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Corona disease has caused a variety of problems for people since it was formed and spread around the world. In this study, diagnosis and differentiation of this disease have been investigated in the form of genomic sequences. The proposed approach is based on a combination of several digital signal processing algorithms that include discrete Fourier transform and comb notch filter. More than 100,000 genomic sequences from different geographical locations and different variants have been tested in this research by various machine learning models. The use of KNN and SVM classifier models has resulted in the accurate diagnosis and differentiation of about 99% of coronavirus samples from the influenza virus. The proposed approach provides the possibility to generalize this method and improve machine learning models and get better results. [ABSTRACT FROM AUTHOR]

Background/Objectives: Understanding the relationship between DNA sequences and gene expression levels is of significant biological importance. Recent advancements have demonstrated the ability of deep learning to predict gene expression levels directly from genomic data. However, traditional methods are limited by basic word encoding techniques, which fail to capture the inherent features and patterns of DNA sequences. Methods: We introduce TExCNN, a novel framework that integrates the pre-trained models DNABERT and DNABERT-2 to generate word embeddings for DNA sequences. We partitioned the DNA sequences into manageable segments and computed their respective embeddings using the pre-trained models. These embeddings were then utilized as inputs to our deep learning framework, which was based on convolutional neural network. Results: TExCNN outperformed current state-of-the-art models, achieving an average R2 score of 0.622, compared to the 0.596 score achieved by the DeepLncLoc model, which is based on the Word2Vec model and a text convolutional neural network. Furthermore, when the sequence length was extended from 10,500 bp to 50,000 bp, TExCNN achieved an even higher average R2 score of 0.639. The prediction accuracy improved further when additional biological features were incorporated. Conclusions: Our experimental results demonstrate that the use of pre-trained models for word embedding generation significantly improves the accuracy of predicting gene expression. The proposed TExCNN pipeline performes optimally with longer DNA sequences and is adaptable for both cell-type-independent and cell-type-dependent predictions. [ABSTRACT FROM AUTHOR]

Recent research is focused on the exploitation of DNA-based molecules for data encryption due to their high capacity to store larger volumes of data and lower computation requirements [1, 2]. This study proposes a Hybrid Image Encryption method (HIE) that convolves DNA sequence encoding with the Rivest-Shamir-Adleman (RSA) algorithm to enhance the security of image encryption. The proposed scheme uses small prime numbers to encrypt the image, which is then encoded as a DNA sequence. Subsequently, the encrypted DNA sequence is stored in a physical medium. The encrypted DNA sequence can then be decrypted using the RSA algorithm and the corresponding private key to recover the original image. The results show that using small prime numbers for RSA encryption of an image and encoding it as a DNA sequence can enhance security and reduce computational time. [ABSTRACT FROM AUTHOR]

This paper proposes a robust image encryption algorithm that utilizes a Novel three-dimensional (3D) Chaotic map and an Enhanced Logistic Sine System (ELSS). We leverage the unpredictability of 3D chaotic dynamics alongside the complexity of ELSS and DNA Sequence to forge a formidable image encryption scheme. Firstly, the image pixels are converted from decimal to hexadecimal notation and sorted in a 1D pixel array carrying a unique sequence of three channels of the RGB image. Secondly, the secret key is appended to XOR, the values with that 1-D pixels array. Thirdly, values are sorted by performing the binary right shift operation and encoded into DNA. Fourthly, a novel chaotic map is used to perform scrambling operations. Lastly, a novel enormous keyspace ELSS is used to perform efficient Border and Cross-Border (B &CB) pixel exchange, further enhancing the encryption quality of the proposed algorithm. Comprehensive security analysis proved that the proposed algorithm exhibits remarkable resilience against powerful known and chosen plaintext attacks and other prevalent cryptanalysis attacks, including differential attacks and exhaustive key search attacks. Henceforth, the proposed algorithm's superior security and low computational cost make it an ideal choice for real-time secure image communication across various platforms, including satellite, multimedia, and military communications. [ABSTRACT FROM AUTHOR]

Abstract Sequence-dependent mechanical properties of DNA could play essential roles in nuclear processes by affecting histone-DNA interactions. Previously, we found that the DNA entry site of the first nucleosomes from the transcription start site (+ 1 nucleosome) in budding yeast enriches AA/TT steps, but not the exit site, and the biased presence of AA/TT in the entry site was associated with the transcription levels of yeast genes. Because AA/TT is a rigid dinucleotide step, we considered that AA/TT causes DNA unwrapping. However, our previous MNase-seq experiments with reconstituted nucleosomes left some doubt regarding this interpretation, owing to its high exonuclease activity. Furthermore, MNase cleavage did not provide direct evidence of its structural state. In this study, Förster resonance energy transfer (FRET) measurements were used to investigate salt-induced conformational changes in nucleosomal DNA containing AA/TT repeats at the entry site. We observed that the AA/TT region wrapped around the histone core was as likely as other DNA sequences at physiological salt concentrations. However, it unwrapped at a lower salt concentration, indicating weaker electrostatic interactions with the histone core. Ethidium-induced nucleosome disruption assay showed that the intercalator had greater access to DNA with AA/TT at the entry site. Taken together, these results suggest that AA/TT at the entry sites induces DNA unwrapping from the histone core on the promoter side, which may promote transcriptional activation in response to the approach of transcription-related proteins.

