comparing methods for identifying transcription factor target genes
DESCRIPTION
Comparing Methods for Identifying Transcription Factor Target Genes. Alena van Bömmel (R 3.3.73) Matthew Huska (R 3.3.18) Max Planck Institute for Molecular Genetics. Transcriptional Regulation. TF not bound = no gene expression. TF bound = gene expression. - PowerPoint PPT PresentationTRANSCRIPT
Max-Planck-Institut für molekulare Genetik
Software Praktikum, 1.2.2013 Folie 1
Comparing Methods for Identifying Transcription Factor Target Genes
Alena van Bömmel (R 3.3.73) Matthew Huska (R 3.3.18)Max Planck Institute for Molecular Genetics
Max-Planck-Institut für molekulare Genetik
Software Praktikum, 1.2.2013
Transcriptional Regulation
TF not bound = no gene expression
TF bound =gene expression
Max-Planck-Institut für molekulare Genetik
Software Praktikum, 1.2.2013
Transcriptional Regulation
TF not bound = no gene expression
TF bound =gene expression
Problem: There are many genes and many TF's, how do we identify the targets of a TF?
Max-Planck-Institut für molekulare Genetik
Software Praktikum, 1.2.2013
Methods for Identifying TF Target Genes
Microarray
PWM Genome Scan
ChIP-seq
Max-Planck-Institut für molekulare Genetik
Software Praktikum, 1.2.2013
PWM Genome Scan
• Purely computational method• Input:
o position weight matrix for your TFo genomic region(s) of interest
• Pros:o No need to do wet lab experiments
• Cons:o Many false positives, not able to take biological conditions into account
Score threshold
Max-Planck-Institut für molekulare Genetik
Software Praktikum, 1.2.2013
PWM genome scan
Folie 6
1) Download the PWMs of your TF of interest from the database (they might include >1 motif)
2) Define the sequences to analyze (promoter sequences)
3) Run the PWM genome scan (hit-based method or affinity prediction method)
4) Rank the genomic sequences by the affinity signal
Suggested Reading:• Roider et al.: Predicting transcription factor
affinities to DNA from a biophysical model. Bioinformatics (2007).
• Thomas-Chollier et al. Transcription factor binding predictions using TRAP for the analysis of ChIP-seq data and regulatory SNPs. Nature Protocols (2011).
Max-Planck-Institut für molekulare Genetik
Software Praktikum, 1.2.2013
PWM-PSCM
Folie 7
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TRAP
Folie 8
1) Convert the PSSM(position specific scoring matrix) to PSEM (position specific energy matrix)
2) Scan the sequences of interest with TRAP
3) Results in 1 score per sequence=binding affinity
4) Doesn’t separate the exact TF binding sites (easier for ranking)
5) Sequences must have the same length!
ANNOTATE=/project/gbrowse/Pipeline/ANNOTATE_v3.02/ReleaseTRAP trap.molgen.mpg.de/cgi-bin/home.cgi
Max-Planck-Institut für molekulare Genetik
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Matrix-scan
Folie 9
1) Use directly the PSSM2) Finds all TFBS which exceed a predefined threshold (e.g. p-value)3) More complicated to create ranked lists of genomic sequences (more
hits in the sequence)4) Exact location of the binding site reported
matrix-scan http://rsat.ulb.ac.be/
Max-Planck-Institut für molekulare Genetik
Software Praktikum, 1.2.2013
Finding the target genes
Folie 10
• target genes will be the top-ranked genes (promoters)
• which are the top-ranked genes? (top-100,500,1000...?)
• There’s no exact definition of promoters, usually 2000bp upstream, 500bp downstream of the TSS
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Microarrays
→ R/Bioconductor (details later)
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Folie 12
Microarrays (2)
• Pros:o There is a lot of microarray data already available (might not have to
generate the data yourself)o Inexpensive and not very difficult to performo Computational workflow is well established
• Cons:o Can not distinguish between indirect regulation and direct regulation
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ChIP-seqMap reads to the genome
Call peaks to determine most likely TF binding locations
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Folie 14
ChIP-seq (2)
• Pros:o Direct measure of genome-wide protein-DNA interaction(*)
• Cons:o Don't know whether binding causes changes in gene expressiono More complicated experimentally and in terms of computational
analysiso Most expensiveo Need an antibody against your protein of interesto Biases are not as well understood as with microarrays
Max-Planck-Institut für molekulare Genetik
Software Praktikum, 1.2.2013
ChIP-seq analysis
Folie 15
1) Download the reads from given source (experiments and controls)
2) Quality control of the reads and statistics (fastqc)
3) Mapping the reads to the reference genome (bwa/Bowtie)
4) Peak calling (MACS)5) Visualization of the peaks in a
genome browser (genome browser, IGV)
6) Finding the closest genes to the peaks(Bioconductor/ChIPpeakAnno)
Visualised peaks in a genome browser
Suggested Reading:• Bailey et alPractical Guidelines for
the Comprehensive Analysis of ChIP-seq Data. PLoS Comput Biol (2013).
