Nucleotide Frequencies Research Articles

Features of influenza virus hemagglutinin genes and their recoding possibilities

The world has already entered the stage of increasing odds for a new pandemic, which prompts to seek out for new flu vaccines, because existing vaccines demonstrate only suboptimal effectiveness. With the Covid-19 pandemic, the possibility of using mRNA vaccines has been opened up, and a prospect of finding hemagglutinin (HA) gene mRNA-based new influenza vaccines seems very attractive. As a rule, the mRNA vaccine is a product of recoding, which ensures the mRNA stability. However, the results of mRNA recoding can be ambiguous. The purpose of this report is to analyze the features of genes and proteins and to consider opportunities and limitations in their recoding. Primary structures of NA proteins and relevant genes were retrieved from Internet publicly available databases. The amino acid composition and frequency of dipeptides, nucleotide and dinucleotide compositions, %GC, translational code and compositions of neighboring di- and tricodones, distribution along primary structure for explicit and synonymous mutations were determined. H1N1 and H3N2 subtypes have both specific and general features (limitations) in their genes, differing not only in the number of protein substitutions, but also in the number and distribution of gene synonymous codons, which do not manifest in the protein primary structure, but appear, apparently, as a hidden factor, which causes the low effectiveness of classical influenza vaccines. The identification of several limitations in gene structure suggests that its any modification (in any gene) must not contradict each of the restrictions established by nature. The frequency of CpG dinucleotides in all studied strains is low, but a potential for optimizing it in H1N1 strains due to the prohibition of the quartet in the gene for arginine-encoding codons is especially limited and can be implemented through synonymous codons of other amino acids (alanine, proline, threonine or serine). Compared to the H1N1 subtype, the H3N2 subtype can be expected to have more possibilities in constructing stable NA gene mRNA.

Machine learning classification of archaea and bacteria identifies novel predictive genomic features

BackgroundArchaea and Bacteria are distinct domains of life that are adapted to a variety of ecological niches. Several genome-based methods have been developed for their accurate classification, yet many aspects of the specific genomic features that determine these differences are not fully understood. In this study, we used publicly available whole-genome sequences from bacteria (N=2546\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$N=2546$$\\end{document}) and archaea (N=109\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$N=109$$\\end{document}). From these, a set of genomic features (nucleotide frequencies and proportions, coding sequences (CDS), non-coding, ribosomal and transfer RNA genes (ncRNA, rRNA, tRNA), Chargaff’s, topological entropy and Shannon’s entropy scores) was extracted and used as input data to develop machine learning models for the classification of archaea and bacteria.ResultsThe classification accuracy ranged from 0.993 (Random Forest) to 0.998 (Neural Networks). Over the four models, only 11 examples were misclassified, especially those belonging to the minority class (Archaea). From variable importance, tRNA topological and Shannon’s entropy, nucleotide frequencies in tRNA, rRNA and ncRNA, CDS, tRNA and rRNA Chargaff’s scores have emerged as the top discriminating factors. In particular, tRNA entropy (both topological and Shannon’s) was the most important genomic feature for classification, pointing at the complex interactions between the genetic code, tRNAs and the translational machinery.ConclusionstRNA, rRNA and ncRNA genes emerged as the key genomic elements that underpin the classification of archaea and bacteria. In particular, higher nucleotide diversity was found in tRNA from bacteria compared to archaea. The analysis of the few classification errors reflects the complex phylogenetic relationships between bacteria, archaea and eukaryotes.

Digitizing DNA Sequences Using Multiset-Based Nucleotide Frequencies for Machine Learning-Based Mutation Detection

Investigating algebraic structures in a non-conventional framework supplements mathematics for hard-nosed practical applications to the fields of theoretical biology and computer science. One such algebraic structure is multigroup whose underlying set is a multiset. The genome is the entire set of DNA instructions found within a cell which contains all the information needed for an individual to develop and function. DNA and RNA are the hereditary materials that play a vital role in the metabolism process of living things, especially protein synthesis. In genomic database DNA sequences are stored in the form of string or text data types. The only data that works with machine learning algorithms is numerical. Thus, it is necessary to transform DNA sequence strings to numerical values. This article is organized in the following manner. Firstly, we prove that standard genetic code is a multigroup and genome architecture of the whole population can be interpreted as the sum of multisets. Next, it is described how a numerical representation of DNA bases relates to its algebraic representation. We further employed Gated Recurrent Unit, Long Short-Term Memory, and Bidirectional Long Short-Term Memory to identify changes between the DNA sequences. Experimental results show that GRU with multiset-based numerical values for DNA bases offers 98% accuracy on testing data. This novel technique will aid in the detection of mutations in complex diseases.

