RNA secondary structure prediction from multi-aligned sequences

It has been well accepted that the RNA secondary structures of most functional non-coding RNAs (ncRNAs) are closely related to their functions and are conserved during evolution. Hence, prediction of conserved secondary structures from evolutionarily related sequences is one important task in RNA bioinformatics; the methods are useful not only to further functional analyses of ncRNAs but also to improve the accuracy of secondary structure predictions and to find novel functional RNAs from the genome. In this review, I focus on common secondary structure prediction from a given aligned RNA sequence, in which one secondary structure whose length is equal to that of the input alignment is predicted. I systematically review and classify existing tools and algorithms for the problem, by utilizing the information employed in the tools and by adopting a unified viewpoint based on maximum expected gain (MEG) estimators. I believe that this classification will allow a deeper understanding of each tool and provide users with useful information for selecting tools for common secondary structure predictions.Comment: A preprint of an invited review manuscript that will be published in a chapter of the book `Methods in Molecular Biology'. Note that this version of the manuscript may differ from the published versio

ViennaRNA Package 2.0

<p>Abstract</p> <p>Background</p> <p>Secondary structure forms an important intermediate level of description of nucleic acids that encapsulates the dominating part of the folding energy, is often well conserved in evolution, and is routinely used as a basis to explain experimental findings. Based on carefully measured thermodynamic parameters, exact dynamic programming algorithms can be used to compute ground states, base pairing probabilities, as well as thermodynamic properties.</p> <p>Results</p> <p>The <monospace>ViennaRNA</monospace> Package has been a widely used compilation of RNA secondary structure related computer programs for nearly two decades. Major changes in the structure of the standard energy model, the <it>Turner 2004 </it>parameters, the pervasive use of multi-core CPUs, and an increasing number of algorithmic variants prompted a major technical overhaul of both the underlying <monospace>RNAlib</monospace> and the interactive user programs. New features include an expanded repertoire of tools to assess RNA-RNA interactions and restricted ensembles of structures, additional output information such as <it>centroid </it>structures and <it>maximum expected accuracy </it>structures derived from base pairing probabilities, or <it>z</it>-<it>scores </it>for locally stable secondary structures, and support for input in <monospace>fasta</monospace> format. Updates were implemented without compromising the computational efficiency of the core algorithms and ensuring compatibility with earlier versions.</p> <p>Conclusions</p> <p>The <monospace>ViennaRNA Package 2.0</monospace>, supporting concurrent computations <monospace>via OpenMP</monospace>, can be downloaded from <url>http://www.tbi.univie.ac.at/RNA</url>.</p

An efficient algorithm for the stochastic simulation of the hybridization of DNA to microarrays

<p>Abstract</p> <p>Background</p> <p>Although oligonucleotide microarray technology is ubiquitous in genomic research, reproducibility and standardization of expression measurements still concern many researchers. Cross-hybridization between microarray probes and non-target ssDNA has been implicated as a primary factor in sensitivity and selectivity loss. Since hybridization is a chemical process, it may be modeled at a population-level using a combination of material balance equations and thermodynamics. However, the hybridization reaction network may be exceptionally large for commercial arrays, which often possess at least one reporter per transcript. Quantification of the kinetics and equilibrium of exceptionally large chemical systems of this type is numerically infeasible with customary approaches.</p> <p>Results</p> <p>In this paper, we present a robust and computationally efficient algorithm for the simulation of hybridization processes underlying microarray assays. Our method may be utilized to identify the extent to which nucleic acid targets (e.g. cDNA) will cross-hybridize with probes, and by extension, characterize probe robustnessusing the information specified by MAGE-TAB. Using this algorithm, we characterize cross-hybridization in a modified commercial microarray assay.</p> <p>Conclusions</p> <p>By integrating stochastic simulation with thermodynamic prediction tools for DNA hybridization, one may robustly and rapidly characterize of the selectivity of a proposed microarray design at the probe and "system" levels. Our code is available at <url>http://www.laurenzi.net</url>.</p