1. Introduction to Bioinformatics
Lecturer: Prof. Yael Mandel-Gutfreund
Teaching Assistance:
Rachelly Normand
Alona Rabner
Course web site :

2. What is Bioinformatics?

3. Course Objectives To introduce the bioinfomatics discipline
To make the students familiar with the major biological questions which can be addressed by bioinformatics tools
To introduce the major tools used in the field

4. Course Structure and Requirements
Class Structure
2 hours lecture
1 hour tutorial
2. Home work
Homework assignments will be given every second week
The homework will be done in pairs.
4/4 homework assignments will be submitted+
Project proposal
2. A final project will be conducted in pairs
* Project will be presented as a poster –poster day 22.3

5. Grading 20 % Homework assignments 80 % final project (10% proposal,
20% supervisor evaluation
70% poster presentation)

6. What is Bioinformatics?

7. What is Bioinformatics?
“The field of science in which biology, computer science, and information technology merge to form a single discipline”

8. Central Paradigm in Molecular Biology
Gene (DNA)
mRNA
Protein
21ST centaury
Genome
Transcriptome
Proteome

9. Information explosion in biology
Human genome length =3*109

10. The revolution in molecular biology
High Throughput Technologies
-Next Generation DNA sequencing > Whole genomes sequencing
-Metagenomics >Sequencing DNA from the
environment
-Microbiome Analysis > Sequencing microbial
communities
-Microrrays/RNA sequencing > RNA expression analysis
- Chip-seq > protein-DNA interactions
- CLIP-seq > Protein-RNA interactions
- Mass Spectrometry > Protein Expression analysis

11. Building models from parts lists
Lazebnik, Cancer Cell, 2002

12. Building models from parts lists

13. Computational tools are needed to distill pathways of interest from large molecular interaction databases
Thinking computationally about biological process may lead to more accurate models,
which in turn can be used to improve the design of algorithms
Navlakha an Bar-Joseph 2011

14. What do we do in Bioinformatics?
- Analyze and interpret the various types of biological data:
Genomic Sequences (DNA)
Transcriptomic Sequences (RNA)
Proteomic sequences (Proteins)
Protein Structures (Proteins)
RNA structure (RNA)
Develop new algorithms and tools
To assess the biological information,
Handel large datasets,
find relationships between data sources etc…

15. What of all this will we learn in the course?
> Pairwise and multiple alignment
> Database search
> Protein alignments
> DNA Sequencing
> Gene expression/ Clustering analysis
> Phylogenetic analysis
> Motif search
> Structural bioinformatics (RNA and proteins)
> Biological networks

16. Manny different applications..
Basic Science
Main Goal: Understand the living cell
Find the function of a new protein
Find the genes/proteins that are unique to human
Medical applications
- Identify the mutations (SNPs) that cause genetic diseases
- Diagnosis ..find the features that characterize disease states
- Find and develop new and better drugs
…..
Agriculture applications
Higher yield crop
Increase shelf life ……

17. Find the function of a new protein
Basic Science
Find the function of a new protein
-Database search

18. Discover Function of a new protein

19. Find the genes/proteins that are unique to human
Basic Science
Find the genes/proteins that are unique to human
-Phylogenetic analysis

20. How can we be so similar--and yet so different?
Perhaps not surprising!!!
How humans
are chimps?
Comparison between the full drafts of the human and chimp genomes
revealed that they differ only by 1.23%
How can we be so similar--and yet so different?

21. Where are we different ?? Where are we similar ???
VERY SIMAILAR
Conserved between many organisms
VERY
DIFFERENT

22. Identify the mutations (SNPs) that cause genetic diseases
Medical applications
Identify the mutations (SNPs) that cause genetic diseases
-Pairwise and multiple alignments
-DNA sequencing

