DNA Codes Based on Stem Similarities Between DNA Sequences

DNA codes consisting of DNA sequences are necessary for DNA computing. The minimum distance parameter of such codes is a measure of how dissimilar the codewords are, and thus is indirectly a measure of the likelihood of undetectedable or uncorrectable errors occurring during hybridization. To compute distance, an abstract metric, for example, longest common subsequence, must be used to model the actual bonding energies of DNA strands. In this paper we continue the development [1,2,3] of similarity functions for q-ary n-sequences The theoretical lower bound on the maximal possible size of codes, built on the space endowed with this metric, is obtained. that can be used (for q = 4) to model a thermodynamic similarity on DNA sequences. We introduce the concept of a stem similarity function and discuss DNA codes [2] based on the stem similarity. We suggest an optimal construction [2] and obtain random coding bounds on the maximum size and rate for such codes

Thermodynamically Stable DNA Code Design using a Similarity Significance Model

DNA code design aims to generate a set of DNA sequences (codewords) with minimum likelihood of undesired hybridizations among sequences and their reverse-complement (RC) pairs (cross-hybridization). Inspired by the distinct hybridization affinities (or stabilities) of perfect double helix constructed by individual single-stranded DNA (ssDNA) and its RC pair, we propose a novel similarity significance (SS) model to measure the similarity between DNA sequences. Particularly, instead of directly measuring the similarity of two sequences by any metric/approach, the proposed SS works in a way to evaluate how more likely will the undesirable hybridizations occur over the desirable hybridizations in the presence of the two measured sequences and their RC pairs. With this SS model, we construct thermodynamically stable DNA codes subject to several combinatorial constraints using a sorting-based algorithm. The proposed scheme results in DNA codes with larger code sizes and wider free energy gaps (hence better cross-hybridization performance) compared to the existing methods.Comment: To appear in ISIT 202

Using SetPSO to determine RNA secondary structure

RNA secondary structure prediction is an important field in Bioinformatics. A number of different approaches have been developed to simplify the determination of RNA molecule structures. RNA is a nucleic acid found in living organisms which fulfils a number of important roles in living cells. Knowledge of its structure is crucial in the understanding of its function. Determining RNA secondary structure computationally, rather than by physical means, has the advantage of being a quicker and cheaper method. This dissertation introduces a new Set-based Particle Swarm Optimisation algorithm, known as SetPSO for short, to optimise the structure of an RNA molecule, using an advanced thermodynamic model. Structure prediction is modelled as an energy minimisation problem. Particle swarm optimisation is a simple but effective stochastic optimisation technique developed by Kennedy and Eberhart. This simple technique was adapted to work with variable length particles which consist of a set of elements rather than a vector of real numbers. The effectiveness of this structure prediction approach was compared to that of a dynamic programming algorithm called mfold. It was found that SetPSO can be used as a combinatorial optimisation technique which can be applied to the problem of RNA secondary structure prediction. This research also included an investigation into the behaviour of the new SetPSO optimisation algorithm. Further study needs to be conducted to evaluate the performance of SetPSO on different combinatorial and set-based optimisation problems.Dissertation (MS)--University of Pretoria, 2009.Computer Scienceunrestricte

Integrated multiple sequence alignment

Sammeth M. Integrated multiple sequence alignment. Bielefeld (Germany): Bielefeld University; 2005.The thesis presents enhancements for automated and manual multiple sequence alignment: existing alignment algorithms are made more easily accessible and new algorithms are designed for difficult cases. Firstly, we introduce the QAlign framework, a graphical user interface for multiple sequence alignment. It comprises several state-of-the-art algorithms and supports their parameters by convenient dialogs. An alignment viewer with guided editing functionality can also highlight or print regions of the alignment. Also phylogenetic features are provided, e.g., distance-based tree reconstruction methods, corrections for multiple substitutions and a tree viewer. The modular concept and the platform-independent implementation guarantee an easy extensibility. Further, we develop a constrained version of the divide-and-conquer alignment such that it can be restricted by anchors found earlier with local alignments. It can be shown that this method shares attributes of both, local and global aligners, in the quality of results as well as in the computation time. We further modify the local alignment step to work on bipartite (or even multipartite) sets for sequences where repeats overshadow valuable sequence information. In the end a technique is established that can accurately align sequences containing eventually repeated motifs. Finally, another algorithm is presented that allows to compare tandem repeat sequences by aligning them with respect to their possible repeat histories. We describe an evolutionary model including tandem duplications and excisions, and give an exact algorithm to compare two sequences under this model

