Introduction to Variant Analysisof Exome- and Amplicon sequencing data

Lecture by:Date:

Dr. Christian Rausch26 April 2016

About CCA-Drylab@VUmc, TraIT, Galaxy Project

• CCA-Drylab at the VU University Medical Center Amsterdam is the Bioinformatics initiative of the Cancer Center Amsterdam (CCA). One goal is to give trainings in bioinformatics and make workflows available to researchers. Galaxy was selected as a tool to achieve this.

• TraIT (Translational Research IT) is a Dutch national project which is part of CTMM, the Center for Translational Molecular Medicine.

• TraIT is a scientific IT project with the goal to enable integration and querying of information across the four major domains of translational research: clinical, imaging, biobanking and experimental (any-omics) with a particular focus on the needs of multi-center projects.

• Galaxy is one of the tools supported and extended by TraIT• Galaxy is an international project lead by teams at Pen State and Johns

Hopkins University (15 FTEs working on the development). It is a graphical bioinformatics analysis platform aimed at Computational Biologists.

Focus of lecture and practical part

• Lecture: from NGS data to Variant analysis• Live demo: we will analyze NGS read data of

the reference NA12878 from “Genome in a Bottle”/GCAT. DNA Analysis software tools will be run interactively through Galaxy, “a web-based platform for data intensive biomedical research”

Image source: A survey of tools for variant analysis of next-generation genome sequencing data, Pabinger et al., Brief Bioinform (2013) doi: 10.1093/bib/bbs086

Recap on Illumina’s sequencing technology

Recap sequencing (Illumina technology) slide 1

Image source: openwetware.org/images/7/7a/DOE_JGI_Illumina_HiSeq_handout.pdf

Recap sequencing (Illumina technology) slide 2

Image source: openwetware.org/images/7/7a/DOE_JGI_Illumina_HiSeq_handout.pdf

Recap sequencing (Illumina technology) slide 3

Image source: openwetware.org/images/7/7a/DOE_JGI_Illumina_HiSeq_handout.pdf

Recap sequencing (Illumina technology) slide 4

To be able to sequence paired reads, a second bridge amplification followed by a ‘flip’ of the template is required

Image source: illumina.com

Properties of Reads (Illumina)

• Typical read length: 50 … 100 … 150 … 200 … 300 bp

• Read quality drops after 100, 150 or 200 bp (depending on model of sequencer)

• Paired reads: Insert size 200 – 500 bp• Mate pairs: Insert size several kbp• Errors in Illumina reads are typically

substitution errors

Properties of Reads (Illumina)

• Paired reads: Insert size 200 – 500 bp

• Mate pairs: Insert size several kbp Source: evomics.org/2014/01/alignment-methods/

Image source: Mate Pair v2 Sample Prep Guide For 2-5 kb Libraries

Comparison Sequencing PlatformsPlatform Amplification

MethodSequencing Method Detection Method Average read length

Illumina (HiSeq, MiSeq etc)

bridge PCR sequencing by synthesis

Light 100-200 bp

Life Tech Ion Torrent / Proton

emulsion PCR Ion semiconductor sequencing

pH 200-400 bp

Roche 454 emulsion PCR Pyrosequencing, cleavage of released pyrophosphate

light 700 bp

Life Tech SOLiD emulsion PCR sequencing by ligation of hybridizing labeled oligos

light 100 bp

Pacific Biosciences PacBio / Sequel System

No amplification, single-molecule sequencing

polymerase incorporating colored NTPs

light 10 kb, 50% >20 kb, 5% > 30 kb

Oxford Nanopore MinION

No amplification, single molecule nanopore sequencing

DNA molecule traverses pore

current > 5.4 kb

Further reading, great lecture: Sequencing technology - Past, Present and Future, http://www.molgen.mpg.de/899148/OWS2013_NGS.pdf

MinION: Oxford Nanopore’s “3rd generation” Sequencer:

Image source: John MacNeill, http://www2.technologyreview.com/article/427677/nanopore-sequencing

