Workshop NGS data analysis - 3

Sequencing data analysisWorkshop – part 3 / peak calling and annotation

Outline

Previously in this workshop…

Peak calling and annotation – the steps

Peak calling and annotation – the workshop

Maté Ongenaert

Previously in this workshop…Introduction – the real cost of sequencing

Data analysis

Raw machine reads… What’s next?

Preprocessing (machine/technology)- adaptors, indexes, conversions,…- machine/technology dependent

Reads with associated qualities (universal)- FASTQ

- QC check

Depending on application (general applicable)- ‘de novo’ assembly of genome (bacterial genomes,…)

- Mapping to a reference genome mapped reads- SAM/BAM/…

High-level analysis (specific for application)- SNP calling- Peak calling

Previously in this workshop…The workflow of NGS data analysis

Previously in this workshop…The workflow of NGS data analysis

Raw sequence reads:

- Represent the sequence ~ FASTA

- Extension: represent the quality, per base ~ FASTQ – Q for qualityScore ~ phred ~ ASCII table ~ phred + 33 = Sanger

@SEQUENCE_IDENTIFIER GATTTGGGGTTCAAAGCAGTATCGATCAAATAGTAAATCCATTTGTTCAACTCACAGTTT + !''*((((***+))%%%++)(%%%%).1***-+*''))**55CCF>>>>>>CCCCCCC65

>SEQUENCE_IDENTIFIER GATTTGGGGTTCAAAGCAGTATCGATCAAATAGTAAATCCATTTGTTCAACTCACAGTTT

Previously in this workshop…Main data formats

- Machine and platform independent and compressed: SRA (NCBI)Get the original FASTQ file using SRATools (NCBI)


- Now moving to a common file format SAM / BAM (Sequence Alignment/Map)- BAM: binary (read: computer-readable, indexed, compressed) ‘form’ of SAM

DESCRIPTION OF THE 11 FIELDS IN THE ALIGNMENT SECTION # QNAME: template name #FLAG #RNAME: reference name # POS: mapping position #MAPQ: mapping quality #CIGAR: CIGAR string #RNEXT: reference name of the mate/next fragment #PNEXT: position of the mate/next fragment #TLEN: observed template length #SEQ: fragment sequence #QUAL: ASCII of Phred-scale base quality+33 #Headers @HD VN:1.3 SO:coordinate @SQ SN:ref LN:45 #Alignment block r001 163 ref 7 30 8M2I4M1D3M = 37 39 TTAGATAAAGGATACTG * r002 0 ref 9 30 3S6M1P1I4M * 0 0 AAAAGATAAGGATA * r003 0 ref 9 30 5H6M * 0 0 AGCTAA * NM:i:1 r004 0 ref 16 30 6M14N5M * 0 0 ATAGCTTCAGC *

- BED files (location / annotation / scores): Browser Extensible DataUsed for mapping / annotation / peak locations / - extension: bigBED (binary)

FIELDS USED: # chr # start # end # name # score # strand track name=pairedReads description="Clone Paired Reads" useScore=1 #chr start end name score strand chr22 1000 5000 cloneA 960 + chr22 2000 6000 cloneB 900 –

- BEDGraph files (location, combined with score)Used to represent peak scores

track type=bedGraph name="BedGraph Format" description="BedGraph format" visibility=full color=200,100,0 altColor=0,100,200 priority=20 #chr start end score chr19 59302000 59302300 -1.0 chr19 59302300 59302600 -0.75 chr19 59302600 59302900 -0.50


- WIG files (location / annotation / scores): wiggleUsed for visulization or summarize data, in most cases count data or normalized count data (RPKM) – extension: BigWig – binary versions (often used in GEO for ChIP-seq peaks)

browser position chr19:59304200-59310700 browser hide all #150 base wide bar graph at arbitrarily spaced positions, #threshold line drawn at y=11.76 #autoScale off viewing range set to [0:25] #priority = 10 positions this as the first graph track type=wiggle_0 name="variableStep" description="variableStep format" visibility=full autoScale=off viewLimits=0.0:25.0 color=50,150,255 yLineMark=11.76 yLineOnOff=on priority=10 variableStep chrom=chr19 span=150 59304701 10.0 59304901 12.5 59305401 15.0 59305601 17.5 59305901 20.0 59306081 17.5


- GFF format (General Feature Format) or GTFUsed for annotation of genetic / genomic features – such as all coding genes in EnsemblOften used in downstream analysis to assign annotation to regions / peaks / …

FIELDS USED: # seqname (the name of the sequence) # source (the program that generated this feature) # feature (the name of this type of feature – for example: exon) # start (the starting position of the feature in the sequence) # end (the ending position of the feature) # score (a score between 0 and 1000) # strand (valid entries include '+', '-', or '.') # frame (if the feature is a coding exon, frame should be a number between 0-2 that represents the reading frame of the first base. If the feature is not a coding exon, the value should be '.'.) # group (all lines with the same group are linked together into a single item) track name=regulatory description="TeleGene(tm) Regulatory Regions" #chr source feature start end scores tr fr group chr22 TeleGene enhancer 1000000 1001000 500 + . touch1 chr22 TeleGene promoter 1010000 1010100 900 + . touch1 chr22 TeleGene promoter 1020000 1020000 800 - . touch2


Peak calling:

Identify genomic regions where the number of sequenced reads (coverage) of the IP-sample is higher than can be estimated from the input (control) samples >> enriched regions >> possibly captured by the IP & thus sequenced with more coverage

Peak annotation:

When such enriched regions are identified, where are they located (intron/exon/…) ? What is the closest gene or the closest promoter region?

