It contains the information about Immuno informatics, immune cell development, Immunological database management and tools used for immuno informatics, all about Microarray and DNA MIcroArray experiment, Micro Array Data Classificationa and Machine learning Overview.

1. 1.IMMUNOINFORMATICS
2.MICROARRAY
3.MACHINE LEARNING OVERVIEW
By Mekhla Diwan

2. IMMUNOINFORMATICS!!

3. HOW DO WE DEFINE IMMUNOINFORMATICS ?
 Immunoinformatics or computational
immunology is a field of science that
encompasses high-throughput genomic and
bioinformatics approaches to immunology.

4. WHAT DOES THE WORD IMMUNOLOGY
MEANS?
 Immunology is a branch of biomedical science that
covers the study of all aspects of the immune
system in all organisms.

5. AIM OF IMMUNOINFORMATICS
 The field's main aim is to convert immunological
data into computational problems, solve these
problems using mathematical and computational
approaches and then convert these results into
immunologically meaningful interpretations.

6. LET US KNOW MORE ABOUT IMMUNOLOGY
 Immunology research is important for
understanding the mechanisms underlying the
defense of human body and to develop drugs for
immunological diseases and maintain health.
Recent findings in genomic and proteomic
technologies have transformed the immunology
research drastically. Sequencing of the human and
other model organism genomes has produced
increasingly large volumes of data relevant to
immunology research and at the same time huge
amounts of functional and clinical data are being
reported in the scientific literature and stored in
clinical records.

8. MORE ABOUT IMMUNOINFORMATICS
 1. Recent advances
in bioinformatics or computational biology were
helpful to understand and organize these large
scale data and gave rise to new area that is
called Computational
immunology or immunoinformatics.
 2. Computational immunology is a branch of
bioinformatics and it is based on similar concepts
and tools, such assequence alignment and protein
structure prediction tools. Immunomics is a
discipline like genomics andproteomics.

9.  3. It is a science, which specifically
combines Immunology with computer
science, mathematics,chemistry,
and biochemistry for large-scale analysis of
immune system functions.
 4. It aims to study the complex protein–protein
interactions and networks and allows a better
understanding of immune responses and their role
during normal, diseased and reconstitution states.
 5.Computational immunology is a part of
immunomics, which is focused on analyzing large
scale experimental data.

10. HISTORY
 Computational immunology began over 90 years
ago with the theoretic modeling of malaria
epidemiology. At that time, the emphasis was on
the use of mathematics to guide the study of
disease transmission. Since then, the field has
expanded to cover all other aspects of immune
system processes and diseases

11. IMMUNOLOGICAL DATABASE
 After the recent advances in sequencing and
proteomics technology, there have been many fold
increase in generation of molecular and
immunological data. The data are so diverse that
they can be categorized in different databases
according to their use in the research. Until now
there are total 31 different immunological
databases noted in the Nucleic Acids Research
(NAR) Database Collection, which are given in the
following table, together with some more immune
related databases.[4] The information given in the
table is taken from the database descriptions
in NAR Database Collection.

12. TOOLS
 Immunoinformatics is using the basic bioinformatics
tools such as ClustalW,[34] BLAST,[35] and TreeView, as
well as specialized immunoinformatics tools, such as
EpiMatrix,[36][37] IMGT/V-QUEST for IG and TR sequence
analysis, IMGT/ Collier-de-Perles and
IMGT/StructuralQuery[38] for IGvariable domain structure
analysis.[39]Methods that rely on sequence comparison
are diverse and have been applied to analyze HLA
sequence conservation, help verify the origins of human
immunodeficiency virus (HIV) sequences, and construct
homology models for the analysis of hepatitis B virus
polymerase resistance to lamivudine and emtricitabine.

13.  There are also some computational models which
focus on protein–protein interactions and networks.
There are also tools which are used for T and B cell
epitope mapping, proteasomal cleavage site
prediction, and TAP– peptide prediction

14. IMMUNE CELL DEVELOPEMENT

15. APPLICATIONS
 To determine ALLERGIES:
The use of immunoinformatics tools can be useful
to predict protein allergenicity and will become
increasingly important in the screening of novel
foods before their wide-scale release for human
use. Thus, there is a major develop such as to
make reliable broad based allergy databases and
combine these with well validated prediction tools in
order to enable the identification of potential
allergens in genetically modified drugs and foods.