• Based on biological nano-machines, reading single DNA molecules

• DNA Sequencer ‘MinION’ size of USB drive

• Extremely fast (first results ~1h)• Much longer reads than currently

established sequencing technologies Puzzle with large parts is easier than with small ones

• Used by more and more leading labs

Example of a relevant recent publication:http://publications.nanoporetech.com/2015/10/05/nanopore-sequencing-detects-structural-variants-in-cancer/

Target enrichmentused for selecting exomes

• Image source: Agilent

Selecting parts of the genome for sequencing

• The Illumina TruSeq Amplicon - Cancer Panel uses Multiplex PCR to amplify a selected part of the genome (a selection of the exons of 48 genes are targeted with 212 amplicons)

Intro Bioinformatics: NGS based Variant Analysis

Workflow: QC & Mapping reads

Input reads (fastq files)

Quality check with FastQC

Quality- & Adapter- trimming

Not OK?

OK?

Map reads to reference genome

using e.g. BWA or Bowtie2

Output:Sorted BAM file (binary SAM

sequence alignment map)

Galaxy sorts mapped reads by coordinates using samtools sort

Mark reads that are likely PCR duplicates (Picard tools

Mark Duplicates)

Source: clc-bio.com

PCR Deduplication

Variant Calling & Annotation pipeline

Reads mapped to reference genome

SAM or BAM file

Freebayes• Analyze mismatches & compute likelihoods of SNP etc.• Call variants (decide if position is altered or not, based on statistical model) • Can be limited to region of interest• Output: VCF• Various statistics on quality of each variant (read depth

etc.), homozygous/heterozygous etc.

Filter variant allele frequencyDiscard variants with variant allele frequency below threshold

SnpSift Annotate:Annotate known Variants with dbSNP/Clinvar db etc.

SnpEff: Effect annotationGenetic variant annotation and effect prediction toolbox. It annotates and predicts the effects of variants on genes (such as amino acid changes).

Quality Measure: Phred Score

Phred quality score Probability that the base is called wrong Accuracy of the base call

10 1 in 10 90%

20 1 in 100 99%

30 1 in 1,000 99.9%

40 1 in 10,000 99.99%

50 1 in 100,000 99.999%

Quality Measure: Phred Score

• Phred score = quality scores• originally developed by the base calling program Phred used with

Sanger sequencing data• Phred quality score Q is defined as a property which is logarithmically

related to the base-calling error probability P

• Error rate P = 10 – (Phred score Q / 10)

• Q = -10 log10 P• Example: Phred score 30 = error rate 10-3 = 1 base in 1000 will be

wrong• Illumina’s ‘Q score’ = Phred score = Quality score in Sanger sequencing• The base calling programs that convert raw data to sequence data (the

‘base callers’) need to be ‘trained’ to give realistic quality values

• format

standard format to store sequence data (DNA and protein seq.)>FASTA header, often contains unique identifiers and descriptions of the sequence

@unique identifiers and optional descriptions of the sequencethe actual DNA sequence

+ separator optionally followed by description

The quality values of the sequence (one character per nucleotide)

standard format to sequencing reads with quality information (‘Q’ stands for Quality)

More info see wikipedia ‘FASTQ_format’

Quality control with FastQC

• Need to check the quality of reads before further analysis

• Program FastQC is quasi standard

• Sequencing platform companies provide also their own tools for quality control

Quality control: FastQC

• Encoding tells which set of characters stands for which Phred scores.

• There’s also Encoding Illumina 1.5 and others.

• Other programs might not automatically recognize the encoding

• In Galaxy there is a possibility to set the encoding of a FastQ file via the ‘pen’ symbol.

Read Quality Control with FastQCExamples of per base sequence quality

of all reads

Historical example of very first Solexa reads 2006 (Solexa acquired by Illumina 2007)

Not so good, might still be usable, depending on application

50 bp

Examples of other quality measures

in FastQC

• Upper 4 graphs from the data set of the practical course:

• Many reads are repeated

• Apparently not uniformly distributed over whole genome

• Overrepresented sequences:Sequenced fragment was too short and sequencing reaction ran into the Adapter/PCR primer

“Mapping reads to the reference” isfinding where their sequence occurs in the genome

Source: Wikimedia, file:Mapping Reads.png

100 bp identified 100 bp identified200 – 500 bp unknown sequence

“Mapping reads to the reference”:naïve text search algorithms are too slow

• Naïve approach: compare each read with every position in the genome– Takes too long, will not find sequences with mismatches

• Search programs typically create an index of the reference sequence (or text) and store the reference sequence (text) in an advanced data structure for fast searching.