Enhancer-promoter interactions (EPIs) are crucial in gene transcription regulation and cell differentiation. Traditional biological experiments are costly and time-consuming, motivating the development of computational prediction methods. However, existing EPI prediction methods inadequately capture the intricate direct interactions between enhancer and promoter sequences, which limits their prediction performance to some extent. In this work, we propose an innovative attention-based approach RAEPI, which uses convolutional neural networks to extract initial features of enhancers and promoters, combined with a specially designed Restricted Attention mechanism with Query-Key-Value constrained to simulate the interactions between them for further feature extraction. To improve cross-cell line prediction, we employ a transfer learning strategy for pre-training. Furthermore, we extracted sequence motifs to evaluate the RAEPI's effectiveness from a visualization perspective. Experimental results show that RAEPI achieves competitive prediction performance to existing methods on the benchmark dataset. [ABSTRACT FROM AUTHOR]

Accurate prediction of transcription factor binding sites (TFBSs) is essential for understanding gene regulation mechanisms and the etiology of diseases. Despite numerous advances in deep learning for predicting TFBSs, their performance can still be enhanced. In this study, we propose MLSNet, a novel deep learning architecture designed specifically to predict TFBSs. MLSNet innovatively integrates multisize convolutional fusion with long short-term memory (LSTM) networks to effectively capture DNA-sparse higher-order sequence features. Further, MLSNet incorporates super token attention and Bi-LSTM to systematically extract and integrate higher-order DNA shape features. Experimental results on 165 ChIP-seq (chromatin immunoprecipitation followed by sequencing) datasets indicate that MLSNet consistently outperforms several state-of-the-art algorithms in the prediction of TFBSs. Specifically, MLSNet reports average metrics: 0.8306 for ACC, 0.8992 for AUROC, and 0.9035 for AUPRC, surpassing the second-best methods by 1.82%, 1.68%, and 1.54%, respectively. This research delineates the effectiveness of combining multi-size convolutional layers with LSTM and DNA shape-based features in enhancing predictive accuracy. Moreover, this study comprehensively assesses the variability in model performance across different cell lines and transcription factors. The source code of MLSNet is available at https://github.com/minghaidea/MLSNet. [ABSTRACT FROM AUTHOR]

Background: Microspore embryogenesis is a process that produces doubled haploids in tissue culture environments and is widely used in cereal plants. The efficient production of green regenerants requires stresses that could be sensed at the level of glycolysis, followed by the Krebs cycle and electron transfer chain. The latter can be affected by Cu(II) ion concentration in the induction media acting as cofactors of biochemical reactions, indirectly influencing the production of glutathione (GSH) and S-adenosyl-L-methionine (SAM) and thereby affecting epigenetic mechanisms involving DNA methylation (demethylation—DM, de novo methylation—DNM). The conclusions mentioned were acquired from research on triticale regenerants, but there is no similar research on barley. In this way, the study looks at how DNM, DM, Cu(II), SAM, GSH, and β-glucan affect the ability of green plant regeneration efficiency (GPRE). Results: The experiment involved spring barley regenerants obtained through anther culture. Nine variants (trials) of induction media were created by adding copper (CuSO4: 0.1; 5; 10 µM) and silver salts (AgNO3: 0; 10; 60 µM), with varying incubation times for the anthers (21, 28, and 35 days). Changes in DNA methylation were estimated using the DArTseqMet molecular marker method, which also detects cytosine methylation. Phenotype variability in β-glucans, SAM and GSH induced by the nutrient treatments was assessed using tentative assignments based on the Attenuated Total Reflectance-Fourier Transform Infrared (ATR-FTIR) spectroscopy. The effectiveness of green plant regeneration ranged from 0.1 to 2.91 plants per 100 plated anthers. The level of demethylation ranged from 7.61 to 32.29, while de novo methylation reached values ranging from 6.83 to 32.27. The paper demonstrates that the samples from specific in vitro conditions (trials) formed tight groups linked to the factors contributing to the two main components responsible for 55.05% of the variance (to the first component DNM, DM, to the second component GSH, β-glucans, Cu(II), GPRE). Conclusions: We can conclude that in vitro tissue culture conditions affect biochemical levels, DNA methylation changes, and GPRE. Increasing Cu(II) concentration in the IM impacts the metabolism and DNA methylation, elevating GPRE. Thus, changing Cu(II) concentration in the IM is fair to expect to boost GPRE. [ABSTRACT FROM AUTHOR]

