RNASeq!)

Li and Durbin 2009 Bioinformatics

Challenge: compare billion of short sequence reads (.fastq file) against human genome (3Gb)

Burrows-Wheeler Transform to “index” the human genome and allow memory-efficient and fast string matching between sequence read and reference genome

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Li & Durbin 2009 Bionformatics

SAM/BAM FILES

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see http://samtools.sourceforge.net/SAMv1.pdf

SAM/BAM FILES

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@ Header (information regarding reference genome, alignment method...)

1) Read ID (QNAME)2) Bitwise FLAG (first/second read in pair, both reads mapped...)3) ReferenceSequence Name (RNAME)4) Position (POS, coordinate)5) MapQuality (MAPQ = -10log10P[wrong mapping position])6) CIGAR (describes alignment – matches, skipped regions, insertions..)7) ReferenceSequence (RNEXT, Ref seq of the pair)8) Position of the pair (PNEXT)9) TemplateLength (TLEN)10) ReadSequence11) QUAL (in Fastq format, '*' if NA)...

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VARIANT CALLING

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Identify sequence variantsDistinguish signal vs noiseVCF filesExamples: SAMtools, SNVmix

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SEQUENCE VARIANTS

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Differences to the reference

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SEQUENCE VARIANTS

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Sanger: is it real??

NGS: read count

Provides confidence (statistics!)

Sensitivity tune-able parameter (dependent on coverage)

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VARIANT CALLING: GATK

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Genome Analysis Toolkit (BROAD Institute)

• Initially developed for 1000 Genomes Project

• Single or multiple sample analysis (cohort)

• Popular tool for germline variant calling

• Evaluates probability of genotype given read data

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see http://www.broadinstitute.org/gatk/and McKenna et al. Genome Research 2010

SOMATIC VARIANT CALLING

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Somatic mutations can occur at low freq. (<10%) due to:

• Tumor heterogeneity (multiple clones)

• Low tumor purity (% normal cells in tumor sample)

Requires different thresholds than germline variant calling when evaluating signal vs noise

Trade-off between sensitivity (ability to detect mutation) and specificity (rate of false positives)

Nature Reviews Cancer 12, 323-334 (May 2012)

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INDELS DETECTION1

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Small insertions/ deletions

The trouble with mapping approaches

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modified from Heng Li (Broad Institute)

INDELS DETECTION

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Small insertions/ deletions

The trouble with mapping approaches

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INDELS DETECTION

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Small insertions/ deletions

The trouble with mapping approaches

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RE-ALIGNMENT

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Re-align considering multi-read context, SNPs & INDELS previous info...

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adapted from Andreas Schreiber

EVALUATING VARIANT QUALITY

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TAKING INTO ACCOUNT:

• Coverage at position

• Number independent reads supporting variant

• Observed allele fraction vs expected (somatic / germline)

• Strand bias

• Base qualities at variant position

• Mapping qualities of reads supporting variant

• Variant position within reads (near ends or at centre)

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VCF FILES

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Variant Call Format

Standard for reporting variants from NGS

Describes metadata of analysis and variant calls

Text file format (open in Text Editor or Excel)

!!! Not a MS Office vCard !!!

see http://www.1000genomes.org/wiki/Analysis/Variant%20Call%20Format/vcf-variant-call-format-version-41

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VCF FILES

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NGS DATA ANALYSIS WORKFLOW

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VARIANT ANNOTATION

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Provide biological & clinical context

Identify disease-causing mutations(among 1000s of variants)

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ANNOTATION OVERVIEW

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VARIANT FILTERING AND PRIORIZATION

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PURPOSE: Identify pathogenic or

disease-associated mutation(s) Reduce candidate variants

to reportable setCOMMON STEPS:

• Remove poor quality variant calls

• Remove common polymorphisms

• Prioritize variants with high functional impact

• Compare against known disease genes

• Consider mode of inheritance (autosomal recessive, X-linked...)

• Consider segregation in family (where multiple samples available)

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NGS DATA ANALYSIS WORKFLOW

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VISUALIZATION – IGV (or Genome Browser, Circos...)

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provided by Katherine Pillman

COMMON PIPELINE6

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bcl2fastq (Illumina)FastQC (open-source)

Exomes (HiSeq): BWA(open-source), GATK (Broad)

Gene panels (MiSeq, PGM): MiSeq Reporter (Illumina) Torrent Suite (Ion Torrent)

Custom scripts and third party tools (Annovar, snpEff, PolyPhen, SIFT...)

Commercial annotation software(GeneticistAssistant, VariantStudio...)

COMMON DATA FORMATS6

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.bcl

.fastq

.BAM

.VCF

.csv

.txt

.xls

.html ...

CONCLUSIONS7

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NGS data - the new currency of (molecular) biology

Broad applications (ecology, evolution, ag sciences, medical research and clinical diagnostics...).

Rapidly evolving (sequencing technologies, library preparation methods, analysis approaches, software).

Different tools/pipelines/parametrization gives different results, (more standards needed).

Bioinformatics pipelines typically combine vendor software, third-party tools and custom scripts.

Requires skills in scripting, Linux/Unix, HPC.

Requires advanced hardware (not always available).

Understanding of data (SE, PE, RNA-Seq) important for successful analysis.