The document provides an overview of the field of bioinformatics. It defines bioinformatics as using computational tools to analyze biological data to answer questions about molecular composition, structure, function, and evolution. Bioinformatics is interdisciplinary, drawing from fields like mathematics, computer science, statistics, and information science. It discusses some key areas of bioinformatics work like analyzing nucleic acid and protein sequences, predicting molecular structures and interactions, and constructing networks to model metabolic and gene regulatory pathways. It also lists some common databases and tools used in bioinformatics as well as examples of computational problems and applications in areas like disease diagnosis, drug discovery, and more.

1. Nimrita Koul
Research Scholar
School of Computing & Information Technology,
REVA University, Bangalore
Bioinformatics
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2. Bio - Informatics
Definition- The research, development and application of
computational tools for analysis, modelling, and simulation of
biological data.
i.e. To Investigate questions about biological composition, structure, function,
and evolution of molecules, cells, tissues, and organisms using mathematics,
informatics, statistics and computer science.
Synonyms – Genomic Data Science, Computational Biology, Computational
Genomics, Statistical Genomics
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3. 3
An Interdisciplinary Field
Image Courtesy – www.researchgate.com

4. The Work Ground of Bioinformatics
Image Credit - https://openoregon.pressbooks.pub/mhccmajorsbio/chapter/dna-organization-inside-a-cell/

5. The flow of information from DNA to RNA to protein in a cell.
Image Credit – www.nature.com
Central Dogma of Molecular Biology

6. The Bioinformatics Cycle

8. DNA/ RNA Structure as Context Free
Grammar
• The problem of describing RNA structure can be modelled as a context free grammar.
This structure is determined by attraction between nucleotides – A(adenine) attracts
U(uracil), C(Cytosine) attracts G(Guanine). Writing this as a CFG rule –
S  aSu | uSa | ε
will generate palindrome like sequences of u’s and a’s of even length.
This grammar becomes increasingly complex as we incorporate other biological
components of a cell into it. Eg.
S → aSu/uSa/N N→aN/uN/ε
S → SS
S → cSg/gSc/
N →aN/uN/cN/gN/Na/Nu/Nc/Ng/ε

9. Bioinformatics Flow Chart
1a. Gene Sequencing
1b. Analysis of nucleic acid sequences
2. Analysis of protein sequences
3. Molecular structure prediction
4. Prediction of Molecular interactions
5. Inference of Metabolic and regulatory networks
6. Gene & Protein expression data
7. Drug screening
Ab initio drug design OR Drug
compound screening in database of
molecules
8. Genetic variability

10. Protein Sequence and Structures Repositories

11. Protein Sequence and Structure Databases

12. Gene Expression Databases

13. Integrated Database Retrieval and Analysis
Systems

14. Pathways Databases and Platforms

16. Computational Research Problems in
Bioinformatics –
• 1. Comparison of Long Sequences of nucleotides/amino acids.
• 2. Construction of Evolutionary Phylogenetic Trees to figure similarity in
genetic traits
• 3. Gene Detection in Sequences of amino acids in DNA.
• 4. Determining 3D structures of DNA, RNA and Proteins.
• 5. Reverse Engineering of GRNs - Inferring Cell Regulation
• 6. Assembling DNA Fragments from various sources/machines
• 7. Creation of Ontologies to annotate the various Transcripts and events
• 8. Development of Specialized Scripting Languages for Bioinformatics tasks

17. Application of Bioinformatics
• Disease Diagnosis and
Prediction
• Antibiotic Resistance
• Waste Cleanup
• Molecular medicine
• Personalized medicine
• Preventative medicine
• Crop improvement
• Forensic analysis
• Bio-weapon creation
• Evolutionary Studies
• Climate change Studies
• Alternative energy
sources
• Improve nutritional
quality of crops
• Development of Drought
resistant crop varieties
• Veterinary Science
• Gene therapy
• Antibiotic resistance
• Insect resistance studies
• Drug Discovery
• Target
Identification
• Target Validation
• Lead Identification
• Lead Optimization

18. Team work for Computational Cancer Biology
statistician
Molecular Oncologist
( Pathway Analysis)
Pathologist
Oncologist
molecular biologist
mathematician
data scientist
Machine Learning Expert
data bases specialist
User friendly interfaces
IT infrastructure (storage,
updates etc)

19. Case Studies

20. Classification of Mental States using EEG Data
Applications - Human – robot interaction, Brain – computer interfacing
Experimental Procedure –
1. Data collection by attaching 4 extra cranial electrodes to head of
subjects (EEG) sensors – the subjects were shown different kinds of
movie clips to evoke different emotional states.
2. Feature Extraction – Through fast Fourier transform and Shannon
Entropy
3. Classification – using CNN, or any conventional classification
algorithm

21. Parkinson’s Prediction