RNA Abstract Shape Analysis - · PDF fileshape space RNAshapes Simple shape analysis Complete...

AdvancedCourse:Shapes

RobertGiegerich

Motivation

Lost in FoldingSpace

Abstractioncomes to rescue

Abstractshapes

Defining shapeabstractions

Properties of theshape space

RNAshapes

Simple shapeanalysis

Completeprobabilisticshape analysis

ShapeProbabilitites

Application:Shape basedindexing

Application:Shape basedmatching

RNA Abstract Shape Analysis

Robert Giegerich

Faculty of Technology & Center of BiotechnologyBielefeld University

robert@TechFak.Uni-Bielefeld.DE

EMBO Practical Course on Computational RNA Biology,Cargese, April 2010

Robert Giegerich Advanced Course: Shapes

RobertGiegerich

Motivation

Abstractshapes

RNAshapes

ShapeProbabilitites

Where do we stand ...

1 Thermodynamic model (X. Flamm)

2 MFE folding, optimal structure, fallacies (G. Steger)

3 representative structural alternatives

4 structure prediction from multiple sequences (D.Mathews)

5 structure comparison (D. Mathews)

6 search by structure (I. Meyer, P. Gardner)

7 . . .

RobertGiegerich

Motivation

Abstractshapes

RNAshapes

ShapeProbabilitites

1 MotivationLost in Folding SpaceAbstraction comes to rescue

2 Abstract shapesDefining shape abstractionsProperties of the shape space

3 RNAshapesSimple shape analysisComplete probabilistic shape analysisShape Probabilitites

4 Application: Shape based indexing

5 Application: Shape based matching

RobertGiegerich

Motivation

Abstractshapes

RNAshapes

ShapeProbabilitites

Better than optimal . . . (1)

Can we get better/more information from thermodynamicfolding than the MFE structure?

How accurate is the MFE structure anyway?

RobertGiegerich

Motivation

Abstractshapes

RNAshapes

ShapeProbabilitites

Can we get better/more information from thermodynamicfolding than the MFE structure?

How accurate is the MFE structure anyway?

RobertGiegerich

Motivation

Abstractshapes

RNAshapes

ShapeProbabilitites

2004 Mfold evaluation by Gutell Lab

Doshi KJ, Cannone JJ, Cobaugh CW, Gutell RR.: Evaluation of the

suitability of free-energy minimization using nearest-neighbor energy

parameters for RNA secondary structure prediction. BMC Bioinformatics.

2004 Aug 5;5:105.

Compares MFE foldings to structures derived by comparativeanalysis and proven by experimental techniques.Findings:

base pair accuracy of about 20% - 71%

no improvement from recently updated thermodynamicparameters

note: did not check for good near-optimal solutions

RobertGiegerich

Motivation

Abstractshapes

RNAshapes

ShapeProbabilitites

Base pair accuracy – what does it mean?

(((( ))))

( )((((

(((())))

(((( ))))

( )....

.... ....

((((...)))) ((((...))))...((((...))))...((((...))))...((((....))))

((((...)))) ..............((((((((((((((((........))))))))))))))))

((((...)))) ............((((((((((((((((........))))))))))))))))..

4 out of 20 BP correct...

....))))))))

.... ))))(( ))

4 out of 20 BP correct...

a reference structure

and two structures

at the same distance 16

two structures at distance 16, but with the same "shape"

(((((( ))

[ [ ] [ ] ]

RobertGiegerich

Motivation

Abstractshapes

RNAshapes

ShapeProbabilitites

Accuracy of MFE folding . . .

RNA folding struggles with

adequacy of thermodynamic parameters . . . ?

uncovered structural motifs – pseudoknots, kissinghairpins!

dynamics of interaction with other molecules . . . ?

RNA transcript processing . . . ?

folding kinetics (co-transcriptional folding) . . . ?

physical properties of the folding space . . . !

RobertGiegerich

Motivation

Abstractshapes

RNAshapes

ShapeProbabilitites

The problem to be solved

We want more comprehensive information about an RNAmolecule’s foldings than just its MFE structure.

