Sequence Data Mining Research Articles

Insights into the RNA Virome of the Corn Leafhopper Dalbulus maidis, a Major Emergent Threat of Maize in Latin America

The maize leafhopper (Dalbulus maidis) is a significant threat to maize crops in tropical and subtropical regions, causing extensive economic losses. While its ecological interactions and control strategies are well studied, its associated viral diversity remains largely unexplored. Here, we employ high-throughput sequencing data mining to comprehensively characterize the D. maidis RNA virome, revealing novel and diverse RNA viruses. We characterized six new viral members belonging to distinct families, with evolutionary cues of beny-like viruses (Benyviridae), bunya-like viruses (Bunyaviridae) iflaviruses (Iflaviridae), orthomyxo-like viruses (Orthomyxoviridae), and rhabdoviruses (Rhabdoviridae). Phylogenetic analysis of the iflaviruses places them within the genus Iflavirus in affinity with other leafhopper-associated iflaviruses. The five-segmented and highly divergent orthomyxo-like virus showed a relationship with other insect associated orthomyxo-like viruses. The rhabdo virus is related to a leafhopper-associated rhabdo-like virus. Furthermore, the beny-like virus belonged to a cluster of insect-associated beny-like viruses, while the bi-segmented bunya-like virus was related with other bi-segmented insect-associated bunya-like viruses. These results highlight the existence of a complex virome linked to D. maidis and paves the way for future studies investigating the ecological roles, evolutionary dynamics, and potential biocontrol applications of these viruses on the D. maidis—maize pathosystem.

Discovery of a novel Wolbachia in Heterodera expands nematode host distribution.

Bioinformatics sequence data mining can reveal hidden microbial symbionts that might normally be filtered and removed as contaminants. Data mining can be helpful to detect Wolbachia, a widespread bacterial endosymbiont in insects and filarial nematodes whose distribution in plant-parasitic nematodes (PPNs) remains underexplored. To date, Wolbachia has only been reported a few PPNs, yet nematode-infecting Wolbachia may have been widespread in the evolutionary history of the phylum based on evidence of horizontal gene transfers, suggesting there may be undiscovered Wolbachia infections in PPNs. The goal of this study was to more broadly sample PPN Wolbachia strains in tylenchid nematodes to enable further comparative genomic analyses that may reveal Wolbachia's role and identify targets for biocontrol. Published whole-genome shotgun assemblies and their raw sequence data from 33 Meloidogyne spp. assemblies, seven Globodera spp. assemblies, and seven Heterodera spp. assemblies were analyzed to look for Wolbachia. No Wolbachia was found in Meloidogyne spp. and Globodera spp., but among seven genome assemblies for Heterodera spp., an H. schachtii assembly from the Netherlands was found to have a large Wolbachia-like sequence that, when re-assembled from reads, formed a complete, circular genome. Detailed analyses comparing read coverage, GC content, pseudogenes, and phylogenomic patterns clearly demonstrated that the H. schachtii Wolbachia represented a novel strain (hereafter, denoted wHet). Phylogenomic tree construction with PhyloBayes showed wHet was most closely related to another PPN Wolbachia, wTex, while 16S rRNA gene analysis showed it clustered with other Heterodera Wolbachia assembled from sequence databases. Pseudogenes in wHet suggested relatedness to the PPN clade, as did the lack of significantly enriched GO terms compared to PPN Wolbachia strains. It remains unclear whether the lack of Wolbachia in other published H. schachtii isolates represents the true absence of the endosymbiont from some hosts.

Progress in application of machine learning in epidemiology

Population based health data collection and analysis are important in epidemiological research. In recent years, with the rapid development of big data, Internet and cloud computing, artificial intelligence has gradually attracted attention of epidemiological researchers. More and more researchers are trying to use artificial intelligence algorithms for genome sequencing and medical image data mining, and for disease diagnosis, risk prediction and others. In recent years, machine learning, a branch of artificial intelligence, has been widely used in epidemiological research. This paper summarizes the key fields and progress in the application of machine learning in epidemiology, reviews the development history of machine learning, analyzes the classic cases and current challenges in its application in epidemiological research, and introduces the current application scenarios and future development trends of machine learning and artificial intelligence algorithms for the better exploration of the epidemiological research value of massive medical health data in China.

