1. Bioinformatics lectures at Rice University Li Zhang Lecture 10: Networks and integrative genomic analysis-2 Genome instability and DNA copy number data http://odin.mdacc.tmc.edu/~llzhang/RiceCourse April 2014

2. Trends in bioinformatics research Increase in sample sizes (more patients) More data types (DNA, RNA, protein, methylation, nucleosome) More features ( more genes, transcript, short reads ) More complicated inference (Translocation, mutation, deletion/insertion, amplification) More involvement of systems biology, which speaks of pathways and networks

3. TCGA to Study More than 20 Cancers over Next Two Years The Cancer Genome Atlas has received $275 million in new funding from the National Institutes of Health — including about $125 million allocated for sequencing — to study more than 20 types of cancer over the next two years. Within the next five years, the project plans to generate comprehensive genomic maps for these cancers

4. URL:cancergenome.nih.gov

5. DNA copy number changes reflect chromosomal abnormality in cancer

6. DNA copy number data obtained from microarrays

7. Characteristics of DNA copy number data 1.Piecewise constant + noise 2.Noise 3.Bias

8. SNP arrays can obtain allele-specific copy number changes BAF = B/(A+B)

9. More characteristics of DNA copy number data 4. Copy changes are often allele-specific, i.e., only one of the homologous chromosome is affected. In some cases, an loss allele can be compensated by gain of the other allele, i.e., CNN-LOH, copy number neutral loss of heterozygosity, or UPD, uni-parental disomy. It is rare that both alleles get affected.

10. More characteristics of DNA copy number data 5. Denoised copy number values often follow comb-like distributions. Exceptions to this rule may indicate presence of multiple clones. Double is loss is very rare. Copy number>6 are considered amplicons, which play critical role in cancer.

11. Next generation sequencing can provide more detailed genome structural changes Paired-end sequencing  translocations Chromothripsis

12. Algorithms 1.CBS: Identify copy number profile segments 2.Data processing to correct for biases. 3.GISTIC: Identify cancer genes with frequent gains/losses

15. Biases distort the DNA copy number profile

17. More characteristics of DNA copy number data 6. Scope of gain or loss vary probably due to different causes. Large chunks may be miss-segregated during mitosis. Small chunks may be affected by mistakes in DNA repair.

18. Cancer genes often reside in regions with Focal SCNAs Tumor suppressorsOncogenes

20. mRNA and DNA copy number Correlation matrices of genome copy number and gene expression in meningiomas. A, Pearson's correlations among DNA copy numbers (Panel A) and between DNA copy number and gene expression (Panel B) are plotted as a function of chromosomal location. Only correlations greater than 0.82 (red) and less than -0.82 (blue) are shown on the intensity map. The association between loss on chromosome 14 and gain on chromosome 1q (black arrow), loss on chromosome 14 and loss on chromosome 6q (brown arrow) and loss on chromosome 7p and gain on chromosome 20 (black arrowhead) are indicated.

22. Copy number breakpoint and gene fusion Wang et al, 2009, Nature Biotechnology.