RobertGiegerich

Motivation

Abstractshapes

RNAshapes

ShapeProbabilitites

Lost in folding space (1)

The folding space of a given sequence is LARGE:

number of foldings is exponential in sequence length

number of near-optimal foldings is exponential in energywindow

Structure asymptotics:

S(n) ≈ 1.104366 ∗ n−3/2 ∗ 2.618034n

Number of secondary structures for ALL sequences of length n.A tyical tRNA of 74 nt has about 4 Mio. feasible structures.Consider the 111 “best” structures, each with 27 - 28 bp:

RobertGiegerich

Motivation

Abstractshapes

RNAshapes

ShapeProbabilitites

gggcccauagcucagugguagagugccuccuuugcaaggaggaugcccuggguucgaaucccagugggucca

((((((((.((...))(((((.((((((((((...))))))).))).)))(((....))))).)))))))).

((((((((.((...)).))((.((((((((((...))))))).))).))))))))((.(((....)))))..

((((((((.((...)).))((.((((((((((...))))))).))).))))))))((..(((...)))))..

((.(((((.((...))(((((.((((((((((...))))))).))).)))(((....))))).)))))))..

.(((((((.((...))(((((.((((((((((...))))))).))).)))(((....))))).)))))))..

((((((((.......((((((.((((((((((...))))))).))).)))(((....)))))))))))))).

(((((((((((((((.(((...((.(((((((...))))))))))))))))))).........)))))))).

((((((((.((...))(((...((.(((((((...)))))))))))).(((((....)).))))))))))).

((((((((.((...))(((...((.(((((((...)))))))))))).(((((....))).)))))))))).

((((((((.((...))((....((((((((((...))))))).)))))(((((....)).))))))))))).

((((((((.((...))((....((((((((((...))))))).)))))(((((....))).)))))))))).

((((((((.((...))(((.((((((((((((...))))))).))((...)))))..)))...)))))))).

((((((((.((...))(((.((((.(((((((...)))))))))(((...)))))..)))...)))))))).

((((((((.((...))(((((.((((((((((...))))))).))).))).((....))))..)))))))).

((((((((.((...))((.((.((((((((((...))))))).))).)).(((....))))).)))))))).

((((((((.((...))(((((.((((((((((...))))))).))).)))((......)))).)))))))).

((((((((.((...))(((((.((((((((((...))))))).))).))).((....)).)).)))))))).

((((((((.((...))(((((.((.(((((((...)))))))))...)))(((....))))).)))))))).

((((((((.((...))(((((..(((((((((...))))))).))..)))(((....))))).)))))))).

((((((((.((...))(((((.(((..((((((....))))))))).)))(((....))))).)))))))).

RobertGiegerich

Motivation

Abstractshapes

RNAshapes

ShapeProbabilitites

((((((((.((...))(((((.(((..((((((...)).))))))).)))(((....))))).)))))))).

((((((((.((...))(((((.(((..((((((...))).)))))).)))(((....))))).)))))))).

((((((((.((...))(((((.(((((((((.....)))))).))).)))(((....))))).)))))))).

((((((((.((...))(((((.(((((((.((....)))))).))).)))(((....))))).)))))))).

((((((((.((...))(((((.(((((((..((...)))))).))).)))(((....))))).)))))))).

((((((((.((...))(((((.(((((((.((...)).)))).))).)))(((....))))).)))))))).

((((((((.((...))(((((.((((((.(((....)))))).))).)))(((....))))).)))))))).

((((((((.((...))(((((.((((((..(((...)))))).))).)))(((....))))).)))))))).

((((((((.((...))(((((.((((((.(((...))).))).))).)))(((....))))).)))))))).

((((((((.((...))(((((.(((((((.((...)))).)).))).)))(((....))))).)))))))).

((((((((.((...))(((((.(((((.((((...)))).)).))).)))(((....))))).)))))))).

((((((((.((...))(((((.(((.((((((...))))))..))).)))(((....))))).)))))))).

((((((((.((...))(((((.(((.((((((...)))).)).))).)))(((....))))).)))))))).

((((((((.((...))(((((.((.(((((((...)))))))..)).)))(((....))))).)))))))).

((((((((.((...))((((..((((((((((...))))))).)))..))(((....))))).)))))))).

((((((((.(((....)))((.((((((((((...))))))).))).))((((....))))..)))))))).

((((((((.(((....)))((.((((((((((...))))))).))).))((((....)).)).)))))))).

((((((((.((((.((.((...))))((((((...)))))))).))..(((((....)).))))))))))).

RobertGiegerich

Motivation

Abstractshapes

RNAshapes

ShapeProbabilitites

((((((((.((((.((.((...))))((((((...)))))))).))..(((((....))).)))))))))).

