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The article discusses a study which examined real-time PCR detection of Plasmodium directly from whole blood and filter paper samples. The study evaluated reagents designed to specifically overcome PCR inhibition and quenching of fluorescence for real-time PCR amplification of Plasmodium DNA directly from blood. The study found that the sensitivity and specificity ranged from 93-100% compared with PCR performed on purified Plasmodium DNA.

Background: Lung cancer is one of the cancers with the highest mortality rate in China. With the rapid development of high-throughput sequencing technology and the research and application of deep learning methods in recent years, deep neural networks based on gene expression have become a hot research direction in lung cancer diagnosis in recent years, which provide an effective way of early diagnosis for lung cancer. Thus, building a deep neural network model is of great significance for the early diagnosis of lung cancer. However, the main challenges in mining gene expression datasets are the curse of dimensionality and imbalanced data. The existing methods proposed by some researchers can't address the problems of high-dimensionality and imbalanced data, because of the overwhelming number of variables measured (genes) versus the small number of samples, which result in poor performance in early diagnosis for lung cancer. Method: Given the disadvantages of gene expression data sets with small datasets, high-dimensionality and imbalanced data, this paper proposes a gene selection method based on KL divergence, which selects some genes with higher KL divergence as model features. Then build a deep neural network model using Focal Loss as loss function, at the same time, we use k-fold cross validation method to verify and select the best model, we set the value of k is five in this paper. Result: The deep learning model method based on KL divergence gene selection proposed in this paper has an AUC of 0.99 on the validation set. The generalization performance of model is high. Conclusion: The deep neural network model based on KL divergence gene selection proposed in this paper is proved to be an accurate and effective method for lung cancer prediction. [ABSTRACT FROM AUTHOR]

Background: With the emergence of Oxford Nanopore technology, now the on-site sequencing of 16S rRNA from environments is available. Due to the error level and structure, the analysis of such data demands some database of reference sequences. However, many taxa from complex and diverse environments, have poor representation in publicly available databases. In this paper, we propose the METASEED pipeline for the reconstruction of full-length 16S sequences from such environments, in order to improve the reference for the subsequent use of on-site sequencing. Results: We show that combining high-precision short-read sequencing of both 16S and full metagenome from the same samples allow us to reconstruct high-quality 16S sequences from the more abundant taxa. A significant novelty is the carefully designed collection of metagenome reads that matches the 16S amplicons, based on a combination of uniqueness and abundance. Compared to alternative approaches this produces superior results. Conclusion: Our pipeline will facilitate numerous studies associated with various unknown microorganisms, thus allowing the comprehension of the diverse environments. The pipeline is a potential tool in generating a full length 16S rRNA gene database for any environment. [ABSTRACT FROM AUTHOR]

Background: Recent evidences indicated that shear stress is critical in orchestrating gene expression in cardiovascular disease. It is necessary to identify the mechanism of shear stress influencing gene expression in physiology and pathophysiology conditions. This paper aimed to identify candidate hub genes and its transcription factors with bioinformatics. Methods: We analyzed microarray expression profile of GSE16706 to identify differentially expressed genes (DEGs) in low shear stress (1 dyne/cm2) treated human umbilical vein endothelial cells (HUVECs) compared with static condition for 24 h. Results: 652 DEGs, including 333 up-regulated and 319 down-regulated DEGs, were screen out. Functional enrichment analysis indicated enrichment items mainly included cytokine-cytokine receptor interaction and cell cycle. Five hub genes (CDC20, CCNA2, KIF11, KIF2C and PLK1) and one significant module (score = 17.39) were identified through protein–protein interaction (PPI) analysis. Key transcriptional factor FOXC1 displayed close interaction with all the hub genes via gene-transcriptional factor network. Single-gene GSEA analysis indicated that CDC20 was linked to the G2M_CHECKPOINT pathway and cell cycle pathway. Conclusions: By using integrated bioinformatic analysis, a new transcriptional factor and hub-genes network related to HUVECs treated with low shear stress were identified. The new regulation mechanism we discovered may be a promising potential therapeutic target for cardiovascular disease. [ABSTRACT FROM AUTHOR]

Background: Gene Set Analysis (GSA) is arguably the method of choice for the functional interpretation of omics results. The following paper explores the popularity and the performance of all the GSA methodologies and software published during the 20 years since its inception. "Popularity" is estimated according to each paper's citation counts, while "performance" is based on a comprehensive evaluation of the validation strategies used by papers in the field, as well as the consolidated results from the existing benchmark studies. Results: Regarding popularity, data is collected into an online open database ("GSARefDB") which allows browsing bibliographic and method-descriptive information from 503 GSA paper references; regarding performance, we introduce a repository of jupyter workflows and shiny apps for automated benchmarking of GSA methods ("GSA-BenchmarKING"). After comparing popularity versus performance, results show discrepancies between the most popular and the best performing GSA methods. Conclusions: The above-mentioned results call our attention towards the nature of the tool selection procedures followed by researchers and raise doubts regarding the quality of the functional interpretation of biological datasets in current biomedical studies. Suggestions for the future of the functional interpretation field are made, including strategies for education and discussion of GSA tools, better validation and benchmarking practices, reproducibility, and functional re-analysis of previously reported data. [ABSTRACT FROM AUTHOR]

Plant genomics plays a pivotal role in enhancing global food security and sustainability by offering innovative solutions for improving crop yield, disease resistance, and stress tolerance. As the number of sequenced genomes grows and the accuracy and contiguity of genome assemblies improve, structural annotation of plant genomes continues to be a significant challenge due to their large size, polyploidy, and rich repeat content. In this paper, we present an overview of the current landscape in crop genomics research, highlighting the diversity of genomic characteristics across various crop species. We also assessed the accuracy of popular gene prediction tools in identifying genes within crop genomes and examined the factors that impact their performance. Our findings highlight the strengths and limitations of BRAKER2 and Helixer as leading structural genome annotation tools and underscore the impact of genome complexity, fragmentation, and repeat content on their performance. Furthermore, we evaluated the suitability of the predicted proteins as a reliable search space in proteomics studies using mass spectrometry data. Our results provide valuable insights for future efforts to refine and advance the field of structural genome annotation. [ABSTRACT FROM AUTHOR]

This document is a response to concerns raised about a previous article on B chromosomes and their evolutionary dynamics. The authors acknowledge the valid concerns and provide corrections and clarifications. They address issues with the availability of sequenced data and inaccuracies in supplementary figures and tables. The authors also discuss errors and corrections in a study on B chromosomes in different species, including mistakes in referencing, figure citation, and data analysis. They emphasize the need for standardized protocols and bioinformatic tools to improve the study of B chromosomes and acknowledge the challenges and gaps in understanding their genomic content. [Extracted from the article]

Purpose: Autism spectrum disorder(ASD) is a disease associated with the neurodevelopment of the brain. The autism spectrum can be observed in early childhood, where the symptoms of the disease usually appear in children within the first year of their life. Currently, ASD can only be diagnosed based on the apparent symptoms due to the lack of information on genes related to the disease. Therefore, in this paper, we need to predict the largest number of disease-causing genes for a better diagnosis. Methods: A hybrid stacking ensemble model with Synthetic Minority Oversampling TEchnique (Stack-SMOTE) is proposed to predict the genes associated with ASD. The proposed model uses the gene ontology database to measure the similarities between the genes using a hybrid gene similarity function(HGS). HGS is effective in measuring the similarity as it combines the features of information gain-based methods and graph-based methods. The proposed model solves the imbalanced ASD dataset problem using the Synthetic Minority Oversampling Technique (SMOTE), which generates synthetic data rather than duplicates the data to reduce the overfitting. Sequentially, a gradient boosting-based random forest classifier (GBBRF) is introduced as a new combination technique to enhance the prediction of ASD genes. Moreover, the GBBRF classifier combined with random forest(RF), k-nearest neighbor, support vector machine(SVM), and logistic regression(LR) to form the proposed Stacking-SMOTE model to optimize the prediction of ASD genes. Results: The proposed Stacking-SMOTE model is evaluated using the Simons Foundation Autism Research Initiative (SFARI) gene database and a set of candidates ASD genes.The results of the proposed model-based SMOTE outperform other reported undersampling and oversampling techniques. Sequentially, the results of GBBRF achieve higher accuracy than using the basic classifiers. Moreover, the experimental results show that the proposed Stacking-SMOTE model outperforms the existing ASD prediction models with approximately 95.5% accuracy. Conclusion: The proposed Stacking-SMOTE model demonstrates that SMOTE is effective in handling the autism imbalanced data. Sequentially, the integration between the gradient boosting and random forest classifier (GBBRF) support to build a robust stacking ensemble model(Stacking-SMOTE). [ABSTRACT FROM AUTHOR]

