Number Of Hairpins Research Articles

Study on DNA Storage Encoding Based IAOA under Innovation Constraints.

With the informationization of social processes, the amount of related data has greatly increased, making traditional storage media unable to meet the current requirements for data storage. Due to its advantages of a high storage capacity and persistence, deoxyribonucleic acid (DNA) has been considered the most prospective storage media to solve the data storage problem. Synthesis is an important process for DNA storage, and low-quality DNA coding can increase errors during sequencing, which can affect the storage efficiency. To reduce errors caused by the poor stability of DNA sequences during storage, this paper proposes a method that uses the double-matching and error-pairing constraints to improve the quality of the DNA coding set. First, the double-matching and error-pairing constraints are defined to solve problems of sequences with self-complementary reactions in the solution that are prone to mismatch at the 3' end. In addition, two strategies are introduced in the arithmetic optimization algorithm, including a random perturbation of the elementary function and a double adaptive weighting strategy. An improved arithmetic optimization algorithm (IAOA) is proposed to construct DNA coding sets. The experimental results of the IAOA on 13 benchmark functions show a significant improvement in its exploration and development capabilities over the existing algorithms. Moreover, the IAOA is used in the DNA encoding design under both traditional and new constraints. The DNA coding sets are tested to estimate their quality regarding the number of hairpins and melting temperature. The DNA storage coding sets constructed in this study are improved by 77.7% at the lower boundary compared to existing algorithms. The DNA sequences in the storage sets show a reduction of 9.7-84.1% in the melting temperature variance, and the hairpin structure ratio is reduced by 2.1-80%. The results indicate that the stability of the DNA coding sets is improved under the two proposed constraints compared to traditional constraints.

ANALYSIS OF NEW PRIMER PAIR CANDIDATES OF rbcL GENE FOR IDENTIFICATION OF MICROALGAE SCENEDESMACEAE

Scenedesmaceae is one of the microalgae groups that has been widely studied as promising biodiesel feedstock. Its morphological identification is often confused by environmental changes, so it requires molecular identification as well. The current study aimed to obtain primer pair candidates that identify the Scenedesmaceae based on the rbcL gene. The research used bioinformatics tools, which harvested rbcL protein sequence data, performed multiple sequence alignments, and designed primers based on conserved and less-conserved regions. The best left and right primers selected based on sequence length, melting temperature, 3' end stability, number of hairpins, and self-dimers, were paired, and three candidates were obtained. The three pairs were examined based on melting temperature difference, number of hetero-dimers, length of amplified nucleotide product, number of hits, and number of genera captured from the GenBank. Sce-16 (F, 5'-TGGTCGTGCTGTTTATGAATGT-3' and 1_RL, 5'-TGCCAAACATGAATACCACCA-3'), which is back-translated according to Hariotina sp. (AOY36008.1), is the most preferred candidate compared to the other two pairs after discussing their advantages and disadvantages. In the future, the proposed primer candidate needs to be validated through in vitro amplification with some optimizations to eliminate potential weaknesses.

Clusters of hairpins induce intrinsic transcription termination in bacteria

Intrinsic transcription termination (ITT) sites are currently identified by locating single and double-adjacent RNA hairpins downstream of the stop codon. ITTs for a limited number of genes/operons in only a few bacterial genomes are currently known. This lack of coverage is a lacuna in the existing ITT inference methods. We have studied the inter-operon regions of 13 genomes covering all major phyla in bacteria, for which good quality public RNA-seq data exist. We identify ITT sites in 87% of cases by predicting hairpin(s) and validate against 81% of cases for which the RNA-seq derived sites could be calculated. We identify 72% of these sites correctly, with 98% of them located ≤ 80 bases downstream of the stop codon. The predicted hairpins form a cluster (when present < 15 bases) in two-thirds of the cases, the remaining being single hairpins. The largest number of clusters is formed by two hairpins, and the occurrence decreases exponentially with an increasing number of hairpins in the cluster. Our study reveals that hairpins form an effective ITT unit when they act in concert in a cluster. Their pervasiveness along with single hairpin terminators corroborates a wider utilization of ITT mechanisms for transcription control across bacteria.

