74,622 research outputs found
Multiple Biolgical Sequence Alignment: Scoring Functions, Algorithms, and Evaluations
Aligning multiple biological sequences such as protein sequences or DNA/RNA sequences is a fundamental task in bioinformatics and sequence analysis. These alignments may contain invaluable information that scientists need to predict the sequences\u27 structures, determine the evolutionary relationships between them, or discover drug-like compounds that can bind to the sequences. Unfortunately, multiple sequence alignment (MSA) is NP-Complete. In addition, the lack of a reliable scoring method makes it very hard to align the sequences reliably and to evaluate the alignment outcomes.
In this dissertation, we have designed a new scoring method for use in multiple sequence alignment. Our scoring method encapsulates stereo-chemical properties of sequence residues and their substitution probabilities into a tree-structure scoring scheme. This new technique provides a reliable scoring scheme with low computational complexity.
In addition to the new scoring scheme, we have designed an overlapping sequence clustering algorithm to use in our new three multiple sequence alignment algorithms. One of our alignment algorithms uses a dynamic weighted guidance tree to perform multiple sequence alignment in progressive fashion. The use of dynamic weighted tree allows errors in the early alignment stages to be corrected in the subsequence stages. Other two algorithms utilize sequence knowledge-bases and sequence consistency to produce biological meaningful sequence alignments. To improve the speed of the multiple sequence alignment, we have developed a parallel algorithm that can be deployed on reconfigurable computer models. Analytically, our parallel algorithm is the fastest progressive multiple sequence alignment algorithm
e-RNA: a collection of web servers for comparative RNA structure prediction and visualisation
e-RNA offers a free and open-access collection of five published RNA sequence analysis tools, each solving specific problems not readily addressed by other available tools. Given multiple sequence alignments, Transat detects all conserved helices, including those expected in a final structure, but also transient, alternative and pseudo-knotted helices. RNA-Decoder uses unique evolutionary models to detect conserved RNA secondary structure in alignments which may be partly protein-coding. SimulFold simultaneously co-estimates the potentially pseudo-knotted conserved structure, alignment and phylogenetic tree for a set of homologous input sequences. CoFold predicts the minimum-free energy structure for an input sequence while taking the effects of co-transcriptional folding into account, thereby greatly improving the prediction accuracy for long sequences. R-chie is a program to visualise RNA secondary structures as arc diagrams, allowing for easy comparison and analysis of conserved base-pairs and quantitative features. The web site server dispatches user jobs to a cluster, where up to 100 jobs can be processed in parallel. Upon job completion, users can retrieve their results via a bookmarked or emailed link. e-RNA is located at http://www.e-rna.org
LaRA 2: parallel and vectorized program for sequence–structure alignment of RNA sequences
Background
The function of non-coding RNA sequences is largely determined by their spatial conformation, namely the secondary structure of the molecule, formed by Watson–Crick interactions between nucleotides. Hence, modern RNA alignment algorithms routinely take structural information into account. In order to discover yet unknown RNA families and infer their possible functions, the structural alignment of RNAs is an essential task. This task demands a lot of computational resources, especially for aligning many long sequences, and it therefore requires efficient algorithms that utilize modern hardware when available. A subset of the secondary structures contains overlapping interactions (called pseudoknots), which add additional complexity to the problem and are often ignored in available software.
Results
We present the SeqAn-based software LaRA 2 that is significantly faster than comparable software for accurate pairwise and multiple alignments of structured RNA sequences. In contrast to other programs our approach can handle arbitrary pseudoknots. As an improved re-implementation of the LaRA tool for structural alignments, LaRA 2 uses multi-threading and vectorization for parallel execution and a new heuristic for computing a lower boundary of the solution. Our algorithmic improvements yield a program that is up to 130 times faster than the previous version.
Conclusions
With LaRA 2 we provide a tool to analyse large sets of RNA secondary structures in relatively short time, based on structural alignment. The produced alignments can be used to derive structural motifs for the search in genomic databases
LaRA 2: parallel and vectorized program for sequence–structure alignment of RNA sequences
Background
The function of non-coding RNA sequences is largely determined by their spatial conformation, namely the secondary structure of the molecule, formed by Watson–Crick interactions between nucleotides. Hence, modern RNA alignment algorithms routinely take structural information into account. In order to discover yet unknown RNA families and infer their possible functions, the structural alignment of RNAs is an essential task. This task demands a lot of computational resources, especially for aligning many long sequences, and it therefore requires efficient algorithms that utilize modern hardware when available. A subset of the secondary structures contains overlapping interactions (called pseudoknots), which add additional complexity to the problem and are often ignored in available software.
