Retrieving DNA sequence from data base.pptx
- 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.
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