Max-Planck-Institut für molekulare Genetik Software Praktikum, Folie 1 Comparing Methods for Identifying Transcription Factor Target Genes Alena van Bömmel (R ) Matthew Huska (R ) Max Planck Institute for Molecular Genetics

Max-Planck-Institut für molekulare Genetik Software Praktikum, Transcriptional Regulation TF not bound = no gene expression TF bound = gene expression

Max-Planck-Institut für molekulare Genetik Software Praktikum, 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, Methods for Identifying TF Target Genes Microarray PWM Genome Scan ChIP-seq

Max-Planck-Institut für molekulare Genetik Software Praktikum, PWM Genome Scan Purely computational method Input: o position weight matrix for your TF o 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, PWM genome scan Folie 6 1) Download the PWMs of your TF of interest from the database (they might include >1 motif) 1) Define the sequences to analyze (promoter sequences) 1) Run the PWM genome scan (hit- based method or affinity prediction method) 1) 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). Roider et al. Thomas-Chollier et al. Transcription factor binding predictions using TRAP for the analysis of ChIP-seq data and regulatory SNPs. Nature Protocols (2011). Thomas-Chollier et al.

Max-Planck-Institut für molekulare Genetik Software Praktikum, PWM-PSCM Folie 7

Max-Planck-Institut für molekulare Genetik Software Praktikum, 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) Doesnt separate the exact TF binding sites (easier for ranking) 5) Sequences must have the same length! ANNOTATE= /project/gbrowse/Pipeline/ANNOTATE_v3.02/Release TRAP trap.molgen.mpg.de/cgi-bin/home.cgitrap.molgen.mpg.de/cgi-bin/home.cgi

Max-Planck-Institut für molekulare Genetik Software Praktikum, Matrix-scan Folie 9 1) Use directly the PSSM 2) 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

Max-Planck-Institut für molekulare Genetik Software Praktikum, Finding the target genes Folie 10 target genes will be the top-ranked genes (promoters) which are the top-ranked genes? (top-100,500, ?) Theres no exact definition of promoters, usually 2000bp upstream, 500bp downstream of the TSS

Max-Planck-Institut für molekulare Genetik Software Praktikum, Microarrays R/Bioconductor (details later)

Max-Planck-Institut für molekulare Genetik Software Praktikum, 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 perform o Computational workflow is well established Cons: o Can not distinguish between indirect regulation and direct regulation

Max-Planck-Institut für molekulare Genetik Software Praktikum, ChIP-seq Map reads to the genome Call peaks to determine most likely TF binding locations

Max-Planck-Institut für molekulare Genetik Software Praktikum, 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 expression o More complicated experimentally and in terms of computational analysis o Most expensive o Need an antibody against your protein of interest o Biases are not as well understood as with microarrays

Max-Planck-Institut für molekulare Genetik Software Praktikum, 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/ChIPp eakAnno) Visualised peaks in a genome browser Suggested Reading: Bailey et alPractical Guidelines for the Comprehensive Analysis of ChIP-seq Data. PLoS Comput Biol (2013).Practical Guidelines for the Comprehensive Analysis of ChIP-seq Data 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).A complete workflow for the analysis of full-size ChIP-seq (and similar) data sets using peak-motifs.

Max-Planck-Institut für molekulare Genetik Software Praktikum, Sequencing data Folie 16 Analysis 1) Quality control with fastqc 2) Filtering of reads with adapter sequences 3) 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)fastqSequence Read Archive

Max-Planck-Institut für molekulare Genetik Software Praktikum, 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!) manual software at the MPI Folie 17 Example of per base seq quality scores FASTQC= /scratch/ngsvin/bin/chip-seq/fastqc/FastQC/fastqc

Max-Planck-Institut für molekulare Genetik Software Praktikum, Folie 18 Mapping with bwa mapping the sequencing reads to a reference genome manual (read it!) manual map the experiments and the controls 1) 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!)SAM format software and data at the MPI: BWA = /scratch/ngsvin/bin/executables/bwa hg19 : /scratch/ngsvin/MappingIndices/hg19.fa bwa index: /scratch/ngsvin/MappingIndices/BWA/hg19

Max-Planck-Institut für molekulare Genetik Software Praktikum, Folie 19 File manipulation with samtools utilities that manipulate SAM/BAM files manual (read it!) manual 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

Max-Planck-Institut für molekulare Genetik Software Praktikum, Folie 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!) manual 1) call the peaks using the experiment (treatment) data vs. control 2) set the parameters e.g. fragment length, treatment of duplication reads 3) analyse the MACS results (BED file with peaks/summits) software at the MPI: MACS = /scratch/ngsvin/bin/executables/macs

Max-Planck-Institut für molekulare Genetik Software Praktikum, 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, Methods for Identifying TF Target Genes Microarray PWM Genome Scan ChIP-seq Threshold s

Max-Planck-Institut für molekulare Genetik Software Praktikum, Bioinformatics Read mapping (Bowtie/bwa) Peak Calling (MACS/Bioconduct or) 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 Software Praktikum, Bioinformatics tools Bowtie bowtie-bio.sourceforge.net/manual.shtmlbowtie-bio.sourceforge.net/manual.shtml bwa bio-bwa.sourceforge.net/bwa.shtmlbio-bwa.sourceforge.net/bwa.shtml MACS github.com/taoliu/MACS/blob/macs_v1/README.rstgithub.com/taoliu/MACS/blob/macs_v1/README.rst TRAP trap.molgen.mpg.de/cgi-bin/home.cgitrap.molgen.mpg.de/cgi-bin/home.cgi matrix-scan Bioconductor (more info in R course) Folie 24 READ THE MANUALS! Databases GEO ENCODE genome.ucsc.edu/ENCODE/genome.ucsc.edu/ENCODE/ SRA JASPAR

Max-Planck-Institut für molekulare Genetik Software Praktikum, Schedule Introduction lecture, R course R & Bioconductor homework submission 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 Final report deadline (tentative) Presentations Final meeting, discussion of final reports Folie 25

Max-Planck-Institut für molekulare Genetik Software Praktikum, GR Group Expression and ChIP-seq data: Luca F, Maranville JC, et al., PLoS ONE, 2013Luca F, Maranville JC, et al., PLoS ONE, 2013 PWM database: jaspar.genereg.netjaspar.genereg.net Folie 26

Max-Planck-Institut für molekulare Genetik Software Praktikum, c-Myc Group Expression data: Cappellen, Schlange, Bauer et al., EMBO reports, 2007Cappellen, Schlange, Bauer et al., EMBO reports, 2007 Musgrove et al., PLoS One, 2008 ChIP-seq data: ENCODE ProjectENCODE Project PWM database: jaspar.genereg.netjaspar.genereg.net Folie 27

Max-Planck-Institut für molekulare Genetik Software Praktikum, Folie 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.bersat.ulb.ac.be binding motifs