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The post-genomic era<br />Epigenetic sequencing applications and data integration<br />WOUD mini-symposium28/09/2011  <br ...
Overview<br /><ul><li>Epigenetics</li></ul>Introduction<br />DNA-methylation<br />Histonemodifications<br />The interplayb...
Epigenetics > Introduction<br />-genetics<br />Heritable changes to the DNA or histones without affecting the DNA seque...
Epigenetics > Introduction<br />
Epigenetics > Introduction<br />DNA-methylation<br />Histone tail modifications<br />
Epigenetics > DNA-methylation<br />DNA-methylationandcancer<br />Global hypomethylation<br />Localhypermethylation<br />
Epigenetics > Interplay<br />Interplaybetween DNA-methylationandhistonemodifications<br />
Epigenetics > Detection / Prognosis / Prediction<br />(Early) detection– diagnostic<br />Diagnostic: who<br />Screening pr...
Epigenetics > Detection / Prognosis / Prediction<br />Prediction<br />Predictive: what<br />Treatment<br />
Epigenetics > Detection / Prognosis / Prediction<br />Prediction<br />Predictive: what<br />Treatment<br />
Epigenetics > Detection / Prognosis / Prediction<br />Prediction<br />Predictive: what<br />Treatment<br />Biomarker<br />
Epigenetics > Detection / Prognosis / Prediction<br />Prediction<br />Predictive: what<br />Treatment<br />
Epigenetics > Detection / Prognosis / Prediction<br />Prediction<br />Chemotherapy respons (MGMT in brain cancer - temozol...
Overview<br /><ul><li>Epigenetics</li></ul>Introduction<br />DNA-methylation<br />Histonemodifications<br />The interplayb...
Sequencing the epigenome<br />DNA-methylation<br />Restriction-based<br />Bisulfite-conversionbased<br />Affinity-based<br...
Sequencing the epigenome<br />Shearing of DNA (Covaris)<br />Sequencing<br />Control of fragment sizeswith high sensitivit...
Sequencing the epigenome<br />Sequencing data analysis<br />
Sequencing the epigenome<br />QC (FastQC)<br />
Sequencing the epigenome<br />Mapping<br />@HWUSI-EAS100R:6:73:941:1973#0/1 <br />GATTTGGGGTTCAAAGCAGTATCGATCAAATAGTAAATCC...
Sequencing the epigenome<br />Mapping<br />bowtie -q -n --fr --phred64-quals -x 250 -t -p 4 hg19-1 qseq_2_MID5_W.fastq -2 ...
Sequencing the epigenome<br />Mapping<br />HWUSI-EAS509:4:34:13795:1029#0/1 + chr16 57608607 GTCAG… IIIII… 0 <br />HWUSI-E...
Sequencing the epigenome<br />Mapping<br />macs14 -t IMR32.bowtie -f BOWTIE -g hs -n IMR32 -w --single-wig <br />Input: ma...
Sequencing the epigenome<br />Mapping<br />Chrstart	end	length	summit	tags	score	fold_enrichment<br />Chr1	14862	15572	711...
Sequencing the epigenome<br />
Sequencing the epigenome<br />PCDHB-cluster (neuroblastoma CLs)<br />
Sequencing the epigenome<br />PCDHB-cluster in neuroblastoma<br />
Sequencing the epigenome<br />Integrating data sources…<br />H3K27 me3<br />H3K36 me3<br />H3K4 me3<br />RNA-seq<br />Prom...
Sequencing the epigenome<br />
Conclusions<br />Sequencingepigenomesreveals a wealth of information<br />There is no suchthing as the epigenome<br />Meth...
Acknowledgments<br /><ul><li>Jean-Pierre Renard
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The post-genomic era: epigenetic sequencing applications and data integration

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The post-genomic era: epigenetic sequencing applications and data integration
by dr. ir. Maté Ongenaert - Center for Medical Genetics, Ghent University

