stem Loops

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12BBI321-Artificial Intelligence

Seminar

on

rRNA Gene Finding : Stem Loops

as SignalsBy

USHA B BIRADAR

1RV12BBI11III Semester,M.Tech Bioinformatics,

Department of Biotechnology

RVCE,Bangalore79.


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RNA Sequences

Composition : A, U, G, C

RNA Sequences

Coding

mRNA - proteins

Non coding

rRNA, tRNA,siRNA..

Functional RNAs

Subclass-

Structural RNAs

November 8, 2013 Dept of Biotechnology[M.TechBioinformatics]

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RNA Secondary Structures

Virtue: Hydrogen bonds

with complementary

basesImplication:Fold upon

itself and form stable

secondary structures


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Stem Loops

Logical expressions1

RNA sequence: n basesIndexed: (1,2,3,4,.e,I,k,p,t,n) where 1


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Internal structures


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Some of the RNA secondary structure components 1,3


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Stem Loop Probabilities1

Stem loop: P(stem loop)

Nucleotides: P(A), P(U), P(C), P(G)

Allowed pairing : A-U,G-C,G-U

Example : say upstream (AAGG),

then possibilities downstream are

(UUCC)

(UUCU)(UUUC) and

(UUUU)


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Example


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Structural RNA Gene Finders

Thermodynamic models

Gibbs free energy( G)

Le et al and Chen et al : Z scores

Many models which were developed later based onG, G+C content etc

Drawbacks: dependent on length, strong evidences forsuboptimal G , computational complexity O(n3)


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RNAFold


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RNAFold


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Stem Loop Centered Approach

RNA genes conserve structure before sequence!!

Stem loops (RNA structures) helices and sheets(protein structures)

Pairing rules

Directionalty 53 Stem loops found in genomic regions coding for structural RNAs are of

higher densitiesand longer lengths than those in the genomic

counterparts No window partitioning required


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Methods

DNA -------transcribe-----> RNA

Valid pairing : A-U, U-A, G-C, C-G, G-U, U-G

High G-C pair composition favored

Artificial Intelligence technique

Search Algorithm implemented and extendedto complex techniques like HMMs, Neural

Networks (for classification purposes)


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1. Basic Stem Loop finding algorithm

Problem:

Find the positions and number of stem loops inthe given RNA sequence

Given: RNA sequence of length n

Allowed bp

Other parametersOutput:

Each stem loop is stored as a tuple


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Search Algorithm1: Finds tetra loops


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2. Analysis with random sequences3. rRNA Stem Loop characters

Studies on model organisms: E. coli, Saccharomycessps, etc

Outcomes:

Size of hairpin loop : 3- 20 bases

Internal loop size : ~7 bases

Bulges : 1-4 bases

%GC bp : ~30-40 %


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Methods continued


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Extended Stem Loop Finding

Algorithm

Incorporate SL of different sizes

Find internal loops and bulges ( alignment method , scores

assigned)

COMPLEXITY OF THE ALGORITHM

Worst case: O(n2)

Real case : O(n) ..i.e lenth of stem loop


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Stem Loop Statistics1

bps Span

cSpacing

fSpacing

Combined metrics:

[(cSpacing OR fSpacing) * bps]


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Statistics

1. Mean metric domains for a given genomicdomain ( )

2. Hypothesis tests

Finding rRNA genes

Calculate all statistical values for the

unknown gene.

Compare against standard with appropriateconfidence limits


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Finding rRNA Genes

Each metric individually used

Complex methods : apply HMMs, Neural

Networkswith different metrics as inputs (

appropriate weights assigned)

Improvisations: Accuracy, Efficiency ,

parameter optimization considerations.


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Neural Network Model


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.

.

.

.

VALID PAIRING

INTERIOR BULGE

LENGTH

cSpacing

fSpacing

Input nodes Output :

rRNAyes/no

Wij

Wij

Input networks

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Tools

Vienna RNA package

MFOLD

SPECIFIC rRNA tools: SILVA rRNA database project (Max Planck Institute for Marine

Microbiology, Bremen, Germany) - provides comprehensive, qualitychecked and regularly updated datasets of aligned small (16S/18S, SSU)and large subunit (23S/28S, LSU) ribosomal RNA (rRNA) sequences for allthree domains of life (Bacteria, Archaea and Eukarya).

RNAmmer 1.2- predicts 5s/8s, 16s/18s, and 23s/28s ribosomal RNA in fullgenome sequences

The Ribosomal Database Project - (RDP(Michigan State University Centrefor Microbial Ecology, U.S.A.). -provides ribosome related data andservices, including online data analysis and aligned and annotatedBacterial and Archaeal small-subunit 16S rRNA sequences.

Ridom- Ribosomal RNA analysis for clinically relevant bacteria -(University of Wrzburg, Germany).

Rifle- (Universitat Bielefeld, Germany) The RIFLE system comparesrestriction patterns of 16S rDNA amplicons against a database oftheoretical restriction patterns generated from a 16S rDNA database


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CASE STUDIES

Sanjun, Rafael, and Antonio V. Bordera(2011).

"Interplay between RNA structure and protein

evolution in HIV-1." Molecular biology and

evolution28.4 : 1333-1338. Yu, Chien-Hung, et al(2011). "Stemloop structures

can effectively substitute for an RNA pseudoknot

in 1 ribosomal frameshifting." Nucleic acids

research39.20 : 8952-8959.


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References1. Kirt M. Noel, Kay C. Wiese (2008).Considering Stem-Loops as Sequence

Signals for Finding Ribosomal RNA Genes.Computational Intelligence in

Biomedicine and Bioinformatics Studies in Computational

Intelligence ,Volume 151.

2. Ronny Lorenz et al (2011).ViennaRNA Package 2.0. Algorithms Mol

Biol. ,vol 6: 26.

3. Yoon, Byung-Jun. (2008).Effective annotation of noncoding RNA familiesusing profile context-sensitive HMMs. Communications, Control and

Signal Processing, 2008. ISCCSP 2008. 3rd International Symposium on.

IEEE, 2008.

4. Yoon, Byung-Jun. (2009).Hidden markov models and their applications in

biological sequence analysis.Current genomics10.6 : 402.

5. Carter, Richard J., Inna Dubchak, and Stephen R. Holbrook(2001). A

computational approach to identify genes for functional RNAs in genomic

sequences.Nucleic Acids Research29.19 : 3928-3938.


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THANK

YOU!!

stem Loops

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