Algorithmes de prediction et de recherche de multi-structures d'ARN

20 Chapter 1. Background – RNAs, non-coding RNAs, and bioinformatics
The method consists of two measures, one for RNA secondary structure conservation and
one for thermodynamic stability of the sequence. The prediction of consensus secondary
structure for alignment is proceeded by using RNAalifold. The two independent diagnostic
features of structural ncRNAs are used to classify an alignment as “structural RNA” or
“other”. For this purpose, RNAz uses a support vector machine (SVM) learning algorithm
which is trained on a large test set of well known ncRNAs.
• Ab initio gene finders. Some methods use essential statistical features of data like GC
percentage to recognize the region of data respecting this feature. The study in [20] uses
a machine learning approach to extract common characteristics (such as base composition
bias), amongst known RNAs for prediction of new RNA genes in the unannotated regions
of prokaryotic and archaeal genomes. Further work has been carried out in this direction
in [98].
1.2.8 Comparison of RNA structures
Comparing two RNA structures involves defining and computing a similarity between two structures.
This similarity is often based on a distance between arborescent structures, as trees or
arc-annotated sequences, and can take into account both the primary and the secondary structure.
Structure comparison can be used to assert or reject the membership of an unknown RNA
to a given ncRNA family, hence predicting the function, or to help evolutionary studies.
Comparison of RNA secondary structure is implemented in tools such as RNAdistance [51,
101, 100], RNAforester [55, 56, 57], Migal [3] and Gardenia [14]. To compute a distance value, it
is also suggested to compare the whole folding space of two sequences through their partition
function and matrix of base pairing probabilities, like in RNAPDIST [16, 51]. Finally, [2] reports
a benchmark of several RNA comparison tools.
1.2.9 Tools for non-secondary structures
As mentioned previously, secondary structures are not the real 3D structure of an RNA. We
now briefly mention methods and tools that go beyond secondary structures, or that combine
secondary structures.
• Prediction of RNA-RNA interactions. Some small non-coding RNAs have a posttranscriptional
regulation role: by base-pairing with mRNAs, they modify the gene expression.
A particular case is miRNA interaction, for which many tools have been proposed
(review in [10]). For generic RNA-RNA interactions, some methods consider only the
interaction site, neglecting intra-RNA interactions, such as in RNAhybrid [91], RNAduplex
[51] or RNAplex [109]. Other approaches take into account both intra-RNA structure
and inter-RNA interactions:
– A first idea is to compute the MFE or the partition function of the concatenation of
the two RNA sequences, as in Pairfold [7] and RNAcofold [51, 12];
– Some approaches, as RNAup [79], consider the problem in two steps, first breaking
intramolecular binding of both RNAs, then adding intermolecular interaction. The
energy gained from hybridization is thus the sum of the energy of the two steps;