Lecture 7. Topics in RNA Bioinformatics (Identification of RNA Structures) The Chinese University of Hong Kong CSCI5050 Bioinformatics and Computational

Lecture 7. Topics in RNA Bioinformatics (Identification of RNA Structures)

The Chinese University of Hong KongCSCI5050 Bioinformatics and Computational Biology

CSCI5050 Bioinformatics and Computational Biology | Kevin Yip-cse-cuhk | Fall 2015 2

Lecture outline1. Sequence-based prediction methods2. RNA footprinting and high-throughput

methods

Last update: 17-Oct-2015

SEQUENCE-BASED PREDICTION METHODS

Part 1


RNA structures• Some RNAs have strong structural features

highly related to their functions


tRNA

snoRNAImage sources: http://www.bio.miami.edu/dana/pix/tRNA.jpg; http://lowelab.ucsc.edu/images/CDBox.jpg; http://www.daviddarling.info/images/ribosome_and_RNA.jpg

rRNA


Secondary vs. tertiary structure• Four levels of molecular structures:– Primary: The sequence– Secondary: Local interactions– Tertiary: Global interactions– Quaternary: Inter-molecule interactions

• Both secondary and tertiary RNA structures are meaningful– However, more work has been devoted to

identifying/predicting RNA secondary structures– Also focus of this lecture



Methods for predicting RNA structures

• Wikipedia contains a comprehensive list: https://en.wikipedia.org/wiki/List_of_RNA_structure_prediction_software

• Main classes:– Models specific to a particular type of RNA– Based on a single sequence• Minimum free energy (MFE)• Partition function

– Based on comparison of multiple sequences


https://en.wikipedia.org/wiki/List_of_RNA_structure_prediction_software

https://en.wikipedia.org/wiki/List_of_RNA_structure_prediction_software


Type-specific models• Example: tRNAscan-SE for finding tRNAs and

predicting tRNA structures• Three main phases:

1. Running tRNAscan and the Pavesi algorithm to find candidate tRNAs

2. Using a covariance model to identify the more confident candidates

3. Trimming the candidates and predicting the detailed secondary structures



Workflow of tRNAscan-SE


Image credit: Lowe and Eddy, Nucleic Acids Research 25(5):955-964, (1997)

Step 1

Step 2


1a. tRNAscan• Features used:– Invariant and semi-

invariant bases– Potential base-pairing

structures consistent with the cloverleaf secondary structure• The aminoacyl arm, the D

arm, the anticodon arm and the T--C arm

– Length and position of potential intron sequences


Image credit: Fichant and Burks, Journal of Molecular Biology 220(3):659-671, (1991)


1a. tRNAscan: sequential tests


Image credit: Fichant and Burks, Journal of Molecular Biology 220(3):659-671, (1991)


1b. The Pavesi algorithm• Frequency tables based on 231 nuclear tRNA

genes:


Image credit: Pavesi et al., Nucleic Acids Research 22(7):1247-1256, (1994)


1b. The Pavesi algorithm: workflow


Image credit: Pavesi et al., Nucleic Acids Research 22(7):1247-1256, (1994)


2. Chomsky hierarchy of languages


Image source: http://www.cs.utexas.edu/users/novak/cs343283.html


2. Context-free grammar• Use of context-free grammars in RNA secondary

structure representation: To capture paring relationships. Example:


Example credit: Sakakibara et al., CPM 289-306, (1994)

Productions P = {S0S1,S1CS2G,S1AS2U,S2AS3U,S3S4S9,S4US5A,S5CS6G,S6AS7,S7US7,

S7GS8,S8G,S8U,S9AS10U,S10CS10G,S10GS11C,S11AS12U,S12US13,S13C}

One possible derivation:S0

S1

CS2G CAS3UG CAS4S9UG CAUS5AS9UG CAUCS6GAS9UG CAUCAS7GAS9UG CAUCAGS8GAS9UG CAUCAGGGAS9UG CAUCAGGGAAS10UUG CAUCAGGGAAGS11CUUG CAUCAGGGAAGAS12UCUUG CAUCAGGGAAGAUS13UCUUG CAUCAGGGAAGAUCUCUUG