Identification of key TE associated with myocarditis based on RNA and single-cell sequencing data mining

Cardiomyopathy is a severe cardiac condition characterized by complex immune regulatory mechanisms. While the role of immune genes is recognized, the specifics of their regulation in cardiomyopathy are not fully understood. Recent studies highlight the significance of transposable elements (TEs) in various diseases, particularly their potential to modulate immune responses. This paper utilizes publicly available databases to explore the role of TEs in myocarditis: RNA Seq data and single-cell sequencing data were analyzed, with a focus on the mouse model of experimental autoimmune myocarditis (EAM). The RNA-Seq analysis revealed substantial upregulation of a range of immune genes in cardiac tissue. Further investigation using single-cell sequencing of cardiac immune cells identified specific expression of certain transposable elements (TEs) across different types of immune cells in the heart. Additionally, there was an overall increase in the expression of the ERVB7-1. LTR-MM transposon across various cells in the EAM model, suggesting a widespread impact of this transposon on the immune response in this disease context. The findings of this study highlight the intricate interplay between transposable elements and the immune system in cardiomyopathy, providing new insights into the molecular mechanisms underlying this condition. The discovery of specific TEs expression in cardiac immune cells and the overall increase in ERVB7-1. LTR-MM expression across the EAM model underscore the potential of these elements in modulating immune responses and contribute to our understanding of cardiomyopathy's pathogenesis. These observations open avenues for further research into the role of TEs in cardiac disases and may lead to novel therapeutic strategies.

DBHC: Discrete Bayesian HMM Clustering

Sequence data mining has become an increasingly popular research topic as the availability of data has grown rapidly over the past decades. Sequence clustering is a type of method within this field that is in high demand in the industry, but the sequence clustering problem is non-trivial and, as opposed to static cluster analysis, interpreting clusters of sequences is often difficult. Using Hidden Markov Models (HMMs), we propose the Discrete Bayesian HMM Clustering (DBHC) algorithm, an approach to clustering discrete sequences by extending a proven method for continuous sequences. The proposed algorithm is completely self-contained as it incorporates both the search for the number of clusters and the search for the number of hidden states in each cluster model in the parameter inference. We provide a working example and a simulation study to explain and showcase the capabilities of the DBHC algorithm. A case study illustrates how the hidden states in a mixture of HMMs can aid the interpretation task of a sequence cluster analysis. We conclude that the algorithm works well as it provides well-interpretable clusters for the considered application.

Integration of single-cell and bulk RNA sequencing data reveals that CYTOR is a potential prognostic and immunotherapeutic response marker for skin cutaneous melanoma.

Skin cutaneous melanoma (SKCM) is a highly malignant tumor that is prone to immune escape and distant metastasis. Immunotherapy is considered to be the best treatment for patients with SKCM. However, not all patients benefit from it. We observed a significant differential expression of the lncRNA CYTOR in patients with SKCM based on single-cell and bulk RNA sequencing data mining results. The results showed that compared to normal tissue lncRNA CYTOR expression was significantly upregulated in SKCM tissue. Subsequently, we validated this finding in clinical samples, and we also found that the expression of lncRNA CYTOR in SKCM was higher as it progressed. lncRNA CYTOR was differentially expressed in patients who responded to immunotherapy, suggesting that it may serve as a biomarker to predict the efficacy of SKCM immunotherapy. In-depth analysis revealed that lncRNA CYTOR expression was strongly correlated with immune cell infiltration, immune response, and immune checkpoint expression. Meanwhile, our experiments revealed that CYTOR affects SKCM cell invasion and clone formation and is associated with the activation of the EMT pathway. In summary, our findings illustrate, for the first time, the value of CYTOR as a potential prognostic and immunotherapeutic response marker in SKCM.

Khai thác dữ liệu chuỗi theo mối quan tâm của người dùng

Sequence data mining, also known as sequential pattern mining, is to find all frequent sub-sequences (called sequential patterns) in a sequence database, the threshold of frequency is specified by the user. In recent years, with the explosion growth of information and big data, this problem trends toward mining with constraints to overcome both effectiveness and efficiency challenges since that the constraints represent for the user’s interest. This paper presents a detailed survey of recent studies on mining sequential pattern and the categories of the constraints. Moreover, it also classifies the main methods and analyzes their advantages and limitations, thereby main approaches and strategies to solve sequential pattern mining problems in the future are presented.