(((((((...((....(((((.((((((((((...))))))).))).)))(((....)))))))))))))).

(((((((...((.((.(((...((.(((((((...))))))))))))))((((....)).))))))))))).

(((((((...((.((.(((...((.(((((((...))))))))))))))((((....)))).))))))))).

(((((((...(((((.(((...((.(((((((...)))))))))))))))(((....)))..))))))))).

(((((((..((...))(((...((.(((((((...))))))))))))((((((....)).))))))))))).

(((((((..((...))(((...((.(((((((...))))))))))))((((((....))).)))))))))).

(((((((..((...))(((...((.(((((((...))))))))))))((((((....)))).))))))))).

(((((((..((...))(((((.((((((((((...))))))).))).)))(((....)))))..))))))).

(((((((((.((....))))..((((((((((...))))))).))).((((((....)).))))))))))).

(((((((((.((....))))..((((((((((...))))))).))).((((((....))).)))))))))).

(((((((((.((....))))..((((((((((...))))))).))).((((((....)))).))))))))).

(((((((((..((...))))..((((((((((...))))))).))).((((((....)).))))))))))).

(((((((((..((...))))..((((((((((...))))))).))).((((((....))).)))))))))).

(((((((((..((...))))..((((((((((...))))))).))).((((((....)))).))))))))).

(((((((((((...))..))..((((((((((...))))))).))).((((((....)).))))))))))).

(((((((((((...))..))..((((((((((...))))))).))).((((((....))).)))))))))).

(((((((((((...))..))..((((((((((...))))))).))).((((((....)))).))))))))).

RobertGiegerich

Motivation

Abstractshapes

RNAshapes

ShapeProbabilitites

(((((((((..((.((.((...))))((((((...))))))))))..((((((....)).))))))))))).

(((((((((..((.((.((...))))((((((...))))))))))..((((((....))).)))))))))).

(((((((((..((.((.((...))))((((((...))))))))))..((((((....)))).))))))))).

(((((((((((...))((((...))))((((((....))))))))..((((((....)).))))))))))).

(((((((((((...))((((...))))((((((....))))))))..((((((....))).)))))))))).

(((((((((((...))((((...))))((((((....))))))))..((((((....)))).))))))))).

(((((((((((...))((((...))))((((((...)).))))))..((((((....)).))))))))))).

(((((((((((...))((((...))))((((((...)).))))))..((((((....))).)))))))))).

(((((((((((...))((((...))))((((((...)).))))))..((((((....)))).))))))))).

(((((((((((...))((((...))))((((((...))).)))))..((((((....)).))))))))))).

(((((((((((...))((((...))))((((((...))).)))))..((((((....))).)))))))))).

(((((((((((...))((((...))))((((((...))).)))))..((((((....)))).))))))))).

(((((((..((((.((.((...))))((((((...)))))))).)).((((((....)).))))))))))).

(((((((..((((.((.((...))))((((((...)))))))).)).((((((....))).)))))))))).

(((((((..((((.((.((...))))((((((...)))))))).)).((((((....)))).))))))))).

((((((...(((....(((((.((((((((((...))))))).))).)))(((....)))))))))))))).

((((((...(((.((.(((...((.(((((((...))))))))))))))((((....)).))))))))))).

((((((...(((.((.(((...((.(((((((...))))))))))))))((((....)))).))))))))).

((((((...((((((.(((...((.(((((((...)))))))))))))))(((....)))..))))))))).

RobertGiegerich

Motivation

Abstractshapes

RNAshapes

ShapeProbabilitites

((((((...((....((((((.((((((((((...))))))).))).)))(((....)))))))))))))). [ [][]]

(((((((((((((((.(((...((.(((((((...)))))))))))))))))))...))......)))))). [ ]

((((((..(((((((.(((...((.(((((((...)))))))))))))))))))...((....)))))))).

(((((..((.((....(((((.((((((((((...))))))).))).)))(((....)))))))))))))).

(((((..((.((.((.(((...((.(((((((...))))))))))))))((((....)).))))))))))).

(((((..((.((.((.(((...((.(((((((...))))))))))))))((((....)))).))))))))).

(((((..((.(((((.(((...((.(((((((...)))))))))))))))(((....)))..))))))))).