An independent base composition of each rate class for improved likelihood-based phylogeny estimation; the 5rf model.

The combination of a time reversible Markov process with a "hidden" mixture of gamma distributed relative site rates plus invariant sites have become the most favoured options for likelihood and other probabilistic models of nucleotide evolution (e.g., tr4gi which approximates a gamma with four rate classes). However, these models assume a homogeneous and stationary distribution of nucleotide (character or base) frequencies. Here, we explore the potential benefits and pitfalls of allowing each rate category (rate class) of a 4gi mixture model to have its own base frequencies. This is achieved by starting each of the five rate classes, at the tree's root, with its own free choice of nucleotide frequencies to create a 4gi5rf model or a 5rf model in shorthand. We assess the practical identifiability of this approach with a BEAST 2 implementation, aiming to determine if it can accurately estimate credibility intervals and expected values for a wide range of plausible parameter values. Practical identifiability, as distinguished from mathematical identifiability, gauges the model's ability to identify parameters in real-world scenarios, as opposed to theoretically with infinite data. One of the most common types of phylogenetic data is mitochondrial DNA (mtDNA) protein coding sequence. It is often assumed current models analyse robustly such data and that higher likelihood/posterior probability models do better. However, this abstract shows that vertebrate mtDNA remains a very difficult type of data to fully model, and that dramatically higher likelihoods do not mean a model is measurably more accurate with respect to recovering key parameters of biological interest (e.g., monophyletic groups, their support and their ages). The 4gi5rf model considerably improves marginal likelihoods and seems to reverse some apparent errors exacerbated by the 4gi model, while introducing others. Problems appear to be linked to non-stationary DNA repair processes that alter the mutation/substitution spectra across lineages and time. We also show such problems are not unique to mtDNA and are encountered in analysing nuclear sequences. Non-stationarity of DNA repair processes mutation/substitution spectra thus pose an active challenge to obtaining reliable inferences of relationships and divergence times near the root of placental mammals, for example. An open source implementation is available under the LGPL 3.0 license in the beastbooster package for BEAST 2, available from https://github.com/rbouckaert/beastbooster.

Cross-species substitution matrix comparison of Tomato leaf curl New Delhi virus (ToLCNDV) with medicinal plant isolates

The main objective of this study was to estimate and compare substitution matrixes of nucleotide frequencies for Tomato leaf curl New Delhi virus (ToLCNDV) with recently identified begomoviral isolates from two medicinal false daisy (Eclipta prostrata) and tomato (Solanum lycopersicum) plants. The ToLCNDV has become a significant limitation to vegetables production in many countries. A polymerase chain reaction was conducted to conserve the existence of begomoviral infection. The acquired amplicon was amplified using primers appropriate to the sequence in order to retrieve the full genome. The sequence analysis has confirmed the presence of ToLCNDV in symptomatic plants. The complete genome sequence having a 2.6–2.7 kb entire genome of ToLCNDV was obtained. An investigation of the phylogenetic and evolutionary history has verified the connection between this virus and other closely related viruses. The available nucleotide frequencies of codon regions (A, T/U, C, G) with newly isolates revealed 20–28% substitution matrixes. There was a minimal difference of nucleotide frequencies’ with already submitted database of this virus. Substitution matrixes, which quantify the probability of nucleotide substitutions evolving over a period of time, offer valuable information about mutation patterns and the forces driving evolution. This comparative analysis enhanced the comprehension of the genetic diversity of ToLCNDV and its possible consequences on medicinal plants. It also assisted in the formulation of efficient control measures and the preservation of begomoviruses in medicinal plant biodiversity. The information presented here is highly valuable for understanding the ToLCNDV biology and epidemiology, and it would also assist in disease management in the future.