Within the domain of molecular biology research, the intricate regulation of transcription continues to present a challenging yet imperative area of study. According to recent scientific studies, the nucleotide double helix shape is a major factor in improving the accuracy and comprehensibility of Transcription Factor Binding Sites (TFBSs). Despite the significant growth in computational methods aiming to concurrently incorporate both DNA sequence and DNA shape features, devising an effective model remains a challenging and unresolved issue. In this paper, we proposed a deep learning prediction model for TFBSs using attention mechanism, convolutional, and RNN-based networks by incorporating the DNA sequence and shape data. Attention mechanisms recognise the long-range dependencies but encounter challenges in focusing on local feature details. On the other hand, convolutional operations are proficient at extracting local features but may inadvertently neglect global information. Recurrent Neural Networks (RNNs) capture long-term dependencies within sequences. We demonstrate that the ability to predict TFBSs is greatly improved by our proposed technique, DeepCTF, using 12 in-vitro datasets collected from Protein Binding Microarray (PBMs) compared to the other state-of-the-art models. [ABSTRACT FROM AUTHOR]

Among the 17 species of Sympetrum found on the Korean peninsula Sympetrum vulgatum Linnaeus, 1758 had not been recorded since its first record in 1932 by Doi, suggesting the potential for misidentification. We observed a total of four specimens on September 21, 25, 30, and November 1 2022 in Goseong, Gangwon-do and collected each one male and female. We described the morphological and ecological characteristics of this species, conducted molecular analysis using mitochondrial COI, and addressed the controversial past record regarding its presence on the Korean Peninsula. [ABSTRACT FROM AUTHOR]

Deciphering the functional effects of noncoding genetic variants stands as a fundamental challenge in human genetics. Traditional approaches, such as Genome-Wide Association Studies (GWAS), Transcriptome-Wide Association Studies (TWAS), and Quantitative Trait Loci (QTL) studies, are constrained by obscured the underlying molecular-level mechanisms, making it challenging to unravel the genetic basis of complex traits. The advent of Next-Generation Sequencing (NGS) technologies has enabled context-specific genome-wide measurements, encompassing gene expression, chromatin accessibility, epigenetic marks, and transcription factor binding sites, to be obtained across diverse cell types and tissues, paving the way for decoding genetic variation effects directly from DNA sequences only. The de novo predictions of functional effects are pivotal for enhancing our comprehension of transcriptional regulation and its disruptions caused by the plethora of noncoding genetic variants linked to human diseases and traits. This review provides a systematic overview of the stateof-the-art models and algorithms for genetic variant effect predictions, including traditional sequence-based models, Deep Learning models, and the cutting-edge Foundation Models. It delves into the ongoing challenges and prospective directions, presenting an in-depth perspective on contemporary developments in this domain. [ABSTRACT FROM AUTHOR]

Summary: The Terminator, a specific DNA sequence, provides the transcriptional termination signal to RNA polymerase, making it a critical aspect of transcriptional regulation. This article proposes AMter, the first end‐to‐end model designed for predicting transcriptional terminators, leveraging attention mechanisms. In AMter, rather than manual feature engineering, two distinct modules based on attention mechanism, known as Frequency‐Attention and Allkmer‐Attention, are employed to automatically learn efficient features. Frequency‐Attention generates informative features by autonomously determining the significance of various frequency features, while Allkmer‐Attention aims to capture the relationships among all k‐mers within a DNA sequence. Features generated by Frequency‐Attention and Allkmer‐Attention demonstrate high informativeness and discriminative capacity, allowing precise discrimination of whether a DNA sequence is a terminator through a simple prediction network. The results of the 5‐fold cross‐validation test indicate the remarkable achievement of our proposed method, attaining 100% accuracy in both the training and validation datasets. Furthermore, AMter demonstrates outstanding prediction accuracy on two independent datasets, with 100% accuracy for Escherichia coli and 99.30% for Bacillus subtilis, marking a significant 94.4% relative improvement over prior methods. Experimental results conclusively demonstrate that AMter surpasses existing approaches, establishing a new state‐of‐the‐art in transcriptional terminator prediction. [ABSTRACT FROM AUTHOR]

A strategy to obtain the DNA base-pair sequence is proposed from the information of the time series data of the size fluctuations (breathing) of local denaturation zones (bubbles). In one of the previous publication of the author,1 it was shown how the DNA stability parameters were evaluated from an equilibrium distribution profile of bubble opening for a known sequence of heteropolymeric DNA. This paper attempts to know whether the reverse could be done, i.e., by knowing the stability parameters and the distribution profile for an unknown DNA sequence, whether the proper base-pair sequence can be evaluated or not. To get the DNA sequence, a non-trivial search process has been used. The problem has been cast as one of optimization problem and stochastic optimizer Genetic Algorithm has been used to optimize the DNA sequence in order to reach the correct or objective equilibrium distribution profile. We have tested the strategy for three DNA sequences, L33B9, L42B18 and the promoter sequence of the T7 bacteriophage. The DNA base pair sequence could be obtained from the information of the time series data of the size fluctuations (breathing) of local denaturation zones (bubbles). The problem has been cast as one of optimization problem and stochastic optimizer Genetic Algorithm. [ABSTRACT FROM AUTHOR]

From the mathematical and statistical point of view, a segment of a DNA strand can be viewed as a sequence of four-state (A, C, G, T) trials. Herein, we consider the distributions of runs and patterns related to the run lengths of multi-state sequences, especially for four states (A, B, C, D). Let Xi,X2,... be a sequence of four state independent and identically distributed trials taking values in the set S = {A, B, C, D}. In this study, we obtain exact formulas for the probability distribution function for the discrete distribution of runs of B's of order k. We obtain longest run statistics, shortest run statistics, and determine the distributions of waiting times and run lengths. [ABSTRACT FROM AUTHOR]

