NGS II Illumina Sequencing Robert Kraaij Department of Internal - - PowerPoint PPT Presentation

DepthOfCoverage

Genetics for Dummies 2017 NGS II – Illumina Sequencing

Robert Kraaij Department of Internal Medicine r.kraaij@erasmusmc.nl

• Data Analysis
• Applications
• Example: Exome Sequencing

Overview

Things to be addressed

NGS: many short reads that might contain errors data analysis will handle these reads and errors

• Data Analysis
• Applications
• Example: Exome Sequencing

Overview

cBot flowcell bridgePCR HiSeq2000

Illumina Sequencing

Per Cycle Imaging

G A T C

Per Cycle Imaging

G good quality G poor quality

Per Cycle Base Calling

Phred Score Incorrect base Accuracy 10 1 in 10 90 % 20 1 in 100 99 % 30 1 in 1000 99.9 % 40 1 in 10000 99.99 % 50 1 in 100000 99.999 % 0 to 93  ASCII 33 to 126 = single character

Quality Scoring

@SEQ_ID GATTTGGGGTTCAAAGCAGTATCGATCAAATAGTAAATCCATTTGTTC +SEQ_ID !''*((((***+))%%%++)(%%%%).1***-+*''))**55CCF>>>

FASTQ File

T A C G G T A C T T G C A T A G A T T A C G G T A C T T G C A T A G C T Alignment or Mapping of Reads R E F E R E N C E G E N O M E (HG19)

chromosome + position + strand

sample.bam

Run QC and filtering

sample.bam

sample.bam

• both reads
• quality scores
• chromosome
• position
• quality flag
• duplicate flag
• off target flag

sortedBAM file

Coverage T A C G G T A C T T G C A T A G A T T A C G G T A C T T G C A T A G C T A C G G T A C T T G C A T A G G A T T A C G G T A C T T G C G G T A C T T G C A T A G C T T T A C G G T A C T T G C A T

5x coverage

Mean Coverage

bases on target size of target

% of Bases Above a Certain Threshold T A C G G T A C T T G C A T A G A T T A C G G T A C T T G C A T A G C T A C G G T A C T T G C A T A G G A T T A C G G T A C T T G C G G T A C T T G C A T A G C T T T A C G G T A C T T G C A T

5x 5x 4x 1x

Variant Calling T A C G G T G C T T G C A T A G A T T A C G G T A C T T G C A T A G C T A C G G T G C T T G C A T A G G A T T A C G G T G C T T G C G G T G C T T G C A T A G C T T T A C G G T G C T T G C A T

G = homozygous alternative

G A T T A C G G T G C C G G T G C T T G C A T A G C T G C A T A G C T - A T T A C G G T G C T T G C A

Variant Calling T A C G G T G C T T G C A T A G A T T A C G G T A C T T G C A T A G C T A C G G T G C T T G C A T A G G A T T A C G G T A C T T G C G G T G C T T G C A T A G C T T T A C G G T A C T T G C A T

A/G = heterozygous

G A T T A C G G T A C C G G T G C T T G C A T A G C T G C A T A G C T - A T T A C G G T G C T T G C A

Variant Calling T A C G G T G C T T G C A T A G A T T A C G G T A C T T G C A T A G C T A C G G T G C T T G C A T A G G A T T A C G G T A C T T G C

A/G = heterozygous?

Variant Calling T A C G G T G C T T G C A T A G A T T A C G G T A C T T G C A T A G C T A C G G T G C T T G C A T A G G A T T A C G G T A C T T G C

G

sequencing quality good poor

sample.vcf

• chromosome
• position
• quality
• annotations

VCF File

Variant Calling T A C G G T G C T T G C A T A G A T T A C G G T A C T T G C A T A G C T A C G G T G C T T G C A T A G T A G G A T T A C G G T A C T T G C G G T G C T T G C A T A G C T G A T T A C G G T A C T T G C A T

deletion = heterozygous

G A T T A C G G T A C C G G T G C T T G C A T A G C T G C A T A G C T - G A T T A C G G T G C T T G C A

Paired-End Sequencing

2 x 100 bp

Variant Calling: Mate Pairs

normal

400 bp

deletion

800 bp

insertion

200 bp

Variant Calling: Mate Pairs

normal

400 bp

translocation

Variant Calling: Split Reads

genome

800 bp

mRNA (cDNA)

• Data Analysis
• Applications
• Example: Exome Sequencing

Overview

Applications

• Re-sequencing  full genome  SNPs and indels
• Re-sequencing  mate pairs  structural variations
• Re-sequencing  regional  SNPs and indels
• Sequencing  de novo assembly
• RNAseq
• ChIPseq
• …seq

www.illumina.com

Example: Exome Sequencing

funding by NGI-NCHA, NWO, BBMRI n > 3,000 samples of random set from RS-I start May 2011; Nimblegen part of “CHARGE-S” effort: >5,000 exomes across 4 cohorts Framingham, CHS, ARIC, Rotterdam Study Expand with exome variants array?

CHARGE

Exome Sequencing

Exome vs Full Genome

exon exon exon genome  3 Gb exome  ~30 Mb

Exome Sequencing Workflow

DNA isolation Library preparation Exome capture Sequencing Data analysis

+ +

Exome capture

Nimblegen SeqCap EZ v2 Capture

• CCDS (Sept 2009)
• miRBase (v14, Sept 2009)
• RefSeq (Jan 2010)
• 2,100,000 probes
• 30,246 coding genes
• 329,028 exons
• 710 miRNAs
• 36.5 Mb primary target
• 44.1 Mb capture target

Illumina TruSeq V3 2x100 PE Sequencing

Data analysis: BWA-GATK pipeline

• BclToFastQ

(CASAVA)

• Chastity Filter

Demultiplexing

• BWA (paired)
• SortSam,

MarkDuplicates (picard)

Alignment

• BaseQualityScore

Recalibration, IndelRealignment (GATK)

Processing

• HaplotypeCaller
• VQSR
• VarEval

Variant-Calling

• ANNOVAR,

VCFtools

• PlinkSeq, SKAT,

R

• Spotfire

Analysis

Sample QC and Variant QC

RSX-2 Samples were sequenced to ~54x Mean Coverage

Average Mean Depth of Coverage across the 44Mb SeqCap Exome Percentage of 44Mb covered 10x or better

Mean Depth of Coverage by Flowcell

Mean Depth of Coverage Flowcell Number (Roughly Chronological Order)

Freemix Values by Flowcell

Estimated Freemix Values Flowcell Number (Roughly Chronological Order)

Determing Heterozygous Concordance versus 550k genotyping arrays

Heterozygous Concordance Flowcell Number (Roughly Chronological Order)

Comparing Concordance versus Freemix reveals cutoff around 13% correction

Heterozygous Concordance Estimated Freemix Values

Sample QC and Variant QC

Number of Detected SNPs per Samples by Flowcell

Flowcell Number (Roughly Chronological Order)

Heterozygous to Homozygous ratio per Sample by Flowcell

Flowcell Number (Roughly Chronological Order)

purines

Transition to Transversion Ratio

pyrimidines

transversion transition

Transition to Transversion Ratio per Sample by Flowcell

Flowcell Number (Roughly Chronological Order)

QC and filtering results

Things to Remember

NGS: many short reads that might contain errors coverage indicates the number of independent reads that cover a base  needed to analyse a genome FASTQ file  sequence + quality scores BAM file  aligned reads VCF file  called variants + annotation