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SRM UNIVERSITY
FACULTY OF ENGINEERING & TECHNOLOGY
DEPARTMENT OF BIOINFORMATICS
BI0510- MICROARRAY BIOINFORMATICS
LECTURE PLAN
Class & Sem: M.Tech & II sem
Subject code: BI0 510
Lecture
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Staff Name:Mrs. S.Shobana
Subject: MicroarrayBioinformatics
Contents
INSTRUCTIONAL OBJECTIVES
DNA MicroArray:
The Technical Foundations
Why are MicroArray Important?
What is a DNAMicroArray
Microarray- Introduction
types of Microarray
Making microarrays
Spotted arrays
Insitu synthesized oligonucleotide arrays
Steps involved in use of microarray
Sample preparation and labeling,
Hybridisation
Washing, image aquisition
Filtering of low complexity sequences
cross-hybridization prediction
Prediction of melting temperature
Secondary structure prediction of probe
Feature extraction steps
Identifying positions of features
Types of segmentation & Background pixel
identification
Data cleaning and transformation
within array normalizationLoess regression
Correcting Spatial effects
between array normalization steps
measuring and quantifying microarray
variability
Calibration experiments and pilot studies
Learning Outcome
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Introduction to the
student about microarray
technology and its uses.
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It gives a brief
description about the making
and using of microarrays.
o Also the different databases
used for creating probes are
taught.
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In the method “insitu
synthesized oligonucleotides”
certain rules have to be
followed for preparing probes
which has been discussed in
this unit.
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Also feature extraction
methods are discussed
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DNA Microarray and
its statistical analysis
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Analysis of RNA data
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Statistical computing
and Statistical Genetics
o Normalization techniques
to correct systemic and
spatial effects
o Quantify non systemic
variability
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Method for measuring variability
Analysis of differentially expressed genes
fundamental concepts and hypothesis rules
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Classic parametric tests- paired and unpaired.
Non parametric tests- bootstrap analysis
Multiplicity of testing- bonferroni adjustment
and ANOVA
Similarity analysis of relationships between
genes using correlation coefficient, rank
coefficient and Euclidean distance
Hierarchial clustering & Linkage methods for
clustering
Machine learning methods of clustering
classification of tissues and samples- methods
Validation and cross validation
Dimensionality reduction- PCA &Individual
gene selection
Pairwise gene selection,Voting algorithms,
genetic algorithms
UNIT V-EXPERIMENTAL DESIGN
Blocking
randomization & blindingchoice of technology using experimental
designs
Power analysis and confidence
data standards- LIMS
storage and sharing- MIAME, MAGE,
Ontologies
Future direction of microarray approach,
Pharmacogenomics,
Toxicogenomics
Data mining
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o Differentially expressed
genes can be obtained from
the numerical data and can
be done using clustering
algorithms.
o Classification of tissues and
genes using different
algorithms and its
validation.
o Also microarray data is a
high dimensional data- thus
dimension reduction is
discussed
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The basic
experimental design for a
microarray experiment
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The ways and means
to store large microarray data
is discussed.
REFERENCES
1. Arun Jogota , Microarray Data Analysis and Visualization, The Bay Press, 2001.
2. Ernst Wit and John McClure, Statistics for Microarrays Design, Analysis and Inference,
John Wiley & Sons, 2004.
3. Steen Knudsen, Guide to analysis of DNA Microarray data, John Wiley & Sons, 2004.
4. Dov Stekel , Microarray Bioinformatics, Cambridge University Press, 2003.
5. S. Draghic, Chapman, Data Analysis tools for DNA Microarray, Hall/ CRC Press, 2002.
E Mail ID: [email protected]
Contact No: 9841988577