Gene drive mosquitoes are increasingly considered a potential transformational tool for vector control of malaria mosquitoes. As part of efforts to promote responsible research in this field, a number of guidance documents have been published by the World Health Organization, National Academies and expert groups. While virtually all recent guidance documents on gene drive research stress the importance of stakeholder engagement activities, no specific guidelines on implementing them have been established. Target Malaria, a not-for-profit research consortium developing a vector-control gene drive approach to eliminate malaria, has reflected on how its stakeholder engagement strategy translates engagement guidance documents into practice. The project analysed and addressed the tension between the context specificities and the international recommendations. The engagement strategy combines published recommendations for responsible gene drive research, information collected from the local context where the project operates and a set of principles guiding the choices made. This strategy was first developed during the early phases of the project's research, years ahead of any activities with gene drive mosquitoes in those countries of operations. These earlier activities, and their related engagement, allow the project to develop and adapt an engagement strategy appropriate for potential gene drive research in its field site countries. This paper offers a description of a stakeholder engagement strategy operationalization based on (1) adaptation to stakeholder preferences, (2) inclusiveness and (3) empowerment and accountability. The authors hope to offer concrete examples to support other projects with the development and implementation of their engagement strategies with particular attention to the co-development principle. [ABSTRACT FROM AUTHOR]

Background: Gene co-expression networks (GCNs) are not easily comparable due to their complex structure. In this paper, we propose a tool, Juxtapose, together with similarity measures that can be utilized for comparative transcriptomics between a set of organisms. While we focus on its application to comparing co-expression networks across species in evolutionary studies, Juxtapose is also generalizable to co-expression network comparisons across tissues or conditions within the same species. Methods: A word embedding strategy commonly used in natural language processing was utilized in order to generate gene embeddings based on walks made throughout the GCNs. Juxtapose was evaluated based on its ability to embed the nodes of synthetic structures in the networks consistently while also generating biologically informative results. Evaluation of the techniques proposed in this research utilized RNA-seq datasets from GTEx, a multi-species experiment of prefrontal cortex samples from the Gene Expression Omnibus, as well as synthesized datasets. Biological evaluation was performed using gene set enrichment analysis and known gene relationships in literature. Results: We show that Juxtapose is capable of globally aligning synthesized networks as well as identifying areas that are conserved in real gene co-expression networks without reliance on external biological information. Furthermore, output from a matching algorithm that uses cosine distance between GCN embeddings is shown to be an informative measure of similarity that reflects the amount of topological similarity between networks. Conclusions: Juxtapose can be used to align GCNs without relying on known biological similarities and enables post-hoc analyses using biological parameters, such as orthology of genes, or conserved or variable pathways. Availability: A development version of the software used in this paper is available at https://github.com/klovens/juxtapose [ABSTRACT FROM AUTHOR]

Background: Hormone-dependent promoters are very efficient in transgene expression. Plasmid-based reporter assays have identified regulatory sequences of the Ovalbumin promoter that are involved in response to estrogen and have shown that the deletion of the steroid-dependent regulatory element (SDRE) and negative regulatory element (NRE) leads to a steroid-independent expression of a reporter. However, the functional roles of these regulatory elements within the native genomic context of the Ovalbumin promoter have not been evaluated. Results: In this study, we show that the negative effects of the NRE element on the Ovalbumin gene can be counteracted by CRISPR interference. We also show that the CRISPR-mediated deletion of SDRE and NRE promoter elements in a non-oviduct cell can lead to the significant expression of the Ovalbumin gene. In addition, the targeted knock-in of a transgene reporter in the Ovalbumin coding region and its expression confirms that the truncated promoter of the Ovalbumin gene can be efficiently used for an estrogen-independent expression of a foreign gene. Conclusions: The methodology applied in this paper allowed the study of promoter regulatory sequences in their native nuclear organization. [ABSTRACT FROM AUTHOR]

Objective: The current paper aims to discuss the development of a virtual simulation experiment teaching system and review its effectiveness in improving the teaching of clinical skills to college medical students. Methods: Collaborators used 3D Studio Max, Unity 3D and Visual Studio to develop four modules: laboratory thinking training, biosafety training, gene testing and experimental assessment. Teaching was conducted and a virtual software program was used for evaluation of the students. Results: The laboratory safety training system, virtual gene experiment system and experimental assessment system were developed. The results of the questionnaire survey show that the software provides good interactivity and guidance. The interest of medical students in study is improved and they received training in clinical experimental thinking. Student evaluation assists their scientific research practice, and can improve the awareness of biosafety. Conclusion: The virtual simulation experiment teaching system, when applied in the teaching of undergraduate and postgraduate experiment courses, can bring about rapid improvements in the following areas: biosafety awareness, interest in learning about experiments and experimental skills, clinical experimental thinking, and comprehensive experimental ability. [ABSTRACT FROM AUTHOR]

Background: Together with application of next-generation sequencing technologies and increased accumulation of genomic variation data in different organism species, an opportunity for effectively identification of superior alleles of functional genes to facilitate marker-assisted selection is emerging, and the clarification of haplotypes of functional genes is becoming an essential target in recent study works. Results: In this paper, we describe an R package 'geneHapR' developed for haplotypes identification, statistics and visualization analysis of candidate genes. This package could integrate genotype data, genomic annotating information and phenotypic variation data to clarify genotype variations, evolutionary-ship, and morphological effects among haplotypes through variants visualization, network construction and phenotypic comparison. 'geneHapR' also provides functions for Linkage Disequilibrium block analysis and visualizing of haplotypes geo-distribution. Conclusions: The R package 'geneHapR' provided an easy-to-use tool for haplotype identification, statistic and visualization for candidate gene and will provide useful clues for gene functional dissection and molecular-assistant pyramiding of beneficial alleles of functional locus in future breeding programs. [ABSTRACT FROM AUTHOR]

Background: The identification of disease-related genes is of great significance for the diagnosis and treatment of human disease. Most studies have focused on developing efficient and accurate computational methods to predict disease-causing genes. Due to the sparsity and complexity of biomedical data, it is still a challenge to develop an effective multi-feature fusion model to identify disease genes. Results: This paper proposes an approach to predict the pathogenic gene based on multi-head attention fusion (MHAGP). Firstly, the heterogeneous biological information networks of disease genes are constructed by integrating multiple biomedical knowledge databases. Secondly, two graph representation learning algorithms are used to capture the feature vectors of gene-disease pairs from the network, and the features are fused by introducing multi-head attention. Finally, multi-layer perceptron model is used to predict the gene-disease association. Conclusions: The MHAGP model outperforms all of other methods in comparative experiments. Case studies also show that MHAGP is able to predict genes potentially associated with diseases. In the future, more biological entity association data, such as gene-drug, disease phenotype-gene ontology and so on, can be added to expand the information in heterogeneous biological networks and achieve more accurate predictions. In addition, MHAGP with strong expansibility can be used for potential tasks such as gene-drug association and drug-disease association prediction. [ABSTRACT FROM AUTHOR]

The article presents information on a study which discusses the existence of proliferating cell nuclear antigen (PCNA) pseudogenes in the human genome. PPCNA is a protein which plays an important role in the eukaryotic DNA replication and cell proliferation. PCNA was first identified as an antigen in serum of patients with systemic lupus erythematosus, and then molecular cloning of the human PCNA gene was also done. The study suggests that there are at least four valid PCNA pseudogenes in human genome including PCNAP1, PCNAP2, LOC392454 and LOC390102.

