corRna: a web server for predicting multiple-point deleterious mutations in structural RNAs

RNA molecules can achieve a broad range of regulatory functions through specific structures that are in turn determined by their sequence. The prediction of mutations changing the structural properties of RNA sequences (a.k.a. deleterious mutations) is therefore useful for conducting mutagenesis experiments and synthetic biology applications. While brute force approaches can be used to analyze single-point mutations, this strategy does not scale well to multiple mutations. In this article, we present corRna a web server for predicting the multiple-point deleterious mutations in structural RNAs. corRna uses our RNAmutants framework to efficiently explore the RNA mutational landscape. It also enables users to apply search heuristics to improve the quality of the predictions. We show that corRna predictions correlate with mutagenesis experiments on the hepatitis C virus cis-acting replication element as well as match the accuracy of previous approaches on a large test-set in a much lower execution time. We illustrate these new perspectives offered by corRna by predicting five-point deleterious mutations—an insight that could not be achieved by previous methods. corRna is available at: http://corrna.cs.mcgill.ca

A global sampling approach to designing and reengineering RNA secondary structures

The development of algorithms for designing artificial RNA sequences that fold into specific secondary structures has many potential biomedical and synthetic biology applications. To date, this problem remains computationally difficult, and current strategies to address it resort to heuristics and stochastic search techniques. The most popular methods consist of two steps: First a random seed sequence is generated; next, this seed is progressively modified (i.e. mutated) to adopt the desired folding properties. Although computationally inexpensive, this approach raises several questions such as (i) the influence of the seed; and (ii) the efficiency of single-path directed searches that may be affected by energy barriers in the mutational landscape. In this article, we present RNA-ensign, a novel paradigm for RNA design. Instead of taking a progressive adaptive walk driven by local search criteria, we use an efficient global sampling algorithm to examine large regions of the mutational landscape under structural and thermodynamical constraints until a solution is found. When considering the influence of the seeds and the target secondary structures, our results show that, compared to single-path directed searches, our approach is more robust, succeeds more often and generates more thermodynamically stable sequences. An ensemble approach to RNA design is thus well worth pursuing as a complement to existing approaches. RNA-ensign is available at http://csb.cs.mcgill.ca/RNAensign.National Science Foundation (U.S.). Graduate Research Fellowship ProgramNatural Sciences and Engineering Research Council of Canada (NSERC) (RGPIN ) (386596-10)Fonds québécois de la recherche sur la nature et les technologies (PR-146375)National Institutes of Health (U.S.) (Grant GM081871)Natural Sciences and Engineering Research Council of Canada (NSERC)National Institutes of Health (U.S.

An unbiased adaptive sampling algorithm for the exploration of RNA mutational landscapes under evolutionary pressure.

International audienceThe analysis of the relationship between sequences and structures (i.e., how mutations affect structures and reciprocally how structures influence mutations) is essential to decipher the principles driving molecular evolution, to infer the origins of genetic diseases, and to develop bioengineering applications such as the design of artificial molecules. Because their structures can be predicted from the sequence data only, RNA molecules provide a good framework to study this sequence-structure relationship. We recently introduced a suite of algorithms called RNAmutants which allows a complete exploration of RNA sequence-structure maps in polynomial time and space. Formally, RNAmutants takes an input sequence (or seed) to compute the Boltzmann-weighted ensembles of mutants with exactly k mutations, and sample mutations from these ensembles. However, this approach suffers from major limitations. Indeed, since the Boltzmann probabilities of the mutations depend of the free energy of the structures, RNAmutants has difficulties to sample mutant sequences with low G+C-contents. In this article, we introduce an unbiased adaptive sampling algorithm that enables RNAmutants to sample regions of the mutational landscape poorly covered by classical algorithms. We applied these methods to sample mutations with low G+C-contents. These adaptive sampling techniques can be easily adapted to explore other regions of the sequence and structural landscapes which are difficult to sample. Importantly, these algorithms come at a minimal computational cost. We demonstrate the insights offered by these techniques on studies of complete RNA sequence structures maps of sizes up to 40 nucleotides. Our results indicate that the G+C-content has a strong influence on the size and shape of the evolutionary accessible sequence and structural spaces. In particular, we show that low G+C-contents favor the apparition of internal loops and thus possibly the synthesis of tertiary structure motifs. On the other hand, high G+C-contents significantly reduce the size of the evolutionary accessible mutational landscapes

Linear algebraic techniques in theoretical computer science and population genetics

Thesis (Ph. D.)--Massachusetts Institute of Technology, Department of Mathematics, 2013.Cataloged from PDF version of thesis.Includes bibliographical references (pages 149-155).In this thesis, we present several algorithmic results for problems in spectral graph theory and computational biology. The first part concerns the problem of spectral sparsification. It is known that every dense graph can be approximated in a strong sense by a sparse subgraph, known as a spectral sparsifier of the graph. Furthermore, researchers have recently developed efficient algorithms for computing such approximations. We show how to make these algorithms faster, and also give a substantial improvement in space efficiency. Since sparsification is an important first step in speeding up approximation algorithms for many graph problems, our results have numerous applications. In the second part of the thesis, we consider the problem of inferring human population history from genetic data. We give an efficient and principled algorithm for using single nucleotide polymorphism (SNP) data to infer admixture history of various populations, and apply it to show that Europeans have evidence of mixture with ancient Siberians. Finally, we turn to the problem of RNA secondary structure design. In this problem, we want to find RNA sequences that fold to a given secondary structure. We propose a novel global sampling approach, based on the recently developed RNAmutants algorithm, and show that it has numerous desirable properties when compared to existing solutions. Our method can prove useful for developing the next generation of RNA design algorithms.by Alex Levin.Ph.D