• An index is basically like a phone book (with people’s addresses) Quickly find address (location) of a person

Example of algorithm using ‘indexed seed tables’ to quickly find locations of exact parts of a read

“Mapping reads to the reference”: frequently used programs

• BLAST, the most famous bioinformatics program since 1990, is used to find similar sequences in DNA and protein data bases– 0.1-1 sec to find a result

– Mapping 60 million reads would take ~ 2 months on one CPU1 too slow for NGS

• Popular tools for read mapping:– Bowtie, Bowtie2, BWA, SOAP2, MAQ, RMAP, GSNAP, Novoalign, and

mrsFAST/mrFAST: Hatem et al. BMC Bioinformatics 2013, 14:184 http://www.biomedcentral.com/1471-2105/14/184

– CLCbio read mapper (commercial)– No tool is the best tool in all example conditions– differences in speed– Differently optimized for mismatches/gap models/Insertions & Deletions/taking

into account read base quality/local realignment of matches etc.

Read Mappers: BWA and Bowtie2

Are based on the Burrows-Wheeler Transformation (BWT)• BWT: special sorting of all letters in the text (sequence)• Similar suffixes (word ends) will be close to each other• Easier to compress• Good for approximate string matching (sequence alignment)• Index (FM index) for finding the locations of matched strings (sequences)

in the genome

Read Mapping: General problems

• Read can match equally well at more than one location (e.g. repeats, pseudo-genes)

• Even fit less well to it’s actual position, e.g. if it carries a break point, insertions and/or deletions

SAM and BAM files

• SAM = Sequence Alignment Map• BAM = Binary SAM = compressed SAM• Sequence Alignment/Map format• contains information about how sequence reads map to a reference

genome• Requires ~1 byte per input base to store sequences, qualities and meta

information.• Supports paired-end reads and color space from SOLiD.• Is produced by bowtie, BWA and other mapping tools

Partly from: genetics.stanford.edu/gene211/lectures/Lecture3_Resequencing_Functional_Genomics-2014.pdf

SAM Format

Example from: genetics.stanford.edu/gene211/lectures/Lecture3_Resequencing_Functional_Genomics-2014.pdf

Example Alignment:

Encoding SAM format:

Harvesting Information from SAM

• Query name, QNAME (SAM) / read_name (BAM). • FLAG provides the following information:

– are there multiple fragments?– are all fragments properly aligned?– is this fragment unmapped?– is the next fragment unmapped?– is this query the reverse strand?– is the next fragment the reverse strand?– is this the last fragment?– is this a secondary alignment?– did this read fail quality controls?– is this read a PCR or optical duplicate

Source: www.cs.colostate.edu/~cs680/Slides/lecture3.pdf

Variant Calling & Annotation

Possible reasons for a mismatch

• True SNP or Mutation• Error generated in library preparation• Base calling error

– May be reduced by better base calling methods, but cannot be eliminated• Misalignment (mapping error):

– Local re-alignment to improve mapping• Error in reference genome sequence

Partly from www.biostat.jhsph.edu/~khansen/LecSNP2.pdf

Variant Calling: Principles• Naïve approach (used in early NGS studies):

– Filter base calls according to quality– Filter by frequency– Typically, a quality Filter of PHRED Q 20 was used (i.e., probability of error 1% ).– Then, the following frequency thresholds were used according to the

frequency of the non-ref base, f(b):

– The frequency heuristic works well if the sequencing depth is high, so that the probability of a heterozygous nucleotide falling outside of the 20% - 80% region is low.