Several species of Cobitis have been reported within Iran, but only four are recognized as valid. The geographical distribution and genetic diversity of these species are not well-known. Two new populations of C. saniae and C. avicennae were reported herein based on their morphological and phylogenetic characters from the Mahabad River, a tributary of the Urmia Lake basin, and the Razavar River, a tributary of the Karkheh drainage, respectively. The genetic diversity and genetic structure of populations of C. saniae, C. avicennae, and C. faridpaki were analysed using the DNA sequences of the mitochondrial COI gene. COI sequences from various watersheds in Iran, Azerbaijan, and Georgia were used. A moderate to high level haplotype diversity and low nucleotide diversity were found in most of the populations of all three species. The AMOVA test and pairwise FST comparison revealed significant genetic structure and genetic distance between the Urmia Lake basin and other C. saniae populations. The results highlight the importance of conserving these species and their habitats, particularly in the Urmia Lake basin, where significant genetic differentiation was observed. [ABSTRACT FROM AUTHOR]

The present research was conducted to study the effect of irrigation with the two types of irrigation regimes, the regular tap water and salty well water, and the use of foliar spraying of the antioxidant glutathione at a concentration of 0, 100, and 200 mg/L and Vitamin B12 at a concentration of 0, 2, and 4 mg/L. Based on molecular characterization, the relative amount of the GPX gene was estimated, and the comparison between the RobyGym cultivar and other strawberry varieties was made by detecting variations in the variety’s genome using the DNA sequence method. The factors were applied individually and overlapped in a split-split plot design in three sectors. The seedlings were irrigated with the irrigation water quality factor starting on July 11, 2021, with normal liquefaction water, and the supplies were irrigated with salty well water on January 12, 2022, and the exact dates for the second season. As for foliar spraying with both glutathione and Vitamin B12, it was carried out on five dates: 12/16, 1/17, 2/17, 7/1, 3/11, and 4/10 for the seasons 2022 and 2023, respectively. The results of analysis of the relative quantity of gene expression in the leaves of seedlings of the RobyGym cultivar showed that the gene expression of the GPX gene was as high as the relative quantity of that salt stress tolerance gene for glutathione and Vitamin B12 (200 mg/L, 4 mg/L, respectively), that is, 31.28-fold. The genetic tree indicates that the Schleck RobyGym variety was close to 100% with international varieties and that there were no genetic differences between the RobyGym variety and international varieties. [ABSTRACT FROM AUTHOR]

Abstract Background The sustainable supply of medicinal plants is important, and cultivating and domesticating them has been suggested as an optimal strategy. However, this can lead to a loss of genetic diversity. Tripterygium wilfordii Hook. f. is a medicinal plant commonly used in traditional Chinese medicine, but its wild populations are dwindling due to excessive harvesting. To protect the species and meet the increasing demand, it is urgent to cultivate it on a large scale. However, distinguishing between T. wilfordii and T. hypoglaucum, two similar species with different medicinal properties, is challenging. Therefore, it is crucial to understand the genetic diversity and population structure of these species for their sustainable utilization. Results In this study, we investigated the genetic diversity and population structure of the two traditional medicinal semiwoody vines plant species, Tripterygium wilfordii and T. hypoglaucum, including wild and cultivated populations using chloroplast DNA (cpDNA) sequences and microsatellite loci. Our results indicated that the two species maintain a high level of genetic divergence, indicating possible genetic bases for the different contents of bioactive compounds of the two species. T. wilfordii showed lower genetic diversity and less subdivided population structures of both markers than T. hypoglaucum. The potential factors in shaping these interesting differences might be differentiated pollen-to-seed migration rates, interbreeding, and history of population divergence. Analyses of cpDNA and microsatellite loci supported that the two species are genetically distinct entities. In addition, a significant reduction of genetic diversity was observed for cultivated populations of the two species, which mainly resulted from the small initial population size and propagated vegetative practice during their cultivation. Conclusion Our findings indicate significant genetic divergence between T. wilfordii and T. hypoglaucum. The genetic diversity and population structure analyses provide important insights into the sustainable cultivation and utilization of these medicinal plants. Accurate identification and conservation efforts are necessary for both species to ensure the safety and effectiveness of crude drug use. Our study also highlighted the importance of combined analyses of different DNA markers in addressing population genetics of medicinal plants because of the contrasts of inheritance and rates of gene flow. Large-scale cultivation programs should consider preserving genetic diversity to enhance the long-term sustainability of T. wilfordii and T. hypoglaucum. Our study proposed that some populations showed higher genetic diversity and distinctness, which can be considered with priority for conservation and as the sources for future breeding and genetic improvement.

