CSE182-L16 Non-coding RNA Biol. Data analysis: Review Assembly - - PowerPoint PPT Presentation

CSE182-L16

Non-coding RNA

• Biol. Data analysis: Review

Protein Sequence Analysis

Sequence Analysis Gene Finding Assembly

Much other analysis is possible

Protein Sequence Analysis

Sequence Analysis Gene Finding Assembly ncRNA Genomic Analysis/ Pop. Genetics

A Static picture of the cell is insufficient

• Each Cell is continuously

active,

– Genes are being transcribed into RNA – RNA is translated into proteins – Proteins are PT modified and transported – Proteins perform various cellular functions

• Can we probe the Cell

dynamically Gene Regulation Proteomic profiling Transcript profiling

ncRNA gene finding

• Gene is transcribed but not translated.
• What are the clues to non-coding genes?

– Look for signals selecting start of transcription and

• translation. Non coding genes are transcribed by Pol III

– Non-coding genes have structure. Look for genomic sequences that fold into an RNA structure

• Structure: Given a sequence, what is the structure

into which it can fold with minimum energy?

tRNA structure

RNA structure: Basics

• Key: RNA is single-stranded. Think of a string over 4

letters, AC,G, and U.

• The complementary bases form pairs.
• Base-pairing defines a secondary structure. The base-

pairing is usually non-crossing.

RNA structure: pseudoknots

Sometimes, unpaired bases in loops form ‘crossing pairs’. These are pseudoknots

RNA structure prediction

• Any set of non-crossing base-pairs

defines a secondary structure.

• Abstract Question:

– Given an RNA string find a structure that maximizes the number of non-crossing base- pairs – Incorporate the true energetics of folding – Incorporate Pseudo-knots

A combinatorial problem

• Input:
• A string over A,C,G,U
• A pairs with U, C pairs with G
• Output:
• A subset of possible base-pairs of maximum

size such that

• No two base-pairs intersect
• How can we compute this set efficiently?

RNA structure

1.

Nussinov’s algorithm

1.

Score B for every base-pair. No penalty for loops. No pesudo-knots.

2.

Let W(i,j) be the score of the best structure of the subsequence from i to j.

for i = n down to 1 { for j = i+1 to n { } }

W (i, j) = max B(r

i,rj) + W (i +1, j -1),

W (i, j -1), W(i +1,j) W (i,k) + W (k +1, j) i £ k < j Ï Ì Ô Ô Ó Ô Ô

Obtaining RNA structure

for i = n downto 1 { for j = i+1 to n { } }

W (i, j) = max B(r

i,rj) + W (i +1, j -1),

W (i, j -1), W(i +1,j) W(i,k) +W(k +1,j) (1) (2) (3) (4) Ï Ì Ô Ô Ó Ô Ô

if (1) { S(i,j) = / else if (2) S(i,j) = | else if(3) S(i,j) = - else S(i,j) = k }

Obtaining RNA Structure

Procedure print_RNA(i,j) { if S(i,j) = / { print “(i,j)”; print_RNA(i+1,j-1); else if (S(i,j) = -) { print_RNA(i+1,j); } else if (S(i,j) = |) { print_RNA(i,j-1); } else { k=S(i,j) print_RNA(i,k); print_RNA(k+1,j); } }

RNA structure: example

1 1 2 3 1 1 2 2 1 1 1 1 i 1 2 3 4 5 6 j 3 4 5 6

A C G A U U A C G A U U 1 2 3 4 5 6 1 2 3 4 5 6

2

RNA Structure: Details

Base-pairing & Loops

• Base-pairs arise from complementary nucleotides
• Single-stranded
• Stack is when 2 base-pairs are contiguous
• Loops arise when there are unpaired bases.
• They are characterized by the number of base-pairs that close it.
• Hairpin: closed by 1 base-pair
• Bulge/Interior Loops (2 base-pairs)
• Multiple Internal loops (k base-pairs)

Scoring Loops, multi-loops

• Zuker-Turner Energy Rules
• http://www.bioinfo.rpi.edu/~zukerm/rna/energy/node2.html
• Stacking Energies
• Energy for Bulges and Interior Loops
• Energy for Multi-loops

Other tricks for obtaining structure

• Alignment and Covariance

RNA: unsolved problems

• The structure problem is still unsolved.