Analysis of single nucleotide polymorphisms in sequences of Pina and Pinb genes of the diploid wheats Triticum monococcum and T. urartu

Puroindolines (puroindoline a and puroindoline b) are low molecular weight proteins that determine the endosperm texture of grain in the tribes Triticeae and Avenae. The aim of our study was to analyse single nucleotide polymorphisms (SNPs) of the coding sequences of the Pina and Pinb genes of the diploid wheats Triticum monococcum (AmAm) and T. urartu (AA) from the NCBI database in comparison with the reference sequences of the common wheat variety Chinese Spring (CS).For this, 62 sequences of the T. monococcum puroindoline a gene (Pina-Am1), 22 sequences of T. urartu Pina (Pina-A1), 32 sequences of the T. monococcum puroindoline b gene (Pinb-Am1) and 13 sequences of T. urartu Pinb (Pinb-A1) were retrieved from the NCBI database. The sequences of the puroindoline a gene DQ363911.1 of the variety CS (the Pina-D1a allele) and the puroindoline b gene DQ363913.1 of CS (the Pinb-D1a allele) from the NCBI database were used as the reference sequences. The sequences were aligned using MEGA 11. In total, 34 SNPs (13 synonymous and 21 nonsynonymous differences, of which 15 result in radical amino acid substitutions and 6 in conservative ones) were identified in the total sample of 84 diploid wheat Pina sequences, some of which were observed in all sequences and some were rare. Among the 45 Pinb sequences, there were 36 SNPs, but, unlike the Pina gene, synonymous substitutions prevailed (22); 7 substitutions led to radical amino acid substitutions and 7 to conservative ones. Substitutions in the sequences of the puroindoline genes relative to the CS genes can be divided into those fixed in both diploid wheat species, those fixed in T. urartu and polymorphic in T. monococcum, and species-specific ones. Significant differences in the frequencies of alternative nucleotides at certain positions (81, 318, 322 and 384 of Pina and 135 and 359 of Pinb) were found between the wild einkorn wheat T. monococcum ssp. aegilopoides and the cultivated wheat T. monococcum ssp. monococcum.

GraphPro: An interpretable graph neural network-based model for identifying promoters in multiple species

Promoters are DNA sequences that bind with RNA polymerase to initiate transcription, regulating this process through interactions with transcription factors. Accurate identification of promoters is crucial for understanding gene expression regulation mechanisms and developing therapeutic approaches for various diseases. However, experimental techniques for promoter identification are often expensive, time-consuming, and inefficient, necessitating the development of accurate and efficient computational models for this task. Enhancing the model's ability to recognize promoters across multiple species and improving its interpretability pose significant challenges. In this study, we introduce a novel interpretable model based on graph neural networks, named GraphPro, for multi-species promoter identification. Initially, we encode the sequences using k-tuple nucleotide frequency pattern, dinucleotide physicochemical properties, and dna2vec. Subsequently, we construct two feature extraction modules based on convolutional neural networks and graph neural networks. These modules aim to extract specific motifs from the promoters, learn their dependencies, and capture the underlying structural features of the promoters, providing a more comprehensive representation. Finally, a fully connected neural network predicts whether the input sequence is a promoter. We conducted extensive experiments on promoter datasets from eight species, including Human, Mouse, and Escherichia coli. The experimental results show that the average Sn, Sp, Acc and MCC values of GraphPro are 0.9123, 0.9482, 0.8840 and 0.7984, respectively. Compared with previous promoter identification methods, GraphPro not only achieves better recognition accuracy on multiple species, but also outperforms all previous methods in cross-species prediction ability. Furthermore, by visualizing GraphPro's decision process and analyzing the sequences matching the transcription factor binding motifs captured by the model, we validate its significant advantages in biological interpretability. The source code for GraphPro is available at https://github.com/liuliwei1980/GraphPro.