Worldwide, Prosopis Genus is widely spread and well known to be of high tolerance to harsh conditions. The P. juliflora and P. cineraria species were introduced to the Mediterranean, while P. farcta is a native one. The genomic structure of the chloroplast of P. juliflora, P. cineraria and P. farcta were targeted in this study. The chloroplast DNA samples were sequenced by genetic analyzer sequencer “Ion S5™ System. The results indicated the size of the genome to be ranged between 162900 bp in P. farcta and 163667 bp in P. cineraria. The full chloroplast of P. juliflora and P. cineraria genome were reported for the first time nationally in Jordan, while globally P. farcta was the first to be analyzed genetically. The present study offers an important portfolio of Prosopis species chloroplast genome analyses, this could help with identification of species and speed up biological and genetic diversity researches. [ABSTRACT FROM AUTHOR]

The Kerinci duck (Anas platyrhynchos), hailing from Jambi Province, represents one of the local duck breeds of Indonesia. This study aimed to ascertain the matrilineal origin of Kerinci ducks through analysis of the mitochondrial NADH Dehydrogenase subunit 2 (MT-ND2) gene. In this pursuit, forward sequences of the MT-ND2 gene (490 bp) were extracted from unsexed Kerinci ducks, totalling forty-eight (48) sequences, procured from blood samples. The findings demonstrated the identification of fifteen (15) distinct haplotypes within the MTND2 gene, with corresponding haplotype diversity (Hd) and nucleotide diversity (pi) values of 0.74 and 0.003, respectively. The resulting phylogenetic tree unveiled that Kerinci ducks exhibit two matrilineal origins: an Asian and an independent Kerinci lineage. Moreover, most Kerinci ducks were categorised within the H4 group (24 birds) of the Asian lineage. Nevertheless, this study also revealed the existence of an independent Kerinci lineage comprising eight (8) duck haplotypes. In conclusion, the analysis of the MT-ND2 gene underscored the genetic introgression of A. zonorhyncha and A. poecilorhyncha in Kerinci ducks. [ABSTRACT FROM AUTHOR]

G-quadruplex (G4), a non-classical deoxyribonucleic acid structure, is widely distributed in the genome and involved in various biological processes. In vivo , high-throughput sequencing has indicated that G4s are significantly enriched at functional regions in a cell-type-specific manner. Therefore, the prediction of G4s based on computational methods is necessary instead of the time-consuming and laborious experimental methods. Recently, G4 CUT&Tag has been developed to generate higher-resolution sequencing data than ChIP-seq, which provides more accurate training samples for model construction. In this paper, we present a new dataset construction method based on G4 CUT&Tag sequencing data and an XGBoost prediction model based on the machine learning boost method. The results show that our model performs well within and across cell types. Furthermore, sequence analysis indicates that the formation of G4 structure is greatly affected by the flanking sequences, and the GC content of the G4 flanking sequences is higher than non-G4. Moreover, we also identified G4 motifs in the high-resolution dataset, among which we found several motifs for known transcription factors (TFs), such as SP2 and BPC. These TFs may directly or indirectly affect the formation of the G4 structure. [ABSTRACT FROM AUTHOR]

This research paper addresses the challenge of objectively evaluating diverse biological characteristics through the classification of DNA sequences. Identifying DNA sequences in genomics research can aid in discovering novel protein activities, such as insulin, which regulates blood sugar levels in the human body. Diabetes, a prevalent chronic illness, is linked to changes in the insulin gene sequence. The study aims to develop a machine-learning model to categorize the insulin gene's DNA sequence and identify type 2 diabetes based on this transformation. The model's performance will be compared to existing machine-learning models. Additionally, the research seeks to identify unique gene variants of the insulin protein associated with diabetes prognosis and investigate the risk factors associated with these gene variants. [ABSTRACT FROM AUTHOR]

Background: The sustainable supply of medicinal plants is important, and cultivating and domesticating them has been suggested as an optimal strategy. However, this can lead to a loss of genetic diversity. Tripterygium wilfordii Hook. f. is a medicinal plant commonly used in traditional Chinese medicine, but its wild populations are dwindling due to excessive harvesting. To protect the species and meet the increasing demand, it is urgent to cultivate it on a large scale. However, distinguishing between T. wilfordii and T. hypoglaucum, two similar species with different medicinal properties, is challenging. Therefore, it is crucial to understand the genetic diversity and population structure of these species for their sustainable utilization. Results: In this study, we investigated the genetic diversity and population structure of the two traditional medicinal semiwoody vines plant species, Tripterygium wilfordii and T. hypoglaucum, including wild and cultivated populations using chloroplast DNA (cpDNA) sequences and microsatellite loci. Our results indicated that the two species maintain a high level of genetic divergence, indicating possible genetic bases for the different contents of bioactive compounds of the two species. T. wilfordii showed lower genetic diversity and less subdivided population structures of both markers than T. hypoglaucum. The potential factors in shaping these interesting differences might be differentiated pollen-to-seed migration rates, interbreeding, and history of population divergence. Analyses of cpDNA and microsatellite loci supported that the two species are genetically distinct entities. In addition, a significant reduction of genetic diversity was observed for cultivated populations of the two species, which mainly resulted from the small initial population size and propagated vegetative practice during their cultivation. Conclusion: Our findings indicate significant genetic divergence between T. wilfordii and T. hypoglaucum. The genetic diversity and population structure analyses provide important insights into the sustainable cultivation and utilization of these medicinal plants. Accurate identification and conservation efforts are necessary for both species to ensure the safety and effectiveness of crude drug use. Our study also highlighted the importance of combined analyses of different DNA markers in addressing population genetics of medicinal plants because of the contrasts of inheritance and rates of gene flow. Large-scale cultivation programs should consider preserving genetic diversity to enhance the long-term sustainability of T. wilfordii and T. hypoglaucum. Our study proposed that some populations showed higher genetic diversity and distinctness, which can be considered with priority for conservation and as the sources for future breeding and genetic improvement. [ABSTRACT FROM AUTHOR]

