1.
Bioinformatics
And Functional
Genomics

2.
Outline…
 Bioinformatics
 Functional Genomics
 Sequence-based tools
 Microarray-based tools
 Gene Ontology
 System Biology approach to
Bioinformatics and Functional
Genomics

3.
What is Bioinformatics?
Bioinformatics
is conceptualizing biology in terms of
molecules (in the sense of physical-chemistry) and
then applying "informatics" techniques (derived
from disciplines such as applied
mathematics, CS, and statistics) to understand and
organize the information associated with these
molecules, on a large-scale.

4.
 The development of new algorithms and statistics
with which to assess relationships among
members of large data sets.
 The analysis and interpretation of various types of
data including nucleotide and amino acid
sequences, protein domains and protein structures.
 The development and implementation of tools that
enable efficient access and management of
different types of information.
Bioinformatics

5.
Functional Genomics
 A field of molecular biology that
attempts to make use of the vast
wealth of data produced by genomic
projects (such as genome sequencing
projects) to
describe gene (and protein) functions
and interactions

6.
Bioinformatics and Functional
Genomics
 Focus on gene function
◦ At genome level, using
◦ High throughput methods
 Conducted using
◦ Sequence-based tools
◦ Microarray-based tools

7.
Bioinformatics and Functional
Genomics
 Because of the large quantity of data
produced by these techniques and the
desire to find biologically meaningful
patterns, bioinformatics is crucial for
analysis of functional genomics data.

8.
Sequence-based tools
• DAVID
-The Database
annotation, visualization and
integrated discovery.
• DG
-DigiNorthern

9.
DigiNorthern
 DigiNorthern (DN) is a web-based tool
for virtually displaying expression
profiles of query genes based on EST
sequences.
 In addition, digital expression data is
available for each UniGene through a
pre-computed data set based on
SAGE and/or ESTs.

10.
Microarray based tools
 Gene Set Enrichment Analysis
(GSEA)
 GSEA considers experiments with
genome wide expression profiles from
samples belonging to two
classes, labeled 1 or 2. Genes are
ranked based on the correlation
between their expression and the
class distinction.
 GEO Gene Expression Omnibus

11.
Gene Ontology
 The Gene Ontology, or GO, is a
major bioinformatics initiative to unify the
representation of gene and gene
product attributes across
all species. More specifically, the project
aims to:
 Maintain and develop its controlled
vocabulary of gene and gene
product attributes;
 Annotate genes and gene products, and
assimilate and disseminate annotation
data;

12.
Gene Ontology
 Gene Ontology based enrichment
analysis are provided by DAVID and
Gene Set Enrichment Analysis
(GSEA).

13.
"A system biology" approach to bioinformatics
and functional genomics in complex human
diseases: arthritis.
 Human and other annotated genome
sequences have facilitated generation of
vast amounts of correlative data, from
human/animal genetics, normal and
disease-affected tissues from complex
diseases such as arthritis using
gene/protein chips and SNP analysis.
 These data sets include genes/proteins
whose functions are partially known at
the cellular level or may be completely
unknown (e.g. ESTs).