Markov Chain-Based Sampling for Exploring RNA Secondary Structure under the Nearest Neighbor Thermodynamic Model and Extended Applications.

Ribonucleic acid (RNA) secondary structures and branching properties are important for determining functional ramifications in biology. While energy minimization of the Nearest Neighbor Thermodynamic Model (NNTM) is commonly used to identify such properties (number of hairpins, maximum ladder distance, etc.), it is difficult to know whether the resultant values fall within expected dispersion thresholds for a given energy function. The goal of this study was to construct a Markov chain capable of examining the dispersion of RNA secondary structures and branching properties obtained from NNTM energy function minimization independent of a specific nucleotide sequence. Plane trees are studied as a model for RNA secondary structure, with energy assigned to each tree based on the NNTM, and a corresponding Gibbs distribution is defined on the trees. Through a bijection between plane trees and 2-Motzkin paths, a Markov chain converging to the Gibbs distribution is constructed, and fast mixing time is established by estimating the spectral gap of the chain. The spectral gap estimate is obtained through a series of decompositions of the chain and also by building on known mixing time results for other chains on Dyck paths. The resulting algorithm can be used as a tool for exploring the branching structure of RNA, especially for long sequences, and to examine branching structure dependence on energy model parameters. Full exposition is provided for the mathematical techniques used with the expectation that these techniques will prove useful in bioinformatics, computational biology, and additional extended applications.

In silico predictions and optimization of aptamers against Streptococcus agalactiae surface protein using computational docking

Abstract Aptamers are single-stranded RNA or DNA oligonucleotides that form unique three-dimensional (3D) structures that can bind a wide range of molecules and compounds The development of aptamers against targeted biomolecules using computational approach is a novel method to obtain viable chemical-based antibody substitutes. The conventional method to derive aptamers using SELEX technology involves multiple rounds of selection and enrichment, which is laborious, costly and time-consuming. It is also inefficient as it will often fail to isolate high binding aptamers. We evaluated a new approach to improve aptamer designs by using in silico refinement of the desired binding requirement on the biomolecule target. The three-dimensional (3D) structures of single strand oligonucleotides were modelled to contain hairpin(s) and predicted to retain folding stability base on free energy secondary structure formation. The size and number of hairpins were then optimized with aptamer sequence length to derive the highest binding score. We utilized the method against Streptoccocus agalactiae surface protein as test model and generated binding score values by molecular docking using AutoDock Vina. The aptamer binding affinities were then evaluated for binding capacity improvement via specific structural design. Molecular docking experiments indicated the feasibility of using in silico technique to select aptamers that can function as synthetic antibodies and revealed 3D structural conformity as an essential requirement in aptamer design.

Design and analysis of linear cascade DNA hybridization chain reactions using DNA hairpins

DNA self-assembly has been employed non-conventionally to construct nanoscale structures and dynamic nanoscale machines. The technique of hybridization chain reactions by triggered self-assembly has been shown to form various interesting nanoscale structures ranging from simple linear DNA oligomers to dendritic DNA structures. Inspired by earlier triggered self-assembly works, we present a system for controlled self-assembly of linear cascade DNA hybridization chain reactions using nine distinct DNA hairpins. NUPACK is employed to assist in designing DNA sequences and Matlab has been used to simulate DNA hairpin interactions. Gel electrophoresis and ensemble fluorescence reaction kinetics data indicate strong evidence of linear cascade DNA hybridization chain reactions. The half-time completion of the proposed linear cascade reactions indicates a linear dependency on the number of hairpins.

Ring-coil transitions in nematic ring polymers: Monte Carlo simulation

A Monte Carlo simulation of small (N≤20) polynematic ring molecules is presented and comparisons are made with their linear counterparts. In particular, the order parameter S, number of hairpins, mean square radius of gyration, and ellipticity are investigated as a function of reduced temperature and stiffness. Ring-coil transitions and related phenomena are observed as the system passes through the nematic-isotropic transition temperature. Odd-even effects are investigated as the homologous sequence is ascended, and pronounced alterations in the magnitude of the parameters are observed with ring size and stiffness