2. Parse tree and RNA structure


Figure credit: Sakakibara et al., The 5th Annual Symposium on Combinatorial Pattern Matching 289-306, (1994)


2. SCFG and CM• Stochastic context-free grammar (SCFG):

context-free grammar with probabilistic derivation

• Covariance model (CM): model for representing RNA sequence and structure profiles based on SCFG



2. SCFC and CM


Input multiple sequence alignment and consensus structure:

Construction of guide tree from consensus structure:

Image credit: INFERNAL user’s guide

Output CM:

Node Description

MATP Pair

MATL Single strand, left

MATR Single strand, right

BIF Bifurcation

ROOT root

BEGL Begin, left

BEGR Begin, right

END End

White state: consensusGray state: indels


Based on a single sequence• Minimum free energy (MFE): Finding the RNA

structure (pairing of bases) that minimizes the free energy– More pairing– More stable pairing• Strong GC pairing• Stable structures such as stacking pairs



Turner’s free energy model• Considering the total

free energy of an RNA structure is the sum of the free energy of the sub-structures


Image credit: http://www.clcbio.com/scienceimages/rna_prediction/RNA_structure_prediction_web.png


Turner’s energy parameters• For hairpin loops:


Table credit: Mathews et al., Journal of Molecular Biology 288(5):911-940, (1999)


Energy minimization• Dynamic programming (without pseudoknots)


Vሺ𝑗ሻ= minቊVሺ𝑗− 1ሻ 𝑗 is unpairedmin1≤𝑖<𝑗ሼVPሺ𝑖,𝑗ሻ+ Vሺ𝑖 − 1ሻሽ 𝑗 pairs with 𝑖 Vpሺ𝑖,𝑗ሻ= min

ە۔

+eSሺ𝑖,𝑗ሻۓ VPሺ𝑖 + 1,𝑗− 1ሻ ሺ𝑖,𝑗ሻ and ሺ𝑖 + 1,𝑗− 1ሻ form stacking pairseH(𝑖,𝑗) ሺ𝑖,𝑗ሻ closes a hairpin loopVBIሺ𝑖,𝑗ሻ ሺ𝑖,𝑗ሻ closes an internal loopVMሺ𝑖,𝑗ሻ ሺ𝑖,𝑗ሻ closes a multi loop

VBIሺ𝑖,𝑗ሻ= min𝑖1,𝑗1:𝑖<𝑖1<𝑗1<𝑗ሼeBIሺ𝑖,𝑗,𝑖1,𝑗1ሻ+ VPሺ𝑖1,𝑗1ሻሽ VMሺ𝑖,𝑗ሻ= min𝑖1,𝑗1,…,𝑖k,𝑗𝑘:𝑖<𝑖1<𝑗1<...<𝑖𝑘<𝑗𝑘<𝑗൝eMሺ𝑖,𝑗,𝑖1,𝑗1,…,𝑖𝑘,𝑗𝑘ሻ+ VPሺ𝑖ℎ,𝑗ℎሻ𝑘

ℎ=1 ൡ


Partition function• Issues of MFE:

– Solution may not be optimal– There can be different structures with similar free energy

• The partition function sums over the relative likelihood of all possible secondary structures:

– S: Possible secondary structures– G(S): Gibb’s free energy change– R: Gas constant– T: Absolute temperature

• Probability of a particular structure s, •


Q= e−∆Gሺ𝑆ሻ/RT𝑆

Prሺ𝑠ሻ= e−∆Gሺ𝑠ሻ/RTQ Prሺ𝑖 and 𝑗 form a pairሻ= σ e−∆Gሺ𝑠ሻ/RT𝑠:ሺ𝑖,𝑗ሻ𝜖𝑠Q Mathews, RNA 10(8):1178-1190, (2004)


Based on multiple sequences• The conservation of a base and the co-

conservation of a base pair in multiple sequences can help resolve ambiguous cases– In fact, a CM can be trained from a multiple

sequence alignment• Main types:– Joint optimization– Consensus/alignment of individual structures