Background: DNA methylation is an epigenetic event involving the addition of a methyl-group to a cytosine-guanine base pair (i.e., CpG site). It is associated with different cancers. Our research focuses on studying non-small cell lung cancer hemimethylation, which refers to methylation occurring on only one of the two DNA strands. Many studies often assume that methylation occurs on both DNA strands at a CpG site. However, recent publications show the existence of hemimethylation and its significant impact. Therefore, it is important to identify cancer hemimethylation patterns.Methods: In this paper, we use the Wilcoxon signed rank test to identify hemimethylated CpG sites based on publicly available non-small cell lung cancer methylation sequencing data. We then identify two types of hemimethylated CpG clusters, regular and polarity clusters, and genes with large numbers of hemimethylated sites. Highly hemimethylated genes are then studied for their biological interactions using available bioinformatics tools.Results: In this paper, we have conducted the first-ever investigation of hemimethylation in lung cancer. Our results show that hemimethylation does exist in lung cells either as singletons or clusters. Most clusters contain only two or three CpG sites. Polarity clusters are much shorter than regular clusters and appear less frequently. The majority of clusters found in tumor samples have no overlap with clusters found in normal samples, and vice versa. Several genes that are known to be associated with cancer are hemimethylated differently between the cancerous and normal samples. Furthermore, highly hemimethylated genes exhibit many different interactions with other genes that may be associated with cancer. Hemimethylation has diverse patterns and frequencies that are comparable between normal and tumorous cells. Therefore, hemimethylation may be related to both normal and tumor cell development.Conclusions: Our research has identified CpG clusters and genes that are hemimethylated in normal and lung tumor samples. Due to the potential impact of hemimethylation on gene expression and cell function, these clusters and genes may be important to advance our understanding of the development and progression of non-small cell lung cancer. [ABSTRACT FROM AUTHOR]

Interest in the assignment and frequency analysis of haplotypes in samples of unrelated individuals has increased immeasurably as a result of the emphasis placed on haplotype analyses by, for example, the International HapMap Project and related initiatives. Although there are many available computer programs for haplotype analysis applicable to samples of unrelated individuals, many of these programs have limitations and/or very specific uses. In this paper, the key features of available haplotype analysis software for use with unrelated individuals, as well as pooled DNA samples from unrelated individuals, are summarised. Programs for haplotype analysis were identified through keyword searches on PUBMED and various internet search engines, a review of citations from retrieved papers and personal communications, up to June 2004. Priority was given to functioning computer programs, rather than theoretical models and methods. The available software was considered in light of a number of factors: the algorithm(s) used, algorithm accuracy, assumptions, the accommodation of genotyping error, implementation of hypothesis testing, handling of missing data, software characteristics and web-based implementations. Review papers comparing specific methods and programs are also summarised. Forty-six haplotyping programs were identified and reviewed. The programs were divided into two groups: those designed for individual genotype data (a total of 43 programs) and those designed for use with pooled DNA samples (a total of three programs). The accuracy of programs using various criteria are assessed and the programs are categorised and discussed in light of: algorithm and method, accuracy, assumptions, genotyping error, hypothesis testing, missing data, software characteristics and web implementation. Many available programs have limitations (eg some cannot accommodate missing data) and/or are designed with specific tasks in mind (eg estimating haplotype frequencies rather than assigning most likely haplotypes to individuals). It is concluded that the selection of an appropriate haplotyping program for analysis purposes should be guided by what is known about the accuracy of estimation, as well as by the limitations and assumptions built into a program. [ABSTRACT FROM AUTHOR]

Background: The availability of biomedical literature in electronic format has made it possible to implement automatic text processing methods to expose implicit relationships among different documents, and more importantly, the functional relationships among the molecules and processes that these documents describe. Results: A computational strategy that identifies functionally related human genes by detecting the implicit relationships among the publications cited under each gene in the Online Mendelian Inheritance in Man (OMIM) was implemented. The implementation was based on a substantially modified version of the kernel document method. The improvements include assigning a calculated weight for a document to indicate its importance in establishing the relationship between two documents, and using multiple kernel documents to reflect the multiple functions of the same gene. An example of using this strategy to identify genes related to the apoptosis pathway in human was given. Conclusions: The results showed that this method can indeed produce meaningful results when applied to human genes. [ABSTRACT FROM AUTHOR]

Background: Childhood systemic lupus erythematosus (cSLE) is a multisystemic, life-threatening autoimmune disease. Compared to adults, SLE in childhood is more active, can cause multisystem involvement including renal, neurological and hematological, and can cause cumulative damage across systems more rapidly. Autophagy, one of the core functions of cells, is involved in almost every process of the immune response and has been shown to be associated with many autoimmune diseases, being a key factor in the interplay between innate and adaptive immunity. Autophagy influences the onset, progression and severity of SLE. This paper identifies new biomarkers for the diagnosis and treatment of childhood SLE based on an artificial neural network of autophagy-related genes. Methods: We downloaded dataset GSE100163 from the Gene Expression Omnibus database and used Protein–protein Interaction Network (PPI) and Least Absolute Shrinkage and Selection Operator (LASSO) to screen the signature genes of autophagy-related genes in cSLE. A new artificial neural network model for cSLE diagnosis was constructed using the signature genes. The predictive efficiency of the model was also validated using the dataset GSE65391. Finally, "CIBERSORT" was used to calculate the infiltration of immune cells in cSLE and to analyze the relationship between the signature genes and the infiltration of immune cells. Results: We identified 37 autophagy-related genes that differed in cSLE and normal samples, and finally obtained the seven most relevant signature genes for cSLE (DDIT3, GNB2L1, CTSD, HSPA8, ULK1, DNAJB1, CANX) by PPI and LASOO regression screening, and constructed an artificial neural network diagnostic model for cSLE. Using this model, we plotted the ROC curves for the training and validation group diagnoses with the area under the curve of 0.976 and 0.783, respectively. Finally, we performed immunoassays on cSLE samples, and the results showed that Plasma cells, Macrophages M0, Dendritic cells activated and Neutrophils were significantly infiltrated in cSLE. Conclusion: We constructed an artificial neural network diagnostic model of seven autophagy-related genes that can be used for the diagnosis of cSLE. Meanwhile, the characteristic genes affect the immune infiltration of cSLE, which may provide new perspectives for the exploration of cSLE treatment and related mechanisms. [ABSTRACT FROM AUTHOR]