In this report, we describe a 6-year-old girl with a medical history of pallor, mild icterus, anemia, blood transfusion and abnormal hemoglobin variant analysis on capillary electrophoresis. She was referred for further analysis. DNA sequencing of the proband revealed a de novo mutation in Codon 88 (CTG > CCG) of the β-globin gene (HBB: c.266T > C) in a heterozygous state compatible with hemoglobin Santa Ana, an unstable hemoglobin. This is the first case of Hb Santa Ana from Iran associated with moderate to severe anemia who underwent splenectomy with clinical improvement. [ABSTRACT FROM AUTHOR]

Enhancers, noncoding DNA fragments, play a pivotal role in gene regulation, facilitating gene transcription. Identifying enhancers is crucial for understanding genomic regulatory mechanisms, pinpointing key elements and investigating networks governing gene expression and disease-related mechanisms. Existing enhancer identification methods exhibit limitations, prompting the development of our novel multi-input deep learning framework, termed Enhancer-MDLF. Experimental results illustrate that Enhancer-MDLF outperforms the previous method, Enhancer-IF, across eight distinct human cell lines and exhibits superior performance on generic enhancer datasets and enhancer–promoter datasets, affirming the robustness of Enhancer-MDLF. Additionally, we introduce transfer learning to provide an effective and potential solution to address the prediction challenges posed by enhancer specificity. Furthermore, we utilize model interpretation to identify transcription factor binding site motifs that may be associated with enhancer regions, with important implications for facilitating the study of enhancer regulatory mechanisms. The source code is openly accessible at https://github.com/HaoWuLab-Bioinformatics/Enhancer-MDLF. [ABSTRACT FROM AUTHOR]

Target enrichment sequencing techniques are gaining widespread use in the field of genomics, prized for their economic efficiency and swift processing times. However, their success depends on the performance of probes and the evenness of sequencing depth among each probe. To accurately predict probe coverage depth, a model called Deqformer is proposed in this study. Deqformer utilizes the oligonucleotides sequence of each probe, drawing inspiration from Watson–Crick base pairing and incorporating two BERT encoders to capture the underlying information from the forward and reverse probe strands, respectively. The encoded data are combined with a feed-forward network to make precise predictions of sequencing depth. The performance of Deqformer is evaluated on four different datasets: SNP panel with 38 200 probes, lncRNA panel with 2000 probes, synthetic panel with 5899 probes and HD-Marker panel for Yesso scallop with 11 000 probes. The SNP and synthetic panels achieve impressive factor 3 of accuracy (F3acc) of 96.24% and 99.66% in 5-fold cross-validation. F3acc rates of over 87.33% and 72.56% are obtained when training on the SNP panel and evaluating performance on the lncRNA and HD-Marker datasets, respectively. Our analysis reveals that Deqformer effectively captures hybridization patterns, making it robust for accurate predictions in various scenarios. Deqformer leads to a novel perspective for probe design pipeline, aiming to enhance efficiency and effectiveness in probe design tasks. [ABSTRACT FROM AUTHOR]

Background: The multiple isoforms are often generated from a single gene via Alternative Splicing (AS) in plants, and the functional diversity of the plant genome is significantly increased. Despite well-studied gene functions, the specific functions of isoforms are little known, therefore, the accurate prediction of isoform functions is exceedingly wanted. Methods: Here we perform the first global analysis of AS of Dichocarpum, a medicinal genus of Ranunculales, by utilizing full-length transcriptome datasets of five Chinese endemic Dichocarpum taxa. Multiple software were used to identify AS events, the gene function was annotated based on seven databases, and the protein-coding sequence of each AS isoform was translated into an amino acid sequence. The self-developed software DIFFUSE was used to predict the functions of AS isoforms. Results: Among 8,485 genes with AS events, the genes with two isoforms were the most (6,038), followed by those with three isoforms and four isoforms. Retained intron (RI, 551) was predominant among 1,037 AS events, and alternative 3 splice sites and alternative 5 splice sites were second. The software DIFFUSE was effective in predicting functions of Dichocarpum isoforms, which have not been unearthed. When compared with the sequence alignment-based database annotations, DIFFUSE performed better in differentiating isoform functions. The DIFFUSE predictions on the terms GO:0003677 (DNA binding) and GO: 0010333 (terpene synthase activity) agreed with the biological features of transcript isoforms. Conclusion: Numerous AS events were for the first time identified from full-length transcriptome datasets of five Dichocarpum taxa, and functions of AS isoforms were successfully predicted by the self-developed software DIFFUSE. The global analysis of Dichocarpum AS events and predicting isoform functions can help understand the metabolic regulations of medicinal taxa and their pharmaceutical explorations.