The past decade is known as the post-genomic era. Ever since the first published human genomes, the pace to determine new genomes ever increased. In addition, a number of new sequencing applications gave access to previously unexplored areas at a genome-wide scale such as whole epigenomes.
In this talk, the data generated from a number of sequencing techniques to determine whole DNA-methylomes and whole genome histone marks will be discussed.
Main goal: to convince scientists that the analysis tools have matured to a level that, using a good manual and insight in the mechanisms behind the analysis, they can do their own basic analyses.
Starting from a raw sequence file, over quality control to mapping to the reference genome, peak calling, visualization and identification of differentially methylated sites: within the time-frame of this talk, the entire process will be demonstrated.
As epigenetics regulates genomic processes and literally is a layer above genetics, able to fine-tune regulatory processes, several layers of information should be look at to understand the underlying mechanisms.
Important aspect in the analysis of epigenetic datasets thus is the integration of several data sources (expression results, re-expression results, DNA-methylation information and histone-modifications).

Published in: Technology

The post-genomic era: epigenetic sequencing applications and data integration

  1. 1. The post-genomic era<br />Epigenetic sequencing applications and data integration<br />WOUD mini-symposium28/09/2011 <br />Maté Ongenaert<br />Center for Medical Genetics<br />Ghent University Hospital, Belgium<br />
  2. 2. Overview<br /><ul><li>Epigenetics</li></ul>Introduction<br />DNA-methylation<br />Histonemodifications<br />The interplaybetweenmethylationandhistonemodifications<br />Applications of epigentics<br />Epigeneticsequencing<br />Sequencingthe epigenome<br />Data analysis andintegration<br />
  3. 3. Epigenetics > Introduction<br />-genetics<br />Heritable changes to the DNA or histones without affecting the DNA sequence<br />A whole range of changes are described<br />DNA-methylation<br />Histonetailmodifications<br />Methylation<br />Acetylation<br />Phosphorylation<br />….<br />Epigenetic changes are interconnected<br />
  4. 4. Epigenetics > Introduction<br />
  5. 5. Epigenetics > Introduction<br />DNA-methylation<br />Histone tail modifications<br />
  6. 6. Epigenetics > DNA-methylation<br />DNA-methylationandcancer<br />Global hypomethylation<br />Localhypermethylation<br />
  7. 7. Epigenetics > Interplay<br />Interplaybetween DNA-methylationandhistonemodifications<br />
  8. 8. Epigenetics > Detection / Prognosis / Prediction<br />(Early) detection– diagnostic<br />Diagnostic: who<br />Screening programs<br />
  9. 9. Epigenetics > Detection / Prognosis / Prediction<br />Prediction<br />Predictive: what<br />Treatment<br />
  10. 10. Epigenetics > Detection / Prognosis / Prediction<br />Prediction<br />Predictive: what<br />Treatment<br />
  11. 11. Epigenetics > Detection / Prognosis / Prediction<br />Prediction<br />Predictive: what<br />Treatment<br />Biomarker<br />
  12. 12. Epigenetics > Detection / Prognosis / Prediction<br />Prediction<br />Predictive: what<br />Treatment<br />
  13. 13. Epigenetics > Detection / Prognosis / Prediction<br />Prediction<br />Chemotherapy respons (MGMT in brain cancer - temozolomide)<br />
  14. 14. Overview<br /><ul><li>Epigenetics</li></ul>Introduction<br />DNA-methylation<br />Histonemodifications<br />The interplaybetweenmethylationandhistonemodifications<br />Applications of epigentics<br />Epigeneticsequencing<br />Sequencingthe epigenome<br />Data analysis andintegration<br />