SingleScan: a comprehensive resource for single-cell sequencing data processing and mining

Single-cell sequencing has shed light on previously inaccessible biological questions from different fields of research, including organism development, immune function, and disease progression. The number of single-cell-based studies increased dramatically over the past decade. Several new methods and tools have been continuously developed, making it extremely tricky to navigate this research landscape and develop an up-to-date workflow to analyze single-cell sequencing data, particularly for researchers seeking to enter this field without computational experience. Moreover, choosing appropriate tools and optimal parameters to meet the demands of researchers represents a major challenge in processing single-cell sequencing data. However, a specific resource for easy access to detailed information on single-cell sequencing methods and data processing pipelines is still lacking. In the present study, an online resource called SingleScan was developed to curate all up-to-date single-cell transcriptome/genome analyzing tools and pipelines. All the available tools were categorized according to their main tasks, and several typical workflows for single-cell data analysis were summarized. In addition, spatial transcriptomics, which is a breakthrough molecular analysis method that enables researchers to measure all gene activity in tissue samples and map the site of activity, was included along with a portion of single-cell and spatial analysis solutions. For each processing step, the available tools and specific parameters used in published articles are provided and how these parameters affect the results is shown in the resource. All information used in the resource was manually extracted from related literature. An interactive website was designed for data retrieval, visualization, and download. By analyzing the included tools and literature, users can gain insights into the trends of single-cell studies and easily grasp the specific usage of a specific tool. SingleScan will facilitate the analysis of single-cell sequencing data and promote the development of new tools to meet the growing and diverse needs of the research community. The SingleScan database is publicly accessible via the website at http://cailab.labshare.cn/SingleScan.

Evidence for an ancient aquatic origin of the RNA viral order Articulavirales.

The emergence of previously unknown disease-causing viruses in mammals is in part the result of a long-term evolutionary process. Reconstructing the deep phylogenetic histories of viruses helps identify major evolutionary transitions and contextualizes the emergence of viruses in new hosts. We used a combination of total RNA sequencing and transcriptome data mining to extend the diversity and evolutionary history of the RNA virus order Articulavirales, which includes the influenza viruses. We identified instances of Articulavirales in the invertebrate phylum Cnidaria (including corals), constituting a novel and divergent family that we provisionally named the "Cnidenomoviridae." We further extended the evolutionary history of the influenza virus lineage by identifying four divergent, fish-associated influenza-like viruses, thereby supporting the hypothesis that fish were among the first hosts of influenza viruses. In addition, we substantially expanded the phylogenetic diversity of quaranjaviruses and proposed that this genus be reclassified as a family-the "Quaranjaviridae." Within this putative family, we identified a novel arachnid-infecting genus, provisionally named "Cheliceravirus." Notably, we observed a close phylogenetic relationship between the Crustacea- and Chelicerata-infecting "Quaranjaviridae" that is inconsistent with virus-host codivergence. Together, these data suggest that the Articulavirales has evolved over at least 600 million years, first emerging in aquatic animals. Importantly, the evolution of the Articulavirales was likely shaped by multiple aquatic-terrestrial transitions and substantial host jumps, some of which are still observable today.

SaPt-CNN-LSTM-AR-EA: a hybrid ensemble learning framework for time series-based multivariate DNA sequence prediction.

Biological sequence data mining is hot spot in bioinformatics. A biological sequence can be regarded as a set of characters. Time series is similar to biological sequences in terms of both representation and mechanism. Therefore, in the article, biological sequences are represented with time series to obtain biological time sequence (BTS). Hybrid ensemble learning framework (SaPt-CNN-LSTM-AR-EA) for BTS is proposed. Single-sequence and multi-sequence models are respectively constructed with self-adaption pre-training one-dimensional convolutional recurrent neural network and autoregressive fractional integrated moving average fused evolutionary algorithm. In DNA sequence experiments with six viruses, SaPt-CNN-LSTM-AR-EA realized the good overall prediction performance and the prediction accuracy and correlation respectively reached 1.7073 and 0.9186. SaPt-CNN-LSTM-AR-EA was compared with other five benchmark models so as to verify its effectiveness and stability. SaPt-CNN-LSTM-AR-EA increased the average accuracy by about 30%. The framework proposed in this article is significant in biology, biomedicine, and computer science, and can be widely applied in sequence splicing, computational biology, bioinformation, and other fields.

