Post on 10-May-2015
description
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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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.