Introduction to Bioinformatics - Tutorial no. 9 RNA Secondary Structure Prediction.

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Introduction to Bioinformatics - Tutorial no. 9 RNA Secondary Structure Prediction

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Transcript of Introduction to Bioinformatics - Tutorial no. 9 RNA Secondary Structure Prediction.

Page 1: Introduction to Bioinformatics - Tutorial no. 9 RNA Secondary Structure Prediction.

Introduction to Bioinformatics - Tutorial no. 9

RNA Secondary Structure Prediction

Page 2: Introduction to Bioinformatics - Tutorial no. 9 RNA Secondary Structure Prediction.

Rfam http://www.sanger.ac.uk/Software/Rfam Rfam is a large collection of multiple

sequence alignments and covariance models covering many common non-coding RNA families. For each family in Rfam you can:

View and download multiple sequence alignments Read family annotation Examine species distribution of family members Follow links to other databases

Page 3: Introduction to Bioinformatics - Tutorial no. 9 RNA Secondary Structure Prediction.

Rfam Browse

Browse the Rfam hierarchy

Page 4: Introduction to Bioinformatics - Tutorial no. 9 RNA Secondary Structure Prediction.

Rfam Search

Search by EMBL ID

Search your own sequence

Page 5: Introduction to Bioinformatics - Tutorial no. 9 RNA Secondary Structure Prediction.

RNA structure prediction Vienna RNA package:

RNAfold -- predict minimum energy secondary structures and pair probabilities

RNAalifold – predict consensus secondary structure RNAeval -- evaluate energy of RNA secondary structures RNAheat -- calculate the specific heat (melting curve) of an RNA

sequence RNAinverse -- inverse fold (design) sequences with predefined

structure RNAdistance -- compare secondary structures RNApdist -- compare base pair probabilities RNAsubopt -- complete suboptimal folding

Web interface: to RNAfold, RNAalifold, RNAinverse Other can be downloaded for Unix and for Windows.

Page 6: Introduction to Bioinformatics - Tutorial no. 9 RNA Secondary Structure Prediction.

RNAfold:

http://www.tbi.univie.ac.at/~ivo/RNA/

Gives best stabilized structure (structure with mfe – minimal free energy)

In addition, uses a partition function and base pair probabilities in the thermodynamic ensemble (default and recommended).

Page 7: Introduction to Bioinformatics - Tutorial no. 9 RNA Secondary Structure Prediction.

Input (sequence only)

Fold Algorithm

RNA or DNA parameters

Target temperature

Advanced fold options

Output formats

Link to your previous run

Email (necessary for large sequences)

Page 8: Introduction to Bioinformatics - Tutorial no. 9 RNA Secondary Structure Prediction.

Output in bracket notation

Output - PostScript

Page 9: Introduction to Bioinformatics - Tutorial no. 9 RNA Secondary Structure Prediction.
Page 10: Introduction to Bioinformatics - Tutorial no. 9 RNA Secondary Structure Prediction.

Free energy (∆G)

Enthalpy (∆H)

Melting (de-hybridization) temperature

Page 11: Introduction to Bioinformatics - Tutorial no. 9 RNA Secondary Structure Prediction.

RNAalifold:Predicts consensus secondary structures for

sets of aligned RNA (ClustalW files).

Information from the alignment:

1. Conserved nucleotide pairs are shown normally.

2. Pairs with consistent mutations, which support the structure, are marked by circles.

3. Pairs with inconsistent mutations are shown in two shades of gray.

Page 12: Introduction to Bioinformatics - Tutorial no. 9 RNA Secondary Structure Prediction.

. - unpaired base

( ) - base i pairs base j

{} - a weaker version of the above

| - a base that is mostly paired but has pairing partners both upstream and downstream

Bracket notation:

(((.((((...))))..))) =

Page 13: Introduction to Bioinformatics - Tutorial no. 9 RNA Secondary Structure Prediction.

Question

Indicate which of the structure represent the same secondary structure

Page 14: Introduction to Bioinformatics - Tutorial no. 9 RNA Secondary Structure Prediction.

Question Yossi is a brilliant student in the “Introduction to

Bioinformatics” course with a great gut feeling. The moment, Yossi saw the following genomic sequence, he understood that it contains a functional RNA. Yossi checked his proposal and discovered that indeed he was right, however when looking at the structure he suddenly realized that something is wrong…… To help Yossi: Repeat the experiment that Yossi ran to check his predictions. Assuming a single mutation had accord in the sequence, find

the mutation that caused the unexpected results. Correct this problem and present the correct results.

Page 15: Introduction to Bioinformatics - Tutorial no. 9 RNA Secondary Structure Prediction.

tRNAscan

Page 16: Introduction to Bioinformatics - Tutorial no. 9 RNA Secondary Structure Prediction.

Question (cont.)

The problem is the internal loop in the stem of the right leaf.

Using tRNAscan tool, we can find that the given sequence contains the tRNA between bases 248 and 324.

Running RNAfold, we receive the following structure:

Page 17: Introduction to Bioinformatics - Tutorial no. 9 RNA Secondary Structure Prediction.

Question (cont.)

Running Blastn we can locate the mutation A->C at position 299 of the given sequence.

The corrected structure:


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