1. Projects

2. Instructions for the final project
Introduction to Bioinformatics
Key dates
lists of suggested projects published *
*You are highly encouraged to choose a project yourself or find a relevant project which can help in your research
Final date to chose a project
23.1– meetings on projects (individual pairs with supervisor)
30.1- Submission project overview (one page)
15.3 Poster submission
22.3 Poster presentation (12-2PM)

3. Working on your project-step by step
Projects are conducted in pairs !
1. Choose a topic (either from the list or your own idea)
2. Preparing for the first meetings
After you have chosen the topic you should start planning the project
Make sure you understand the problem and read the necessary biological background
As most projects require working on a specific data set you should
search for the most suitable data for your project .
At this stage you should download the data and explore it
Now that you have the information you should formulate your working plan and think of the relevant tools you would need to use
Prepare questions
Being prepared for the meeting is the first important step for succeeding in the project!!!

4. The proposal should be one page and include -Title -Main question
Write a proposal
Following the meeting with your supervisor you will need to write a short one page proposal , describing your project
The proposal should be one page and include
-Title
-Main question
-Major Tools you are planning to use to answer the questions
4. Working on your proposal
Following the submission of the proposal and the feedback from your supervisor you can start working on the project following your proposed plan. Your initial results should guide you towards your next steps.
During the work you are highly encouraged to consult with your supervisor.
Important! make sure to summarize the results and extract the relevant information needed to answer your question, it is recommended to save the raw data for your records , but don't present raw data in your poster

5. ! Remember NO is also an answer as long as you are sure it is NO.
5. Summarize the project
When you feel you explored all tools you can apply to answer your question you should summarize your results and get to conclusions.
At this stage it is highly recommended to set a meeting with your supervisor .
! Remember NO is also an answer as long as you are sure it is NO. .

6. 6. Summarizing final project in a poster
Prepare in PPT poster size cm
Title of the project
Names and affiliation of the students presenting
The poster should include 5 sections :
Background should include description of your question (can add figure)
Goal and Research Plan:
Describe the main objective and the research plan
Results (main section) : Present your results in 3-4 figures, describe each figure (figure legends) and give a title to each result
Conclusions : summarized in points the conclusions of your project
References : List the references of paper/databases/tools used for your project
Examples of posters will be presented in class

7. Motif Search

8. What are Motifs
Motif (dictionary) A recurrent thematic element, a common theme

9. Find a common motif in the text

10. Find a short common motif in the text

11. Motifs in biological sequences
Sequence motifs represent a short common sequence (length 4-20) which is highly represented in the data

12. Motifs in biological sequences
What can we learn from these motifs?
Regulatory motifs on DNA or RNA
Functional sites in proteins

13. Regulatory Motifs on DNA
Transcription Factors (TF) are regulatory protein that bind to regulatory motifs near the gene and act as a switch bottom (on/off)
TF binding motifs are usually 6 – 20 nucleotides long
located near target gene, mostly upstream the transcription start site
Transcription
Start Site
TF1
TF2
Gene X
TF1
motif
TF2
motif

14. What can we learn from these motifs?
About half of all cancer patients have a mutation in a gene
called p53 which codes for a key Transcription factors.
The mutations are in the DNA binding region and allows
tumors to survive and continue growing even after chemotherapy
severely damages their DNA
P53
Transcription Factor
Target Gene
Binding sites (moifs)

15. Why is P53 involved in so many cancer types?
p53 regulated over
100 different genes
(hub)
We are interested to identify the genes
regulated by p53

16. Can we find TF targets using a bioinformatics approach?

17. Finding TF targets using a bioinformatics approach?
Scenario 1 : Binding motif is known (easier case)
Scenario 2 : Binding motif is unknown (hard case)