DNA was extracted from tissue samples from specimens of newly-collected Bathynomus kensleyi from Queensland and subsequently the COI and 16S rRNA sequences were successfully cloned. The holotype of B. kensleyi was also sampled for COI only. Comparison of the sequences showed that, for the COI sequences, B. jamesi and B. kensleyi have more than 59 different DNA positions amongst 596 known reading sequences. The Kimura two parameter (K2P) distance analysis confirmed that B. jamesi and B. kensleyi are two species. Indian records of Bathynomus are reviewed and three of the four identified species from India are shown to be misidentifications. Bathynomus decemspinosus, B. doederlini and B. kensleyi are found to not occur in India and the only accepted record is that of Bathynomus keablei Lowry & Dempsey, 2006. We conclude that, based on molecular analysis and morphological comparisons, the correct species identity of Indian species other than Bathynomus keablei remains unknown.

In many biological research studies that rely on DNA sequence data, calculating the edit distance between two sequences is a vital component. However, computing the edit distance involves dynamic programming, which can be computationally intensive. To address this challenge, numerous works have focused on embedding sequences into the vector space while preserving the distance metric. This means that the edit distance between sequences is analogous to the distance between their corresponding vectors. In this study, we propose a novel Needleman-Wunsch Attention (NWA) framework for sequence embedding that leverages the relationship between the Needleman-Wunsch (NW) matrix and attention maps to improve the accuracy and efficiency of edit distance approximation methods. Our approach applies to any deep learning-based sequence embedding network and provides a general solution to improve the accuracy and efficiency of edit distance approximation methods. We validate the effectiveness of our proposed method by applying it to various existing embedding networks, demonstrating improved edit distance-preserving embedding in an actual dataset. The code is publicly available at https://github.com/thisislim/nw-attention/.

Abstract Background Luteibacter jiangsuensis is a gram-negative aerobic bacillus that was first isolated from soil samples at a pesticide factory in China and reported in 2011. Here, we describe the first case of L. jiangsuensis infection in human. Case presentation A 59-year-old Japanese woman undergoing treatment for Crohn’s disease was admitted to our hospital with fever. Clinical examination indicated catheter-related bloodstream infection. The catheter was removed and meropenem was initiated. Morphologically identical glucose non-fermentative gram-negative bacilli were detected from two sets of aerobic blood culture and catheter-tip cultures. MALDI-TOF mass spectrometry failed to identify the bacterium, which was later identified as L. jiangsuensis by 16 S rRNA gene sequencing. Antimicrobial susceptibility test revealed that the isolate was resistant to carbapenem, therefore meropenem was switched to intravenous levofloxacin (500 mg/day). After 14 days of treatment with levofloxacin, the patient was discharged. Conclusions This is the first case of L. jiangsuensis infection in human. The strain was identified by 16 S rRNA gene sequence analysis.

The adult, pupa and larva of a new species, Gnaptorina (Gnaptorina) lhorongica Li, sp. nov., from northeastern Xizang, China are described and illustrated. The species was identified using molecular phylogenetic analyses based on three mitochondrial fragments and one nuclear gene fragment (COI, Cytb, 16S, and 28S-D2). The taxonomic status of the new species is confirmed using a combination of molecular and morphological datasets. This study provides valuable molecular and morphological data for phylogenetic studies of the tribe Blaptini. [ABSTRACT FROM AUTHOR]

Objectives: The prevalence of G6PD deficiency has not been reported in Yangjiang, a western city in Guangdong province. This study aims to investigate the molecular characteristics of G6PD deficiency in this region. Methods: Blood samples were collected from adults at a local hospital to screen for G6PD deficiency. The deficient samples were subjected to further analysis using PCR and reverse dot blot to determine the specific G6PD variants. Results: Among the 3314 male subjects, 250 cases of G6PD deficiency were found using the G6PD enzyme quantitative assay, resulting in a prevalence of 7.54% (250/3314) in the Yangjiang region. The prevalence of G6PD deficiency in females was 3.42% (176/5145). Out of the 268 cases of G6PD deficiency tested for G6PD mutations, reverse dot blot identified 20 different G6PD variants. The most common G6PD variant was c.1388G>A (81/268), followed by c.1376G>T (48/268), c.95A>G (32/268), c.1024C>T (9/268), c.392G>T (7/268), and c.871G>A/c.1311C>T (6/268). It was observed that c.871G>A was always linked to the polymorphism of c.1311C>T in this population. Conclusion: This investigation into G6PD deficiency in this area is expected to significantly improve our understanding of the prevalence and molecular characterization of this condition. [ABSTRACT FROM AUTHOR]

In biological science, the study of DNA sequences is considered an important factor because it carries the genomic details that can be used by researchers and doctors for the early prediction of disease using DNA classification. The NCBI has the world's largest database of genetic sequences, but the security of this massive amount of data is currently the greatest issue. One of the options is to encrypt these genetic sequences using blockchain technology. As a result, this paper presents a survey on healthcare data breaches, the necessity for blockchain in healthcare, and the number of research studies done in this area. In addition, the report suggests DNA sequence classification for earlier disease identification and evaluates previous work in the field. [ABSTRACT FROM AUTHOR]

