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

2. Lecture outline Sequence-based prediction methods
RNA footprinting and high-throughput methods
Last update: 17-Oct-2015
CSCI5050 Bioinformatics and Computational Biology | Kevin Yip-cse-cuhk | Fall 2015

3. Sequence-based Prediction Methods
Part 1
Sequence-based Prediction Methods

4. RNA structures
Some RNAs have strong structural features highly related to their functions
tRNA
snoRNA
rRNA
Image sources:
Last update: 17-Oct-2015
CSCI5050 Bioinformatics and Computational Biology | Kevin Yip-cse-cuhk | Fall 2015

5. 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
Last update: 20-Oct-2015
CSCI5050 Bioinformatics and Computational Biology | Kevin Yip-cse-cuhk | Fall 2015

6. Methods for predicting RNA structures
Wikipedia contains a comprehensive list:
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
Last update: 17-Oct-2015
CSCI5050 Bioinformatics and Computational Biology | Kevin Yip-cse-cuhk | Fall 2015

7. Type-specific models
Example: tRNAscan-SE for finding tRNAs and predicting tRNA structures
Three main phases:
Running tRNAscan and the Pavesi algorithm to find candidate tRNAs
Using a covariance model to identify the more confident candidates
Trimming the candidates and predicting the detailed secondary structures
Last update: 17-Oct-2015
CSCI5050 Bioinformatics and Computational Biology | Kevin Yip-cse-cuhk | Fall 2015

8. Workflow of tRNAscan-SE
Step 1
Step 2
Image credit: Lowe and Eddy, Nucleic Acids Research 25(5): , (1997)
Last update: 17-Oct-2015
CSCI5050 Bioinformatics and Computational Biology | Kevin Yip-cse-cuhk | Fall 2015

9. 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): , (1991)
Last update: 17-Oct-2015
CSCI5050 Bioinformatics and Computational Biology | Kevin Yip-cse-cuhk | Fall 2015

10. 1a. tRNAscan: sequential tests
Image credit: Fichant and Burks, Journal of Molecular Biology 220(3): , (1991)
Last update: 17-Oct-2015
CSCI5050 Bioinformatics and Computational Biology | Kevin Yip-cse-cuhk | Fall 2015

11. 1b. The Pavesi algorithm
Frequency tables based on 231 nuclear tRNA genes:
Image credit: Pavesi et al., Nucleic Acids Research 22(7): , (1994)
Last update: 17-Oct-2015
CSCI5050 Bioinformatics and Computational Biology | Kevin Yip-cse-cuhk | Fall 2015

12. 1b. The Pavesi algorithm: workflow
Image credit: Pavesi et al., Nucleic Acids Research 22(7): , (1994)
Last update: 17-Oct-2015
CSCI5050 Bioinformatics and Computational Biology | Kevin Yip-cse-cuhk | Fall 2015

13. 2. Chomsky hierarchy of languages
Image source:
Last update: 17-Oct-2015
CSCI5050 Bioinformatics and Computational Biology | Kevin Yip-cse-cuhk | Fall 2015

14. 2. Context-free grammar
Use of context-free grammars in RNA secondary structure representation: To capture paring relationships. Example:
One possible derivation: S0  S1  CS2G  CAS3UG  CAS4S9UG  CAUS5AS9UG  CAUCS6GAS9UG  CAUCAS7GAS9UG  CAUCAGS8GAS9UG  CAUCAGGGAS9UG  CAUCAGGGAAS10UUG  CAUCAGGGAAGS11CUUG  CAUCAGGGAAGAS12UCUUG  CAUCAGGGAAGAUS13UCUUG  CAUCAGGGAAGAUCUCUUG
Productions P = { S0S1, S1CS2G, S1AS2U, S2AS3U, S3S4S9, S4US5A, S5CS6G, S6AS7, S7US7, S7GS8, S8G, S8U, S9AS10U, S10CS10G, S10GS11C, S11AS12U, S12US13, S13C}
Example credit: Sakakibara et al., CPM , (1994)
Last update: 17-Oct-2015
CSCI5050 Bioinformatics and Computational Biology | Kevin Yip-cse-cuhk | Fall 2015

15. 2. Parse tree and RNA structure
Figure credit: Sakakibara et al., The 5th Annual Symposium on Combinatorial Pattern Matching , (1994)
Last update: 17-Oct-2015
CSCI5050 Bioinformatics and Computational Biology | Kevin Yip-cse-cuhk | Fall 2015

16. 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
Last update: 20-Oct-2015
CSCI5050 Bioinformatics and Computational Biology | Kevin Yip-cse-cuhk | Fall 2015

17. 2. SCFC and CM
Output CM:
White state: consensus
Gray state: indels
Input multiple sequence alignment and consensus structure:
Construction of guide tree from consensus structure:
Node
Description
MATP
Pair
MATL
Single strand, left
MATR
Single strand, right
BIF
Bifurcation
ROOT
root
BEGL
Begin, left
BEGR
Begin, right
END
End
Image credit: INFERNAL user’s guide
Last update: 17-Oct-2015
CSCI5050 Bioinformatics and Computational Biology | Kevin Yip-cse-cuhk | Fall 2015