23. Due to 1 swapping of an A for a T
Sickle Cell Anemia
Due to 1 swapping of an A for a T

24. Healthy Individual
>gi| |ref|NM_ | Homo sapiens hemoglobin, beta (HBB), mRNA
ACATTTGCTTCTGACACAACTGTGTTCACTAGCAACCTCAAACAGACACCATGGTGCATCTGACTCCTGA
GGAGAAGTCTGCCGTTACTGCCCTGTGGGGCAAGGTGAACGTGGATGAAGTTGGTGGTGAGGCCCTGGGC
AGGCTGCTGGTGGTCTACCCTTGGACCCAGAGGTTCTTTGAGTCCTTTGGGGATCTGTCCACTCCTGATG
CTGTTATGGGCAACCCTAAGGTGAAGGCTCATGGCAAGAAAGTGCTCGGTGCCTTTAGTGATGGCCTGGC
TCACCTGGACAACCTCAAGGGCACCTTTGCCACACTGAGTGAGCTGCACTGTGACAAGCTGCACGTGGAT
CCTGAGAACTTCAGGCTCCTGGGCAACGTGCTGGTCTGTGTGCTGGCCCATCACTTTGGCAAAGAATTCA
CCCCACCAGTGCAGGCTGCCTATCAGAAAGTGGTGGCTGGTGTGGCTAATGCCCTGGCCCACAAGTATCA
CTAAGCTCGCTTTCTTGCTGTCCAATTTCTATTAAAGGTTCCTTTGTTCCCTAAGTCCAACTACTAAACT
GGGGGATATTATGAAGGGCCTTGAGCATCTGGATTCTGCCTAATAAAAAACATTTATTTTCATTGC
>gi| |ref|NP_ | beta globin [Homo sapiens]
MVHLTPEEKSAVTALWGKVNVDEVGGEALGRLLVVYPWTQRFFESFGDLSTPDAVMGNPKVKAHGKKVLG
AFSDGLAHLDNLKGTFATLSELHCDKLHVDPENFRLLGNVLVCVLAHHFGKEFTPPVQAAYQKVVAGVAN
ALAHKYH

25. Diseased Individual
>gi| |ref|NM_ | Homo sapiens hemoglobin, beta (HBB), mRNA
ACATTTGCTTCTGACACAACTGTGTTCACTAGCAACCTCAAACAGACACCATGGTGCATCTGACTCCTGA
GGTGAAGTCTGCCGTTACTGCCCTGTGGGGCAAGGTGAACGTGGATGAAGTTGGTGGTGAGGCCCTGGGC
AGGCTGCTGGTGGTCTACCCTTGGACCCAGAGGTTCTTTGAGTCCTTTGGGGATCTGTCCACTCCTGATG
CTGTTATGGGCAACCCTAAGGTGAAGGCTCATGGCAAGAAAGTGCTCGGTGCCTTTAGTGATGGCCTGGC
TCACCTGGACAACCTCAAGGGCACCTTTGCCACACTGAGTGAGCTGCACTGTGACAAGCTGCACGTGGAT
CCTGAGAACTTCAGGCTCCTGGGCAACGTGCTGGTCTGTGTGCTGGCCCATCACTTTGGCAAAGAATTCA
CCCCACCAGTGCAGGCTGCCTATCAGAAAGTGGTGGCTGGTGTGGCTAATGCCCTGGCCCACAAGTATCA
CTAAGCTCGCTTTCTTGCTGTCCAATTTCTATTAAAGGTTCCTTTGTTCCCTAAGTCCAACTACTAAACT
GGGGGATATTATGAAGGGCCTTGAGCATCTGGATTCTGCCTAATAAAAAACATTTATTTTCATTGC
>gi| |ref|NP_ | beta globin [Homo sapiens]
MVHLTPVEKSAVTALWGKVNVDEVGGEALGRLLVVYPWTQRFFESFGDLSTPDAVMGNPKVKAHGKKVLG
AFSDGLAHLDNLKGTFATLSELHCDKLHVDPENFRLLGNVLVCVLAHHFGKEFTPPVQAAYQKVVAGVAN
ALAHKYH

26. Diagnosis ..find the features that characterize disease states
Medical Applications
Diagnosis ..find the features that characterize disease states
-Gene expression/clustering analysis
-Motif search

27. Samples were taken from patients with adenocarcinoma.
hundreds of genes
that differentiate between
cancer tissues in different
stages of the tumor were found.
The arrow shows an example
of a tumor cells which
were not detected correctly by
histological or other
clinical parameters.
Ramaswamy et al, 2003 Nat Genet 33:49-54

28. Find and develop new and better drugs
Medical applications
Find and develop new and better drugs
-DNA sequencing
-Gene expression
-Structural Bioinformatics
-Biological networks

29. Bioinformatics can dramatically reduce the cost and time for developing a new drug
Putative drug-target candidates
Billions of $
Pre-discovery
Discovery
VALIDATION
Clinical trials
Approval
Envisagenics Inc.

30. Bioinformatics can dramatically reduce the cost and time for developing a new drug
Putative drug-target candidates
Pre-discovery
Discovery
VALIDATION
Clinical trials
Approval
Millions of $
Good putative drug-target candidates
Envisagenics Inc.

31. Keats ( )
Kafka ( )
Orwell ( )
Mozart ( )
Schubert ( )
Chopin ( )

32. Did you know?
Infectious diseases are still number 1 cause of premature death
(0-44 years of age) worldwide.
Annually kill >13 million people
(~33% of all deaths)

33. The ribosome is a target for approximately half of antibiotics characterized to date
Antibiotics targets of the large ribosomal subunit

34. Using bioinformatics to find new target sites on the ribosome
Bad site
Good site

35. Manny different applications.. And beyond…
Personalized medicine

37. How can bioinformatics contribute to Medicine?
MAKE THE DATA WORK FOR US