RNA FOOTPRINTING AND HIGH-THROUGHPUT METHODS

Part 2


RNA footprinting• A traditional way to study RNA secondary structures– Preferentially cleave or mark nucleotides with a particular

structural property


Image credit: Novikova et al., International Journal of Molecular Sciences 14(12):23672-23684, (2013)


Some probes that can be usedProbe Size (Dalton) Structural preference

DMS (dimethyl sulfate) 126 Mark unpaired bases

IM7 222 Mark unpaired bases

RNase V1 15,900 Cleave paired bases

RNase ONE 27,000 Cleave unpaired bases

Nuclease S1 32,000 Cleave unpaired bases

Nuclease P1 36,000 Cleave unpaired bases



High-throughput RNA footprinting• Enzyme-based: After enzymatic treatment,

sequence the resulting fragments to identify the cleavage sites– And thus bases with the structural property

• Chemical-probe-based: Chemical adduct can terminate reverse-transcription. The termination point can be identified by sequencing



Parallel Analysis of RNA Structure (PARS)


Image credit: Kertesz et al., Nature 467(7311):103-107, (2010)


DMS-seq


Image credit: Rouskin et al., Nature 505(7485):701-705, (2014)


Potential confounding factors• General:– Expression level of transcripts• Need control/comparison

– Sequence bias– Issues in read alignment• Blind tail – Fragments that are too short cannot be

aligned correctly

– Experimental efficiency



Potential confounding factors• Method-specific:– DMS modifies mainly only adenines and cytosines– Increasing read count towards 3’ end in DMS-seq– Natural polymerase drop-off in chemical-probe-

based methods– Preference due to secondary vs. tertiary structure

(e.g., steric hindrance in enzyme-based methods)



Data normalization• Normalization strategies:– Transcript level: Comparison using• Standard RNA-seq data• Control experiment (with some steps not carried out)• Data from two different enzymes (PARS)

– Increasing read count: Smoothing by local window– Polymerase drop-off: Modeling it explicitly



Example: Poisson linear model• Modeling local sequence bias

– i: measured read count of nucleotide i– : actual expression level of transcript– bik: the k-th nucleotide within the length-K local

sub-sequence around nucleotide i– kh: bias coefficient


log𝜔𝑖 = 𝛼+ 𝛽𝑘ℎ𝐼ሺ𝑏𝑖𝑘 = ℎሻ+ 𝜀ℎ∈ሼ𝐴,𝐶,𝐺ሽ𝐾

𝑘=1

Li et al., Genome Biology 11(5):R50, (2010)


In vivo vs. in vitro data


Image credit: Rouskin et al., Nature 505(7485):701-705, (2014)


Using structure-probing data• The high-throughput RNA footprinting

(structure-probing) data only tell whether a base is paired or not, but not with which other base

• The data can be used to help RNA secondary structure prediction



Using structure-probing data• Several ways to use the data:– Free energy penalty– Pseudo free energy terms– Discrepancy minimization– Identifying closest structure centroid



Example: StructureFold• Overall workflow:


Image credit: Tang et al., Bioinformatics 31(16):2668-2675, (2015)


Example: RNApbfold• Minimizing discrepancies between predicted

( ) and measured ( ) probabilities of bases being unpaired:


𝐹ሺ𝜖Ԧሻ= 𝜖𝜇2𝜏𝜇2𝜇 + 1𝜎𝑖2ሺ𝑝𝑖ሺ𝜖Ԧሻ− 𝑞𝑖ሻ2𝑛

𝑖=1

𝑝𝑖ሺ𝜖Ԧሻ 𝑞𝑖

Variance terms

indicating uncertainty

Washietl et al., Nucleic Acids Research 40(10):4261-4272, (2012)

Perturbation of the energy

parameter values


Example: RNApbfold• Sample results:


Image credit: Washietl et al., Nucleic Acids Research 40(10):4261-4272, (2012)


Summary• Sequence-based RNA secondary structure

prediction– For specific types of RNA– Single sequence• Minimum free energy (MFE)• Partition function

– Multiple sequences• High-throughput RNA structure probing– Modification of objective function– Selection of appropriate structures


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Lecture 7. Topics in RNA Bioinformatics (Identification of RNA Structures) The Chinese University of Hong Kong CSCI5050 Bioinformatics and Computational