– De novo prediction does not work as well. – Co-variance models require prior alignment.

• Many undiscovered non-coding genes

– miRNA, and others have only just been discovered. – Very hard to detect signal for these genes – Random sequence folds into low energy structures.

Other ncRNA: miRNA

• ncRNA ~22 nt in length
• Pairs to sites within the 3’ UTR,

specifying translational repression.

• Similar to siRNA (involved in RNAi)
• Unlike siRNA, miRNA do not need

perfect base complementarity

• Until recently, no computational

techniques to predict miRNA

• Most predictions based on cloning

small RNAs from size fractionated samples

Gene Regulation

Gene expression

• The expression of

transcripts and protein in the cell is not static. It changes in response to signals.

• The expression can be

measured using micro- arrays.

• What causes the change

in expression?

Transcriptional machinery

• DNA polymerase (II) scans the genome, initiating

transcription, and terminating it.

• The same machinery is used for every gene, so while Pol II

is required, it is not sufficient to confer specificity

TF binding

• Other transcription

factors interact with the core machinery and upstream DNA to provide specificity.

• TFs bind to TF binding

sites which are clustered in upstream enhancer and promoter elements.

• The enhancer elements

may be located many kb upstream of the core- promoter

Upstream elements Transcription factors

TF binding sites

• TF binding sites are

weak signal (about 10 bp with 5bp conserved)

• If two genes are co-

regulated, they are likely to share binding sites

• Discovery of binding

site motifs is an important research problem. TGAGGAG TCAGGAG TCAGGTG TGAGGTG TCAGGTG g1 g2 g3 g4 g5

http://www.gene-regulation.com/pub/databases.html#transfac

Discovering TF binding sites

• Identification of these TF binding

sites/switches is critical.

• Requires identification of co-regulated

genes (genes containing the same set of switches).

• How do we find co-regulated genes?

Idea1: Use orthologous genes from different species

ACGGCAGCTCGCCGCCGCGC ||||| || ||||||| || ACGGC-GGGCGCCGCCCCGC ACGGCAGCTCGCCGCCGC-C | || | ||||||| | AGTGC-GGGCGCCGCCTCAT ACGGC-GC-TCGCCGCCGCGC | | | || | | AT-ACGAAGTAGCGG-ATGGT

1. The species are too close (EX: humans and chimps). Binding & non-binding sites are both conserved. 2. The species are distant. Binding sites are conserved but not

• ther sequence.

3. The species are very distant. Even binding sites are not

• conerved. The genes have

alternative regulators.

Idea2: Measure expression of genes

• Northern Blot:

– Quantitative expression of a few genes

Microarray

• Expression level of all genes

Protein Expression using MS

Pathways

• Proteins interact to

transduce signal, catalyze reactions, etc.

• The interactions can be

captured in a database.

• Queries on this

database are about looking for interesting sub-graphs in a large graph.

Biological databases in NAR

• http://www3.oup.co.uk/nar/database/c
• 548 databases in various categories

Rfam Genbank SwissProt Stanford microarray db PDB Kegg dbSNP/OMIM/seattleSNPs SWISS 2D-page

Summary

• Biological databases cannot be

understood without understanding the data, and the tools for querying and accessing these data.

• While database technology (XML,

Relational OO databases, text formats) is used to store this data, its use is (often) transparent for Bioinformatics people.

• In this course, we looked at various

data-streams, and pointed to databases that store these data- streams

• Nucleic Acids Research brings out

a database issue every January

2004: 548 databases