Upregulated HAVCR2: A Prognostic and Immune-Related Marker in Testicular Germ Cell Tumors

Testicular germ cell tumors (TGCTs) are the most common solid malignant tumor in young men aged 20–34 years. Currently, the diagnosis and differentiation of TGCTs depend on immunohistochemical analysis, and the treatment methods are mainly surgery and chemoradiotherapy. Although immunotherapy has been applied in clinic, the response rate is not high, and also there is a lack of effective biomarkers. In this study, through high-throughput transcriptome sequencing and public data mining, the expression of hepatitis A virus cellular receptor 2 (HAVCR2) was found to be significantly upregulated in TGCT tissue and correlated with poor prognosis. Additionally, the expression level of HAVCR2 was negatively correlated with its DNA methylation but positively correlated with its copy number level. The HAVCR2 expression level was significantly positively correlated with the infiltration of six types of immune cells, including B cells, CD4+ T cells, CD8+ T cells, neutrophils, macrophages, and myeloid dendritic cells. Also, the higher its expression level, the higher the immune cell abundance. Tumor immune dysfunction and exclusion (TIDE) scoring analysis showed that HAVCR2 was related to the responsiveness to TGCT immunotherapy. Drug sensitivity analysis revealed that HAVCR2 was related to the sensitivity of multiple antitumor drugs. This study demonstrated that HAVCR2 has high potential as a biomarker for the diagnosis, prognosis, and treatment of TGCTs.

Loss of MiR-155 Sensitizes FLT3-ITD+AML to Chemotherapy and FLT3 Inhibitors via Glycolysis Blocking by Targeting PIK3R1.

FLT3 tyrosine kinase inhibitors in combination with chemotherapy have shown some success in patients with FLT3 mutations. But a variety of mechanisms have led to the rapid resistance to the treatment. One of the most prominent is the metabolic alteration on aerobic glycolysis. We aim to explore the role of a high expressing microRNA, miR-155, in mediating resistance to chemotherapy and FLT3 inhibitor treatment. The deep sequencing data mining revealed the connection between glycolysis and drug resistance. MV411 cells with miR-155 knockout (KO) not only had increased sensitivity to FLT3 inhibitors but also Adriamycin (ADM) treatment. When combined with glycolysis inhibition the treatment response in MV411 cells further increased. Whereas in miR-155 KO cells, a lower glucose consumption level and lactic acid level were observed, and western blotting showed a decreased expression of key enzymes in glycolysis pathways. A negative correlation between PIK3R1 and miR-155 level can be observed in the sequencing data from FLT3-ITD+ AML patients. Moreover, luciferase reporter assay revealed that the 3'UTR of PIK3R1 mRNA can interact with the seed sequence of miR-155-5p. In conclusion, the loss of miR-155 increased treatment sensitivity to both chemotherapy and FLT3 inhibitors in FLT3-ITD+ AML cells via glycolysis blocking by targeting PIK3R1.

Nuclear envelope, chromatin organizers, histones, and DNA: The many achilles heels exploited across cancers.

In eukaryotic cells, the genome is organized in the form of chromatin composed of DNA and histones that organize and regulate gene expression. The dysregulation of chromatin remodeling, including the aberrant incorporation of histone variants and their consequent post-translational modifications, is prevalent across cancers. Additionally, nuclear envelope proteins are often deregulated in cancers, which impacts the 3D organization of the genome. Altered nuclear morphology, genome organization, and gene expression are defining features of cancers. With advances in single-cell sequencing, imaging technologies, and high-end data mining approaches, we are now at the forefront of designing appropriate small molecules to selectively inhibit the growth and proliferation of cancer cells in a genome- and epigenome-specific manner. Here, we review recent advances and the emerging significance of aberrations in nuclear envelope proteins, histone variants, and oncohistones in deregulating chromatin organization and gene expression in oncogenesis.