Algorithms for string matching are considered one of the most extensively researched topics in the field of computer science due to their substantial role in various applications, such as information retrieval, editing, security, firewalls, and biological applications. String matching involves examining the optimal alignment by comparing the characters in the pattern and the text. Over the past two decades, it has gained considerable attention due to technological advancements. The need to address string-matching problems has also emerged because of its wide-ranging applications. This study presents the E-ARFO hybrid string-matching algorithm, which combines the best features of two original algorithms, namely, E-AbdulRazzaq and fast online hybrid matching. Compared with other algorithms, the proposed method demonstrates outstanding performance in terms of the number of attempts and character comparisons conducted across multiple databases, including DNA and protein sequences. Results indicate that irrespective of the number of attempts or character comparisons made, E-ARFO consistently ranks first for short and lengthy patterns in most databases. Results also reveal reduced runtimes and competitive character comparisons. Moreover, results underscore the potential effect of E_ARFO on computational biology, offering a new paradigm for precision and efficiency in string matching. [ABSTRACT FROM AUTHOR]

Callyntrura (s.l.) Börner, 1906 is the largest genus of the subfamily Salininae and contains 11 subgenera and 98 species from all over the world (mainly Asia), with eight species recorded from China. In the present paper, three new species of Callyntrura (s.l.) are described from China: C. (Japonphysa) xinjianensis sp. nov.; C. (J.) tongguensis sp. nov. and C. (J.) raoi sp. nov. Their differences in colour pattern, chaetotaxy and other characters are slight, however distances of COI mtDNA support their validation as three new distinct species. A key to the Chinese Callyntrura (s.l.) is provided. [ABSTRACT FROM AUTHOR]

Background: Luteibacter jiangsuensis is a gram-negative aerobic bacillus that was first isolated from soil samples at a pesticide factory in China and reported in 2011. Here, we describe the first case of L. jiangsuensis infection in human. Case presentation: A 59-year-old Japanese woman undergoing treatment for Crohn's disease was admitted to our hospital with fever. Clinical examination indicated catheter-related bloodstream infection. The catheter was removed and meropenem was initiated. Morphologically identical glucose non-fermentative gram-negative bacilli were detected from two sets of aerobic blood culture and catheter-tip cultures. MALDI-TOF mass spectrometry failed to identify the bacterium, which was later identified as L. jiangsuensis by 16 S rRNA gene sequencing. Antimicrobial susceptibility test revealed that the isolate was resistant to carbapenem, therefore meropenem was switched to intravenous levofloxacin (500 mg/day). After 14 days of treatment with levofloxacin, the patient was discharged. Conclusions: This is the first case of L. jiangsuensis infection in human. The strain was identified by 16 S rRNA gene sequence analysis. [ABSTRACT FROM AUTHOR]

This study presents a novel statistical and computational approach using nonparametric regression, which capitalizes on correlation structure to deal with the high-dimensional data often found in pharmacogenomics, for instance, in Crohn's inflammatory bowel disease. The empirical correlation between the test statistics, investigated via simulation, can be used as an estimate of noise. The theoretical distribution of −log10(p-value) is used to support the estimation of that optimal bandwidth for the model, which adequately controls type I error rates while maintaining reasonable power. Two proposed approaches, involving normal and Laplace-LD kernels, were evaluated by conducting a case-control study using real data from a genome-wide association study on Crohn's disease. The study successfully identified single nucleotide polymorphisms on the NOD2 gene associated with the disease. The proposed method reduces the computational burden by approximately 33% with reasonable power, allowing for a more efficient and accurate analysis of genetic variants influencing drug responses. The study contributes to the advancement of statistical methodology for analyzing complex genetic data and is of practical advantage for the development of personalized medicine. [ABSTRACT FROM AUTHOR]

Large-scale structural variations, such as chromosomal translocations, can have profound effects on fitness and phenotype, but are difficult to identify and characterize. Here, we describe a simple and effective method aimed at identifying translocations using only the dosage of sequence reads mapped on the reference genome. We binned reads on genomic segments sized according to sequencing coverage and identified instances when copy number segregated in populations. For each dosage-polymorphic 1 Mb bin, we tested independence, effectively an apparent linkage disequilibrium (LD), with other variable bins. In nine potato (Solanum tuberosum) dihaploid families translocations affecting pericentromeric regions were common and in two cases were due to genomic misassembly. In two populations, we found evidence for translocation affecting euchromatic arms. In cv. PI 310467, a nonreciprocal translocation between chromosomes (chr.) 7 and 8 resulted in a 5-3 copy number change affecting several Mb at the respective chromosome tips. In cv. "Alca Tarma," the terminal arm of chr. 4 translocated to the tip of chr. 1. Using oligonucleotide-based fluorescent in situ hybridization painting probes (oligo-FISH), we tested and confirmed the predicted arrangement in PI 310467. In 192 natural accessions of Arabidopsis thaliana, dosage haplotypes tended to vary continuously and resulted in higher noise, while apparent LD between pericentromeric regions suggested the effect of repeats. This method, LD-CNV, should be useful in species where translocations are suspected because it tests linkage without the need for genotyping.