Bioinformatics - Karolinska...

Bioinformatics in next generation sequencing projects

Rickard SandbergAssistant ProfessorDepartment of Cell and Molecular BiologyKarolinska Institutet

May 2013

Thursday, May 16, 13

Standard sequence library generation

Thursday, May 16, 13

Illumina Sequencing Technology

Thursday, May 16, 13

Illumina (Solexa) Sequencing

Thursday, May 16, 13

Illumina paired-end and index-read sequencing

Thursday, May 16, 13

Once sequenced the problembecomes computational

Computational analyses is the bottleneck• Rapid improvement in sequencing• Still need for customized analysis for most projects

Thursday, May 16, 13

Overview of computational analyses

Image analysisBase calling

Primary Analyses: Mapping(Assembly)

Data typespeci!c analyses(e.g. peak calling,

calculate expression)

Custom projectspeci!c analyses

ChIP-Seq peak calling

RNA-Seq expression levelsgenome sequence

assembled contig

Thursday, May 16, 13

Preliminary Analyses

Raw Image (TB)

Platform-specific analysis using the vendors programs

Sequences and Quality scoresText File (GB)

Real Time Analysis

Thursday, May 16, 13

Sequenced reads

>EAS54_6_R1_2_1_413_324

CCCTTCTTGTCTTCAGCGTTTCTCCFasta file:

Fastq file:

SOLiD

csfasta file>1_39_146_F3T22100200202311030112002022222002021>1_39_194_F3T11022322003020303320012223122202221

SOLiD, QV file>1_39_146_F314 6 21 27 5 18 6 15 22 27 18 17 14 18 26 15 24 19 18 18 8 20 17 12 20 6 14 13 23 6 11 12 7 13 4 >1_39_194_F326 27 16 27 23 22 23 25 22 10 5 21 4 17 20 26 26 17 25 27 23 25 14 24 26 4 4 4 4 4 4 4 4 4 14

GAACTCTGCCTTTTTCAGTGATGAGGAAAGGAGTTCTCTCTGGTCCCCAG

aaab^_U_aa [U [ _Z ] a `WU_^X`GT^_ \ TM^ ^ \ ___ \ Z \ YQVVXUBBBB

Read identifier

Quality scores

@HWI - EAS269:1:120:1786:18#0/1

+HWI - EAS269:1:120:1786:18#0/1

Thursday, May 16, 13

Phred Quality Score, Q

Each base call has an estimate of the probability of being wrong (error probability, p)

Q = -10 * log10(p)

Phred Quality Score Probability of incorrect base call Base call accuracy10 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 %

Thursday, May 16, 13

FastQ encodings

Thursday, May 16, 13

Fastq quality control (FastQC)

http://www.youtube.com/watch?v=bz93ReOv87YVideo tutorial:

Thursday, May 16, 13

Quality scores for each sequence position

Thursday, May 16, 13

Quality scores for each sequence position:A good run

Thursday, May 16, 13

GC for reads

Thursday, May 16, 13

Percent A,C,G,T at each position

Thursday, May 16, 13

Relative enrichment of kmers

Thursday, May 16, 13

Overview of computational analyses

Image analysisBase calling

Primary Analyses: MappingAssembly

Data typespeci!c analyses(e.g. peak calling,

calculate expression)

Custom projectspeci!c analyses

ChIP-Seq peak calling

RNA-Seq expression levelsgenome sequence

assembled contig

Thursday, May 16, 13

Short Read Assembly

Velvet and SOAPdenovode novo genomic assembler specially designed for short read sequencing technologies

Nature 2009

Thursday, May 16, 13

Two principal approaches for transcriptome reconstruction

Thursday, May 16, 13

Genome-independent transcriptome reconstruction

Garbherr et al. Nature Biotechnology, July 2011

Default k = 25

Thursday, May 16, 13

Finding novel non-annotated genes or transcript variants

Thursday, May 16, 13

Mapping of millions of short reads

Task: Map millions of short sequences (25-100 nt) onto a genome (3 000 Mbp ) or transcriptome

Mismatches (sequencing errors and SNPs)

Unique / Repetitive matches

Indels (Normal variation, CNVs)

Large rearrangements (translocations)

BLAST, BLAT tools not designed for these tasks

Thursday, May 16, 13

Mapping of RNA-Seq reads

Garber et al. 2011 Nat Methods

STAR

Thursday, May 16, 13

Genome Chromosome Fasta Files

+

Known and putative splice junctions Fasta File

2. map reads towardsgenome + junction compilation

GTAAGT-----------AG Exon n+1

1. compile sets of junctions

Exon n

Mapping of splice junctions

Thursday, May 16, 13

Tophat !rst MethodIdentifying the transcriptome

A B C identify candidate exons

via genomic mapping

A B C A B C Generate possible

pairings of exons

Align “unmappable”

reads to possible junctions

A B C A B C

Thursday, May 16, 13

Longer readsLonger reads

GATGTTCTCAGTGTCC GATGTAATCAGTGTCC AACCCTCTCAGTGTCC

>HWI-EAS229_75_30DY0AAXX:7:1:0:949

Very long (100Kb+) intron

By segmenting the long reads, and mapping the segments independently, we can

look harder for junctions we might have missed with shorter reads

Running time

independent of

intron size

Thursday, May 16, 13

Mapping to transcriptomeExons 5’UTR 3’UTRIntronsGene:

DNA (genome)W

C

pre-mRNA

Transcription

AAAAA

RNA processing (splicing, polyadenylation)

mRNA AAAAA

Exons 5’UTR 3’UTRIntronsGene:

DNA (genome)W

C

Thursday, May 16, 13

Microexons and junction coverage

Exons 5’UTR 3’UTRIntronsGene:

DNA (genome)W

C

2 or more splice junctions within the same read

in-house mapping tophat mapping

Thursday, May 16, 13

Microexons and junction coverage

Exons 5’UTR 3’UTRIntronsGene:

DNA (genome)W

C

2 or more splice junctions within the same read

in-house mapping tophat mapping

Different read length will have different problems!Thursday, May 16, 13

Mapping'speed 308'M'reads'/'hour%'uniquely'mapping 60%'multimapping 25%'unmapped 15

Example of STAR aligned single-cell RNA-Seq data

281 719 splice junctions279 356 with GT/AG 2 123 with GC/AG 215 with AT/AC

Thursday, May 16, 13

Storing mapped Alignments

Formats for storing alignments should include:

genomic coordinates

mismatches, insertion, deletions etc.

quality information

Thursday, May 16, 13

Samtools

Sequence Alignment Map (SAM)

Generic Alignment format

Supports long and short reads

Human readable, "exible and compact

Emerging standard

h"p://samtools.sourceforge.net/

Li  H.*,  Handsaker  B.*,  Wysoker  A.,  Fennell  T.,  Ruan  J.,  Homer  N.,  Marth  G.,  Abecasis  G.,  Durbin  R.  and  1000  Genome  Project  Data  Processing  Subgroup  (2009)  The  Sequence  alignment/map  (SAM)  format  and  SAMtools.  BioinformaScs,  25,  2078-­‐9.  [PMID:  19505943]

Thursday, May 16, 13

SAM Example

16 chr Y 616000 255 22M731N28M

* 0 0 ATTTCGACCATGATCATCGAACCTTCCCCTGGATCCACTTCCACGATCAC

#9 ; -7 +2@4 : 2=20 - 14= : ><?< ; : BB? : 4<BB?ABBBBABCBBBBC=BB NM: i : 0

XS: A:-

Bit field, where 16

means reverse strand

Start position

Alignment structure. Here: 22 aligned bases,

then 731 bases intron, then 28 aligned bases

HWI - EAS269:1:114:1242:1582#0

Thursday, May 16, 13

CIGAR Format

M, match/mismatch

I, insertion

D, deletion

S, softclip

...

Ref: GCATTCAGATGCAGTACGC

Read: ccTCAG--GCAGTAgtg

Pos: 5

CIGAR: 2S4M3D6M3S

50M

Thursday, May 16, 13

Samtools for SAM/BAM !les

Library and software package (C, Java)

Creating, sorting, indexing SAM & BAM

Visualizing alignments in command

SNP calling

Short indel detection

BAM (Binary representation of SAM) ~25% #le size reduction

Thursday, May 16, 13

Read mapping statistics

e.g. using RSeQC (package)

GC content (%)

Den

sity

of R

eads

0 20 40 60 80 100

0.00

0.01

0.02

0.03

0.04

0.05

0.06

0.07

●

● ●

●

●

●

●

●

●

●

●

●

●●

●

● ●

●●

● ● ● ●●

●●

●●

●●

●● ●

●

●

● ● ●● ●

●●

●

0 10 20 30 40

0.15

0.20

0.25

0.30

0.35

0.40

0.45

Position of Read

Nuc

leot

ide

Freq

uenc

y

●

●

●

●●

●

●

●

●

●

●

●

●

●

●

●

●

● ● ● ●●

●●

● ●●

●●

●● ● ● ● ●

● ● ● ●●

●●

●

●

●

●

●

●

●

●●

●

●

●

●

●

●

●●

● ●

●●

●●

●●

●● ●

● ● ● ●●

● ● ● ● ● ● ● ● ●● ●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

● ●●

●● ● ● ● ●

●●

● ●● ● ●

● ●● ●

●

● ● ●●

●

●●

●

●

●

●

●

ATGC

Thursday, May 16, 13

Read mapping statistics:Read mapping across genes

0 20 40 60 80 100

2000

4000

6000

8000

1000

0

percentile of gene body (5'−>3')

