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Background: The explosive growth of next-generation sequencing data has resulted in ultra-large-scale datasets and significant computational challenges. As the cost of next-generation sequencing (NGS) has decreased, the amount of genomic data has surged globally. However, the cost and complexity of the computational resources required continue to be substantial barriers to leveraging big data. A promising solution to these computational challenges is cloud computing, which provides researchers with the necessary CPUs, memory, storage, and software tools. Results: Here, we present Closha 2.0, a cloud computing service that offers a user-friendly platform for analyzing massive genomic datasets. Closha 2.0 is designed to provide a cloud-based environment that enables all genomic researchers, including those with limited or no programming experience, to easily analyze their genomic data. The new 2.0 version of Closha has more user-friendly features than the previous 1.0 version. Firstly, the workbench features a script editor that supports Python, R, and shell script programming, enabling users to write scripts and integrate them into their pipelines. This functionality is particularly useful for downstream analysis. Second, Closha 2.0 runs on containers, which execute each tool in an independent environment. This provides a stable environment and prevents dependency issues and version conflicts among tools. Additionally, users can execute each step of a pipeline individually, allowing them to test applications at each stage and adjust parameters to achieve the desired results. We also updated a high-speed data transmission tool called GBox that facilitates the rapid transfer of large datasets. Conclusions: The analysis pipelines on Closha 2.0 are reproducible, with all analysis parameters and inputs being permanently recorded. Closha 2.0 simplifies multi-step analysis with drag-and-drop functionality and provides a user-friendly interface for genomic scientists to obtain accurate results from NGS data. Closha 2.0 is freely available at https://www.kobic.re.kr/closha2. [ABSTRACT FROM AUTHOR]

Background: The variant call format (VCF) file is a structured and comprehensive text file crucial for researchers and clinicians in interpreting and understanding genomic variation data. It contains essential information about variant positions in the genome, along with alleles, genotype calls, and quality scores. Analyzing and visualizing these files, however, poses significant challenges due to the need for diverse resources and robust features for in-depth exploration. Results: To address these challenges, we introduce variant graph craft (VGC), a VCF file visualization and analysis tool. VGC offers a wide range of features for exploring genetic variations, including extraction of variant data, intuitive visualization, and graphical representation of samples with genotype information. VGC is designed primarily for the analysis of patient cohorts, but it can also be adapted for use with individual probands or families. It integrates seamlessly with external resources, providing insights into gene function and variant frequencies in sample data. VGC includes gene function and pathway information from Molecular Signatures Database (MSigDB) for GO terms, KEGG, Biocarta, Pathway Interaction Database, and Reactome. Additionally, it dynamically links to gnomAD for variant information and incorporates ClinVar data for pathogenic variant information. VGC supports the Human Genome Assembly Hg37 and Hg38, ensuring compatibility with a wide range of data sets, and accommodates various approaches to exploring genetic variation data. It can be tailored to specific user needs with optional phenotype input data. Conclusions: In summary, VGC provides a comprehensive set of features tailored to researchers working with genomic variation data. Its intuitive interface, rapid filtering capabilities, and the flexibility to perform queries using custom groups make it an effective tool in identifying variants potentially associated with diseases. VGC operates locally, ensuring data security and privacy by eliminating the need for cloud-based VCF uploads, making it a secure and user-friendly tool. It is freely available at https://github.com/alperuzun/VGC. [ABSTRACT FROM AUTHOR]

Background: The advancement of next-generation sequencing (NGS) technologies provides opportunities for large-scale Pharmacogenetic (PGx) studies and pre-emptive PGx testing to cover a wide range of genotypes present in diverse populations. However, NGS-based PGx testing is limited by the lack of comprehensive computational tools to support genetic data analysis and clinical decisions. Methods: Bioinformatics utilities specialized for human genomics and the latest cloud-based technologies were used to develop a bioinformatics pipeline for analyzing the genomic sequence data and reporting PGx genotypes. A database was created and integrated in the pipeline for filtering the actionable PGx variants and clinical interpretations. Strict quality verification procedures were conducted on variant calls with the whole genome sequencing (WGS) dataset of the 1000 Genomes Project (G1K). The accuracy of PGx allele identification was validated using the WGS dataset of the Pharmacogenetics Reference Materials from the Centers for Disease Control and Prevention (CDC). Results: The newly created bioinformatics pipeline, Pgxtools, can analyze genomic sequence data, identify actionable variants in 13 PGx relevant genes, and generate reports annotated with specific interpretations and recommendations based on clinical practice guidelines. Verified with two independent methods, we have found that Pgxtools consistently identifies variants more accurately than the results in the G1K dataset on GRCh37 and GRCh38. Conclusions: Pgxtools provides an integrated workflow for large-scale genomic data analysis and PGx clinical decision support. Implemented with cloud-native technologies, it is highly portable in a wide variety of environments from a single laptop to High-Performance Computing (HPC) clusters and cloud platforms for different production scales and requirements. [ABSTRACT FROM AUTHOR]

