A Seeded Genetic Algorithm for RNA Secondary Structural Prediction with Pseudoknots

This work explores a new approach in using genetic algorithm to predict RNA secondary structures with pseudoknots. Since only a small portion of most RNA structures is comprised of pseudoknots, the majority of structural elements from an optimal pseudoknot-free structure are likely to be part of the true structure. Thus seeding the genetic algorithm with optimal pseudoknot-free structures will more likely lead it to the true structure than a randomly generated population. The genetic algorithm uses the known energy models with an additional augmentation to allow complex pseudoknots. The nearest-neighbor energy model is used in conjunction with Turner’s thermodynamic parameters for pseudoknot-free structures, and the H-type pseudoknot energy estimation for simple pseudoknots. Testing with known pseudoknot sequences from PseudoBase shows that it out performs some of the current popular algorithms

HMM with auxiliary memory: a new tool for modeling RNA structures

For a long time, proteins have been believed to perform most of the important functions in all cells. However, recent results in genomics have revealed that many RNAs that do not encode proteins play crucial roles in the cell machinery. The so-called ncRNA genes that are transcribed into RNAs but not translated into proteins, frequently conserve their secondary structures more than they conserve their primary sequences. Therefore, in order to identify ncRNA genes, we have to take the secondary structure of RNAs into consideration. Traditional approaches that are mainly based on base-composition statistics cannot be used for modeling and identifying such structures and models with more descriptive power are required. In this paper, we introduce the concept of context-sensitive HMMs, which is capable of describing pairwise interactions between distant symbols. It is demonstrated that the proposed model can efficiently model various RNA secondary structures that are frequently observed

Prediction of RNA pseudoknots by Monte Carlo simulations

In this paper we consider the problem of RNA folding with pseudoknots. We use a graphical representation in which the secondary structures are described by planar diagrams. Pseudoknots are identified as non-planar diagrams. We analyze the non-planar topologies of RNA structures and propose a classification of RNA pseudoknots according to the minimal genus of the surface on which the RNA structure can be embedded. This classification provides a simple and natural way to tackle the problem of RNA folding prediction in presence of pseudoknots. Based on that approach, we describe a Monte Carlo algorithm for the prediction of pseudoknots in an RNA molecule.Comment: 22 pages, 14 figure

An O(n^5) algorithm for MFE prediction of kissing hairpins and 4-chains in nucleic acids

Efficient methods for prediction of minimum free energy (MFE) nucleic secondary structures are widely used, both to better understand structure and function of biological RNAs and to design novel nano-structures. Here, we present a new algorithm for MFE secondary structure prediction, which significantly expands the class of structures that can be handled in O(n^5) time. Our algorithm can handle H-type pseudoknotted structures, kissing hairpins, and chains of four overlapping stems, as well as nested substructures of these types

Quantitative nucleotide level analysis of regulation of translation in response to depolarization of cultured neural cells

Studies on regulation of gene expression have contributed substantially to understanding mechanisms for the long-term activity-dependent alterations in neural connectivity that are thought to mediate learning and memory. Most of these studies, however, have focused on the regulation of mRNA transcription. Here, we utilized high-throughput sequencing coupled with ribosome footprinting to globally characterize the regulation of translation in primary mixed neuronal-glial cultures in response to sustained depolarization. We identified substantial and complex regulation of translation, with many transcripts demonstrating changes in ribosomal occupancy independent of transcriptional changes. We also examined sequence-based mechanisms that might regulate changes in translation in response to depolarization. We found that these are partially mediated by features in the mRNA sequence—notably upstream open reading frames and secondary structure in the 5′ untranslated region—both of which predict downregulation in response to depolarization. Translationally regulated transcripts are also more likely to be targets of FMRP and include genes implicated in autism in humans. Our findings support the idea that control of mRNA translation plays an important role in response to neural activity across the genome

PATTERNA: transcriptome-wide search for functional RNA elements via structural data signatures.

Establishing a link between RNA structure and function remains a great challenge in RNA biology. The emergence of high-throughput structure profiling experiments is revolutionizing our ability to decipher structure, yet principled approaches for extracting information on structural elements directly from these data sets are lacking. We present PATTERNA, an unsupervised pattern recognition algorithm that rapidly mines RNA structure motifs from profiling data. We demonstrate that PATTERNA detects motifs with an accuracy comparable to commonly used thermodynamic models and highlight its utility in automating data-directed structure modeling from large data sets. PATTERNA is versatile and compatible with diverse profiling techniques and experimental conditions