1 / 21

Other genomic arrays: Methylation, chIP on chip …

Other genomic arrays: Methylation, chIP on chip …. U B io Training Courses. SNP-arrays and copy number. Genotyping arrays can detect CNVs. Copy numbers from SNP arrays. Illumina SNP arrays: Hybridization to Universal IllumiCode TM. Illumina uses the same technology for methylation arrays

tucker
Download Presentation

Other genomic arrays: Methylation, chIP on chip …

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Other genomic arrays: Methylation, chIP on chip… UBio Training Courses

  2. SNP-arrays and copy number Genotyping arrays can detect CNVs

  3. Copy numbers from SNP arrays

  4. Illumina SNP arrays: Hybridization to Universal IllumiCodeTM Illumina uses the same technology for methylation arrays (bi-sulfited nucleotides are like SNPs) Intensity <-> Copy number

  5. Calculation of aCGH-like ratios Median R CEPH Individual R cell line (NCI60)

  6. Methylation arrays

  7. METHYLATION MICROARRAYS • BeadArrays • Until 12 samples per chip. • 27,578 CpG loci, >14.000 genes • 2 beads per locus (methylated/no methylated) • Random distribution (50 mer) • Input: Bisulphyted DNA • Includes probes for the promoter regions of miRNA 110 genes Infinium HumanMethylation27 BeadChip

  8. METHYLATION MICROARRAYS Illumina Golden Gate Assay • Until 147,456 DNA methylation measures simultaneously. • Resolution: 1 CpG • Until 96 samples simultaneously • GoldenGate Methylation Cancer Panel I 1,505 CpG loci selected from 807 gene • Allows custom designs

  9. METHYLATION MICROARRAYS SOFTWARE Bead Studio  Genome Studio Methylation module http://www.illumina.com/pages.ilmn?ID=196 Lumi package (Import, background correction, normalization) Beadarray package (Import, QC) Methylumi (Import, QC ,normalization, differential meth.)

  10. METHYLATION MICROARRAYS DIFFERENTIAL METHYLATION Bead Studio  Genome Studio Methylation module http://www.illumina.com/pages.ilmn?ID=196 Beta values: β= Imethylated/Imethylated+Ino_methylated Hypermethylated Hypomethylated β 0 1 0.3 0.7

  11. METHYLATION MICROARRAYS NORMALIZATION Methylumi normalization • Calculate medians for Cy3 and Cy5 at high an low betas • Cy5 medians adjusted to Cy3 channel (dye bias) • Recalculate betas with new intensities

  12. METHYLATION MICROARRAYS DIFFERENTIAL METHYLATION Wilcoxon rank-test (UBio) Limma (Pomelo) Permutations (Pomelo) βs Median βs class A Median βs class B FDR<0.05 + Differentially methylated genes

  13. ChIP on chip

  14. ChIP on Chip We thank Chris Glass lab, UCSD, for the original slide

  15. ChIP on Chip Discover protein/DNA interactions!!

  16. ChIP on Chip software Chip Analytics WORKFLOW I. 1. Pre-normalization. Background substraction: Foreground – background Default: Median blank substraction  Each channel – median negative controls 2. Normalization (dye-byas and interarray normalization) Default : Median dye-byas, median interarray. Recommended: Loess

  17. ChIP on Chip software Chip Analytics WORKFLOW II. 3. Error modelling To identify which probes are most representative of binding events: P(X)=P-value of a single probe matching event P(Xneighb)= Positive signals in a probe should be corroborated by the signals of probes that are its genomic neighbors, provided they are close enough P(Xneighb) follows a Gaussian distribution Both the P(X) and the P(Xneighb) values of a probe need to satisfy significance thresholds in order for a probe to be considered as representing a binding event

  18. ChIP on Chip software Chip Analytics WORKFLOW III. 4. Segment identification (clusters of enriched probes) bp 5. Gene identification -Segment, Gene or Probe report (Gene or probe ID, Chr, Start, End, p(X)…)

  19. CoCas http://www.ciml.univ-mrs.fr/software/cocas/index.html Agilent platform Normalization QC Report Genome Visualization Peak Finder Benoukraf et al. Bioinformatics 2009.

  20. Weeder: Motif discovery in sequences from co-regulated genes (single specie). WeederH: Motif discovery in sequences from homologous genes. Pscan: Motif discovery in sequences from co-regulated genes (JASPAR,TRANSFAC matrices) UBio training courses: See “Course on Introduction to Sequence Analysis”

  21. Thanks ! Visit UBio web ! http://bioinfo.cnio.es/

More Related