Morphometric and DNA-based identification of horseshoe crab Carcinoscorpius rotundicauda (Latreille, 1802) and Tachypleus gigas (Müller, 1785) from Bangladesh coast

Horseshoe crabs are the living fossils of xiphosurans that have survived for over 200 million years without morphological change and records fossil dating back 500 million years. A study was conducted on morphometric and molecular characterization of horseshoe crab Carcinoscorpius rotundicauda (Latreille, 1802) and Tachypleus gigas (Müller, 1785) from Bangladesh waters. A total of ten samples of C. rotundicauda and one sample of T. gigas were collected from Dublar Char, Sundarbans and Cox’s bazar from June 2016 to May 2017 and February 2024, respectively. Among them, four specimens were male and seven female. Morphometric measurements were taken from all the collected samples and differentiated male and female based on the second pair of legs, the pedipalps. The species were genetically identified using mitochondrial cytochrome c oxidase subunit I (COI) gene. An average length of about 670.5 bp nucleotide sequences were obtained. Average percentage of nucleotide frequencies were T (30.3), C (16.3), A (34.4) and G (19.0) for C. rotundicauda and T (34.45), C (22.01), A (27.91) and C (15.63) for T. gigas. GC content analysis showed that average GC 36.20% and AT 63.80%. The average GC content at the 1st, 2nd and 3rd codon position were 38.82%, 37.87% and 31.92% respectively. This is the first report of DNA barcoding of a living fossil C. rotundicauda and T. gigas from Bangladesh and will serve as baseline information for future research, conservation and management of this invaluable primitive creature. Bioresearch Commu. 10(2): 1489-1494, 2024 (July)

GB5mCPred: Cross-species 5mc Site Predictor Based on Bootstrap-based Stochastic Gradient Boosting Method for Poaceae

Background: One of the most prevalent epigenetic alterations in all three kingdoms of life is 5mC, which plays a part in a wide range of biological functions. Although in-vitro techniques are more effective in detecting epigenetic alterations, they are time and money-intensive. Artificial intelligence-based in silico approaches have been used to overcome these obstacles. background: One of the most prevalent epigenetic alterations in all three kingdoms of life is 5mC, which plays a part in a wide range of biological functions. Although in-vitro techniques are more effective in detecting epigenetic alterations, they are time and money intensive. Artificial intelligence-based in silico approaches have been used to overcome these obstacles. Aim: This study aimed to develop an ML-based predictor for the detection of 5mC sites in Poaceae. Objective: The objective of this study was the evaluation of machine learning and deep learning models for the prediction of 5mC sites in rice. Method: In this study, the vectorization of DNA sequences has been performed using three distinct feature sets- Oligo Nucleotide Frequencies (k = 2), Mono-nucleotide Binary Encoding, and Chemical Properties of Nucleotides. Two deep learning models, long short-term memory (LSTM) and Bidirectional LSTM (Bi-LSTM), as well as nine machine learning models, including random forest, gradient boosting, naïve bayes, regression tree, k-Nearest neighbour, support vector machine, adaboost, multiple logistic regression, and artificial neural network, were investigated. Also, bootstrap resampling was used to build more efficient models along with a hybrid feature selection module for dimensional reduction and removal of irrelevant features of the vector space. Result: Random Forest gains the maximum accuracy, specificity and MCC, i.e., 92.6%, 86.41% and 0.84. Gradient Boosting obtained the maximum sensitivity, i.e., 96.85%. The Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) technique showed that the best three models were Random Forest, Gradient Boosting, and Support Vector Machine in terms of accurate prediction of 5mC sites in rice. We developed an R-package, ‘GB5mCPred,’ and it is available in CRAN (https://cran.r-project.org/web/packages/GB5mcPred/index.html). Also, a user-friendly prediction server was made based on this algorithm (http://cabgrid.res.in:5474/). Conclusion: With nearly equal TOPSIS scores, Random Forest, Gradient Boosting, and Support Vector Machine ended up being the best three models. The major rationale may be found in their architectural design since they are gradual learning models that can capture the 5mC sites more correctly than other learning models.