Background: CHARGE syndrome (CS) is an autosomal dominant genetic condition whose recognition in the neonatal period is complicated by considerable phenotypic variability. Pediatric patients with genetic disorders have a known high incidence of hypoglycemia, due to many concurring factors. To date, neonatal hypoglycemia is a feature poorly explored in the literature associated with CS. This paper adds to the existing literature on hypoglycemia in CS and provides a brief review of the mechanisms through which CS, as well as the main genetic syndromes associated with neonatal hypoglycemia, may determine it. Case presentation: The patient was a term newborn, first-born daughter to non-consanguineous parents. At birth, axial hypotonia with slight hypertonia of the limbs, and dysplastic auricles were noted. The incidental finding of asymptomatic hypoglycemia led to the initiation of glucose infusion on the II day of life, continued for a total of 8 days (maximum infusion rate: 8 mg/kg/min). In-depth endocrinological examinations showed poor cortisol response to the hypoglycemic stimulus, with normal GH values, thyroid function and ACTH. In view of the suspected hypoadrenalism, oral hydrocortisone therapy was initiated. Inappropriately low values of plasmatic and urinary ketones supported the hypothesis of concomitant transient hyperinsulinism, not requiring therapy. A brain MRI was performed, documenting thinning of the optic nerves, non-displayable olfactory bulbs and dysmorphic corpus callosum. An eye examination revealed bilateral chorioretinal coloboma. Temporal bone CT scan showed absence of the semicircular canals. The unexpected findings of coloboma and absence of semicircular canals led to the suspicion of CS, later confirmed by the molecular analysis of CHD7. Conclusions: It seems important to consider CS in the differential diagnosis of persistent hypoglycemia in newborns with specific anomalies. At the same time, it is advisable to consider the risk of hypoglycemia in children with CS, as well as other genetic syndromes. Awareness of the many possible causes of hypoglycemia in newborns with genetic conditions may help steer the investigations, allowing for an appropriate and timely treatment. [ABSTRACT FROM AUTHOR]

Background: Osteoarthritis, a common degenerative disease of articular cartilage, is characterized by degeneration of articular cartilage, changes in subchondral bone structure, and formation of osteophytes, with main clinical manifestations including increasingly serious swelling, pain, stiffness, deformity, and mobility deficits of the knee joints. With the advent of the big data era, the processing of mass data has evolved into a hot topic and gained a solid foundation from the steadily developed and improved machine learning algorithms. Aiming to provide a reference for the diagnosis and treatment of osteoarthritis, this paper using machine learning identifies the key feature genes of osteoarthritis and explores its relationship with immune infiltration, thereby revealing its pathogenesis at the molecular level. Methods: From the GEO database, GSE55235 and GSE55457 data were derived as training sets and GSE98918 data as a validation set. Differential gene expressions of the training sets were analyzed, and the LASSO regression model and support vector machine model were established by applying machine learning algorithms. Moreover, their intersection genes were regarded as feature genes, the receiver operator characteristic (ROC) curve was drawn, and the results were verified using the validation set. In addition, the expression spectrum of osteoarthritis was analyzed by immunocyte infiltration and the co-expression correlation between feature genes and immunocytes was construed. Conclusion: EPYC and KLF9 can be viewed as feature genes for osteoarthritis. The silencing of EPYC and the overexpression of KLF9 are associated with the occurrence of osteoarthritis and immunocyte infiltration. [ABSTRACT FROM AUTHOR]

This article examines the reproducibility of a previous study on B chromosomes in three species. The authors discovered inconsistencies and errors in the original study, resulting in false positive results and unsupported claims. They stress the significance of rigorous reproducibility practices in bioinformatics analyses and propose enhancements to the review process for such studies. The article includes supplementary materials that compare the authors' analyses with those of the original study. [Extracted from the article]

Background: FragGeneScan is currently the most accurate and popular tool for gene prediction in short and error-prone reads, but its execution speed is insufficient for use on larger data sets. The parallelization which should have addressed this is inefficient. Its alternative implementation FragGeneScan+ is faster, but introduced a number of bugs related to memory management, race conditions and even output accuracy. Results: This paper introduces FragGeneScanRs, a faster Rust implementation of the FragGeneScan gene prediction model. Its command line interface is backward compatible and adds extra features for more flexible usage. Its output is equivalent to the original FragGeneScan implementation. Conclusions: Compared to the current C implementation, shotgun metagenomic reads are processed up to 22 times faster using a single thread, with better scaling for multithreaded execution. The Rust code of FragGeneScanRs is freely available from GitHub under the GPL-3.0 license with instructions for installation, usage and other documentation (https://github.com/unipept/FragGeneScanRs). [ABSTRACT FROM AUTHOR]

Background: Loss of efficacy of diagnostic tests may lead to untreated or mistreated malaria cases, compromising case management and control. There is an increasing reliance on rapid diagnostic tests (RDTs) for malaria diagnosis, with the most widely used of these targeting the Plasmodium falciparum histidine-rich protein 2 (PfHRP2). There are numerous reports of the deletion of this gene in P. falciparum parasites in some populations, rendering them undetectable by PfHRP2 RDTs. The aim of this study was to identify P. falciparum parasites lacking the P. falciparum histidine rich protein 2 and 3 genes (pfhrp2/3) isolated from asymptomatic and symptomatic school-age children in Kinshasa, Democratic Republic of Congo. Methods: The performance of PfHRP2-based RDTs in comparison to microscopy and PCR was assessed using blood samples collected and spotted on Whatman 903™ filter papers between October and November 2019 from school-age children aged 6–14 years. PCR was then used to identify parasite isolates lacking pfhrp2/3 genes. Results: Among asymptomatic malaria carriers (N = 266), 49%, 65%, and 70% were microscopy, PfHRP2_RDT, and pfldh-qPCR positive, respectively. The sensitivity and specificity of RDTs compared to PCR were 80% and 70% while the sensitivity and specificity of RDTs compared to microscopy were 92% and 60%, respectively. Among symptomatic malaria carriers (N = 196), 62%, 67%, and 87% were microscopy, PfHRP2-based RDT, pfldh-qPCR and positive, respectively. The sensitivity and specificity of RDTs compared to PCR were 75% and 88%, whereas the sensitivity and specificity of RDTs compared to microscopy were 93% and 77%, respectively. Of 173 samples with sufficient DNA for PCR amplification of pfhrp2/3, deletions of pfhrp2 and pfhrp3 were identified in 2% and 1%, respectively. Three (4%) of samples harboured deletions of the pfhrp2 gene in asymptomatic parasite carriers and one (1%) isolate lacked the pfhrp3 gene among symptomatic parasite carriers in the RDT positive subgroup. No parasites lacking the pfhrp2/3 genes were found in the RDT negative subgroup. Conclusion: Plasmodium falciparum histidine-rich protein 2/3 gene deletions are uncommon in the surveyed population, and do not result in diagnostic failure. The use of rigorous PCR methods to identify pfhrp2/3 gene deletions is encouraged in order to minimize the overestimation of their prevalence. [ABSTRACT FROM AUTHOR]

Background: Fatty acid composition and content affect rapeseed oil quality. Fatty acid synthesis-related genes in rapeseed have been studied globally by researchers. Nevertheless, rapeseed oil is mainly composed of seven different fatty acids (FA), and each fatty acid was regulated by different genes. Furthermore, different FA affect each other, which needs continuous and in-depth research to obtain more clear results in Brassica napus. Results: In this paper, broad-scale miRNA expression profiles were constructed and 21 differentially expressed miRNAs were detected. GO enrichment analysis showed that most up-regulated proteins were involved in transcription factor activity and catalytic activity. KEGG pathway enrichment analysis indicated that 20 pathways involving 36 target genes were enriched, of which the bna00592 pathway may be involved in fatty acid metabolism. The results were verified using a quantitative real-time PCR (RT-qPCR) analysis, we found that the target gene of bna-miR156b > c > g was the OPR (12-oxo-phytodienoic acid reductase). Four copies of OPR gene were found, and the over-expression vectors (pCAMBIA1300-35 s-OPR and pCAMBIA1300-RNAi-OPR) were constructed to verify their functions. In T1 and T2 generation, the content of linoleic acid (LA) increased significantly in OE but deceased in OPRi. Conclusions: This is the first study to provide four copies of the OPR gene that regulates LA metabolism, can be used for the molecular mechanism of LA and optimizing fatty acid profiles in oilseed for breeding programs. [ABSTRACT FROM AUTHOR]