MicrobioTA: an atlas of the microbiome in multiple disease tissues of Homo sapiens and Mus musculus.

microbioTA (http://bio-annotation.cn/microbiota) was constructed to provide a comprehensive, user-friendly resource for the application of microbiome data from diseased tissues, helping users improve their general knowledge and deep understanding of tissue-derived microbes. Various microbes have been found to colonize cancer tissues and play important roles in cancer diagnoses and outcomes, with many studies focusing on developing better cancer-related microbiome data. However, there are currently no independent, comprehensive open resources cataloguing cancer-related microbiome data, which limits the exploration of the relationship between these microbes and cancer progression. Given this, we propose a new strategy to re-align the existing next-generation sequencing data to facilitate the mining of hidden sequence data describing the microbiome to maximize available resources. To this end, we collected 417 publicly available datasets from 25 human and 14 mouse tissues from the Gene Expression Omnibus database and use these to develop a novel pipeline to re-align microbiome sequences facilitating in-depth analyses designed to reveal the microbial profile of various cancer tissues and their healthy controls. microbioTA is a user-friendly online platform which allows users to browse, search, visualize, and download microbial abundance data from various tissues along with corresponding analysis results, aimimg at providing a reference for cancer-related microbiome research.

Unlocking the Hidden Genetic Diversity of Varicosaviruses, the Neglected Plant Rhabdoviruses.

The genus Varicosavirus is one of six genera of plant-infecting rhabdoviruses. Varicosaviruses have non-enveloped, flexuous, rod-shaped virions and a negative-sense, single-stranded RNA genome. A distinguishing feature of varicosaviruses, which is shared with dichorhaviruses, is a bi-segmented genome. Before 2017, a sole varicosavirus was known and characterized, and then two more varicosaviruses were identified through high-throughput sequencing in 2017 and 2018. More recently, the number of known varicosaviruses has substantially increased in concert with the extensive use of high-throughput sequencing platforms and data mining approaches. The novel varicosaviruses have revealed not only sequence diversity, but also plasticity in terms of genome architecture, including a virus with a tentatively unsegmented genome. Here, we report the discovery of 45 novel varicosavirus genomes which were identified in publicly available metatranscriptomic data. The identification, assembly, and curation of the raw Sequence Read Archive reads has resulted in 39 viral genome sequences with full-length coding regions and 6 with nearly complete coding regions. The highlights of the obtained sequences include eight varicosaviruses with unsegmented genomes, which are linked to a phylogenetic clade associated with gymnosperms. These findings have resulted in the most complete phylogeny of varicosaviruses to date and shed new light on the phylogenetic relationships and evolutionary landscape of this group of plant rhabdoviruses. Thus, the extensive use of sequence data mining for virus discovery has allowed us to unlock of the hidden genetic diversity of varicosaviruses, the largely neglected plant rhabdoviruses.

How to identify and characterize novel transaminases? Two novel transaminases with opposite enantioselectivity for the synthesis of optically active amines

Amine transaminases (TAs) are attractive biocatalysts for the synthesis of chiral aromatic amines representing highly valuable motifs of APIs. The increased industrial need of novel methods to produce chiral amines for APIs resulted in an emerged discovery of new TAs. Joining the current wave of TA related research, this study reports the identification of genes encoding an (S)-selective TA from Pseudomonas psychrotolerans TA (PpS-TA) and an (R)-selective TA from Shinorizobium sp. TA (SrR-TA) by sequence data mining. Functional analysis of the novel TAs revealed their pH profile, thermal stability, optimal buffer system, DMSO tolerance, and operational stability in kinetic resolution (KR) of racemic 1-phenylethane-1-amines. The (S)-selective PpS-TA maintained its operational stability even at high temperature and pH values, enabling conversions from racemates approaching the optimal ∼50% value of a highly selective KR. Although with lower activity, the (R)-selective SrR-TA remained active at higher DMSO co-solvent concentrations up to 30%, enabling elevated substrate concentrations for aromatic amines of low water-solubility. The kinetic parameters of the novel PpS-TA and SrR-TA in KRs of several racemic 1-arylethane-1-amines (±)-2a-d and pyruvate (co-substrate) revealed superior catalytic efficiencies (kcat values) compared to the well-characterized (S)-TA from Chromobacterium violaceum (CvS-TA) indicating the biocatalytic potential of the two newly characterized TAs.