Utilizing genomic signatures to gain insights into the dynamics of SARS-CoV-2 through Machine and Deep Learning techniques

The global spread of the SARS-CoV-2 pandemic, originating in Wuhan, China, has had profound consequences on both health and the economy. Traditional alignment-based phylogenetic tree methods for tracking epidemic dynamics demand substantial computational power due to the growing number of sequenced strains. Consequently, there is a pressing need for an alignment-free approach to characterize these strains and monitor the dynamics of various variants. In this work, we introduce a swift and straightforward tool named GenoSig, implemented in C++. The tool exploits the Di and Tri nucleotide frequency signatures to delineate the taxonomic lineages of SARS-CoV-2 by employing diverse machine learning (ML) and deep learning (DL) models. Our approach achieved a tenfold cross-validation accuracy of 87.88% (± 0.013) for DL and 86.37% (± 0.0009) for Random Forest (RF) model, surpassing the performance of other ML models. Validation using an additional unexposed dataset yielded comparable results. Despite variations in architectures between DL and RF, it was observed that later clades, specifically GRA, GRY, and GK, exhibited superior performance compared to earlier clades G and GH. As for the continental origin of the virus, both DL and RF models exhibited lower performance than in predicting clades. However, both models demonstrated relatively higher accuracy for Europe, North America, and South America compared to other continents, with DL outperforming RF. Both models consistently demonstrated a preference for cytosine and guanine over adenine and thymine in both clade and continental analyses, in both Di and Tri nucleotide frequencies signatures. Our findings suggest that GenoSig provides a straightforward approach to address taxonomic, epidemiological, and biological inquiries, utilizing a reductive method applicable not only to SARS-CoV-2 but also to similar research questions in an alignment-free context.

MAST: Phylogenetic Inference with Mixtures Across Sites and Trees.

Hundreds or thousands of loci are now routinely used in modern phylogenomic studies. Concatenation approaches to tree inference assume that there is a single topology for the entire dataset, but different loci may have different evolutionary histories due to incomplete lineage sorting (ILS), introgression, and/or horizontal gene transfer; even single loci may not be treelike due to recombination. To overcome this shortcoming, we introduce an implementation of a multi-tree mixture model that we call mixtures across sites and trees (MAST). This model extends a prior implementation by Boussau et al. (2009) by allowing users to estimate the weight of each of a set of pre-specified bifurcating trees in a single alignment. The MAST model allows each tree to have its own weight, topology, branch lengths, substitution model, nucleotide or amino acid frequencies, and model of rate heterogeneity across sites. We implemented the MAST model in a maximum-likelihood framework in the popular phylogenetic software, IQ-TREE. Simulations show that we can accurately recover the true model parameters, including branch lengths and tree weights for a given set of tree topologies, under a wide range of biologically realistic scenarios. We also show that we can use standard statistical inference approaches to reject a single-tree model when data are simulated under multiple trees (and vice versa). We applied the MAST model to multiple primate datasets and found that it can recover the signal of ILS in the Great Apes, as well as the asymmetry in minor trees caused by introgression among several macaque species. When applied to a dataset of 4 Platyrrhine species for which standard concatenated maximum likelihood (ML) and gene tree approaches disagree, we observe that MAST gives the highest weight (i.e., the largest proportion of sites) to the tree also supported by gene tree approaches. These results suggest that the MAST model is able to analyze a concatenated alignment using ML while avoiding some of the biases that come with assuming there is only a single tree. We discuss how the MAST model can be extended in the future.

RhizoBindingSites v2.0 Is a Bioinformatic Database of DNA Motifs Potentially Involved in Transcriptional Regulation Deduced From Their Genomic Sites.

