High throughput sequencing: informatics & software aspects Gabor T. Marth Boston College Biology Department BI543 Fall 2013 January 29, 2013.

High throughput sequencing:informatics & software aspects

Gabor T. MarthBoston College Biology Department

BI543 Fall 2013January 29, 2013

Traditional DNA sequencing

Genetics of living organisms

DNA

Chromosomes

Radioactive label gel sequencing

Four-color capillary sequencing

~1 Mb ~100 Mb >100 Mb ~3,000 Mb

ABI 3700 four-color sequence trace

Individual human resequencing

Next-generation DNA sequencing

New sequencing technologies…

… vast throughput, many applications

read length

base

s per

mach

ine r

un

10 bp 1,000 bp100 bp

1 Gb

100 Mb

10 Mb

10 Gb

Illumina, SOLiD

ABI / capillary

454

1 Mb

100 Gb

1 Tb

DNA ligation DNA base extension

Church, 2005

Sequencing chemistries

Template clonal amplification

Church, 2005

Massively parallel sequencing

Church, 2005

Chemistry of paired-end sequencing

Double strand DNA is folded into a bridge shape then separated into single strands. The end of each strand is then sequenced.

(Figure courtesy of Illumina)

Paired-end reads

• fragment amplification: fragment length 100 - 600 bp• fragment length limited by amplification efficiency

• circularization: 500bp - 10kb (sweet spot ~3kb)• fragment length limited by library complexity

Korbel et al. Science 2007

Features of NGS data

Short sequence reads100-200bp25-35bp (micro-reads)

Huge amount of sequence per runUp to gigabases per run

Huge number of reads per runUp to 100’s of millions

Higher error as compared with Sanger sequencing

Error profile different to Sanger

Application areas of next-gen sequencing

Application areas• Genome resequencing

• variant discovery• somatic mutation detection• mutational profiling

• De novo assembly

• Identification of protein-bound DNA• chromatin structure• methylation• transcription binding sites

• RNA-Seq• expression• transcript discovery

Mikkelsen et al. Nature 2007

Cloonan et al. Nature Methods, 2008

SNP and short-INDEL discovery

Structural variation detection

• structural variations (deletions, insertions, inversions and translocations) from paired-end read map locations

• copy number (for amplifications, deletions) from depth of read coverage

Identification of protein-bound DNA

genome sequence

aligned reads

Chromatin structure (CHIP-SEQ)(Mikkelsen et al. Nature 2007)

Transcription binding sites. (Robertson et al. Nature Methods, 2007)

Novel transcript discovery (genes)

Mortazavi et al. Nature Methods

• novel exons• novel transcripts containing known exons

Novel transcript discovery (miRNAs)

Ruby et al. Cell, 2006

Expression profiling

aligned reads

aligned reads

Jones-Rhoads et al. PLoS Genetics, 2007

gene gene

• tag counting (e.g. SAGE, CAGE)• shotgun transcript sequencing

De novo genome sequencing

assembled sequence contigs

short reads

longer reads

read pairs

Lander et al. Nature 2001

The informatics of sequencing

Re-sequencing informatics pipeline

REF

(ii) read mapping

IND

(i) base calling

IND(iii) SNP and short INDEL calling

(v) data viewing, hypothesis generation

(iv) SV callingGigaBayesGigaBayes

The variation discovery toolbox

• base callers

• read mappers

• SNP callers

• SV callers

• assembly viewers

Raw data processing / base calling

Trace extraction

Base calling

• These steps are usually handled well by the machine manufacturers’ software

• What most analysts want to see is base calls and well-calibrated base quality values

Sequence traces are machine-specific

Base calling is increasingly left to machine manufacturers

…where they give you the cover on the box

Read mapping…

Is like a jigsaw puzzle…

Some pieces are easier to place than others…

…pieces with unique features

pieces that look like each other…

Repeats multiple mapping problem

Lander et al. 2001

Paired-end (PE) reads

fragment length: 100 – 600bp

Korbel et al. Science 2007

fragment length: 1 – 10kb

PE reads are now the standard for whole-genome short-read sequencing

Mapping quality values

0.8 0.19 0.01

SNP calling

SNP calling: what goes into it?

sequencing errortrue polymorphism

Base qualities

Base coverage

Prior expectation

Bayesian SNP calling

Siablevarall

]T,G,C,A[S ]T,G,C,A[SiiiorPr

iiorPr

i

iiorPr

i

NiorPrNiorPr

NN

iorPr

i Ni

N

N

N )S,...,S(P)S(P

)R|S(P...

