Transcript Bioinformatics lectures at Rice University

Bioinformatics lectures at Rice
University
Li Zhang
Lecture 11: Networks and integrative genomic
analysis-3
Genomic data
http://odin.mdacc.tmc.edu/~llzhang/RiceCourse
How to find the modules?
Testing results of the method
URL:cancergenome.nih.gov
The network approach
Mapping interactions
Module detection
DCTN2 module is a new module discovered by the automated process
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.
Summary of the course
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, swarmcomputing, cellular-computing).
• Commonly used software tools and technologies in this
field include Java, XML, Perl, C, C++, Python, R, MySQL,
SQL, CUDA, MATLAB, and Microsoft Excel.
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
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