1) The document describes a method called RactIPAce that uses integer programming to predict RNA-RNA interactions by maximizing base pairing probabilities while incorporating accessibility probabilities. 2) RactIPAce was shown to improve prediction of interaction sites over other methods like RactIP, RNAplex, and IntaRNA on benchmark datasets with true positive interactions. 3) However, the methods still have difficulties discriminating true interactions from false ones, indicating a need for novel techniques to address challenges in RNA-RNA interaction prediction.

1. Institute of Biochemical Engineering (IBVT)
Allmandring 31, 70569 Stuttgart, Germany
www.ibvt.uni-stuttgart.de An accessibility-
incorporated method for
accurate prediction of
RNA-RNA interactions
from sequence data
Richard Schäfer
29/11/2016
Literature
Seminar

2. 29-Nov-162, IBVT, Stuttgart
Integer Programming (IP) with incorporation of precomputed accessibility
Prediction of RNA-RNA interactions

3. 29-Nov-163, IBVT, Stuttgart
The function of ncRNAs are often determined by their structure
Motivation - Versatility of RNA
• RNA is able to fold into diverse
structures to be involved in a
number of biological processes
• Experimental determination of
RNA structure is tedious and time-
consuming (e.g., Cryo-EM, SHAPE)
• Computational methods1 are used
to predict structure models with
the focus on secondary structure
• Many ncRNAs interact with other
RNAs (miRNA, siRNA) following
the same base-pairing rules as
with intramolecular interactions
Washietl S, Will S, Hendrix D, Goff, L, Rinn J, Berger B and
Kellis M. (2012) Computational analysis of noncoding RNAs.
WIREs RNA. doi: 10.1002/wrna.1134
1
Zuker M and Stiegler P (1981) Optimal computer folding of
large RNA sequences using thermodynamics and auxiliary
information. Nucleic Acids Res., 9, 133-148

4. 29-Nov-164, IBVT, Stuttgart
Classification
Computational Approaches for Prediction of RNA-RNA Interactions
• Concatenation Methods: Prediction of inter- and intramolecular base-pairs by
concatenating the input sequences and applying classical RNA secondary structure
algorithms1 (e.g., Mfold, RNAfold). PairFold and RNAcofold implement this strategy
• Favorable Hybridization Methods: Finding the most stable configuration of the
interaction complex ranging from simple interactions (e.g., RNAhybrid, UNAfold,
RNAduplex) to complex joint structures (e.g., IRIS, SRIG)
• Accessibility Methods: Prediction of intermolecular base-pairs, with consideration
of the accessibility of interaction sites (e.g., IntaRNA, RNAup, RNAplex)

5. • Experimentally verified interactions sites are significantly more accessible
29-Nov-165, IBVT, Stuttgart
Differences between functional and non-functional interaction sites
Accessibility
Richter and Backofen (2012) Accessibility and conservation: General features of bacterial small RNA–mRNA
interactions. RNA Biology, 9:7

6. 29-Nov-166, IBVT, Stuttgart
Maximize the objective function under some linear constraints
Prediction Model using RactIPAce
Kato Y, Mori T, Sato K, Maegawa S, Hosokawa H and Akutsu T (2016) An accessibility-incorporated method for accurate prediction of
RNA-RNA interactions from sequence data. Bioinformatics, 2016, 1-8

7. • Linear Programming is a method for maximizing/minimizing a function
subject to linear constraints
• Maximize y
subject to -x + y ≤ 1
3x + 2y ≤ 12
2x + 3y ≤ 12
• Integer Programming expects all
variables to be integers
• Maximize to the sum of intra-
and intermolecular base-
pairing probabilities
29-Nov-167, IBVT, Stuttgart
Optimization of a linear objective function
(Integer) Linear Programming (ILP)
Creative Commons Integer Programming license under CC BY 2.0

8. Query RNA sequence a = a1,…,an
Let x = (xij)i<j (1 ≤ i < j ≤ n)
=
x11 ⋯ 0
⋮ ⋱ ⋮
xn1 ⋯ xnn
Query RNA sequence b = b1,…,bm
Let y = (yij)i<j (1 ≤ i < j ≤ n)
=
y11 ⋯ 0
⋮ ⋱ ⋮
ym1 ⋯ ymm
29-Nov-168, IBVT, Stuttgart
Matrices for secondary structures
Abstraction of inter- and intramolecular base-pairs
Hybridization between a and b
Let z = (zik)
=
z11 ⋯ z1𝑚
⋮ ⋱ ⋮
zn1 ⋯ zn𝑚
where xij = 1 means that bases ai and
aj form an intramolecular base-pair.
where yij = 1 means that bases bi and
bj form an intramolecular base-pair.
where zik = 1 means that bases ai and
bk form an intermolecular base-pair.