18. 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 GC pairing
Stable structures such as stacking pairs
Last update: 17-Oct-2015
CSCI5050 Bioinformatics and Computational Biology | Kevin Yip-cse-cuhk | Fall 2015

19. 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:
Last update: 17-Oct-2015
CSCI5050 Bioinformatics and Computational Biology | Kevin Yip-cse-cuhk | Fall 2015

20. Turner’s energy parameters
For hairpin loops:
Table credit: Mathews et al., Journal of Molecular Biology 288(5): , (1999)
Last update: 17-Oct-2015
CSCI5050 Bioinformatics and Computational Biology | Kevin Yip-cse-cuhk | Fall 2015

21. Energy minimization Dynamic programming (without pseudoknots)
Last update: 17-Oct-2015
CSCI5050 Bioinformatics and Computational Biology | Kevin Yip-cse-cuhk | Fall 2015

22. 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,
Mathews, RNA 10(8): , (2004)
Last update: 17-Oct-2015
CSCI5050 Bioinformatics and Computational Biology | Kevin Yip-cse-cuhk | Fall 2015

23. 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
Last update: 17-Oct-2015
CSCI5050 Bioinformatics and Computational Biology | Kevin Yip-cse-cuhk | Fall 2015

24. RNA Footprinting and High-Throughput Methods
Part 2
RNA Footprinting and High-Throughput Methods

25. 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): , (2013)
Last update: 17-Oct-2015
CSCI5050 Bioinformatics and Computational Biology | Kevin Yip-cse-cuhk | Fall 2015

26. Some probes that can be used
Size (Dalton)
Structural preference
DMS (dimethyl sulfate)
126
Mark unpaired bases
IM7
222
RNase V1
15,900
Cleave paired bases
RNase ONE
27,000
Cleave unpaired bases
Nuclease S1
32,000
Nuclease P1
36,000
Last update: 17-Oct-2015
CSCI5050 Bioinformatics and Computational Biology | Kevin Yip-cse-cuhk | Fall 2015

27. 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
Last update: 17-Oct-2015
CSCI5050 Bioinformatics and Computational Biology | Kevin Yip-cse-cuhk | Fall 2015

28. Parallel Analysis of RNA Structure (PARS)
Image credit: Kertesz et al., Nature 467(7311): , (2010)
Last update: 17-Oct-2015
CSCI5050 Bioinformatics and Computational Biology | Kevin Yip-cse-cuhk | Fall 2015

29. DMS-seq Image credit: Rouskin et al., Nature 505(7485):701-705, (2014)
Last update: 17-Oct-2015
CSCI5050 Bioinformatics and Computational Biology | Kevin Yip-cse-cuhk | Fall 2015

30. 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
Last update: 17-Oct-2015
CSCI5050 Bioinformatics and Computational Biology | Kevin Yip-cse-cuhk | Fall 2015

31. 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)
Last update: 17-Oct-2015
CSCI5050 Bioinformatics and Computational Biology | Kevin Yip-cse-cuhk | Fall 2015

32. 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
Last update: 17-Oct-2015
CSCI5050 Bioinformatics and Computational Biology | Kevin Yip-cse-cuhk | Fall 2015

33. 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
Li et al., Genome Biology 11(5):R50, (2010)
Last update: 17-Oct-2015
CSCI5050 Bioinformatics and Computational Biology | Kevin Yip-cse-cuhk | Fall 2015

34. In vivo vs. in vitro data
Image credit: Rouskin et al., Nature 505(7485): , (2014)
Last update: 17-Oct-2015
CSCI5050 Bioinformatics and Computational Biology | Kevin Yip-cse-cuhk | Fall 2015

35. 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
Last update: 17-Oct-2015
CSCI5050 Bioinformatics and Computational Biology | Kevin Yip-cse-cuhk | Fall 2015

36. Using structure-probing data
Several ways to use the data:
Free energy penalty
Pseudo free energy terms
Discrepancy minimization
Identifying closest structure centroid
Last update: 17-Oct-2015
CSCI5050 Bioinformatics and Computational Biology | Kevin Yip-cse-cuhk | Fall 2015

37. Example: StructureFold
Overall workflow:
Image credit: Tang et al., Bioinformatics 31(16): , (2015)
Last update: 17-Oct-2015
CSCI5050 Bioinformatics and Computational Biology | Kevin Yip-cse-cuhk | Fall 2015

38. Example: RNApbfold
Minimizing discrepancies between predicted ( ) and measured ( ) probabilities of bases being unpaired:
Perturbation of the energy parameter values
Variance terms indicating uncertainty
Washietl et al., Nucleic Acids Research 40(10): , (2012)
Last update: 17-Oct-2015
CSCI5050 Bioinformatics and Computational Biology | Kevin Yip-cse-cuhk | Fall 2015

39. Example: RNApbfold Sample results:
Image credit: Washietl et al., Nucleic Acids Research 40(10): , (2012)
Last update: 17-Oct-2015
CSCI5050 Bioinformatics and Computational Biology | Kevin Yip-cse-cuhk | Fall 2015

40. 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
Last update: 17-Oct-2015
CSCI5050 Bioinformatics and Computational Biology | Kevin Yip-cse-cuhk | Fall 2015