RhizoBindingSites is a de novo depurified database of conserved DNA motifs potentially involved in the transcriptional regulation of the Rhizobium, Sinorhizobium, Bradyrhizobium, Azorhizobium, and Mesorhizobium genera covering 9 representative symbiotic species, deduced from the upstream regulatory sequences of orthologous genes (O-matrices) from the Rhizobiales taxon. The sites collected with O-matrices per gene per genome from RhizoBindingSites were used to deduce matrices using the dyad-Regulatory Sequence Analysis Tool (RSAT) method, giving rise to novel S-matrices for the construction of the RizoBindingSites v2.0 database. A comparison of the S-matrix logos showed a greater frequency and/or re-definition of specific-position nucleotides found in the O-matrices. Moreover, S-matrices were better at detecting genes in the genome, and there was a more significant number of transcription factors (TFs) in the vicinity than O-matrices, corresponding to a more significant genomic coverage for S-matrices. O-matrices of 3187 TFs and S-matrices of 2754 TFs from 9 species were deposited in RhizoBindingSites and RhizoBindingSites v2.0, respectively. The homology between the matrices of TFs from a genome showed inter-regulation between the clustered TFs. In addition, matrices of AraC, ArsR, GntR, and LysR ortholog TFs showed different motifs, suggesting distinct regulation. Benchmarking showed 72%, 68%, and 81% of common genes per regulon for O-matrices and approximately 14% less common genes with S-matrices of Rhizobium etli CFN42, Rhizobium leguminosarum bv. viciae 3841, and Sinorhizobium meliloti 1021. These data were deposited in RhizoBindingSites and the RhizoBindingSites v2.0 database (http://rhizobindingsites.ccg.unam.mx/).

Ense-i6mA: Identification of DNA N6-methyl-adenine Sites Using XGB-RFE Feature Se-lection and Ensemble Machine Learning.

DNA N6-methyladenine (6mA) is an important epigenetic modification that plays a vital role in various cellular processes. Accurate identification of the 6mA sites is fundamental to elucidate the biological functions and mechanisms of modification. However, experimental methods for detecting 6mA sites are high-priced and time-consuming. In this study, we propose a novel computational method, called Ense-i6mA, to predict 6mA sites. Firstly, five encoding schemes, i.e., one-hot encoding, gcContent, Z-Curve, K-mer nucleotide frequency, and K-mer nucleotide frequency with gap, are employed to extract DNA sequence features. Secondly, to our knowledge, it is the first time that eXtreme gradient boosting coupled with recursive feature elimination is applied to 6mA sites prediction domain to remove noisy features for avoiding over-fitting, reducing computing time and complexity. Then, the best subset of features is fed into base-classifiers composed of Extra Trees, eXtreme Gradient Boosting, Light Gradient Boosting Machine, and Support Vector Machine. Finally, to minimize generalization errors, the prediction probabilities of the base-classifiers are aggregated by averaging for inferring the final 6mA sites results. We conduct experiments on two species, i.e., Arabidopsis thaliana and Drosophila melanogaster, to compare the performance of Ense-i6mA against the recent 6mA sites prediction methods. The experimental results demonstrate that the proposed Ense-i6mA achieves area under the receiver operating characteristic curve values of 0.967 and 0.968, accuracies of 91.4% and 92.0%, and Mathew's correlation coefficient values of 0.829 and 0.842 on two benchmark datasets, respectively, and outperforms several existing state-of-the-art methods.

Two Intra-Individual ITS1 rDNA Sequence Variants Identified in the Female and Male Rotylenchulus reniformis Populations of Alabama.

Around 300 different plant species are infected by the plant-parasitic reniform nematode (Rotylenchulus reniformis), including cotton. This is a devasting nematode with a preference for cotton; it is commonly found in Alabama farms and causes severe reduction in yields. Its first internal transcribed spacer (ITS1) region can be sequenced, and potential mutations can be found in order to study the population dynamics of the reniform nematode. The goal of our study was to sequence the ITS1 rDNA region in male and female RNs that were collected from BelleMina, Hamilton, and Lamons locations in Alabama. After separating the single male and female RNs from the samples collected from the three selected listed sites above, the ITS1 region was amplified selectively using specific primers, and the resulting products were cloned and sequenced. Two distinct bands were observed after DNA amplification of male and female nematodes at 550 bp and 730 bp, respectively. The analysis of sequenced fragments among the three populations showed variation in average nucleotide frequencies of female and male RNs. Singletons within the female and male Hamilton populations ranged from 7.8% to 10%, and the variable sites ranged from 13.4% to 26%. However, female and male BelleMina populations had singletons ranging from 7.1% to 19.7% and variable regions in the range of 13.9% to 49.3%. The female and male Lamons populations had singletons ranging from 2.5% to 8.7% and variable regions in the range of 2.9% to 14.2%. Phylogenetic (neighbor-joining) analysis for the two ITS1 fragments (ITS-550 and ITS-730) showed relatively high intra-nematode variability. Different clone sequences from an individual nematode often had greater similarity with other nematodes than with their own sequences. RNA fold analysis of the ITS1 sequences revealed varied stem and loop structures, suggesting both conserved and variable regions in the variants identified from female and male RNs, thus underscoring the presence of significant intra- and inter-nematodal variation among RN populations in Alabama.