9. 29-Nov-169, IBVT, Stuttgart
Definition of the Probabilities for the IP Formulation
Base-Pairing and Hybridization Probabilities
• Base-pairing probabilities:
pij
a = 2 P x a)
6∈89:(<)
pij
b = 2 P y b)
>∈89:(?)
• Hybridization probabilities:
pi𝑘
a, b = 2 P z a, b)
B∈C9D(<,?)
• Probabilities can be calculated with
RNAfold and RNAcofold,
respectively, in the ViennaRNA
package
• Factorization of the probability distribution:
P x, y, z a, b ) ≈ P x a) P(y b P z a, b)
set of all secondary
structures of a with
ai and aj paired
set of all hybridized
structures of a and b
with ai and bk paired

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Profile of an interaction site
Accessibility Probabilities
uij
(a) =
1
Z
2 eI
J 6
KL
6∈89:M
<
where Z = 2 eI
J 6
KL
6∈8 <
free energy of x
gas temperature
set of all secondary
structures whose
subsequence
ai,…,aj is unpaired
probability that
the sequence
interval ai,…,aj is
unpaired
set of all secondary
structures of a
and uii(a) = 1 − 2 pij
(a)
:ST
base-pairing
probabilities
between bases ai
and aj in sequence a

11. 29-Nov-1611, IBVT, Stuttgart
IP Formulation
• Maximize
2 pij
a
− θ xij + 2 pij
b − θ yij + 𝛼
TXY
2 pij
a, b
− η zik
T,TXY
subject to 13 constraints
• Each base can be paired with at most one base – (1),(2)
• No intramolecular pseudoknots and crossing interactions – (3),(4),(5)
• Bases in intramolecular interactions are not accessible - (6),(7)
• Regions in intermolecular interactions are always accessible – (8),(9)
• Maximum number of accessible regions and no overlaps – (10)-(13)

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sRNA-mRNA pairs with experimentally verified interaction sites
Prediction Results with Positive Data
SEN PPV MCC time (s)
RactIPAce 0.619 0.842 0.709 14.794
RactIP 0.544 0.388 0.446 11.139
RNAplex-a 0.675 0.687 0.670 6.349
RNAplex 0.687 0.609 0.634 0.707
RNAup 0.325 0.307 0.311 29.901
IntaRNA 0.696 0.686 0.672 19.494
𝑆𝐸𝑁 =
`a
`abcd
𝑃𝑃𝑉 =
`a
`abca
𝑀𝐶𝐶 ≈ 𝑆𝐸𝑁 𝑥 𝑃𝑃𝑉

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New dataset of seven RNA pairs
Prediction Results with Positive and Negative Data
RNA-mRNA ID (%) In vitro RactIPAce RactIP RNAplex-a RNAplex RNAup IntaRNA
CP003938-cirA 88 - -1.004 -0.516 -0.004 1.400 1.012 0.247
FR877557-ompT 87 - -1.939 0.314 -0.678 0.155 -0.376 -1.475
CU928163-manX 89 + -1.253 2.105 -1.527 -0.341 -2.286 -2.337
FR775191-ptsG 70 + -1.998 -0.350 -3.065 -0.283 -3.235 -3.589
CP000946-STM3216 88 - 0.476 0.529 -1.496 -0.246 -2.500 -1.310
BC109583-nmpC1 76 - 0.349 0.080 1.030 -0.701 1.454 1.247
AJ277528-nmpC2 75 - -0.205 1.109 -1.783 1.163 0.717 -1.301
Kato Y, Mori T, Sato K, Maegawa S, Hosokawa H and Akutsu T (2016) An accessibility-incorporated method for accurate prediction of
RNA-RNA interactions from sequence data. Bioinformatics, 2016, 1-8

14. Discriminative power of each method
Receiver Operator Characteristic
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AUC
RactIPAce 0.697
RactIP 0.614
RNAplex-a 0.773
RNAplex 0.527
RNAup 0.743
IntaRNA 0.882
all 0.891
Kato Y, Mori T, Sato K, Maegawa S, Hosokawa H and Akutsu T (2016) An
accessibility-incorporated method for accurate prediction of RNA-RNA
interactions from sequence data. Bioinformatics, 2016, 1-8

15. Outlook
23-Jun-1615, IBVT, Stuttgart
• RNA-RNA interaction prediction (RIP) on the basis of Integer
Programming (IP) – incorporation of accessibility significantly improves
the prediction strength
• Accurate prediction of interaction sites but problems with discrimination
• Methods still need a range of novel ideas to address the challenges in RIP
(SEN ≈ 0.7) – limits of generalized algorithms? Approaches for specific
classes of interactions
• Techniques of machine learning for prediction algorithms
(e.g., Neural Networks, Support Vector Machines for supervised learning)