• Thomas-Chollier et al. A complete workflow for the analysis of full-size ChIP-seq (and similar) data sets using peak-motifs. Nature Protocols (2012).
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Sequencing data
Folie 16
Analysis1) Quality control with fastqc 2) Filtering of reads with adapter
sequences3) Mapping of the reads to the
reference genome (bwa or Bowtie) Example of fastq data file
• raw data=reads usually very large file (few GB)
• format fastq (ENCODE) or SRA (Sequence Read Archive of NCBI)
Max-Planck-Institut für molekulare Genetik
Software Praktikum, 1.2.2013
Quality control with fastqc• per base quality• sequence quality (avg. > 20)• sequence length• sequence duplication level
(duplication by PCR)• overrepresented
sequences/kmers (adapter sequences)
• produces a html report• manual (read it!)
• software at the MPI
Folie 17
Example of per base seq quality scores
FASTQC=/scratch/ngsvin/bin/chip-seq/fastqc/FastQC/fastqc
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Mapping with bwa• mapping the sequencing reads to a reference genome• manual (read it!)• map the experiments and the controls1) reference genome in fasta format (hg19)2) create an index of the reference file for faster mapping (only if not
available)3) align the reads (specify parameters e.g. for # of mismatches, read
trimming, threads used...)4) generate alignments in the SAM format (different commands for
single-end and pair-end reads!)
software and data at the MPI: BWA = /scratch/ngsvin/bin/executables/bwa
hg19: /scratch/ngsvin/MappingIndices/hg19.fabwa index: /scratch/ngsvin/MappingIndices/BWA/hg19
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File manipulation with samtools
• utilities that manipulate SAM/BAM files• manual (read it!)1) merge the replicates in one file (still separate experiment and control)2) convert the SAM file into BAM file (binary version of SAM, smaller)3) sort and index the BAM file
now the sequencing files are ready for further analysis
software at the MPI: SAMTOOLS = /scratch/ngsvin/bin/executables/samtools
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Peak finding with MACS
• find the peaks, i.e. the regions with a high density of reads, where the studied TF was bound
• manual (read it!)1) call the peaks using the experiment (treatment) data vs. control2) set the parameters e.g. fragment length, treatment of duplication reads3) analyse the MACS results (BED file with peaks/summits)
software at the MPI:
MACS = /scratch/ngsvin/bin/executables/macs
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Software Praktikum, 1.2.2013 Folie 21
Finding the target genes• find the genes which are in the closest distance to the
(significant) peaks • how to define the closest distance? (+- X kb)• use ChIPpeakAnno in Bioconductor or bedtools
Max-Planck-Institut für molekulare Genetik
Software Praktikum, 1.2.2013
Methods for Identifying TF Target Genes
Microarray
PWM Genome Scan
ChIP-seq Thresholds
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Bioinformatics
• Read mapping (Bowtie/bwa)
• Peak Calling (MACS/Bioconductor)
• Peak-Target Analysis (Bioconductor)
Folie 23
• Microarray data analysis (Bioconductor)
• Differential Genes (R)
• GSEA
• PWM Genome Scan (TRAP/MatScan)
• Statistics (R)
• Data Integration (R/Python/Perl)
• Statistical Analysis (R)
Max-Planck-Institut für molekulare Genetik
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Bioinformatics tools
• Bowtie bowtie-bio.sourceforge.net/manual.shtml• bwa bio-bwa.sourceforge.net/bwa.shtml• MACS github.com/taoliu/MACS/blob/macs_v1/README.rst• TRAP trap.molgen.mpg.de/cgi-bin/home.cgi• matrix-scan http://rsat.ulb.ac.be/• Bioconductor www.bioconductor.org/ (more info in R course)
Folie 24
READ THE MANUALS!
Databases• GEO www.ncbi.nlm.nih.gov/geo/• ENCODE genome.ucsc.edu/ENCODE/• SRA www.ncbi.nlm.nih.gov/sra• JASPAR http://jaspar.genereg.net/
Max-Planck-Institut für molekulare Genetik
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Schedule
• 03.03. Introduction lecture, R course • 04.03. R & Bioconductor homework submission• 11.03. Presentation of the detailed plan of each group
(which TF, cell line, tools, data, data integration, team work ) 10:30am, 11:30am
• every Tuesday 10:30am, 11:30am progress meetings• 17.04. Final report deadline• 24.04. (tentative) Presentations• 28.04. Final meeting, discussion of final reports
Folie 25
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GR Group
• Expression and ChIP-seq data: Luca F, Maranville JC, et al., PLoS ONE, 2013
• PWM database: jaspar.genereg.net
Folie 26
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c-Myc Group
• Expression data: Cappellen, Schlange, Bauer et al., EMBO reports, 2007
• Musgrove et al., PLoS One, 2008• ChIP-seq data: ENCODE Project • PWM database: jaspar.genereg.net
Folie 27
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Additional analysisBinding motifs• are the overrepresented
motifs in the ChIP-peak regions different?
• do we find any co-factors?
Recommended tool: RSAT rsat.ulb.ac.be
binding motifs
binding motifsbinding motifs