read

num

ber

Thursday, May 16, 13

Read mapping statistics

partial_novel 2%

complete_novel 9%

known 89%

splicing junctions

Thursday, May 16, 13

Read mapping statistics: duplicate and unique reads

0 100 200 300 400 500

Frequency

Num

ber o

f Rea

ds (l

og10

)●

●

●

●

●

●

●

●

●●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●●

●

●

●

●

●●

●

●

●

●●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●●

●

●

●

●

●

●

●

●

●

●●

●

●

●

●

●

●

●

●

●

●

●●

●

●

●

●

●

●

●

●

●●

●●

●

●

●●

●●

●●

●

●

●

●

●●

●●●

●

●●

●●

●

●●●●

●

●

●

●

●●●

●●

●

●

●

●

●

●

●

●

●

●

●●● ●

●●●●

●

●

●●●

●

●● ●●

●

●●●● ●●

●

●●●●●

●

●●●● ● ●

●

● ●● ●● ●

●

● ● ● ● ●

●

●

●

● ●● ●● ●

●

Sequence−baseMapping−base

01

23

45

23

983

Rea

ds %

Thursday, May 16, 13

Read mapping statistics: q values on mapped reads

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43

Position of Read

313233343536373839404142434445464748495051525354555657585960616263646566676869707172

Phre

d Q

ualit

y Sc

ore

Thursday, May 16, 13

Overview of computational analyses

Image analysisBase calling

Primary Analyses: MappingAssembly

Data typespeci!c analyses(e.g. peak calling,

calculate expression)

Custom projectspeci!c analyses

ChIP-Seq peak calling

RNA-Seq expression levelsgenome sequence

assembled contig

Thursday, May 16, 13

Visualization

Integrated Genome Viewer (Broad Inst.)

Custom tracks at UCSC Genome Browser

Thursday, May 16, 13

Peak characteristics differ with signal

Thursday, May 16, 13

Peak characteristics differ with signal

H3K4me3: Sharp promoter peaksH3K36me3: Broad transcription elongation signal

Thursday, May 16, 13

Important !le formats

Sequences: FastQ

Aligned reads: SAM/BAM

Genome annotations: Bed, Gff

Coverage: Wig, (Tdf )

http://genome.ucsc.edu/FAQ/FAQformat.html

Thursday, May 16, 13

BED format

chrom  -­‐  The  name  of  the  chromosome  (e.g.  chr3,  chrY,  chr2_random)  or  scaffold  (e.g.  scaffold10671).

chromStart  -­‐  The  starSng  posiSon  of  the  feature  in  the  chromosome  or  scaffold.  The  first  base  in  a  chromosome  is  numbered  0.

chromEnd  -­‐  The  ending  posiSon  of  the  feature  in  the  chromosome  or  scaffold.  The  chromEnd  base  is  not  included  in  the  display  of  the  feature.  

For  example,  the  first  100  bases  of  a  chromosome  are  defined  as  chromStart=0,  chromEnd=100,  and  span  the  bases  numbered  0-­‐99.

http://genome.ucsc.edu/FAQ/FAQformat.html

track name=pairedReads description="Clone Paired Reads" useScore=1chr22 1000 5000

Thursday, May 16, 13

BED continued

strand - Defines the strand - either '+' or '-'.thickStart - The starting position at which the feature is drawn thickly (for example, the start codon in gene displays).thickEnd - The ending position at which the feature is drawn thickly (for example, the stop codon in gene displays).itemRgb - An RGB value of the form R,G,B (e.g. 255,0,0). If the track line itemRgb attribute is set to "On", this RBG value will determine the display color of the data contained in this BED line. NOTE: It is recommended that a simple color scheme (eight colors or less) be used with this attribute to avoid overwhelming the color resources of the Genome Browser and your Internet browser.blockCount - The number of blocks (exons) in the BED line.blockSizes - A comma-separated list of the block sizes. The number of items in this list should correspond to blockCount.blockStarts - A comma-separated list of block starts. All of the blockStart positions should be calculated relative to chromStart. The number of items in this list should correspond to blockCount.

track name=pairedReads description="Clone Paired Reads" useScore=1chr22 2000 6000 cloneB 900 - 2000 6000 0 2 433,399, 0,3601

Thursday, May 16, 13

Variable step Fixed step

variableStep chrom=chr2300701 12.5300702 12.5300703 12.5300704 12.5300705 12.5is equivalent to:variableStep chrom=chr2 span=5300701 12.5

fixedStep chrom=chr3 start=400601 step=100112233

WIG format (coverage format)

Wiggle format (WIG) allows the display of continuous-valued data in a track format

Thursday, May 16, 13

Data Repositories

Short Read Archive (fastq) [discontinued!]http://www.ncbi.nlm.nih.gov/sraEuropean Nucleotide Archive

Gene Expression Omnibus (bed, wig, fastq)http://www.ncbi.nlm.nih.gov/geo/

Thursday, May 16, 13

SEQAnswers, an active forum for discussions on next-generation sequencing methods and bioinformatics

http://seqanswers.com/Thursday, May 16, 13

Thursday, May 16, 13

Garbherr et al. Nature Biotechnology, July 2011

Genome-independent transcriptome reconstruction: accuracy and coverage

Thursday, May 16, 13

Genome-independent transcriptome reconstruction: accuracy and coverage

Garbherr et al. Nature Biotechnology, July 2011

Thursday, May 16, 13