1. Bioinformatics lectures at Rice University Li Zhang Lecture 11: Networks and integrative genomic analysis-3 Genomic data http://odin.mdacc.tmc.edu/~llzhang/RiceCourse

3. How to find the modules?

4. Testing results of the method

5. URL:cancergenome.nih.gov

7. The network approach

8. Mapping interactions

9. Module detection

12. DCTN2 module is a new module discovered by the automated process

13. Limitations of the study Network analysis is only as good as the network itself. Human interaction and pathway data remain sparse and fragmented, and we must assume that the Human Interaction Network (HIN) used here represents a small portion of the full human interactome [47]. Interactions and pathways in our network are completely devoid of the context in which they were originally described, and we can only use the HIN as an approximate model for in vivo interactions. As a quality filter, we have also specifically. Distinguishing genes implicated by copy number alterations remains problematic, even when candidate genes are filtered through a network. For example, KIT, KDR and PDGFRA are all located at 4q12, a region of frequent amplification in GBM, and it is difficult to determine which one(s) are the true targets.

14. Summary of the course

15. What is bioinformatics? Bioinformatics is the application of computer science and information technology to the field of biology and medicine. Bioinformatics deals with algorithms, databases and information systems, web technologies, artificial intelligence and soft computing, information and computation theory, software engineering, data mining, image processing, modeling and simulation, signal processing, discrete mathematics, control and system theory, circuit theory, and statistics, for generating new knowledge of biology and medicine, and improving & discovering new models of computation (e.g. DNA computing, neural computing, evolutionary computing, immuno-computing, swarm- computing, cellular-computing).computer scienceinformation technologybiology medicine Commonly used software tools and technologies in this field include Java, XML, Perl, C, C++, Python, R, MySQL, SQL, CUDA, MATLAB, and Microsoft Excel.JavaXMLPerlCC++PythonRMySQL SQLCUDAMATLABMicrosoft Excel

16. Statistical concepts and algorithms Shannon entropy Mutual information, ARACNE, correlated mutations Maximum information coefficient GISTIC Hidden Markov Models Network analysis: redundant genes Network analysis: Gen Set Enrichment Analysis Network analysis: Modularity

17. Biological context High throughput genomics technologies (microarrays and next generation sequencing) Gene expression data DNA copy number data (characteristics and interpretation) Gene expression regulation network (ARACNE) Information coded in a gene sequence HMM used in decoding DNA sequences Integrative genomics Network analysis