Graphical methods in RNA structure matching

Eukaryotic genomes are pervasively transcribed; almost every base can be found in an RNA transcript. This is a surprising observation since most of the genome does not encode proteins. This RNA must serve an important regulatory function – important because producing non-coding RNA is an energy intensive process, and in the absence of strong selection one would expect it to disappear. RNA families with common functions have specifically conserved structural motifs, which are directly related to the functional roles of RNA in catalysis and regulation. Because the conserved structures depend on base-pairing, similar RNA structures may have little or no detectable sequence similarity, making the identification of conserved RNAs difficult. This is a particularly serious problem when studying regulatory structures in RNA. In many cases, such as that of cellular internal ribosome entry sites, although we can identify RNAs that have similar regulatory responses, it is difficult to tell whether the RNAs have common structural features using current methods. Available tools for identifying common structures based on RNA sequence suffer from one or more of the following problems: they do not consider pseudoknots, which are important in many catalytic and regulatory structures; they do not consider near minimum free energy structures, which is important as many RNAs exist as an ensemble of structures of nearly equal energy; they require many examples of known structures in order to train a computational model; they require impractical amounts of computational time, precluding their use on long sequences or genomic scale; or they use a similarity function that cannot identify RNAs as having similar structure, even when they are from one of the well characterized known classes. The approach presented here has the potential to address all of these issues, allowing novel RNA structures that are shared between RNAs with little or no sequence similarity to be discovered. This provides a powerful tool to investigate and explain the pervasive transcription observed in eukaryotic genomes

Clustering and analysis of g quadruplex sequences.

G quadruplex structures are secondary structures located throughout the genome of various organisms with involvement in regulatory functions in different transcription, translation, genome stability, epigenetic regulation as well as cell division. Even with the diverse acknowledgement of G4 structure in vivo, there are no current search tools for G quadruplexes based on already identified G quadruplexes and identified families across different genomes based on sequence diversity. Construction of families of G4 sequences and identifying their polymorphisms within disease and disorders will lead to a better understanding of their functional roles and will further research into the biophysical modeling of interactions with oligonucleotide treatments of disease. The first project aims to develop a framework for clustering G quadruplex (G4) sequences into families based on sequence, structure, and thermodynamic properties. No current search tools exist to filter G4s based on their properties, and the diversity of G4 sequences across the genome is not fully understood. To address this gap, we utilized a combination of clustering and annotation methods to identify 95 families of G4 sequences within the human genome. Profiles for each family were created using hidden Markov models, and their thermodynamic properties, functional annotations, and transcription factor binding motifs were analyzed. The second project aims to investigate the effect of single nucleotide variations (SNVs) on G4 structures in disease contexts. Although the role of G4s in cancer and metabolic disorders are well-established, the effect of SNVs on G4s has not been extensively studied. Using the COSMIC and CLINVAR databases, we identified over 37,000 G4 SNVs and analyzed their effects on G4 secondary structures. We found that a significant proportion of SNVs result in G4 loss or gain, and we identified genes enriched for destabilizing SNVs in G4-forming regions. We also analyzed mutational patterns in the G4 structure and found a higher selective pressure on the coding region of the template strand. Our findings provide insights into the effects of SNVs on G4 structures and highlight potential targets for therapeutic intervention in diseases associated with G4 dysregulation