CSE182-L16

Non-coding RNA

Biol. Data analysis: Review

Protein SequenceAnalysis

Sequence Analysis

Gene Finding

Assembly

Much other analysis is possible

Protein SequenceAnalysis

Sequence Analysis

Gene Finding

Assembly

ncRNA

GenomicAnalysis/Pop. Genetics

A Static picture of the cell is insufficient

• Each Cell is continuouslyactive,– Genes are being

transcribed into RNA– RNA is translated into

proteins– Proteins are PT

modified andtransported

– Proteins performvarious cellularfunctions

• Can we probe the Celldynamically

GeneRegulation Proteomic

profilingTranscript 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 andtranslation. Non coding genes are transcribed by Pol III

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

• Structure: Given a sequence, what is the structureinto which it can fold with minimum energy?

tRNA structure

RNA structure: Basics

• Key: RNA is single-stranded. Think of a string over 4letters, 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-pairsdefines 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 algorithm1. 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(ri,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(ri,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 StructureProcedure 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

011231122

01111

0

i 1 2 3 4 5 6j

3

4

5

6

A C G A U UA 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 fractionatedsamples

Gene Regulation

Gene expression

• The expression oftranscripts and protein inthe cell is not static. Itchanges in response tosignals.

• The expression can bemeasured using micro-arrays.

• What causes the changein expression?

Transcriptional machinery

• DNA polymerase (II) scans the genome, initiatingtranscription, and terminating it.

• The same machinery is used for every gene, so while Pol IIis required, it is not sufficient to confer specificity

TF binding

• Other transcriptionfactors interact with thecore machinery andupstream DNA toprovide specificity.

• TFs bind to TF bindingsites which are clusteredin upstream enhancer andpromoter elements.

• The enhancer elementsmay be located many kbupstream of the core-promoter

Upstream elements

Transcription factors

TF binding sites• TF binding sites are

weak signal (about 10bp with 5bpconserved)

• If two genes are co-regulated, they arelikely to share bindingsites

• Discovery of bindingsite motifs is animportant researchproblem.

TGAGGAGTCAGGAG

TCAGGTGTGAGGTGTCAGGTG

g1

g2

g3

g4

g5

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

Discovering TF binding sites

• Identification of these TF bindingsites/switches is critical.

• Requires identification of co-regulatedgenes (genes containing the same set ofswitches).

• How do we find co-regulated genes?

Idea1: Use orthologous genes from differentspecies

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 bothconserved.

2. The species are distant. Bindingsites are conserved but notother sequence.

3. The species are very distant.Even binding sites are notconerved. The genes havealternative regulators.

Idea2: Measure expression of genes

• Northern Blot:– Quantitative

expression of afew genes

Microarray

• Expression level of all genes

Protein Expression using MS

Pathways

• Proteins interact totransduce signal,catalyze reactions, etc.

• The interactions can becaptured in a database.

• Queries on thisdatabase are aboutlooking for interestingsub-graphs in a largegraph.

Biological databases in NAR

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

RfamGenbank

SwissProt

Stanford microarray db

PDBKegg

dbSNP/OMIM/seattleSNPs

SWISS 2D-page

Summary

• Biological databases cannot beunderstood without understandingthe data, and the tools forquerying and accessing these data.

• While database technology (XML,Relational OO databases, textformats) is used to store this data,its use is (often) transparent forBioinformatics people.

• In this course, we looked at variousdata-streams, and pointed todatabases that store these data-streams

• Nucleic Acids Research brings outa database issue every January

2004: 548 databases