Comparing Methods for Identifying Transcription Factor Target Genes

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

2. Transcriptional Regulation TF notbound = no gene expression TF bound = gene expression

3. Transcriptional Regulation TF notbound = 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?

4. Methods for Identifying TF Target Genes PWM Genome Scan Microarray ChIP-seq

5. PWM Genome Scan • Purely computational method • Input: • position weight matrix for your TF • genomic region(s) of interest • Pros: • No need to do wet lab experiments • Cons: • Many false positives, not able to take biological conditions into account Score threshold

6. PWM genome scan • Download the PWMs of your TF of interest from the database (they might include >1 motif) • Define the sequences to analyze (promoter sequences) • Run the PWM genome scan (hit-based method or affinity prediction method) • 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).

7. PWM-PSCM

8. TRAP • Convert the PSSM(position specific scoring matrix) to PSEM (position specific energy matrix) • Scan the sequences of interest with TRAP • Results in 1 score per sequence=binding affinity • Doesn’t separate the exact TF binding sites (easier for ranking) • Sequences must have the same length! ANNOTATE=/project/gbrowse/Pipeline/ANNOTATE_v3.02/Release TRAP trap.molgen.mpg.de/cgi-bin/home.cgi

9. Matrix-scan • Use directly the PSSM • Finds all TFBS which exceed a predefined threshold (e.g. p-value) • More complicated to create ranked lists of genomic sequences (more hits in the sequence) • Exact location of the binding site reported matrix-scan http://rsat.ulb.ac.be/

10. Finding the target genes • 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

11. Microarrays → R/Bioconductor (details later)

12. Folie 12 Microarrays (2) • Pros: • There is a lot of microarray data already available (might not have to generate the data yourself) • Inexpensive and not very difficult to perform • Computational workflow is well established • Cons: • Can not distinguish between indirect regulation and direct regulation

13. ChIP-seq Map reads to the genome Call peaks to determine most likely TF binding locations

14. Folie 14 ChIP-seq (2) • Pros: • Direct measure of genome-wide protein-DNA interaction(*) • Cons: • Don't know whether binding causes changes in gene expression • More complicated experimentally and in terms of computational analysis • Most expensive • Need an antibody against your protein of interest • Biases are not as well understood as with microarrays

15. ChIP-seq analysis • Download the reads from given source (experiments and controls) • Quality control of the reads and statistics (fastqc) • Mapping the reads to the reference genome (bwa/Bowtie) • Peak calling (MACS) • Visualization of the peaks in a genome browser (genome browser, IGV) • Finding the closest genes to the peaks(Bioconductor/ChIPpeakAnno) Visualised peaks in a genome browser • Suggested Reading: • Bailey et alPracticalGuidelines for the Comprehensive Analysis of ChIP-seq Data.PLoSComputBiol(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).

16. Sequencing data • raw data=reads usually very large file (few GB) • formatfastq (ENCODE) or SRA (Sequence Read Archive of NCBI) Analysis • Quality control with fastqc • Filteringof reads with adapter sequences • Mapping of the reads to the reference genome (bwa or Bowtie) Example of fastq data file

17. 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 Example of per base seq quality scores FASTQC=/scratch/ngsvin/bin/chip-seq/fastqc/FastQC/fastqc

18. Mapping with bwa • mapping the sequencing reads to a reference genome • manual(read it!) • map the experiments and the controls • reference genome in fasta format (hg19) • create an index of the reference file for faster mapping (only if not available) • align the reads (specify parameters e.g. for # of mismatches, read trimming, threads used...) • 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.fa bwaindex: /scratch/ngsvin/MappingIndices/BWA/hg19

19. File manipulation with samtools • utilities that manipulate SAM/BAM files • manual (read it!) • merge the replicates in one file (still separate experiment and control) • convert the SAM file into BAM file (binary version of SAM, smaller) • 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

20. 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!) • call the peaks using the experiment (treatment) data vs. control • set the parameters e.g. fragment length, treatment of duplication reads • analyse the MACS results (BED file with peaks/summits) • software at the MPI: MACS = /scratch/ngsvin/bin/executables/macs

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

22. Methods for Identifying TF Target Genes PWM Genome Scan Microarray ChIP-seq Thresholds

23. Bioinformatics • Read mapping (Bowtie/bwa) • Peak Calling (MACS/Bioconductor) • Peak-Target Analysis (Bioconductor) • Microarray data analysis (Bioconductor) • Differential Genes (R) • GSEA • PWM Genome Scan (TRAP/MatScan) • Statistics (R) • Data Integration (R/Python/Perl) • Statistical Analysis (R)

24. Bioinformatics tools READ THE MANUALS! • Bowtiebowtie-bio.sourceforge.net/manual.shtml • bwabio-bwa.sourceforge.net/bwa.shtml • MACS github.com/taoliu/MACS/blob/macs_v1/README.rst • TRAPtrap.molgen.mpg.de/cgi-bin/home.cgi • matrix-scan http://rsat.ulb.ac.be/ • Bioconductorwww.bioconductor.org/(more info in R course) Databases • GEOwww.ncbi.nlm.nih.gov/geo/ • ENCODE genome.ucsc.edu/ENCODE/ • SRAwww.ncbi.nlm.nih.gov/sra • JASPAR http://jaspar.genereg.net/

25. 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

26. GR Group • Expression and ChIP-seqdata: Luca F, Maranville JC, et al., PLoS ONE, 2013 • PWM database: jaspar.genereg.net

27. 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

28. Additional analysis • Binding 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 motifs binding motifs