– Problems with frequency heuristic:• For low sequencing depth, leads to undercalling of heterozygous genotypes• Use of quality threshold leads to loss of information onindividual read/base qualities• Does not provide a measure of confidence in the callIn parts from: compbio.charite.de/contao/index.php/genomics.html

Variant Calling: Principles

• Today’s Variant Callers rely on probability calculations• Use of Bayes’ Theorem:

– E.g. MAQ: One of the first widely used read mappers and variant callers

– See introduction at: https://en.wikipedia.org/wiki/SNV_calling_from_NGS_data

• Takes into account a quality score for whole read alignment & quality of base at the individual position

• Calls the most likely genotype given observed substitutions• Reliability score can be calculated

Variant Calling & Annotation: Popular Tools

• SAMtools (Mpileup & Bcftools)• GATK• Freebayes• Varscan2• Mutec• MAQ (historic)

VCF = Variant Call Format

• Variant Call Format / BCF = binary version

Variant Annotation with: dbSNP and snpEff

• dbSNP = the Single Nucleotide Polymorphism Database @ NCBI

• Different collections of SNPs are available: ‘all humans’, human subpopulations, different clinical significance (www.ncbi.nlm.nih.gov/variation/docs/human_variation_vcf).

• snpEff is a program that can annotate a collection of SNVs according to information available in dbSNP and information extracted from the location of the SNV (Exon, Intron, silent/non-sense mutation etc.)

Variant annotation with: ANNOVARa versatile tool to annotate genetic variants

(SNPs and CNVs)

• Input:– variants as VCF file– various databases with statistical and predictive annotations: dbSNP, 1000 genomes, exome variant

server (Univ. of Washington), …• Output:

– In coding region? Which gene? How frequently observed in 1000 genomes project? (and more statistics).• According to coordinates from RefSeq genes, UCSC genes, ENSEMBL genes, GENCODE genes or

others, etc.– In non-coding region? Conserved region?

• According to conservation in 44 species, transcription factor binding sites, GWAS hits, etc.– Predicted Effect?

• SIFT predicts whether an amino acid substitution affects protein function. SIFT prediction is based on the degree of conservation of amino acid residues in sequence alignments derived from closely related sequences, collected through PSI-BLAST. SIFT can be applied to naturally occurring nonsynonymous polymorphisms or laboratory-induced missense mutations

• PolyPhen-2 (Polymorphism Phenotyping v2) is a tool which predicts possible impact of an amino acid substitution on the structure and function of a human protein using straightforward physical and comparative considerations.

• PhyloP: Detection of nonneutral substitution rates on mammalian phylogenies.

Annotation with DGIdb: mining the druggable genomeDrug Gene Interaction Database:

Matching disease genes with potential drugs.

• Searches genes against a compendium of drug-gene interactions and identify potentially 'druggable' genes

• DGIdb 2.0 Overview:• New Sources of Drug-Gene Interactions

– CIViC, an open access, open source, community-driven web resource for Clinical Interpretation of Variants in Cancer.

– DoCM, the Database of Curated Mutations, a highly curated database of known, disease-causing mutations.• Automatic updating of sources

– CIViC - drug-gene interactions– DoCM - drug-gene interactions– DrugBank - drug-gene interactions– Gene Ontology - druggable gene categories– Entrez Gene - genes– PubChem - drugs

• Search drug-gene interactions by drug identifiers – Go to the search interactions page and click on the Drugs button to try it out

Genome in a Bottle Consortium & GCAT

Genome in a Bottle Consortium hosted by the National Institute of Standards and Technology, (NIST), is creating reference materials and data for human genome sequencing, as well as methods for genome comparison and benchmarkingExample:• Sequenced ‘pilot genome’ NA12878 with 11 sequencing

technologies.• Variant reference set created with seven read mappers

and three variant callers

Genome Comparison and Analytic Testing (GCAT) platform wants to facilitate development of performance metrics and comparisons of analysis tools across these metrics. Provides interactive visualizations of benchmark and performance testing results.

Practical part (Galaxy live demo)

Note: Public Galaxy Servers and how to install Galaxy yourself:• https://galaxyproject.org/