Barkodlama Bölgesi Olarak Potentilla L. Cinsinde psbA-trnH IGS Sekanslarının Değerlendirmesi

Barkodlama Bölgesi Olarak Potentilla L. Cinsinde psbA-trnH IGS Sekanslarının Değerlendirmesi

Molecular characterization and evolutionary history analysis of a novel Kudzu mosaic virus (KMV) infecting the Kudzu (Pueraria montana)

ObjectivesPlant viruses are a significant contributor to the economic losses experienced in numerous plant species. Begomoviruses exhibit a wide range of host species, encompassing both cultivated and non-cultivated plants. The kudzu plant, scientifically known as Pueraria montana, is indigenous to the Southeast Asian region. Moreover, it exhibits varied attributes with regards to its utilization in the fields of medicine, food, cosmetics, and the herbal industry. MethodsDuring a routine survey, some P. montana plants having typical symptoms of yellowing, mosaic, and mottle were observed in areas including Fort-Minro (FM) and Anari of district Dera Ghazi Khan and Maari (MA) of district Rajanpur. The genomic DNA was extracted from the collected samples. The polymerase chain reaction (PCR) and rolling circular amplification (RCA) was carried out to isolate the DNA and DNA- β from the collected plant specimens. An evolutionary research was conducted to assess the lineage of this virus inside a novel geographical region. ResultsA 500-bp amplicon size was obtained from the samples of the FM and MA, while the approximately 2.7-kb amplicon size of its full genome was obtained from the samples of both locations. The results of the sequence analysis demonstrated conclusively that the plants in question are infected with Kudzu mosaic virus (KMV). The evolutionary history analysis has confirmed the virus's lineage in a new region, and the nucleotide frequencies of obtained sequences were variable. ConclusionThe presence of a novel bipartite begomovirus in the tropical region has been substantiated using a phylogenetic and evolutionary investigation. The observation of a white fly population on infected plants has also suggested that the transmission of this virus through the migration of this insect vector can be a viable mechanism.

Türkiye’de İstalacı bir tür olan Nysius cymoides (Spinola, 1837) (Hemiptera: Lygaeidae)’in Moleküler Tanımlaması

The false chinch bug, Nysius cymoides (Spinola, 1837) (Hemiptera: Lygaeidae), is an important pest with a wide host range. It was first discovered in Italy but now causing agricultural damage worldwide, including Turkey. Specimens of Nysius cymoides were collected from surveys of the sunflower and corn fields in Malkara-Keşan province, in Edirne in July 2020. Because of the population increase of the pest and their damages to sunflowers and corn fields, the pest identification have become an important issue. We investigated molecular characterization and identification of the pest by using mitochondrial cytochrome oxidase I (COI) barcode region. Adult and late immature stage of the specimens were examined under a stereozoom microscope and morphologically identified as well. The partial COI gene (659 bp) was amplified by using LCO1490/ HCO 2198 and PCR products were sequenced directly for molecular characterization. The sequence was registered GenBank database with accession number OL989232. Molecular analyzes were performed using MEGA X software. As a result, the nucleotide frequencies were A = 33.81%, T/U = 35.79%, C = 16.03%, and G = 14.37%. The distance-based species limitation method applied by using ASAP and ABGD, identified 5 species in the partition with the lowest ASAP score (1.5). The barcode gap distance of this partition with the best ASAP score was determined as 1.7%. The threshold distance was determined as 1.62%. The highest nucleotide identity of the studied specimen (OL989232) was detected as 99.8% with the specimen of Nysius cymoides from France (KJ541649.1) in GenBank. The objective of this study was the first attempt to identify Nysius cymoides specimens collected in Edirne based on DNA barcode regions.