(((((..((((...))(((...((.(((((((...))))))))))))((((((....)).))))))))))). [[][][]]

(((((..((((...))(((...((.(((((((...))))))))))))((((((....))).)))))))))).

(((((..((((...))(((...((.(((((((...))))))))))))((((((....)))).))))))))).

(((((..((((...))(((((.((((((((((...))))))).))).)))(((....)))))..))))))).

(((((..((((((.((.((...))))((((((...)))))))).)).((((((....)).))))))))))).

(((((..((((((.((.((...))))((((((...)))))))).)).((((((....))).)))))))))).

(((((..((((((.((.((...))))((((((...)))))))).)).((((((....)))).))))))))).

(((((.((.((...))(((((.((((((((((...))))))).))).)))(((....))))).)).))))).

((((..(((((((((.(((...((.(((((((...)))))))))))))))))))...))((....)))))).

((((..(((((((((.(((...((.(((((((...)))))))))))))))))))...)).((...)))))).

((((.(((.((...))(((((.((((((((((...))))))).))).)))(((....))))).))).)))).

(((.((.((.((....(((((.((((((((((...))))))).))).)))(((....)))))))))))))).

(((.((.((.((.((.(((...((.(((((((...))))))))))))))((((....)).))))))))))).

RobertGiegerich

Motivation

Abstractshapes

RNAshapes

ShapeProbabilitites

(((.((.((.((.((.(((...((.(((((((...))))))))))))))((((....)))).))))))))).

(((.((.((.(((((.(((...((.(((((((...)))))))))))))))(((....)))..))))))))).

(((.((.((((...))(((...((.(((((((...))))))))))))((((((....)).))))))))))).

(((.((.((((...))(((...((.(((((((...))))))))))))((((((....))).)))))))))).

(((.((.((((...))(((...((.(((((((...))))))))))))((((((....)))).))))))))).

(((.((.((((...))(((((.((((((((((...))))))).))).)))(((....)))))..))))))).

(((.((.((((((.((.((...))))((((((...)))))))).)).((((((....)).))))))))))).

(((.((.((((((.((.((...))))((((((...)))))))).)).((((((....))).)))))))))).

(((.((.((((((.((.((...))))((((((...)))))))).)).((((((....)))).))))))))).

(((.((((.((...))(((((.((((((((((...))))))).))).)))(((....))))).)).))))).

(((((.((.((...))(((((.((((((((((...))))))).))).)))(((....))))).)))).))).

((((((((.((...)).))((.((((((((((...))))))).))).)))))(((..((....)))))))).

(((...(((((((((.(((...((.(((((((...)))))))))))))))))))...))(((...)))))).

(((.((((.((...))(((((.((((((((((...))))))).))).)))(((....))))).)))).))).

((.(((((.((...))(((((.((((((((((...))))))).))).)))(((....))))).))))).)).

.(((((((.((...))(((((.((((((((((...))))))).))).)))(((....))))).))))).)).

RobertGiegerich

Motivation

Abstractshapes

RNAshapes

ShapeProbabilitites

Lost in folding space (2)

What we observe from the simple tRNA example:

LARGE number of close-to-optimal foldings

FEW structural classes holding many similar foldings

Can we condense the folding space to good representatives ofthese classes?

RobertGiegerich

Motivation

Abstractshapes

RNAshapes

ShapeProbabilitites

Alternatives to a single MFE structure prediction:

BP probabilities and dotplots (McCaskill)

sampling of near-optimal structures (Mfold)

complete enumeration within a threshold (RNAsubopt)

stochastic sampling and clustering a posteriori (Sfold)

classified folding by abstract shape (RNAshapes)

RobertGiegerich

Motivation

Abstractshapes

RNAshapes

ShapeProbabilitites

Classification by abstract shape

U U GG

Multiple Loop

Stacking Region

Hairpin Loop

Internal Loop

Bulge Loop (left)

Bulge Loop (right)

What is a shape LIKE this .............. or NOT like this.....?Robert Giegerich Advanced Course: Shapes

RobertGiegerich

Motivation

Abstractshapes

RNAshapes

ShapeProbabilitites

Levels of abstraction

Level 0 Level 1

All types ofFull structure

Level 3

All helix

Level 4

Multi− and

internal loops,

no bulges

Level 5

arrangement

interruptions

RobertGiegerich

Motivation

Abstractshapes

RNAshapes

ShapeProbabilitites

String representation of shapes

CGUCUUAAACUCAUCACCGUGUGGAGCUGCGACCCUUCCCUAGAUUCGAAGACGAG((((((...(((..(((...))))))...(((..((.....))..)))))))))..