  15. 15. Sequencing the epigenome<br />DNA-methylation<br />Restriction-based<br />Bisulfite-conversionbased<br />Affinity-based<br />MeDIP-seq (Antibody)<br />MBD-seq (Methyl Binding Domain)<br /><ul><li>Chromatinmarks</li></ul>ChIP-seq<br />
  16. 16. Sequencing the epigenome<br />Shearing of DNA (Covaris)<br />Sequencing<br />Control of fragment sizeswith high sensitivity DNA chips<br />Concentration determination of the fragmented DNA with Fluostar Optima plate reader<br />MBD2 immunoprecipitation reaction (MethylCollector Kit)<br />
  17. 17. Sequencing the epigenome<br />Sequencing data analysis<br />
  18. 18. Sequencing the epigenome<br />QC (FastQC)<br />
  19. 19. Sequencing the epigenome<br />Mapping<br />@HWUSI-EAS100R:6:73:941:1973#0/1 <br />GATTTGGGGTTCAAAGCAGTATCGATCAAATAGTAAATCCATTTGTTCAACTCACAGTT <br />+HWUSI-EAS100R:6:73:941:1973#0/1 <br />!''*((((***+))%%%++)(%%%%).1***-+*''))**55CCF>>>>>>CCCCCCC6<br />Identifier (Illumina machine name, lane, tile etc.)<br />Sequence<br />Sequencing quality<br />
  20. 20. Sequencing the epigenome<br />Mapping<br />bowtie -q -n --fr --phred64-quals -x 250 -t -p 4 hg19-1 qseq_2_MID5_W.fastq -2 qseq_2_MID5_C.fastq IMR32.bowtie <br />Input FASTQ files (two: paired end)<br />Mapping parameters:<br />- q: quality aware (--phred64-quals: Iluminaquality scores instead of Phred)--fr: map on forward and reverse strand of the reference genome- x: map paired-end reads maximum 250 bp apart<br />- p 4: use 4 processes to map (parallelization)<br />- hg19: reference genome<br />
  21. 21. Sequencing the epigenome<br />Mapping<br />HWUSI-EAS509:4:34:13795:1029#0/1 + chr16 57608607 GTCAG… IIIII… 0 <br />HWUSI-EAS509:4:34:13795:1029#0/3/2 - chr16 57608757 GTCCT… IIIII… 0 <br />HWUSI-EAS509:4:34:6016:1041#0/3/2 + chr10 94410976 GTTTC… IIIII… 0 <br />HWUSI-EAS509:4:34:6016:1041#0/1 - chr10 94411127 TGTTT… IIIHH… 0 <br />HWUSI-EAS509:4:34:7281:1043#0/1 + chr4 54043731 GTCTA… IIIII… 0 <br />Chromosomal location (strand, chromosome, pos)<br />Quality of the mapping<br />
  22. 22. Sequencing the epigenome<br />Mapping<br />macs14 -t IMR32.bowtie -f BOWTIE -g hs -n IMR32 -w --single-wig <br />Input: mapped “treatment” reads and format of mapping (you can also provide a control sample)<br />Parameters:-g hs: human reference genome (for size estimation)- n: name of output files<br />- w: create wig-files for visualisation (counts)<br />
  23. 23. Sequencing the epigenome<br />Mapping<br />Chrstart end length summit tags score fold_enrichment<br />Chr1 14862 15572 711 227 17 86.37 13.55 <br />Chr1 135001 135399 399 197 12 83.27 15.43 <br />Chr1 229428 229950 523 329 10 62.41 14.03 <br />
  24. 24. Sequencing the epigenome<br />
  25. 25. Sequencing the epigenome<br />PCDHB-cluster (neuroblastoma CLs)<br />
  26. 26. Sequencing the epigenome<br />PCDHB-cluster in neuroblastoma<br />
  27. 27. Sequencing the epigenome<br />Integrating data sources…<br />H3K27 me3<br />H3K36 me3<br />H3K4 me3<br />RNA-seq<br />Promoter region<br />Gene Body<br />Active gene<br />
  28. 28. Sequencing the epigenome<br />
  29. 29. Conclusions<br />Sequencingepigenomesreveals a wealth of information<br />There is no suchthing as the epigenome<br />Methylome<br />Hydroxymethylome<br />Different histonemodifications<br />Don’tforget the interplayand the dynamics…<br />Start exploring the data byyourselfas youknow the application the best<br />
  30. 30. Acknowledgments<br /><ul><li>Jean-Pierre Renard
  31. 31. Sarah De Keulenaer
  32. 32. Anneleen Decock
  33. 33. Frank Speleman
  34. 34. Jo Vandesompele
  35. 35. Tim De Meyer
  36. 36. Geert Trooskens
  37. 37. Wim Van Criekinge
  38. 38. Leander Van Neste
  39. 39. Johan Vandersmissen</li>

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