Day 5-2

1. Day 5-2 What bioinformatics tools can be used for analysing ChIP data? What bioinformatics tools can be used for analysing ChIP data?

2. After this seminar • You should be able to • Understand the differences between CHip-chip and CHip-Seq and identify key decision making steps for choosing a platform • Identify bioinformatics steps needed for handling CHip-chip and Chip-Seq datasets • Understand underlying data from genome tiling arrays • Understand how to search for binding sites in genomic data • Understand the need for skills in handling large datasets

3. General problem • Find accessible regions of DNA that are bound to your protein. • What method is best? • What sort of bioinformatics skills are required? • What is real signal and what is noise? • What do we do with the regions once you have identified them? Zheng, M. et al. (2007) ChIP-chip: data, model, and analysis. Biometrics, Vol 63, 787-796.

4. Experimental methods give different types of data • ChIP-chip • microarray data defining genomic regions • probe (with position usually defined) + expression • ChIP-Seq • high throughput DNA sequence • ACGATGTCA sequence fragments (from Solexa/SOLID/454)‏ • sequence position undefined (search required)‏ • The same issues exist for microarray vs. deep sequencing in gene expression experiments • coverage • cost • practicality‏

5. Raw (sequence) data • Flat files, processed from base-calls to fasta format • Solexa • ~25-30 bp reads • Barcode is used to pool samples in one sequence run • ACGT = Expt1 • TGAC = Expt2 • ACGT|Sequence • TGAC|Sequence

6. Choice of experiment • Choice of experiment depends on the focus you require • Whole genome broad coverage (of known genome)‏ • or focused genomic region? • or discovery based (known or unknown genome)‏ • How much coverage do you need? • Fewer broad experiments vs. many focused experiments? • Custom chips can be easily designed for focused regions and custom applications.

7. Chip- Workflow CHip-chip CHip-seq • Select antibody • Select chip or design and select probes • Map Array probes to genomic positon (BLAST/BLAT or lookup table from chip supplier)‏ • Identify peaks from data and minimise false positives • Analyse peaks to predict binding sites • Select antibody • Decide how deep to sequence ($$$ vs. coverage)‏ • Sequence fragments • Map Sequence to genomic position (BLAST/BLAT)‏ • Identify peaks from data and minimise false positives • Analyse peaks to predict binding sites

8. Chip- Ringo Workflow example

9. Chip- output • Peaks on the genome • “Score” for each genomic position BMC Bioinformatics 2007, 8:219

10. Antibody selection • Success depends on your antibody • Select antibodies that are suitable for CHip-chip experiments • Only a small number so far! • List available from • http://www.chiponchip.org/antibody.html

11. Microarray companies • DNA microarrays suitable for ChIP-chip assays: • Affymetrix • Human Chr21&22 tiling microarrays (oligonucleotide arrays)‏ • Human ENCODE tiling arrays (oligonucleotide arrays) • Agilent • Custom oligonucleotide arrays • Nimblegen Systems, Inc. • Human promoter microarrays • Human ENCODE microarrays • Custom oligonucleotide arrays • Aviva Systems Biology • Hu5K promoter arrays (PCR product arrays)‏ • Hu20K promoter arrays (Oligo arrays)

12. Probe Design • Tiling • high-resolution arrays • target genomic regions of interest • whole genome or specific targeted regions? • Agilent eArray probe database • >21 million tiled CGH and ChIP-on-chip probes • Do it yourself • unassembled genomes, etc...‏

13. Mapping to genome • The genome is still not constant, especially for many organisms • You must map the probe/sequence to genomic location using • standard alignment software (BLAST/BLAT/vmatch/...)‏ • or rely on datafiles from the vendor (reccomended for most cases)‏ • R packages exist for annotating probes to genomic location

14. Mapping to genome • For sequence based methods this step is critical (and slow)‏ • need unix server to run (or VMware) • Do I need access to a computing cluster? • choice of parameters for short sequences • Filter raw sequences -> representative sequence set • Do I need to pre-filter data (some seqs will account for most of the compute time)‏ • must be aware of speed vs. specificity for large datasets Genome

15. Normalisation • A normalization procedure: • (a) The MA plot before normalization shows a need for rotation to correct dye-bias. • (b) To determine the correct angle of rotation, the σ(M) vs σ(A) plot of the differences between probes is generated This circumvents the effect of binding signal in determining the rotating angle for original MA plot in (a). • (c) The MA plot after rotation by the angle determined in (b). The green line is the fitting line after rotation. • (d) The MA plot after normalization.. • BMC Bioinformatics. 2007; 8: 219. MA plot is a scatterplot with transformed axes. The X-axis represents the average log intensity from 2 channels while Y-axis represents the log-ratios.

16. Peak detection • What regions of DNA contain signal peaks? • How to define a statistically significant peak? Zheng, M. et al. (2007) ChIP-chip: data, model, and analysis. Biometrics, Vol 63, 787-796.

17. Normalisation • Before normalization • the mock control appears to show the same differential enrichment between genic and intergenic regions as the histone occupancy, suggesting that the differential enrichment may be an artifact. • After normalization • the mock control no longer shows significant differential enrichment while H3 and H4 profiles still do • Peng et al. BMC Bioinformatics 2007 8:219 doi:10.1186/1471-2105-8-219

18. Noise • Contamination • Do sequences match the expected genome? • Sequencing errors • Can you determine where a sequencing error is? • Multiple-mapping sequences • Many sequences do not unique genome matches • Dye specific bias • ChIP-chip data for chromatin-associated proteins and histone modifications present additional challenges • as they often display broad regions of enrichment. This is in contrast to the isolated and sharp peaks that are typical for the binding of transcription factors.

19. Peak detection - replicates • Use replicates to improve detection • Peaks that are consistent between replications are more likely to be true Zheng, M. et al. (2007) ChIP-chip: data, model, and analysis. Biometrics, Vol 63, 787-796.

20. What next? • Given that you've identified accessible regions in the genome • What information can be gathered from this sequence? • Use discovery methods to look for common patterns in the regions • MEME, etc • Use TFBS databases to look for known transcription factor binding sites in the sequence • Transfac • High coverage • Noisy database • Jaspar • Low coverage • Higher quality‏

21. R packages for chip-chip • Ringo • Well documented workflow and good tutorial • BAC • Perfect example of minimal documentation • Bayesian Analysis of ChIP-chip data

22. Summary • You should be able to • Understand the differences between CHip-chip and CHip-Seq and identify key decision making steps for choosing a platform • Identify bioinformatics requirements for handling CHip-chip and Chip-Seq datasets • Find transcription factor binding sites in genomic data • Understand the need for skills in handling large datasets