18. Scenario 1 : Binding motif is known
Given a motif find the binding sites in an input sequence

19. Challenges in biological sequences
Motifs are usually not exact words
…….

20. How to present non exact motifs?

21. How to present non exact motifs?
Position Specific Scoring Matrix (PSSM)
Probability for each base in each position
Seq 1 AAAGCCC
Seq 2 CTATCCA
Seq 3 CTATCCC
Seq 4 CTATCCC
Seq 5 GTATCCC
Seq 6 CTATCCC
Seq 7 CTATCCC
Seq 8 CTATCCC
Seq 9 TTATCTG 1 2 3 4 5 6 A T G C

22. The PSSM can be also represented as a sequence logo
-A letter’s height indicates the information it contains

23. Presenting a sequence motif as a logo
PSSM
PWM 1 2 3 4 5 6 A G C T 1 2 3 4 5 6 A
0.75
0.25 G C T
TTCACG
TACATG
TACAGG
TACAAG
Divide each score by background
probability 0.25
PWM= Position Weight Matrix
Letter Height
Log2S
T position 1=Log24=2
T position 5=Log21=0

24. חידה
מהו המקסימום גובה שנוכל לקבל בלוגו שמתאר מוטיב שהתקבל מרצפי חלבונים??

25. How to search for a motif in a sequence given a PSSM:
Given a string s of length l = 7
s = s1s2…sl
Pr(s | W) =
Example:
Pr(CTAATCCG) =
0.67 x 0.89 x 1 x 1 x 0.89
x 1 x 0.89 x 0.11 1 9 A 6 8 7 C G T
.11 1 A
.67
.89
.78 C G T W
Counts of each base
In each column
Probability of each base
In each column
Wk = probability of base  in column k

26. How to search for a motif in a sequence given a PSSM:
Given sequence S (e.g., 1000 base-pairs long)
For each substring s of S,
Compute Pr(s|W)
Define if Pr(s|W) > threshold
The threshold is calculated based on the probability to find it in random !! And can be different for each motif.
Open question: What do we do when searching motifs in DNA?

27. Scenario 2 : Binding motif is unknown
“Ab initio motif finding”
Why is it hard???

28. Are common motifs the right thing to search for ?

29. ?

30. Solutions:
-Searching for motifs which are enriched in one set but not in a random set
- Use experimental information to rank the sequences according to their binding affinity and search for enriched motifs at the top of the list

31. Chromatin Immunoprecipitation followed by sequencing
ChIP-Seq
Chromatin Immunoprecipitation followed by sequencing
Finding regions in the genome to which a DNA binding protein (transcription factor) binds to.

32. Cross-linking immunoprecipitation
CLIP-Seq
Cross-linking immunoprecipitation
Identifying the regions in the transcriptome to which an RNA binding protein (e.g. splicing factor) binds to.

33. Finding the p53 binding motif in a set of p53 target sequences which are ranked according to binding affinity
Best
Binders
ChIP –seq
Weak
Binders

34. a word search approach to search for enriched motif in a ranked list
Ranked sequences list
CTGTGA
CTGTGA
CTGTGA
CTGTGA
Candidate k-mers
CTACGC
ACTTGA
ACGTGA
ACGTGC
CTGTGC
CTGTGA
CTGTAC
ATGTGC
ATGTGA
CTATGC
CTGTGA
CTGTGC
CTGTGA
CTGTGA
CTGTGA

35. uses the minimal hyper geometric statistics (mHG) to find enriched motifs
The total number of
input sequences
The number of sequences containing the motif
The number of sequences at
the top of the list
The number of sequences containing the motif among the top sequences
Ranked sequences list
CTGTGA
CTGTGA
CTGTGA
CTGTGA
CTGTGA
CTGTGA
CTGTGA
CTGTGA

36. The enriched motifs are combined to get a PSSM which represents the binding motif

38. P[ED]XK[RW][RK]X[ED]
Protein Motifs
Protein motifs are usually 6-20 amino acids long and
can be represented as a consensus/profile:
P[ED]XK[RW][RK]X[ED]
or as PWM