Background: Backfat thickness is an important carcass composition trait for pork production and is commonly included in swine breeding programmes. In this paper, we report the results of a large genome-wide association study for backfat thickness using data from eight lines of diverse genetic backgrounds., Methods: Data comprised 275,590 pigs from eight lines with diverse genetic backgrounds (breeds included Large White, Landrace, Pietrain, Hampshire, Duroc, and synthetic lines) genotyped and imputed for 71,324 single-nucleotide polymorphisms (SNPs). For each line, we estimated SNP associations using a univariate linear mixed model that accounted for genomic relationships. SNPs with significant associations were identified using a threshold of p < 10 -6 and used to define genomic regions of interest. The proportion of genetic variance explained by a genomic region was estimated using a ridge regression model., Results: We found significant associations with backfat thickness for 264 SNPs across 27 genomic regions. Six genomic regions were detected in three or more lines. The average estimate of the SNP-based heritability was 0.48, with estimates by line ranging from 0.30 to 0.58. The genomic regions jointly explained from 3.2 to 19.5% of the additive genetic variance of backfat thickness within a line. Individual genomic regions explained up to 8.0% of the additive genetic variance of backfat thickness within a line. Some of these 27 genomic regions also explained up to 1.6% of the additive genetic variance in lines for which the genomic region was not statistically significant. We identified 64 candidate genes with annotated functions that can be related to fat metabolism, including well-studied genes such as MC4R, IGF2, and LEPR, and more novel candidate genes such as DHCR7, FGF23, MEDAG, DGKI, and PTN., Conclusions: Our results confirm the polygenic architecture of backfat thickness and the role of genes involved in energy homeostasis, adipogenesis, fatty acid metabolism, and insulin signalling pathways for fat deposition in pigs. The results also suggest that several less well-understood metabolic pathways contribute to backfat development, such as those of phosphate, calcium, and vitamin D homeostasis., (© 2021. The Author(s).)

Background: RNA sequencing is currently the method of choice for genome-wide profiling of gene expression. A popular approach to quantify expression levels of genes from RNA-seq data is to map reads to a reference genome and then count mapped reads to each gene. Gene annotation data, which include chromosomal coordinates of exons for tens of thousands of genes, are required for this quantification process. There are several major sources of gene annotations that can be used for quantification, such as Ensembl and RefSeq databases. However, there is very little understanding of the effect that the choice of annotation has on the accuracy of gene expression quantification in an RNA-seq analysis. Results: In this paper, we present results from our comparison of Ensembl and RefSeq human annotations on their impact on gene expression quantification using a benchmark RNA-seq dataset generated by the SEQC consortium. We show that the use of RefSeq gene annotation models led to better quantification accuracy, based on the correlation with ground truths including expression data from >800 real-time PCR validated genes, known titration ratios of gene expression and microarray expression data. We also found that the recent expansion of the RefSeq annotation has led to a decrease in its annotation accuracy. Finally, we demonstrated that the RNA-seq quantification differences observed between different annotations were not affected by the use of different normalization methods. Conclusion: In conclusion, our study found that the use of the conservative RefSeq gene annotation yields better RNA-seq quantification results than the more comprehensive Ensembl annotation. We also found that, surprisingly, the recent expansion of the RefSeq database, which was primarily driven by the incorporation of sequencing data into the gene annotation process, resulted in a reduction in the accuracy of RNA-seq quantification. [ABSTRACT FROM AUTHOR]

Purpose: To explore the specific role and regulatory mechanism of oxysterol binding protein like 5 (OSBPL5) in non-small cell lung cancer (NSCLC).Methods and Results: Quantitative real-time polymerase chain reaction (qRT-PCR) analysis demonstrated that OSBPL5 expression was notably elevated in NSCLC tissues and cell lines, and Kaplan-Meier analysis manifested that high OSBPL5 expression was closely related to the poor prognosis of NSCLC patients. Besides, according to the results from western blot analysis, cell counting kit-8, EdU and Transwell assays, knockdown of OSBPL5 suppressed NSCLC cell proliferation, migration, invasion and epithelial-mesenchymal transition (EMT) process. Additionally, by performing qRT-PCR analysis, luciferase reporter and RNA pull-down assays, we verified that OSBPL5 was a downstream target of miR-526b-3p and long noncoding RNA (lncRNA) LMCD1-AS1 served as a sponge for miR-526b-3p. Moreover, from rescue assays, we observed that OSBPL5 overexpression offset LMCD1-AS1 knockdown-mediated inhibition in cell proliferation, migration, invasion and EMT in NSCLC.Conclusions: This paper was the first to probe the molecular regulatory mechanism of OSBPL5 involving the LMCD1-AS1/miR-526b-3p axis in NSCLC and our results revealed that the LMCD1-AS1/miR-526b-3p/OSBPL5 axis facilitates NSCLC cell proliferation, migration, invasion and EMT, which may offer a novel therapeutic direction for NSCLC. [ABSTRACT FROM AUTHOR]

Background: Exploring the relationship between disease and gene is of great significance for understanding the pathogenesis of disease and developing corresponding therapeutic measures. The prediction of disease-gene association by computational methods accelerates the process., Results: Many existing methods cannot fully utilize the multi-dimensional biological entity relationship to predict disease-gene association due to multi-source heterogeneous data. This paper proposes FactorHNE, a factor graph-aggregated heterogeneous network embedding method for disease-gene association prediction, which captures a variety of semantic relationships between the heterogeneous nodes by factorization. It produces different semantic factor graphs and effectively aggregates a variety of semantic relationships, by using end-to-end multi-perspectives loss function to optimize model. Then it produces good nodes embedding to prediction disease-gene association., Conclusions: Experimental verification and analysis show FactorHNE has better performance and scalability than the existing models. It also has good interpretability and can be extended to large-scale biomedical network data analysis.

Background: The genetic factors leading to hypertension have been extensively studied, and large numbers of research papers have been published on the subject. One of hypertension researchers' primary research tasks is to locate key hypertension-related genes in abstracts. However, gathering such information with existing tools is not easy: (1) Searching for articles often returns far too many hits to browse through. (2) The search results do not highlight the hypertension-related genes discovered in the abstract. (3) Even though some text mining services mark up gene names in the abstract, the key genes investigated in a paper are still not distinguished from other genes. To facilitate the information gathering process for hypertension researchers, one solution would be to extract the key hypertension-related genes in each abstract. Three major tasks are involved in the construction of this system: (1) gene and hypertension named entity recognition, (2) section categorization, and (3) gene-hypertension relation extraction. Results: We first compare the retrieval performance achieved by individually adding template features and position features to the baseline system. Then, the combination of both is examined. We found that using position features can almost double the original AUC score (0.8140vs.0.4936) of the baseline system. However, adding template features only results in marginal improvement (0.0197). Including both improves AUC to 0.8184, indicating that these two sets of features are complementary, and do not have overlapping effects. We then examine the performance in a different domain--diabetes, and the result shows a satisfactory AUC of 0.83. Conclusion: Our approach successfully exploits template features to recognize true hypertension-related gene mentions and position features to distinguish key genes from other related genes. Templates are automatically generated and checked by biologists to minimize labor costs. Our approach integrates the advantages of machine learning models and pattern matching. To the best of our knowledge, this the first systematic study of extracting hypertension-related genes and the first attempt to create a hypertension-gene relation corpus based on the GAD database. Furthermore, our paper proposes and tests novel features for extracting key hypertension genes, such as relative position, section, and template features, which could also be applied to key-gene extraction for other diseases. [ABSTRACT FROM AUTHOR]

An amendment to this paper has been published and can be accessed via the original article. [ABSTRACT FROM AUTHOR]