Shape Type 5: [[][]]Shape Type 4: [[][[]]]Shape Type 3: [[[]][[]]]Shape Type 2: [[ []][ [] ]]Shape Type 1: [ [ [ ]] [ [ ] ]]

U G U G G A G

******

RobertGiegerich

Motivation

Abstractshapes

RNAshapes

ShapeProbabilitites

Formalizing the notion of (abstract) shape

Shape abstraction retains nesting and adjacency of stems

Shape abstraction disregards all sizes (of stems, loops, . . . )Shape abstraction may retain or disregard presence and type ofbulges and internal loops, i.e. helix interruptions

RNAshapes provides shape abstraction levels 1 through 5

RobertGiegerich

Motivation

Abstractshapes

RNAshapes

ShapeProbabilitites

Shape abstraction retains nesting and adjacency of stemsShape abstraction disregards all sizes (of stems, loops, . . . )

Shape abstraction may retain or disregard presence and type ofbulges and internal loops, i.e. helix interruptions

RobertGiegerich

Motivation

Abstractshapes

RNAshapes

ShapeProbabilitites

Shape abstraction retains nesting and adjacency of stemsShape abstraction disregards all sizes (of stems, loops, . . . )Shape abstraction may retain or disregard presence and type ofbulges and internal loops, i.e. helix interruptions

RobertGiegerich

Motivation

Abstractshapes

RNAshapes

ShapeProbabilitites

Shape abstraction mathematics

General:

tree-like domains of structures F and shapes Ptree homomorphism π : F → P

For each sequence s:

folding space of sequence s: F (s)

shape space of sequence s: P(s) = π(F (s))

shape class of p in F (s):f (x , p) = {x |x ∈ F (S), π(x) = p}

shape representative structure:shrep = class member of minimal free energy, formally

shrep(s, p)

RobertGiegerich

Motivation

Abstractshapes

RNAshapes

ShapeProbabilitites

Structures and shapes as trees and strings

Level 0

((((.(((....)))((...(...))))))) [ [ ] [ ] ]

Level 3

abstract

Level 5

abstract

[ [ ] [ [ ] ] ]

[ _ [_] [ _ [_] ] ] level 1

RobertGiegerich

Motivation

Abstractshapes

RNAshapes

ShapeProbabilitites

Shape algorithmics

Implementation of shape analysis:

shape abstractions are tree homomorphisms

integrate well with DP algorithms

allows for a priori rather than a posteriori analysis

compute shapes in parallel with energyperform analyses on per-shape basis

Any RNA folding program can implement shape abstractionCurrently: use RNAshapes.

RobertGiegerich

Motivation

Abstractshapes

RNAshapes

ShapeProbabilitites

Properties of shapes and shreps

Good properties:

shape classes are disjoint

shreps are interesting representatives

shapes have sequence-independent representation

shapes are meaningful across different sequences (ofdifferent length)

shapes and shreps can be computed efficiently

Bad properties:

shapes are too abstract

shapes are not abstract enough

RobertGiegerich

Motivation

Abstractshapes

RNAshapes

ShapeProbabilitites

Properties of shapes and shreps

Good properties:

shape classes are disjoint

shreps are interesting representatives

shapes have sequence-independent representation

shapes are meaningful across different sequences (ofdifferent length)

shapes and shreps can be computed efficiently

Bad properties:

shapes are too abstract

shapes are not abstract enough

RobertGiegerich

Motivation

Abstractshapes

RNAshapes

ShapeProbabilitites

Simple shape analysis with RNAshapes

The three top shreps of our tRNA example:

Shape GGGCCCAUAGCUCAGUGGUAGAGUGCCUCCUUUGCAAGGAGGAUGCCCUGGGUUCGAAUCCCAGUGGGUCCA[] (((((((((((((((.((((.....(((((((...))))))).))))))))))).........)))))))). -35.9 kcal/mol[[][]] ((((((((.....((.((((.....(((((((...))))))).))))))(((.......))).)))))))). -32.2 kcal/mol[[][][]] ((((((...((((.......)))).(((((((...))))))).....(((((.......))))).)))))). -31.7 kcal/mol