Results
We present the SeqAn-based software LaRA 2 that is significantly faster than comparable software for accurate pairwise and multiple alignments of structured RNA sequences. In contrast to other programs our approach can handle arbitrary pseudoknots. As an improved re-implementation of the LaRA tool for structural alignments, LaRA 2 uses multi-threading and vectorization for parallel execution and a new heuristic for computing a lower boundary of the solution. Our algorithmic improvements yield a program that is up to 130 times faster than the previous version.
Conclusions
With LaRA 2 we provide a tool to analyse large sets of RNA secondary structures in relatively short time, based on structural alignment. The produced alignments can be used to derive structural motifs for the search in genomic databases
Multiple structural alignment for distantly related all b structures using TOPS pattern discovery and simulated annealing
Topsalign is a method that will structurally align diverse protein structures, for example, structural alignment of protein superfolds. All proteins within a superfold share the same fold but often have very low sequence identity and different biological and biochemical functions. There is often signi®cant structural diversity around the common scaffold of secondary structure elements of the fold. Topsalign uses topological descriptions of proteins. A pattern discovery algorithm identi®es equivalent secondary structure elements between a set of proteins and these are used to produce an initial multiple structure alignment. Simulated annealing is used to optimize the alignment. The output of Topsalign is a multiple structure-based sequence alignment and a 3D superposition of the structures. This method has been tested on three superfolds: the b jelly roll, TIM (a/b) barrel and the OB fold. Topsalign outperforms established methods on very diverse structures. Despite the pattern discovery working only on b strand secondary structure elements, Topsalign is shown to align TIM (a/b) barrel superfamilies, which contain both a helices and b strands
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High throughput sequencing analysis of RNA libraries reveals the influences of initial library and PCR methods on SELEX efficiency.
The systemic evolution of ligands by exponential enrichment (SELEX) technique is a powerful and effective aptamer-selection procedure. However, modifications to the process can dramatically improve selection efficiency and aptamer performance. For example, droplet digital PCR (ddPCR) has been recently incorporated into SELEX selection protocols to putatively reduce the propagation of byproducts and avoid selection bias that result from differences in PCR efficiency of sequences within the random library. However, a detailed, parallel comparison of the efficacy of conventional solution PCR versus the ddPCR modification in the RNA aptamer-selection process is needed to understand effects on overall SELEX performance. In the present study, we took advantage of powerful high throughput sequencing technology and bioinformatics analysis coupled with SELEX (HT-SELEX) to thoroughly investigate the effects of initial library and PCR methods in the RNA aptamer identification. Our analysis revealed that distinct "biased sequences" and nucleotide composition existed in the initial, unselected libraries purchased from two different manufacturers and that the fate of the "biased sequences" was target-dependent during selection. Our comparison of solution PCR- and ddPCR-driven HT-SELEX demonstrated that PCR method affected not only the nucleotide composition of the enriched sequences, but also the overall SELEX efficiency and aptamer efficacy
HiTRACE-Web: an online tool for robust analysis of high-throughput capillary electrophoresis
To facilitate the analysis of large-scale high-throughput capillary
electrophoresis data, we previously proposed a suite of efficient analysis
software named HiTRACE (High Throughput Robust Analysis of Capillary
Electrophoresis). HiTRACE has been used extensively for quantitating data from
RNA and DNA structure mapping experiments, including mutate-and-map contact
inference, chromatin footprinting, the EteRNA RNA design project and other
high-throughput applications. However, HiTRACE is based on a suite of
command-line MATLAB scripts that requires nontrivial efforts to learn, use, and
extend. Here we present HiTRACE-Web, an online version of HiTRACE that includes
standard features previously available in the command-line version as well as
additional features such as automated band annotation and flexible adjustment
of annotations, all via a user-friendly environment. By making use of
parallelization, the on-line workflow is also faster than software
implementations available to most users on their local computers. Free access:
http://hitrace.or
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