Compositional Structure of the Genome: A Review.

As the genome carries the historical information of a species' biotic and environmental interactions, analyzing changes in genome structure over time by using powerful statistical physics methods (such as entropic segmentation algorithms, fluctuation analysis in DNA walks, or measures of compositional complexity) provides valuable insights into genome evolution. Nucleotide frequencies tend to vary along the DNA chain, resulting in a hierarchically patchy chromosome structure with heterogeneities at different length scales that range from a few nucleotides to tens of millions of them. Fluctuation analysis reveals that these compositional structures can be classified into three main categories: (1) short-range heterogeneities (below a few kilobase pairs (Kbp)) primarily attributed to the alternation of coding and noncoding regions, interspersed or tandem repeats densities, etc.; (2) isochores, spanning tens to hundreds of tens of Kbp; and (3) superstructures, reaching sizes of tens of megabase pairs (Mbp) or even larger. The obtained isochore and superstructure coordinates in the first complete T2T human sequence are now shared in a public database. In this way, interested researchers can use T2T isochore data, as well as the annotations for different genome elements, to check a specific hypothesis about genome structure. Similarly to other levels of biological organization, a hierarchical compositional structure is prevalent in the genome. Once the compositional structure of a genome is identified, various measures can be derived to quantify the heterogeneity of such structure. The distribution of segment G+C content has recently been proposed as a new genome signature that proves to be useful for comparing complete genomes. Another meaningful measure is the sequence compositional complexity (SCC), which has been used for genome structure comparisons. Lastly, we review the recent genome comparisons in species of the ancient phylum Cyanobacteria, conducted by phylogenetic regression of SCC against time, which have revealed positive trends towards higher genome complexity. These findings provide the first evidence for a driven progressive evolution of genome compositional structure.

CPPVec: an accurate coding potential predictor based on a distributed representation of protein sequence

Long non-coding RNAs (lncRNAs) play a crucial role in numbers of biological processes and have received wide attention during the past years. Since the rapid development of high-throughput transcriptome sequencing technologies (RNA-seq) lead to a large amount of RNA data, it is urgent to develop a fast and accurate coding potential predictor. Many computational methods have been proposed to address this issue, they usually exploit information on open reading frame (ORF), protein sequence, k-mer, evolutionary signatures, or homology. Despite the effectiveness of these approaches, there is still much room to improve. Indeed, none of these methods exploit the contextual information of RNA sequence, for example, k-mer features that counts the occurrence frequencies of continuous nucleotides (k-mer) in the whole RNA sequence cannot reflect local contextual information of each k-mer. In view of this shortcoming, here, we present a novel alignment-free method, CPPVec, which exploits the contextual information of RNA sequence for coding potential prediction for the first time, it can be easily implemented by distributed representation (e.g., doc2vec) of protein sequence translated from the longest ORF. The experimental findings demonstrate that CPPVec is an accurate coding potential predictor and significantly outperforms existing state-of-the-art methods.

Computational prediction of promotors in Agrobacterium tumefaciens strain C58 by using the machine learning technique.

Promotors are those genomic regions on the upstream of genes, which are bound by RNA polymerase for starting gene transcription. Because it is the most critical element of gene expression, the recognition of promoters is crucial to understand the regulation of gene expression. This study aimed to develop a machine learning-based model to predict promotors in Agrobacterium tumefaciens (A. tumefaciens) strain C58. In the model, promotor sequences were encoded by three different kinds of feature descriptors, namely, accumulated nucleotide frequency, k-mer nucleotide composition, and binary encodings. The obtained features were optimized by using correlation and the mRMR-based algorithm. These optimized features were inputted into a random forest (RF) classifier to discriminate promotor sequences from non-promotor sequences in A. tumefaciens strain C58. The examination of 10-fold cross-validation showed that the proposed model could yield an overall accuracy of 0.837. This model will provide help for the study of promoters in A. tumefaciens C58 strain.