1. RiboSearch Ben Daniel Ariel Kirshner Naomi
Instructor : Dr. Danny Barash
Adaya Cohen

2. Introduction Biological Introduction Method Layout
“The merge strategy”
Results and Conclusions

3. RNA A single-stranded nucleic acid made up of 4 nucleotides :
Purines : adenine (A), guanine (G)
Pyramidines: cytosine (C), and uracil (U).
WC pairs:
A-U G-C

4. Introduction Biological
Old scheme
Protein carry out all biological functions
RNA : only a stage between DNA to protein with no catalytic function
DNA
RNA
Protein

5. Biological introduction
New scheme
Since the discovery of self-splicing RNAs in the early 1980’s, a number of new structural and catalytic RNAs have been discovered.
Recent studies focusing on non-coding and small RNAs have led to discovery of RNA molecules that posses essential regulatory functions
DNA
RNA
Protein

6. RNA Secondary Structure
Hairpin
Internal loop
Bulge loop
Junction
Stem (double strand)
pseudoknot
The secondary structure of many RNAs is usually more conserved than their sequence

7. Riboswitch
Aptamer
Coding section 3’ 5’
Expression platform
5’ UTR
3’ UTR
RNA control elements that regulates gene expression, without the participation of proteins
Utilize a unique mechanism where by small molecules bind to aptamer/box region causing a conformational switch
Were found initially in 5’ UTR of bacteria with successive discoveries in prokaryotes
There are evidence suggesting riboswitches could be found in eukaryotes.

8. Riboswitch mechanism
Guanine bind to aptamer region with cause conformational change in the expression platform, which regulates the guanine metabolism.

9. G-box Regulates genes related to purine metabolism and transport
Binds purines
Consists of 2 hairpins and 1 internal junction

10. RiboSearch Goal Finding G-box in eukaryotic genomes Method
Combining existing search methods into one overall package

11. Search Methods Whiffer – CS department, BGU
RNAMotif – Macke et al. , 2001
RNAProfile – Pavesi et al. , 2004
STR2 – CS department, BGU

12. Whiffer Input Pattern that consists of : Output
Sequence information
Variable gaps
Base pairing brackets representing WC pairs
Output
Candidates locations that meet constraints imposed by the method
<<<< [2] TA [5] GTNTCTAC [3] <<<<< [3] CCNNNAA [3] >>>>> [5] >>>>

13. Whiffer
Method
Uses simple matching ,based on the constraints ,as opposed to dynamic programming.

14. RNAMotif Input Database of nucleotide sequences
Description file that consists of:
Descriptor section
Score section (optional)
Output
Candidates that meet the conditions of the descriptor and the scoring scheme

15. RNAMotif Sample descriptor file : descr h5 (minlen=6, maxlen=8)
ss (minlen=4, maxlen=6) h3
score {
gcnt = 0; glen = 0;
for( i = 1; i <= NSE; i++ ){
llen=length( se[i] );
glen=glen+llen;
for( j = 1; j <= glen; j++ ){
b = se[i,j,1];
if( b == "g" || b == "c" ) gcnt++; {
SCORE = 1.0 * gcnt / glen;
if( SCORE < .4 ) REJECT; } ss h5 h3

16. RNAMotif Method Two-stage algorithm Stage I : Compilation stage
Analyzing the specific motif, called a descriptor and converting it into a search tree based on the helical nesting of the motif

17. RNAMotif Method Two-stage algorithm Stage II : DFS
Depth first search of the tree that was created by the compilation stage
Each time a complete solution to the descriptor is found, the candidate is passed to an optional score section for scoring and ranking
In absence of score section the candidate is accepted

18. RNAProfile Input Number of distinct hairpins a motif has to contain
Set of unaligned RNA sequences expected to share a common motif

19. RNAProfile
Output
Regions that are most conserved throughout the sequences, according to
sequence of the regions
Secondary structure that can be formed according to base-pairing and thermodynamic rules

20. RNAProfile Method Two phases Phase I :
Extracting a set of candidate regions from each input sequence, whose predicted optimal secondary structure contains the number of hairpins given as input
Phase II :
The regions selected are compared with each other to find the group of most similar ones, formed by a region taken from each sequence

21. Method Summery Whiffer RNAMotif RNAProfile
Combines sequence and structure similarity
Very high specifity – potential candidates may be ruled out
RNAMotif
Similarity based mostly on structural elements, according to the descriptor
RNAProfile
Similarity based on both sequence and structure
Recommended as a post-processing step

22. Structure (bracket notation)
The merge strategy
Query:
Sequence
Structure (bracket notation)
Input
(((..((((…)))).))
Parsing
Whiffer
RNAMotif
Parsing
Candidates

23. Candidates
The location contained within a gene
The gene is relevant to the requested function (purine metabolism)
Filtering
RNAProfile
Post processing
Final candidates

24. Biological experiments
Final candidates
Sequence alignment
Biological experiments

25. Results – prokaryote Bacillus Halodurans
Merge
RNAMotif
Whiffer 7 4
Candidates 2
True positives 3 5
False positives
False negatives

26. Results – eukaryote Arabidopsis Thaliana
Merge
RNAMotif
Run #2
Run #1
Whiffer -
70000 30
Candidates 11 17
Final candidates

27. Results – eukaryote Arabidopsis Thaliana
Most promising candidates
Arabidopsis Thaliana

28. c2__ _
queryGBox CGTGGATATGGCACGCAAGTTTCTACCGGGCACCGTAAATGTCCGACTAT 50
c2__ _ _ TTCAGGTC-CATCTTTGGCTAGACCGAAGTCAGATAATTTGGCGTTAT 47
* * * ** * * **** * * *** * ***
queryGBox G
c2__ _ _ AGTCCTGAA 56

29. c3_ _
c3_sequences
GGATGAGGAACCAATTGACCCTGGATTTCAAGATT-TACAAAAGAACGTA 49
queryGBox CGTGGATATGGCACGCAAGTTTCTACCGGGCACCGTA 37
** *** **** ** *** * ****
c3_sequences AGCATCC
queryGBox AATGTCCGACTATG 51
* ***

30. RiboSearch - Conclusions
Filters false positives
Sequences are by far less conserved within eukaryotes than prokaryotes
The merge strategy is essential in eukaryotic genomes search

31. Our thanks
Dr. Danny Barash
Adaya Cohen