Peak callingThe workflow

Peak calling:

Coverage

From the BAM file: mapping against the reference genomeBoth the IP-sample and the control (Input) must be mapped, duplicates will be ignored by most peak callers

Peak caller will determine coverage for both samples- Store them for visualisation (WIG files; BIGWIG files or similar)

Enriched

Find out which regions are enriched (or within the sample or versus a control (Input) sample statistics ~ model of tag distributions and normalisation strategy


Peak calling:

Enriched

Find out which regions are enriched (or within the sample or versus a control (Input) sample statistics ~ model of tag distributions and normalisation strategy


Density profiles Peak assignment Control data adjustment Significance relative to control

data Statistical model / test

Program Reference Window-

based Tag

clustering Gaussian

kernel Strand-specific

Peak height or FE

Bacground subtract

Genomic dupl/deletions FDR

Normalized control

Statistical model on

control

Conditional binomial

Local poisson

Chromome poisson HMM T-test

Cisgenome [73] X X X X X X Minimal ChipSeq

Peak Finder [74] X X X

E-range [75] X X X X X MACS [76] X X X X X QuEST [77] X X X X X Hpeak [78] X X X X

Sole-Search [79] X X X X X

PeakSeq [80] X X X X SISSRS [81] X X X

spp package [82] X X X X X


Usage: macs14 <-t tfile> [-n name] [-g genomesize] [options]

Example: macs14 -t ChIP.bam -c Control.bam -f BAM -g h -n test -w --call-subpeaks

macs14 -- Model-based Analysis for ChIP-Sequencing

Options: --version show program's version number and exit -h, --help show this help message and exit. -t TFILE, --treatment=TFILE ChIP-seq treatment files. REQUIRED. When ELANDMULTIPET is selected, you must provide two files separated by comma, e.g. s_1_1_eland_multi.txt,s_1_2_eland_multi.txt -c CFILE, --control=CFILE Control files. When ELANDMULTIPET is selected, you must provide two files separated by comma, e.g. s_2_1_eland_multi.txt,s_2_2_eland_multi.txt -n NAME, --name=NAME Experiment name, which will be used to generate output file names. DEFAULT: "NA" -f FORMAT, --format=FORMAT Format of tag file, "AUTO", "BED" or "ELAND" or "ELANDMULTI" or "ELANDMULTIPET" or "ELANDEXPORT" or "SAM" or "BAM" or "BOWTIE". The default AUTO option will let MACS decide which format the file is. Please check the definition in 00README file if you choose EL AND/ELANDMULTI/ELANDMULTIPET/ELANDEXPORT/SAM/BAM/BOWTI E. DEFAULT: "AUTO"

Peak annotation

Enriched

Peak locations > in which features is my peak located; is it close to a gene; provide me some statistics on how far my peaks are from annotated TSSes

R/BioConductorChipPeakAnno package

PeakAnalyzer



Outline




Maté Ongenaert

Further downstream processingPeak overlaps


Is this observed overlap larger than one can expect if the datasets were random?

Peak caller gives each peak a score

Randomy distribute this score accross the peaks of the same peakset (factor) and, for a percentage of top-peaks, calculate overlapping peaks in real dataset and with random distributed scores

Further downstream processingIdentify sequence motifs (region around ‘peak’, searched for motifs)


Further downstream processingIdentify differentially bound regions between conditions/factors/…

Further downstream processingPeak overlaps


Real 10% 15% 20% 30% 50% 75%

7 18 25 52 102 201

Means 0,347 1,153 2,699 9,297 42,377 140,888

Factor diff 20,17291066 15,6114484 9,262689885 5,593202108 2,406966043 1,426665152

FDR 10% 15% 20% 30% 50% 75%

0 0 0 0 0 0

10% 10% 15% 20% 30% 50% 75%

282 333 506 907 1000 1000

20% 10% 15% 20% 30% 50% 75%

59 33 125 332 1000 1000

30% 10% 15% 20% 30% 50% 75%

4 2 9 27 981 1000

50% 10% 15% 20% 30% 50% 75%

2 0 0 0 95 1000

75% 10% 15% 20% 30% 50% 75%

0 0 0 0 0 148


Outline




Maté Ongenaert

Blokde Van…

ETER

Workshop NGS data analysis - 3

Education

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