Database is organized collection of information which can be easily retrude

1. Retrieving RNA sequence from
data base
PRESENTED BY
Mahnoor BAIG
Roll no. 181607
Department:
zoology
Semester 8th

2. Table of contents:-
Introduction
Methods of RNA structure
prediction
 Maximize base pair
approach
 Minimization of energy

3. Data base:

4.  RNA
 Single strand of nucleotide
 Contain ribose sugar
 Four types of nitrogenous bases i-e
 Adenine
 Guanine
 Cytosine
 Uracil
 & phosphate group.

5.  Most of RNA transcripts are
identifiable as protein coding mRNA.
 It is needed to distinguish them from
smaller number of well characterized
non protein coding RNAs such as
 Transfer RNA
 Ribosomal RNA
 Recent genomic studies have revealed
the existence of thousands of non-
coding or non functional transcript
whose function & significance are
unclear.

6.  Linear sequence of bases
i-e adenine, guanine, cytosine &
uracil is the primary structure of
RNA.
 In RNA world guanine & cytosine
form base pair by triple H-bond.
 Adenine & uracil form base pair by
double H-bond while guanine &
uracil form base pair by single H-
bond & it occurs rarely.

7. METHODS OF RNA STRUCTURE
PREDICTION:-
Two principal approaches of RNA
structure prediction are
 Maximize base pair approach
 Minimization of energy

8. 1. Maximize base pair
approach:-
 This approach is based on a given
RNA sequence; we need to determine
maximal base pair. We then allign the
bases according to their ability to pair
with each other.
 It helps to determine optimal structure
using dynamic programming
approach or Nussinov alogrithm.

9. Nussinov alogrithm:-
 To find configurations with greatest
number of paired bases.
 The number of possible configurations
to be expected grown exponentially with
the length of sequence.
 It also calculates the best structure for
small sub-sequences.
 It also work outwards to larger & larger
sub-sequences.

10.  The dynamic alogrithm has two stages.
 In the final stage, we will recrusively
calculate the maximal number of base
pairs that can be formed for sub-
sequences.
 In the trace back stage, we trace back
through the calculated matrix to obtain
one of the maximally base paired
structure.
 Consider two short sub-sequences in a
long sequence ACGGU……ACGU

11.  For sub-sequence of length 1
 A, C, G, G, U,…….A, C, G, U, C.
 # of base pairs
 0, 0, 0, 0, 0,………,0, 0, 0, 0, 0.
 For sub-sequence of length 2
 AC, CG, GG, GU,….,AC, CG, GU, UC
 # of base pairs
 0, 1, 0, 1…………,0, 1, 1, 0
 For sub-sequence of length 3
ACG,CGG,GGU,….UAC, ACG,CGU,GUC

12.  Example.,
GUC
 G-C+U 1+0 =1 head paired
 G+UC 0+0 =0 head unpaired
 GU+C 1+0 =1 tail unpaired

13. 2. Minimize energy
approach:-
 In this approach, all possible choices of
complimentry sequences are
considered.
 So we need to consider all possible
choices of complimentary sequences &
find the most stable structure.
 Base pairs appear in ‘clusters’;
 we call them stacks, which are the
energetically favourable.

14.  Most of the stability of RNA secondary
structure is determined by stacks as it
contribute to the negative free energy.
 Unpaired bases form destabilizing loops
& these contribute to the positive free
energy.
 Energy minimization algorithm predicts
the secondary structure by minimizing
the free energy (G).
 G is calculated as the sum of the
individual contributions of loops, base-
pair and secondary structure element.

16. 
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