Background: Various analytical methods exist that first quantify gene expression and then analyze differentially expressed genes from Affymetrix GeneChip® gene expression analysis array data. These methods differ in the choice of probe measure (quantification of probe hybridization), summarizing multiple probe intensities into a gene expression value, and analysis of differential gene expression. Research papers that describe these methods focus on performance, and how their approaches differ from others. To better understand the common features and differences between various methods, and to evaluate their impact on the results of gene expression analysis, we describe a class of models, referred to as generalized probe models (GPMs), which encompass various currently available methods. Results: Using an empirical dataset, we compared different formulations of GPMs, and GPMs with three other commonly used methods, i.e. MAS 5.0, dChip, and RMA. The comparison shows that, on a genome-wide scale , different methods yield similar results if the same probe measures are chosen. Conclusion: In this paper we present a general framework, i.e. GPMs, which encompasses various methods. GPMs permit the use of a wide range of probe measures and facilitate appropriate comparison between commonly used methods. We demonstrate that the dissimilar results stem primarily from different choice of probe measures, rather than other factors. [ABSTRACT FROM AUTHOR]

Background: At present, the bioinformatics research on the relationship between aging-related diseases and genes is mainly through the establishment of a machine learning multi-label model to classify each gene. Most of the existing methods for predicting pathogenic genes mainly rely on specific types of gene features, or directly encode multiple features with different dimensions, use the same encoder to concatenate and predict the final results, which will be subject to many limitations in the applicability of the algorithm. Possible shortcomings of the above include: incomplete coverage of gene features by a single type of biomics data, overfitting of small dimensional datasets by a single encoder, or underfitting of larger dimensional datasets. Methods: We use the known gene disease association data and gene descriptors, such as gene ontology terms (GO), protein interaction data (PPI), PathDIP, Kyoto Encyclopedia of genes and genomes Genes (KEGG), etc, as input for deep learning to predict the association between genes and diseases. Our innovation is to use Mashup algorithm to reduce the dimensionality of PPI, GO and other large biological networks, and add new pathway data in KEGG database, and then combine a variety of biological information sources through modular Deep Neural Network (DNN) to predict the genes related to aging diseases. Result and conclusion: The results show that our algorithm is more effective than the standard neural network algorithm (the Area Under the ROC curve from 0.8795 to 0.9153), gradient enhanced tree classifier and logistic regression classifier. In this paper, we firstly use DNN to learn the similar genes associated with the known diseases from the complex multi-dimensional feature space, and then provide the evidence that the assumed genes are associated with a certain disease. [ABSTRACT FROM AUTHOR]

Background: The cereal family Poaceae is one of the largest and most diverse angiosperm families. The central component of centromere specification and function is the centromere-specific histone H3 (CENH3). Some cereal species (maize, rice) have one copy of the gene encoding this protein, while some (wheat, barley, rye) have two. We applied a homology-based approach to sequenced cereal genomes, in order to finally trace the mutual evolution of the structure of the CENH3 genes and the nearby regions in various tribes. Results: We have established that the syntenic group or the CENH3 locus with the CENH3 gene and the boundaries defined by the CDPK2 and bZIP genes first appeared around 50 Mya in a common ancestor of the subfamilies Bambusoideae, Oryzoideae and Pooideae. This locus came to Pooideae with one copy of CENH3 in the most ancient tribes Nardeae and Meliceae. The βCENH3 gene as a part of the locus appeared in the tribes Stipeae and Brachypodieae around 35–40 Mya. The duplication was accompanied by changes in the exon-intron structure. Purifying selection acts mostly on αCENH3s, while βCENH3s form more heterogeneous structures, in which clade-specific amino acid motifs are present. In barley species, the βCENH3 gene assumed an inverted orientation relative to αCENH3 and the CDPK2 gene was substituted with LHCB-l. As the evolution and domestication of plant species went on, the locus was growing in size due to an increasing distance between αCENH3 and βCENH3 because of a massive insertion of the main LTR-containing retrotransposon superfamilies, gypsy and copia, without any evolutionary preference on either of them. A comparison of the molecular structure of the locus in the A, B and D subgenomes of the hexaploid wheat T. aestivum showed that invasion by mobile elements and concomitant rearrangements took place in an independent way even in evolutionarily close species. Conclusions: The CENH3 duplication in cereals was accompanied by changes in the exon-intron structure of the βCENH3 paralog. The observed general tendency towards the expansion of the CENH3 locus reveals an amazing diversity of ways in which different species implement the scenario described in this paper. [ABSTRACT FROM AUTHOR]

Background: This paper proposes a workflow to identify genes that respond to specific treatments in plants. The workflow takes as input the RNA sequencing read counts and phenotypical data of different genotypes, measured under control and treatment conditions. It outputs a reduced group of genes marked as relevant for treatment response. Technically, the proposed approach is both a generalization and an extension of WGCNA. It aims to identify specific modules of overlapping communities underlying the co-expression network of genes. Module detection is achieved by using Hierarchical Link Clustering. The overlapping nature of the systems' regulatory domains that generate co-expression can be identified by such modules. LASSO regression is employed to analyze phenotypic responses of modules to treatment. Results: The workflow is applied to rice (Oryza sativa), a major food source known to be highly sensitive to salt stress. The workflow identifies 19 rice genes that seem relevant in the response to salt stress. They are distributed across 6 modules: 3 modules, each grouping together 3 genes, are associated to shoot K content; 2 modules of 3 genes are associated to shoot biomass; and 1 module of 4 genes is associated to root biomass. These genes represent target genes for the improvement of salinity tolerance in rice. Conclusions: A more effective framework to reduce the search-space for target genes that respond to a specific treatment is introduced. It facilitates experimental validation by restraining efforts to a smaller subset of genes of high potential relevance. [ABSTRACT FROM AUTHOR]

Background: Acute myeloid leukemia (AML) is a heterogeneous cancer of the blood, though specific recurring cytogenetic abnormalities in AML are strongly associated with attaining complete response after induction chemotherapy, remission duration, and survival. Therefore recurring cytogenetic abnormalities have been used to segregate patients into favorable, intermediate, and adverse prognostic risk groups. However, it is unclear how expression of genes is associated with these prognostic risk groups. We postulate that expression of genes monotonically associated with these prognostic risk groups may yield important insights into leukemogenesis. Therefore, in this paper we propose penalized Bayesian ordinal response models to predict prognostic risk group using gene expression data. We consider a double exponential prior, a spike-and-slab normal prior, a spike-and-slab double exponential prior, and a regression-based approach with variable inclusion indicators for modeling our high-dimensional ordinal response, prognostic risk group, and identify genes through hypothesis tests using Bayes factor. Results: Gene expression was ascertained using Affymetrix HG-U133Plus2.0 GeneChips for 97 favorable, 259 intermediate, and 97 adverse risk AML patients. When applying our penalized Bayesian ordinal response models, genes identified for model inclusion were consistent among the four different models. Additionally, the genes included in the models were biologically plausible, as most have been previously associated with either AML or other types of cancer. Conclusion: These findings demonstrate that our proposed penalized Bayesian ordinal response models are useful for performing variable selection for high-dimensional genomic data and have the potential to identify genes relevantly associated with an ordinal phenotype. [ABSTRACT FROM AUTHOR]