DNA Computing: Concepts for Medical Applications

The branch of informatics that deals with construction and operation of computers built of DNA, is one of the research directions which investigates issues related to the use of DNA as hardware and software. This concept assumes the use of DNA computers due to their biological origin mainly for intelligent, personalized and targeted diagnostics frequently related to therapy. Important elements of this concept are (1) the retrieval of unique DNA sequences using machine learning methods and, based on the results of this process, (2) the construction/design of smart diagnostic biochip projects. The authors of this paper propose a new concept of designing diagnostic biochips, the key elements of which are machine-learning methods and the concept of biomolecular queue automata. This approach enables the scheduling of computational tasks at the molecular level by sequential events of cutting and ligating DNA molecules. We also summarize current challenges and perspectives of biomolecular computer application and machine-learning approaches using DNA sequence data mining.

An Adaptable Antibody‐Based Platform for Flexible Synthetic Peptide Delivery Built on Agonistic CD40 Antibodies

AbstractThe agonistic potentials of therapeutic anti‐CD40 antibodies have been profiled in relation to antibody isotype and epitope specificity. Still, clinical impact relies on a well‐balanced clinical efficacy versus target‐mediated toxicity. As CD40‐mediated immune activation must rely on a combination of stimulation of antigen‐presenting cells (APCs) alongside antigen presentation, for efficient T cell priming, alternative approaches to improve the therapeutic outcome of CD40‐targeting strategies should focus on providing optimal antigen presentation together with CD40 stimulation. Herein, a bispecific antibody targeting CD40 as a means to deliver cargo (i.e., synthetic peptides) into APCs through a non‐covalent, high‐affinity interaction between the antibody and the cargo peptide, further referred to as the Adaptable Drug Affinity Conjugate (ADAC) technology, has been developed. The ADAC platform demonstrated a target‐specific CD4+ and CD8+ T cell expansion in vitro and significantly improved peptide‐specific CD8+ T cell proliferation in vivo. In addition, the strategy dramatically improved the in vitro and in vivo half‐life of the synthetic peptides. Future applications of ADAC involve pandemic preparedness to viral genetic drift as well as neoepitope vaccination strategies where the bispecific antibody is an off‐the‐shelf product, and the peptide antigen is synthesized based on next‐generation sequencing data mining.

A Novel Literary Translation Text Classification Method Based on Distributed Incremental Sequence Data Mining Algorithm

With the rapid development of electronic information technology and Internet technology, people's ability to generate and collect data is also increasing. With the rapid development of international exchanges, the number of literary translation texts has also increased dramatically. The information contained in the huge data of literary translation texts is huge, but these data are disorganized at this stage. Therefore, the classification of literary translation texts has become the key to efficient management of translated text information. Literary translation text classification is the process of classifying a given text as one or several of several predetermined text categories according to the content of the text. As one of the key steps in processing huge amounts of text data, text classification is generally regarded as the orderly organization of text sets, that is, grouping similar and related texts together. In this way, the problem of information clutter can be solved to a greater extent, and the efficiency of users' discovery, filtering, and analysis of text information resources can be effectively improved. At present, the basis of text classification is mainly based on the characteristics of words in the article, to analyze the correlation between words and categories. This approach ignores information such as word order and collocations in literary translation texts. To solve this problem, this paper introduces the distributed incremental sequence data mining algorithm into the classification of literary translation texts. This method can fully mine the features of syntactic order and other features based on considering the words and phrasing characteristics of articles in different application fields. The text classification effect is strengthened by discovering more effective information. The experimental results show that the method can improve the classification performance of literary translation texts.

Sequence graph transform (SGT): a feature embedding function for sequence data mining

Sequence feature embedding is a challenging task due to the unstructuredness of sequences—arbitrary strings of arbitrary length. Existing methods are efficient in extracting short-term dependencies but typically suffer from computation issues for the long-term. Sequence Graph Transform (SGT), a feature embedding function, that can extract a varying amount of short- to long-term dependencies without increasing the computation is proposed. SGT’s properties are analytically proved for interpretation under normal and uniform distribution assumptions. SGT features yield significantly superior results in sequence clustering and classification with higher accuracy and lower computation as compared to the existing methods, including the state-of-the-art sequence/string Kernels and LSTM.