Course: Bioinformatics for Biomedical Research (2014). Session: 2.3- Introduction to NGS Variant Calling Analysis. Statistics and Bioinformatisc Unit (UEB) & High Technology Unit (UAT) from Vall d'Hebron Research Institute (www.vhir.org), Barcelona.

1. Hospital Universitari Vall d’Hebron
Institut 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 Briansó
ferran.brianso@vhir.org
15/05/2014
INTRODUCTION TO NGS
VARIANT CALLING ANALYSIS

2. 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
5
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3
5
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PRESENTATION OUTLINE
4
7

3. NGS WORKFLOW OVERVIEW1
3Extracted from Dr Kassahn's publicly shared slides (2013)

4. LIBRARY PREPARATION2
4
Select target
Hybridization-based cature or PCR
Add adapters
Contain binding sequences
Barcodes
Primer sequences
Amplify material
2

5. 5
Select target
Hybridization-based cature or PCR
Add adapters
Contain binding sequences
Barcodes
Primer 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

6. 6
Select target
Hybridization-based cature or PCR
Add adapters
Contain binding sequences
Barcodes
Primer sequences
Amplify material
LIBRARY PREPARATION2

7. TEMPLATE PREPARATION
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Attachment of library
e.g. To Illumina Flowcell
Amplification of library molecules
e.g. Brigde amplification
2

8. BRIDGE AMPLIFICATION
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2

9. SEQUENCING
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Sequencing-by-Synthesis
Detection by:
• Illumina – fluorescence
• Ion Torrent – pH
• ROCHE 454 – PO4 and light
2

10. SEQUENCING-BY-SYNTHESIS (ILLUMINA)
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2

11. 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!
3

12. NGS DATA ANALYSIS WORKFLOW4
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13. DE-MULTIPLEXING (BARCODE SPLITTING)
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4

14. FASTQ FORMAT
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4
see en.wikipedia.org/wiki/FASTQ_format

15. 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
4

16. NGS DATA ANALYSIS WORKFLOW4
16

17. READ MAPPING (BASIC ALIGNMENT)4
17
Comparison against
reference genome
(! not assembly !)
Many aligners
(short reads, longer reads, RNAseq...)
Examples: BWA, Bowtie
SAM/BAM files

18. BURROWS-WHEELER ALIGNMENT TOOL (BWA)
18
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
4
Li & Durbin 2009 Bionformatics

19. SAM/BAM FILES
19
4
see http://samtools.sourceforge.net/SAMv1.pdf

20. 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) ReadSequence
11) QUAL (in Fastq format, '*' if NA)
...
4

21. VARIANT CALLING
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Identify sequence variants
Distinguish signal vs noise
VCF files
Examples: SAMtools, SNVmix
4

22. SEQUENCE VARIANTS
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Differences to the reference
4

23. SEQUENCE VARIANTS
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Sanger: is it real??
NGS: read count
Provides confidence (statistics!)
Sensitivity tune-able parameter
(dependent on coverage)
4

24. 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
4
see http://www.broadinstitute.org/gatk/
and McKenna et al. Genome Research 2010

25. 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)
4

26. INDELS DETECTION1
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Small insertions/ deletions
The trouble with mapping approaches
4
modified from Heng Li (Broad Institute)

27. INDELS DETECTION
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Small insertions/ deletions
The trouble with mapping approaches
4

28. INDELS DETECTION
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Small insertions/ deletions
The trouble with mapping approaches
4

29. RE-ALIGNMENT
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Re-align considering multi-read context, SNPs & INDELS previous info...
4
adapted from Andreas Schreiber

30. 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)
4

31. 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
4

32. VCF FILES
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4

33. NGS DATA ANALYSIS WORKFLOW
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4

34. VARIANT ANNOTATION
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Provide biological & clinical context
Identify disease-causing mutations
(among 1000s of variants)
4

35. ANNOTATION OVERVIEW
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4

36. 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)
4

37. NGS DATA ANALYSIS WORKFLOW
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38. VISUALIZATION – IGV (or Genome Browser, Circos...)
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5
provided by Katherine Pillman

39. 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...)

40. COMMON DATA FORMATS6
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.bcl
.fastq
.BAM
.VCF
.csv
.txt
.xls
.html
...

41. 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.