UNASSIGNED: At the intersection of neural monitoring and decoding, event-related potential (ERP) based on electroencephalography (EEG) has opened a window into intrinsic brain function. The stability of ERP makes it frequently employed in the field of neuroscience. However, project-specific custom code, tracking of user-defined parameters, and the large diversity of commercial tools have limited clinical application.
UNASSIGNED: We introduce an open-source, user-friendly, and reproducible MATLAB toolbox named EPAT that includes a variety of algorithms for EEG data preprocessing. It provides EEGLAB-based template pipelines for advanced multi-processing of EEG, magnetoencephalography, and polysomnogram data. Participants evaluated EEGLAB and EPAT across 14 indicators, with satisfaction ratings analyzed using the Wilcoxon signed-rank test or paired t-test based on distribution normality.
UNASSIGNED: EPAT eases EEG signal browsing and preprocessing, EEG power spectrum analysis, independent component analysis, time-frequency analysis, ERP waveform drawing, and topological analysis of scalp voltage. A user-friendly graphical user interface allows clinicians and researchers with no programming background to use EPAT.
UNASSIGNED: This article describes the architecture, functionalities, and workflow of the toolbox. The release of EPAT will help advance EEG methodology and its application to clinical translational studies.

Wolfberry, also known as goji berry or Lycium barbarum, is a highly valued fruit with significant health benefits and nutritional value. For more efficient and comprehensive usage of published L. barbarum genomic data, we established the Wolfberry database. The utility of the Wolfberry Genome Database (WGDB) is highlighted through the Genome browser, which enables the user to explore the L. barbarum genome, browse specific chromosomes, and access gene sequences. Gene annotation features provide comprehensive information about gene functions, locations, expression profiles, pathway involvement, protein domains, and regulatory transcription factors. The transcriptome feature allows the user to explore gene expression patterns using transcripts per kilobase million (TPM) and fragments per kilobase per million mapped reads (FPKM) metrics. The Metabolism pathway page provides insights into metabolic pathways and the involvement of the selected genes. In addition to the database content, we also introduce six analysis tools developed for the WGDB. These tools offer functionalities for gene function prediction, nucleotide and amino acid BLAST analysis, protein domain analysis, GO annotation, and gene expression pattern analysis. The WGDB is freely accessible at https://cosbi7.ee.ncku.edu.tw/Wolfberry/. Overall, WGDB serves as a valuable resource for researchers interested in the genomics and transcriptomics of L. barbarum. Its user-friendly web interface and comprehensive data facilitate the exploration of gene functions, regulatory mechanisms, and metabolic pathways, ultimately contributing to a deeper understanding of wolfberry and its potential applications in agronomy and nutrition.

BACKGROUND: The rapid growth of omics technologies has led to the use of bioinformatics as a powerful tool for unravelling scientific puzzles. However, the obstacles of bioinformatics are compounded by the complexity of data processing and the distinct nature of omics data types, particularly in terms of visualization and statistics.
OBJECTIVE: We developed a comprehensive and free platform, CFViSA, to facilitate effortless visualization and statistical analysis of omics data by the scientific community.
METHODS: CFViSA was constructed using the Scala programming language and utilizes the AKKA toolkit for the web server and MySQL for the database server. The visualization and statistical analysis were performed with the R program.
RESULTS: CFViSA integrates two omics data analysis pipelines (microbiome and transcriptome analysis) and an extensive array of 79 analysis tools spanning simple sequence processing, visualization, and statistics available for various omics data, including microbiome and transcriptome data. CFViSA starts from an analysis interface, paralleling a demonstration full course to help users understand operating principles and scientifically set the analysis parameters. Once analysis is conducted, users can enter the task history interface for figure adjustments, and then a complete series of results, including statistics, feature tables and figures. All the graphic layouts were printed with necessary statistics and a traceback function recording the options for analysis and visualization; these statistics were excluded from the five competing methods.
CONCLUSIONS: CFViSA is a user-friendly bioinformatics cloud platform with detailed guidelines for integrating functions in multi-omics analysis with real-time visualization adjustment and complete series of results provision. CFViSA is available at http://www.cloud.biomicroclass.com/en/CFViSA/.

Rapid progresses in RNA-Seq and computational methods have assisted in quantifying A-to-I RNA editing and altered RNA editing sites have been widely observed in various diseases. Nevertheless, functional characterization of the altered RNA editing sites still remains a challenge. Here, we developed perturbations of RNA editing sites (PRES; http://bio-bigdata.hrbmu.edu.cn/PRES/) as the webserver for decoding functional perturbations of RNA editing sites based on editome profiling. After uploading an editome profile among samples of different groups, PRES will first annotate the editing sites to various genomic elements and detect differential editing sites under the user-selected method and thresholds. Next, the downstream functional perturbations of differential editing sites will be characterized from gain or loss miRNA/RNA binding protein regulation, RNA and protein structure changes, and the perturbed biological pathways. A prioritization module was developed to rank genes based on their functional consequences of RNA editing events. PRES provides user-friendly functionalities, ultra-efficient calculation, intuitive table and figure visualization interface to display the annotated RNA editing events, filtering options and elaborate application notebooks. We anticipate PRES will provide an opportunity for better understanding the regulatory mechanisms of RNA editing in human complex diseases.

BACKGROUND: Understanding the transcriptome has become an essential step towards the full interpretation of the biological function of a cell, a tissue or even an organ. Many tools are available for either processing, analysing transcriptome data, or visualizing analysis results. However, most existing tools are limited to data from a single sequencing platform and only several of them could handle more than one analysis module, which are far from enough to meet the requirements of users, especially those without advanced programming skills. Hence, we still lack an open-source toolkit that enables both bioinformatician and non-bioinformatician users to process and analyze the large transcriptome data from different sequencing platforms and visualize the results.
RESULTS: We present a Linux-based toolkit, RNA-combine, to automatically perform the quality assessment, downstream analysis of the transcriptome data generated from different sequencing platforms, including bulk RNA-seq (Illumina platform), single cell RNA-seq (10x Genomics) and Iso-Seq (PacBio) and visualization of the results. Besides, this toolkit is implemented with at least 10 analysis modules more than other toolkits examined in this study. Source codes of RNA-combine are available on GitHub: https://github.com/dongxuemin666/RNA-combine .
CONCLUSIONS: Our results suggest that RNA-combine is a reliable tool for transcriptome data processing and result interpretation for both bioinformaticians and non-bioinformaticians.

Dengue endemic is a serious healthcare concern in tropical and subtropical countries. Although well-established laboratory tests can provide early diagnosis of acute dengue infections, access to these tests is limited in developing countries, presenting an urgent need to develop simple, rapid, and robust diagnostic tools. Point-of-care (POC) devices, particularly paper-based POC devices, are typically rapid, cost-effective and user-friendly, and they can be used as diagnostic tools for the prompt diagnosis of dengue at POC settings. Here, we review the importance of rapid dengue diagnosis, current dengue diagnostic methods, and the development of paper-based POC devices for diagnosis of dengue infections at the POC.