)S(P

)R|S(P...

)S,...,S(P)S(P)R|S(P

...)S(P)R|S(P

)SNP(P

1

1

1

1 11

11

11

AAAAA

CCCCC

TTTTT

GGGGG

polymorphic permutation

monomorphic permutationBayesian

posterior probability

Base call + Base quality Expected polymorphism rate

Base composition Depth of coverage

http://bioinformatics.bc.edu/~marth/PolyBayes

Marth et al., Nature Genetics, 1999

• First statistically rigorous SNP discovery tool• Correctly analyzes alternative cDNA splice forms

The PolyBayes software

SNP calling (continued)

P(G1=aa|B1=aacc; Bi=aaaac; Bn= cccc)P(G1=cc|B1=aacc; Bi=aaaac; Bn= cccc)P(G1=ac|B1=aacc; Bi=aaaac; Bn= cccc)

P(Gi=aa|B1=aacc; Bi=aaaac; Bn= cccc)P(Gi=cc|B1=aacc; Bi=aaaac; Bn= cccc)P(Gi=ac|B1=aacc; Bi=aaaac; Bn= cccc)

P(Gn=aa|B1=aacc; Bi=aaaac; Bn= cccc)P(Gn=cc|B1=aacc; Bi=aaaac; Bn= cccc)P(Gn=ac|B1=aacc; Bi=aaaac; Bn= cccc)

P(SNP)

“genotype probabilities”

P(B1=aacc|G1=aa)P(B1=aacc|G1=cc)P(B1=aacc|G1=ac)

P(Bi=aaaac|Gi=aa)P(Bi=aaaac|Gi=cc)P(Bi=aaaac|Gi=ac)

P(Bn=cccc|Gn=aa)P(Bn=cccc|Gn=cc)P(Bn=cccc|Gn=ac)

“genotype likelihoods”

Pri

or(

G1,.

.,G

i,..,

Gn)

-----a----------a----------c----------c-----

-----a----------a----------a----------a----------c-----

-----c----------c----------c----------c-----

Insertion/deletion (INDEL) variants

These variants have been on the “radar screen” for decades

Accurate automated detection is difficultDifferent mutation mechanisms

Often appear in repetitive sequence and therefore difficult to align

Often multi-allelic

Deleted allele has no base quality values

Alignment methods became more refined

Original alignment

After left realignment

After haplotype-aware realignment

Medium length INDELs still a problem

Guillermo Angel

Structural variation detection

Feuk et al. Nature Reviews Genetics, 2006

Structural variant detection (cont’d)

Detection Approaches

Read Depth: good for big CNVs

Sample Reference

Lmap

read

contig

• Paired-end: all types of SV

• Split-Readsgood break-point resolution

• deNovo Assembly~ the future

SV slides courtesy of Chip Stewart, Boston College

SV detection – resolution

Expected CNVsKaryotype

Micro-arraySequencing

Rela

tive n

um

bers

of

even

ts

CNV event length [bp]

Standard data formats

Reads: FASTQ

Alignments: SAM/BAM

Variants: VCF

Tools for analyzing & manipulating 1000G data

• samtools: http://samtools.sourceforge.net/• BamTools: http://sourceforge.net/projects/bamtools/• GATK: http://www.broadinstitute.org/gsa/wiki/index.php/The_Genome_Analysis_Toolkit

• VCFTools: http://vcftools.sourceforge.net/• VcfCTools: https://github.com/AlistairNWard/vcfCTools

Alignments: SAM/BAM

Variants: VCF