Background: Although the genome of Saccharomyces cerevisiae (S. cerevisiae) was the first one of a eukaryote organism that was fully sequenced (in 1996), a complete understanding of the potential of encoded biomolecular mechanisms has not yet been achieved. Here, we wish to quantify how far the goal of a full list of S. cerevisiae gene functions still is. Results: The scientific literature about S. cerevisiae protein-coding genes has been mapped onto the yeast genome via the mentioning of names for genomic regions in scientific publications. The match was quantified with the ratio of a given gene name's occurrences to those of any gene names in the article. We find that ~ 230 elite genes with ≥ 75 full publication equivalents (FPEs, FPE = 1 is an idealized publication referring to just a single gene) command ~ 45% of all literature. At the same time, about two thirds of the genes (each with less than 10 FPEs) are described in just 12% of the literature (in average each such gene has just ~ 1.5% of the literature of an elite gene). About 600 genes have not been mentioned in any dedicated article. Compared with other groups of genes, the literature growth rates were highest for uncharacterized or understudied genes until late nineties of the twentieth century. Yet, these growth rates deteriorated and became negative thereafter. Thus, yeast function discovery for previously uncharacterized genes has returned to the level of ~ 1980. At the same time, literature for anyhow well-studied genes (with a threshold T10 (≥ 10 FPEs) and higher) remains steadily growing. Conclusions: Did the early full genome sequencing of yeast boost gene function discovery? The data proves that the moment of publishing the full genome in reality coincides with the onset of decline of gene function discovery for previously uncharacterized genes. If the current status of literature about yeast molecular mechanisms can be extrapolated into the future, it will take about another ~ 50 years to complete the yeast gene function list. We found that a small group of scientific journals contributed extraordinarily to publishing early reports relevant to yeast gene function discoveries. [ABSTRACT FROM AUTHOR]

Background: Pathway enrichment analysis (PEA) is a well-established methodology for interpreting a list of genes and proteins of interest related to a condition under investigation. This paper aims to extend our previous work in which we introduced a preliminary comparative analysis of pathway enrichment analysis tools. We extended the earlier work by providing more case studies, comparing BiP enrichment performance with other well-known PEA software tools. Methods: PEA uses pathway information to discover connections between a list of genes and proteins as well as biological mechanisms, helping researchers to overcome the problem of explaining biological entity lists of interest disconnected from the biological context. Results: We compared the results of BiP with some existing pathway enrichment analysis tools comprising Centrality-based Pathway Enrichment, pathDIP, and Signaling Pathway Impact Analysis, considering three cancer types (colorectal, endometrial, and thyroid), for a total of six datasets (that is, two datasets per cancer type) obtained from the The Cancer Genome Atlas and Gene Expression Omnibus databases. We measured the similarities between the overlap of the enrichment results obtained using each couple of cancer datasets related to the same cancer. Conclusion: As a result, BiP identified some well-known pathways related to the investigated cancer type, validated by the available literature. We also used the Jaccard and meet-min indices to evaluate the stability and the similarity between the enrichment results obtained from each couple of cancer datasets. The obtained results show that BiP provides more stable enrichment results than other tools. [ABSTRACT FROM AUTHOR]

Background: Identifying or prioritizing genes for chronic obstructive pulmonary disease (COPD), one type of complex disease, is particularly important for its prevention and treatment.Methods: In this paper, a novel method was proposed to Prioritize genes using Expression information in Protein-protein interaction networks with disease risks transferred between genes (abbreviated as PEP). A weighted COPD PPI network was constructed using expression information and then COPD candidate genes were prioritized based on their corresponding disease risk scores in descending order.Results: Further analysis demonstrated that the PEP method was robust in prioritizing disease candidate genes, and superior to other existing prioritization methods exploiting either topological or functional information. Top-ranked COPD candidate genes and their significantly enriched functions were verified to be related to COPD. The top 200 candidate genes might be potential disease genes in the diagnosis and treatment of COPD.Conclusions: The proposed method could provide new insights to the research of prioritizing candidate genes of COPD or other complex diseases with expression information from sequencing or microarray data. [ABSTRACT FROM AUTHOR]

Background: Gmelina arborea Roxb is a fast-growing tree species of commercial importance for tropical countries due to multiple industrial uses of its wood. Wood is primarily composed of thick secondary cell walls of xylem cells which imparts the strength to the wood. Identification of the genes involved in the secondary cell wall biosynthesis as well as their cognate regulators is crucial to understand how the production of wood occurs and serves as a starting point for developing breeding strategies to produce varieties with improved wood quality, better paper pulping or new potential uses such as biofuel production. In order to gain knowledge on the molecular mechanisms and gene regulation related with wood development in white teak, a de novo sequencing and transcriptome assembly approach was used employing secondary cell wall synthesizing cells from young white teak trees. Results: For generation of transcriptome, RNA-seq reads were assembled into 110,992 transcripts and 49,364 genes were functionally annotated using plant databases; 5071 GO terms and 25,460 SSR markers were identified within xylem transcripts and 10,256 unigenes were assigned to KEGG database in 130 pathways. Among transcription factor families, C2H2, C3H, bLHLH and MYB were the most represented in xylem. Differential gene expression analysis using leaves as a reference was carried out and a total of 20,954 differentially expressed genes were identified including monolignol biosynthetic pathway genes. The differential expression of selected genes (4CL, COMT, CCoAOMT, CCR and NST1) was validated using qPCR. Conclusions: We report the very first de novo transcriptome of xylem-related genes in this tropical timber species of commercial importance and constitutes a valuable extension of the publicly available transcriptomic resource aimed at fostering both basic and breeding studies. [ABSTRACT FROM AUTHOR]

Background: Small interfering RNA (siRNA) can be used to post-transcriptional gene regulation by knocking down targeted genes. In functional genomics, biomedical research and cancer therapeutics, siRNA design is a critical research topic. Various computational algorithms have been developed to select the most effective siRNA, whereas the efficacy prediction accuracy is not so satisfactory. Many existing computational methods are based on feature engineering, which may lead to biased and incomplete features. Deep learning utilizes non-linear mapping operations to detect potential feature pattern and has been considered perform better than existing machine learning method. Results: In this paper, to further improve the prediction accuracy and facilitate gene functional studies, we developed a new powerful siRNA efficacy predictor based on a deep architecture. First, we extracted hidden feature patterns from two modalities, including sequence context features and thermodynamic property. Then, we constructed a deep architecture to implement the prediction. On the available largest siRNA database, the performance of our proposed method was measured with 0.725 PCC and 0.903 AUC value. The comparative experiment showed that our proposed architecture outperformed several siRNA prediction methods. Conclusions: The results demonstrate that our deep architecture is stable and efficient to predict siRNA silencing efficacy. The method could help select candidate siRNA for targeted mRNA, and further promote the development of RNA interference. [ABSTRACT FROM AUTHOR]

Background: Pharmacogenomics describes the link between gene variations (polymorphisms) and drug responses. In view of the implementation of precision medicine in personalized healthcare, pharmacogenetic tests have recently been introduced in the clinical practice. However, the translational aspects of such tests have been limited due to the lack of robust population-based evidence.Materials: In this paper we present a novel pharmacogenetic panel (iDNA Genomics-PGx-CNS or PGx-CNS), consisting of 24 single nucleotide polymorphisms (SNPs) on 13 genes involved in the signaling or/and the metabolism of 28 approved drugs currently administered to treat diseases of the Central Nervous System (CNS). We have tested the PGx-CNS panel on 501 patient-derived DNA samples from a southeastern European population and applied biostatistical analyses on the pharmacogenetic associations involving drug selection, dosing and the risk of adverse drug events (ADEs).Results: Results reveal the occurrences of each SNP in the sample and a strong correlation with the European population. Nonlinear principal component analysis strongly indicates co-occurrences of certain variants. The metabolization efficiency (poor, intermediate, extensive, ultra-rapid) and the frequency of clinical useful pharmacogenetic, associations in the population (drug relevance), are also described, along with four exemplar clinical cases illustrating the strong potential of the PGx-CNS panel, as a companion diagnostic assay. It is noted that pharmacogenetic associations involving copy number variations (CNVs) or the HLA gene were not included in this analysis.Conclusions: Overall, results illustrate that the PGx-CNS panel is a valuable tool supporting therapeutic medical decisions, urging its broad clinical implementation. [ABSTRACT FROM AUTHOR]