16.  2. Infectious diseases and host responses
Algorithms have been developed for knowing which
kinds of disease and hosts are present in our body.
3. Immune system function
The affinity of HLA-binding peptides for TAP was
found to differ according to the HLA supertype
concerned using this method. This research could
have important implications for the design of
peptide based immuno-therapeutic drugs and
vaccines.

17.  4. Cancer Informatics
 Immunoinformatics have been useful in increasing
success of tumour vaccination.

18. MICROARRAY!!
 The most commonly used global gene expression profiling method in
current genomics research is the DNA microarray-based approach.

19. WHAT IS A MICROARRAY?
 A microarray (or gene chip) is a slide attached with
a high-density array of immobilized DNA oligomers
(sometimes cDNAs) representing the entire
genome of the species under study.

20. INTERNAL STRUCTURE OF MICROARRAY
 1.Each oligomer is spotted on the slide and serves
as a probe for binding to a unique, complementary
cDNA.
 2. The entire cDNA population, labeled with
fluorescent dyes or radioisotopes, is allowed to
hybridize with the oligo probes on the chip.
 3. The amount of fluorescent or radiolabels at each
spot position reflects the amount of corresponding
mRNA in the cell.
 Using this analysis, patterns of global gene
expression in a cell can be examined.

21. DNAMICROARRAYEXPERIMENTINVOLVESAMULTISTE
PPROCEDURE

22. MICROARRAY DATA CLASSIFICATION
 One of the key features of DNA microarray analysis
is to study the expression of many genes in parallel
and identify groups of genes that exhibit similar
expression patterns.
 1.Supervised and Unsupervised Classification
 A supervised analysis refers to classification of data
into a set of predefined categories. For example,
depending on the purpose of the experiment,the
data can be classified into predefined “diseased” or
“normal” categories.

23.  An unsupervised analysis does not assume
predefined categories, but identifies data categories
according to actual similarity patterns. The
unsupervised analysis is also called clustering,
which is to group patterns into clusters of genes
with correlated profiles.

24. MACHINE LEARNING OVERVIEW!!

25. WHAT IS MACHINE LEARNING?
 Machine learning is an adaptive process that
enables computers to learn from experience, learn
by example, and learn by analogy.
 There are new computational approaches to solve
the computational problems on biological
approaches by handling complex data.
 It presents modelling methods, such as supervised
classification, clustering and probabilistic graphical
models for knowledge discovery, as well as
deterministic and stochastic heuristics for
optimization. Applications in genomics, proteomics,
systems biology, evolution and text mining are also
shown.

26. THE EXISTING RESEARCH ON BIOINFORMATICS
THAT HAS APPLIED MACHINE LEARNING
 Research , Area, Application Reference
 Sequence alignment:
1.BLAST, http://www.ncbi.nlm.nih.gov/BL
2.AST/FASTA, http://www.ebi.ac.uk/fasta33/
 Multiple sequence alignment:
1.ClustalW, http://www.ebi.ac.uk/clustalw/
2.MultAlin.http://prodes.toulouse.inra.fr/multalin/multalin.html
3.DiAlignhttp://www.genomatix.de/cgibin/dialign/dialign.pl
o Gene finding
1.Genscan http://genes.mit.edu/GENSCAN.html
2.GenomeScan http://genes.mit.edu/genomescan/

27.  Protein domain analysis and identification:
1.GeneMark http://www.ebi.ac.uk/genemark/Pfam
http://www.sanger.ac.uk/Software/Pfam/
2.BLOCKS http://www.blocks.fhcrc.org/
3.ProDom http://prodes.toulouse.inra.fr/prodom/current/html/ home.php
 Pattern identification:
1.Gibbs Sampler http://bayesweb.wadsworth.org/gibbs/gibbs.html
2.AlignACE http://atlas.med.harvard.edu/cgibin/alignace.pl
3.MEME http://meme.sdsc.edu/meme/website/intro.html
 Protein folding prediction:
1.Predict protein:http://www.emblheidelberg.de/predictprotein/ -
dictprotein.html
2.SwissModle http://www.expasy.org/swissmod/

28. WHY MACHINE LEARNING?
 There are some biological problems in which
experts can specify only input/output pairs, but not
the relationships between inputs and outputs,such
as the prediction of protein structure and structural
and functional sequences.
 This limitation can be addressed by machine
learning methods.
 They are able to adjust their internal structure to
produce approximate results for the given
problems.

29. THANK YOU!!