RobertGiegerich

Motivation

Abstractshapes

RNAshapes

ShapeProbabilitites

Shape [ ]

GCAUGU A U

GGCC C

CGGGUU C

GAUCCCC G

RobertGiegerich

Motivation

Abstractshapes

RNAshapes

ShapeProbabilitites

Shape [[ ][ ]]

GGGCCCAUAG

UGCCUCCUU

AAGGAGG

AUGCCCU

G G GU U

AAUCCC

AGUGGGUCCA

RobertGiegerich

Motivation

Abstractshapes

RNAshapes

ShapeProbabilitites

Shape [[ ][ ][ ]]

GGGCCCAUA

GCUCAGU

U AG A G U

GCCUCCUU

AAGGAGGAUGC

CC U G G G

AAUCCCAG

UGGGUCCA

RobertGiegerich

Motivation

Abstractshapes

RNAshapes

ShapeProbabilitites

Shape Space Statistics

Condensation of the folding space:Structure asymptotics:

S(n) ≈ 1.104366 ∗ n−3/2 ∗ 2.618034n

Level-k shape asymptotics:

P1(n) ≈ 0.98542 ∗ n−3/2 ∗ 2.40591n

P5(n) ≈ 2.44251 ∗ n−3/2 ∗ 1.32218n

Empirically, numbers are much smaller for a concrete sequenceSee some statistics within 5% kcal/mol of MFE:

RobertGiegerich

Motivation

Abstractshapes

RNAshapes

ShapeProbabilitites

Numbers of shapes versus structures

0 50 100 150 200 250 300

Sequence length [nt]

ShapesStructures

RobertGiegerich

Motivation

Abstractshapes

RNAshapes

ShapeProbabilitites

Shapes versus structures, logarithmic scale

0 20 40 60 80 100 120

Sequence length N [nt]

StructuresShapes

0.0391 * 1.3968912N

0.2064 * 1.1067094N

RobertGiegerich

Motivation

Abstractshapes

RNAshapes

ShapeProbabilitites

Homogenity in shape classes

The “Boltzman Ensemble” on Ice

RobertGiegerich

Motivation

Abstractshapes

RNAshapes

ShapeProbabilitites

Homogenity in shape classes

The “Boltzman Ensemble” on Ice

RobertGiegerich

Motivation

Abstractshapes

RNAshapes

ShapeProbabilitites

Best k shreps

Björn Voß

[] [[][]] [[][][]]

RNAshapes

RobertGiegerich

Motivation

Abstractshapes

RNAshapes

ShapeProbabilitites

Complete probabilistic shape analysis

“How much would you trust a structure with aprobability of 0.1 ∗ 10−12, even when it is optimal?”

Chip Lawrence, Benasque 2003 and ISMB 2007

RobertGiegerich

Motivation

Abstractshapes

RNAshapes

ShapeProbabilitites

From energy to probability

According to Boltzmann statistics, sequence s has structure xwith probability

Prob(x) = (e−Ex/RT )/Q

where Ex is folding energy, T is temperature, R universal gasconstant, and Q the “partition function”,

Q =∑

x∈F (s)

e−Ex/RT

Accumulated shape probabilities

Prob(p) =∑

π(x)=p Prob(x) for all p ∈ P(s)

RobertGiegerich

Motivation

Abstractshapes

RNAshapes

ShapeProbabilitites

New information from shape probabilities

Overtaking: Shape probabilities may contradict energy ranking

[ ]E= -22.90 kcal/mol

P= 0.2370279

[ ][ ][ ]E= -22.50 kcal/mol

P= 0.0999191

[ ][ ]E= -22.30 kcal/mol

P= 0.5511424

Gets 2nd Gets 3rd

Gets 1stBjörn Voß

RobertGiegerich

Motivation

Abstractshapes

RNAshapes

ShapeProbabilitites

A propos “complete”

Probabilistic shape analysis is computationally expensive

probabilities give full information about folding space, but

we can not compute only the k most likely shapes

computation feasible up to 400 nts ...

but check for RapidShapes by Stefan Janssen

RobertGiegerich

Motivation

Abstractshapes

RNAshapes

ShapeProbabilitites

Requirements

Complete probabilistic shape analysis

requires a non-ambiguous grammar with correct dangles atall places

applies “classified” dynamic programming

takes time O(1.1n ∗ n3) where n = |s|

RobertGiegerich

Motivation

Abstractshapes

RNAshapes

ShapeProbabilitites

Results from complete probabilistic analysis

Some observations:

Sequence Shape 1 Prob. Shape 2 Prob.lin-4 precursor [] 0.99999994tRNA-ala [] 0.989744 [[]] 0.008994typical mRNA [][[][]] 0.432154 [[[][]][]] 0.149831HIV-1 Leader [][[][[][]]]] 0.6164 [][[[][[][]]][]] 0.3492

RobertGiegerich

Motivation

Abstractshapes

RNAshapes

ShapeProbabilitites

The RNAshapes package

Modes of operation:

Computation of low-energy shape representative structures

Computation of accumulated shape probabilities

Computation of consensus shapes

No heuristics involvedAvailable athttp://bibiserv.techfak.uni-bielefeld.de/RNAshapes/

RobertGiegerich

Motivation

Abstractshapes

RNAshapes

ShapeProbabilitites

Application: shape based indexing

Assume we have a ncRNA candidate in some novel organism,(⇒ lecture by C. Sharma)and want to know whether it resembles something known:

main resource: Rfam database with 600 structural RNAfamilies

families represented by curated structural alignments (cf.Rfam lecture)

search via covariance models (cf. probabilistic modelslecture)

search effort O(n4) per model

RobertGiegerich

Motivation

Abstractshapes

RNAshapes

ShapeProbabilitites

Filter techniques

Filter techniques are used to skip unsuccessful searches

1 BLAST filter

2 Ravenna HMM filter

3 shape index based filtering – RNAsifter by Stefan Janssen

Details on (1) and (2) in the Rfam Database lecture

RobertGiegerich

Motivation

Abstractshapes

RNAshapes

ShapeProbabilitites

Shape index construction

RobertGiegerich

Motivation

Abstractshapes

RNAshapes

ShapeProbabilitites

Shape index based search

_[_[_[_[_[]]_[_[]_]_]_]_]_[]__[_[_[_[_[]]_[_[]_]_]_]_]_[]_[_[_[_[_[]]_[_[]_]]_]_]_[]_

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53,116 more shapes

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59,337 more shapes

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93,840 more shapes

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112,489 more shapes

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12,156 more shapes

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RobertGiegerich

Motivation

Abstractshapes

RNAshapes

ShapeProbabilitites

Filtered search performance

0.40 0.50 0.60 0.70 0.80 0.90 1.00

k-best-shape-index1-SS_cons-shape-index1-consensus-shape-index1-hybrid-shape-index1-union-shape-index1-RNAalifold-shape-index

cmsearch --hmmfilterk-RNAlishapes-shape-index

RobertGiegerich

Motivation

Abstractshapes

RNAshapes

ShapeProbabilitites

Average run times

0 100 200 300 400 500 600 700 800 900

RNAsiftercmsearch

HMM-filterBLAST-filter

RobertGiegerich

Motivation

Abstractshapes

RNAshapes

ShapeProbabilitites

Shape based matching

Search by structure ...

Assume you have a (single) transcript with a well-definedstructure

How to search for structural homologues in relatedorganisms?

Create a specialized folding program via Locomotif athttp://bibiserv.cebitec.uni-bielefeld.de/locomotif

RobertGiegerich

Motivation

Abstractshapes

RNAshapes

ShapeProbabilitites

References on abstract shape analysis

Abstract Shapes of RNA. Giegerich R, Voss B, Rehmsmeier M.Nucleic Acids Research 2004, Vol. 32, No 15, 1 - 9.

Complete Probabilistic Analysis of RNA Abstract Shapes. Voss,Giegerich, Rehmsmeier. BMC Biology, 2006, Feb 15;4(1):5

RNAshapes: an integrated RNA analysis package based onabstract shapes. Steffen P, Voss B, Rehmsmeier M, Reeder J,Giegerich R. Bioinformatics 2006, Feb 15;22(4):500-3.

Shape based indexing for faster search of RNA family databases.Janssen S, Reeder J, Giegerich R, BMC Bioinformatics, 2008

Locomotif

Rapidshapes

RobertGiegerich

Motivation

Abstractshapes

RNAshapes

ShapeProbabilitites

The End

Thanks for your attention.

RNA Abstract Shape Analysis - · PDF fileshape space RNAshapes Simple shape analysis Complete...

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