Background: RNA-seq data are increasingly used to derive prognostic signatures for cancer outcome prediction. A limitation of current predictors is their reliance on reference gene annotations, which amounts to ignoring large numbers of non-canonical RNAs produced in disease tissues. A recently introduced kind of transcriptome classifier operates entirely in a reference-free manner, relying on k-mers extracted from patient RNA-seq data.Methods: In this paper, we set out to compare conventional and reference-free signatures in risk and relapse prediction of prostate cancer. To compare the two approaches as fairly as possible, we set up a common procedure that takes as input either a k-mer count matrix or a gene expression matrix, extracts a signature and evaluates this signature in an independent dataset.Results: We find that both gene-based and k-mer based classifiers had similarly high performances for risk prediction and a markedly lower performance for relapse prediction. Interestingly, the reference-free signatures included a set of sequences mapping to novel lncRNAs or variable regions of cancer driver genes that were not part of gene-based signatures.Conclusions: Reference-free classifiers are thus a promising strategy for the identification of novel prognostic RNA biomarkers. [ABSTRACT FROM AUTHOR]

Background: Asparagine is a very important nitrogen transport and storage compound in plants due to its high nitrogen/carbon ratio and stability. Asparagine intracellular concentration depends on a balance between asparagine biosynthesis and degradation. The main enzymes involved in asparagine metabolism are asparagine synthetase (ASN), asparaginase (NSE) and serine-glyoxylate aminotransferase (SGAT). The study of the genes encoding for these enzymes in the model legume Lotus japonicus is of particular interest since it has been proposed that asparagine is the principal molecule used to transport reduced nitrogen within the plant in most temperate legumes. Results: A differential expression of genes encoding for several enzymes involved in asparagine metabolism was detected in L. japonicus. ASN is encoded by three genes, LjASN1 was the most highly expressed in mature leaves while LjASN2 expression was negligible and LjASN3 showed a low expression in this organ, suggesting that LjASN1 is the main gene responsible for asparagine synthesis in mature leaves. In young leaves, LjASN3 was the only ASN gene expressed although at low levels, while all the three genes encoding for NSE were highly expressed, especially LjNSE1. In nodules, LjASN2 and LjNSE2 were the most highly expressed genes, suggesting an important role for these genes in this organ. Several lines of evidence support the connection between asparagine metabolic genes and photorespiration in L. japonicus: a) a mutant plant deficient in LjNSE1 showed a dramatic decrease in the expression of the two genes encoding for SGAT; b) expression of the genes involved in asparagine metabolism is altered in a photorespiratory mutant lacking plastidic glutamine synthetase; c) a clustering analysis indicated a similar pattern of expression among several genes involved in photorespiratory and asparagine metabolism, indicating a clear link between LjASN1 and LjSGAT genes and photorespiration. Conclusions: The results obtained in this paper indicate the existence of a differential expression of asparagine metabolic genes in L. japonicus and point out the crucial relevance of particular genes in different organs. Moreover, the data presented establish clear links between asparagine and photorespiratory metabolic genes in this plant. [ABSTRACT FROM AUTHOR]

An amendment to this paper has been published and can be accessed via the original article. [ABSTRACT FROM AUTHOR]

Background: The amount of available data, which can facilitate answering scientific research questions, is growing. However, the different formats of published data are expanding as well, creating a serious challenge when multiple datasets need to be integrated for answering a question. Results: This paper presents a semi-automated framework that provides semantic enhancement of biomedical data, specifically gene datasets. The framework involved a concept recognition task using machine learning, in combination with the BioPortal annotator. Compared to using methods which require only the BioPortal annotator for semantic enhancement, the proposed framework achieves the highest results. Conclusions: Using concept recognition combined with machine learning techniques and annotation with a biomedical ontology, the proposed framework can provide datasets to reach their full potential of providing meaningful information, which can answer scientific research questions. [ABSTRACT FROM AUTHOR]

Background: Model averaging has attracted increasing attention in recent years for the analysis of high-dimensional data. By weighting several competing statistical models suitably, model averaging attempts to achieve stable and improved prediction. In this paper, we develop a two-stage model averaging procedure to enhance accuracy and stability in prediction for high-dimensional linear regression. First we employ a high-dimensional variable selection method such as LASSO to screen redundant predictors and construct a class of candidate models, then we apply the jackknife cross-validation to optimize model weights for averaging. Results: In simulation studies, the proposed technique outperforms commonly used alternative methods under high-dimensional regression setting, in terms of minimizing the mean of the squared prediction error. We apply the proposed method to a riboflavin data, the result show that such method is quite efficient in forecasting the riboflavin production rate, when there are thousands of genes and only tens of subjects. Conclusions: Compared with a recent high-dimensional model averaging procedure (Ando and Li in J Am Stat Assoc 109:254–65, 2014), the proposed approach enjoys three appealing features thus has better predictive performance: (1) More suitable methods are applied for model constructing and weighting. (2) Computational flexibility is retained since each candidate model and its corresponding weight are determined in the low-dimensional setting and the quadratic programming is utilized in the cross-validation. (3) Model selection and averaging are combined in the procedure thus it makes full use of the strengths of both techniques. As a consequence, the proposed method can achieve stable and accurate predictions in high-dimensional linear models, and can greatly help practical researchers analyze genetic data in medical research. [ABSTRACT FROM AUTHOR]

Background: Genetic resources are important natural assets. Discovery of new enzyme gene sequences has been an ongoing effort in biotechnology industry. In the genomic age, genomes of microorganisms from various environments have been deciphered. Increasingly, it has become more and more difficult to find novel enzyme genes. In this work, we attempted to use the easily accessible banknotes to search for novel microbial gene sequences. Results: We used high-throughput genomic sequencing technology to comprehensively characterize the diversity of microorganisms on the US dollars and Chinese Renminbis (RMBs). In addition to finding a vast diversity of microbes, we found a significant number of novel gene sequences, including an unreported superoxide dismutase (SOD) gene, whose catalytic activity was further verified by experiments. Conclusions: We demonstrated that banknotes could be a good and convenient genetic resource for finding economically valuable biologicals. [ABSTRACT FROM AUTHOR]

Background: The most popular tool used for measuring personality traits is the Five-Factor Model (FFM). It includes neuroticism, extraversion, openness, agreeableness and conscientiousness. Many studies indicated the association of genes encoding neurotransmitter receptors/transporters with personality traits. The relationship connecting polymorphic DNA sequences and FFM features has been described in the case of genes encoding receptors of cannabinoid and dopaminergic systems. Moreover, dopaminergic system receives inputs from other neurotransmitters, like GABAergic or serotoninergic systems. Methods: We searched PubMed Central (PMC), Science Direct, Scopus, Cochrane Library, Web of Science and EBSCO databases from their inception to November 19, 2020, to identify original studies, as well as peer-reviewed studies examining the FFM and its association with gene polymorphisms affecting the neurotransmitter functions in central nervous system. Results: Serotonin neurons modulate dopamine function. In gene encoding serotonin transporter protein, SLC6A4, was found polymorphism, which was correlated with openness to experience (in Sweden population), and high scores of neuroticism and low levels of agreeableness (in Caucasian population). The genome-wide association studies (GWASs) found an association of 5q34-q35, 3p24, 3q13 regions with higher scores of neuroticism, extraversion and agreeableness. However, the results for chromosome 3 regions are inconsistent, which was shown in our review paper. Conclusions: GWASs on polymorphisms are being continued in order to determine and further understand the relationship between the changes in DNA and personality traits. [ABSTRACT FROM AUTHOR]

An amendment to this paper has been published and can be accessed via the original article. [ABSTRACT FROM AUTHOR]