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


Illumina Sequencing Technology


Illumina (Solexa) Sequencing


Illumina paired-end and index-read sequencing


Once sequenced the problembecomes computational

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


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


Preliminary Analyses

Raw Image (TB)

Platform-specific analysis using the vendors programs

Sequences and Quality scoresText File (GB)

Real Time Analysis


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


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 %


FastQ encodings


Fastq quality control (FastQC)

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


http://www.youtube.com/watch?v=bz93ReOv87Y

http://www.youtube.com/watch?v=bz93ReOv87Y

Quality scores for each sequence position


Quality scores for each sequence position:A good run


GC for reads


Percent A,C,G,T at each position


Relative enrichment of kmers




Primary Analyses: MappingAssembly






assembled contig


Short Read Assembly

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

Nature 2009


Two principal approaches for transcriptome reconstruction


Genome-independent transcriptome reconstruction

Garbherr et al. Nature Biotechnology, July 2011

Default k = 25


Finding novel non-annotated genes or transcript variants


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


Mapping of RNA-Seq reads

Garber et al. 2011 Nat Methods

STAR


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


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


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


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


Microexons and junction coverage


DNA (genome)W

C

2 or more splice junctions within the same read

in-house mapping tophat mapping


Microexons and junction coverage


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


Storing mapped Alignments

Formats for storing alignments should include:

genomic coordinates

mismatches, insertion, deletions etc.

quality information


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]


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


CIGAR Format

M, match/mismatch

I, insertion

D, deletion

S, softclip

...

Ref: GCATTCAGATGCAGTACGC

Read: ccTCAG--GCAGTAgtg

Pos: 5

CIGAR: 2S4M3D6M3S

50M


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


Read mapping statistics

e.g. using RSeQC (package)

GC content (%)

Den

sity

of R

eads

0 20 40 60 80 100

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Position of Read

Nuc

leot

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Freq

uenc

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ATGC


Read mapping statistics:Read mapping across genes

0 20 40 60 80 100

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percentile of gene body (5'−>3')

read

num

ber


Read mapping statistics

partial_novel 2%

complete_novel 9%

known 89%

splicing junctions


Read mapping statistics: duplicate and unique reads

0 100 200 300 400 500

Frequency

Num

ber o

f Rea

ds (l

og10

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Sequence−baseMapping−base

01

23

45

23

983

Rea

ds %


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




Primary Analyses: MappingAssembly






assembled contig


Visualization

Integrated Genome Viewer (Broad Inst.)

Custom tracks at UCSC Genome Browser


Peak characteristics differ with signal


Peak characteristics differ with signal

H3K4me3: Sharp promoter peaksH3K36me3: Broad transcription elongation signal


Important !le formats

Sequences: FastQ

Aligned reads: SAM/BAM

Genome annotations: Bed, Gff

Coverage: Wig, (Tdf )

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




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.


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




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


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


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/


http://www.ncbi.nlm.nih.gov/geo/

http://www.ncbi.nlm.nih.gov/geo/

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

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

http://seqanswers.com

http://seqanswers.com


Genome-independent transcriptome reconstruction: accuracy and coverage


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