Single-cell RNA sequencing (scRNA-seq) has emerged as a powerful tool to gain biological insights at the cellular level. However, due to technical limitations of the existing sequencing technologies, low gene expression values are often omitted, leading to inaccurate gene counts. Existing methods, including advanced deep learning techniques, struggle to reliably impute gene expressions due to a lack of mechanisms that explicitly consider the underlying biological knowledge of the system. In reality, it has long been recognized that gene–gene interactions may serve as reflective indicators of underlying biology processes, presenting discriminative signatures of the cells. A genomic data analysis framework that is capable of leveraging the underlying gene–gene interactions is thus highly desirable and could allow for more reliable identification of distinctive patterns of the genomic data through extraction and integration of intricate biological characteristics of the genomic data. Here we tackle the problem in two steps to exploit the gene–gene interactions of the system. We first reposition the genes into a 2D grid such that their spatial configuration reflects their interactive relationships. To alleviate the need for labeled ground truth gene expression datasets, a self-supervised 2D convolutional neural network is employed to extract the contextual features of the interactions from the spatially configured genes and impute the omitted values. Extensive experiments with both simulated and experimental scRNA-seq datasets are carried out to demonstrate the superior performance of the proposed strategy against the existing imputation methods. [ABSTRACT FROM AUTHOR]

Background: Visualising patient genomic data in a cohort with embedding data analytics models can provide relevant and sensible patient comparisons to assist a clinician with treatment decisions. As immersive technology is actively used around the medical world, there is a rising demand for an efficient environment that can effectively display genomic data visualisations on immersive devices such as a Virtual Reality (VR) environment. The VR technology will allow clinicians, biologists, and computer scientists to explore a cohort of individual patients within the 3D environment. However, demonstrating the feasibility of the VR prototype needs domain users' feedback for future user-centred design and a better cognitive model of human–computer interactions. There is limited research work for collecting and integrating domain knowledge into the prototype design. Objective: A usability study for the VR prototype–-Virtual Reality to Observe Oncology data Models (VROOM) was implemented. VROOM was designed based on a preliminary study among medical users. The goals of this usability study included establishing a baseline of user experience, validating user performance measures, and identifying potential design improvements that are to be addressed to improve efficiency, functionality, and end-user satisfaction. Methods: The study was conducted with a group of domain users (10 males, 10 females) with portable VR devices and camera equipment. These domain users included medical users such as clinicians and genetic scientists and computing domain users such as bioinformatics and data analysts. Users were asked to complete routine tasks based on a clinical scenario. Sessions were recorded and analysed to identify potential areas for improvement to the data visual analytics projects in the VR environment. The one-hour usability study included learning VR interaction gestures, running visual analytics tool, and collecting before and after feedback. The feedback was analysed with different methods to measure effectiveness. The statistical method Mann–Whitney U test was used to analyse various task performances among the different participant groups, and multiple data visualisations were created to find insights from questionnaire answers. Results: The usability study investigated the feasibility of using VR for genomic data analysis in domain users' daily work. From the feedback, 65% of the participants, especially clinicians (75% of them), indicated that the VR prototype is potentially helpful for domain users' daily work but needed more flexibility, such as allowing them to define their features for machine learning part, adding new patient data, and importing their datasets in a better way. We calculated the engaged time for each task and compared them among different user groups. Computing domain users spent 50% more time exploring the algorithms and datasets than medical domain users. Additionally, the medical domain users engaged in the data visual analytics parts (approximately 20%) longer than the computing domain users. [ABSTRACT FROM AUTHOR]

Genomic data analysis is a field that deals with the processing and interpretation of large amounts of genetic data. Studies in this field offer researchers the opportunity to work in many fields. In addition, bibliometric analyses are an important tool in identifying publications and trends in the field of genomic data analysis. The purpose of this bibliometric review is to analyse the current status and trends to identify trends and prevalence of worldwide research in the field of genomic data analysis and to guide the advancement of research in this field. In this bibliometric study, genomic data analysis studies conducted globally between 1993 -2023 were examined. As a result of the searches made using the keyword "genomic data analysis", 258 studies were found and 173 articles were used by eliminating the unsuitable and non-article studies. The articles in the database were analyzed using information such as title, author names, publication year, journal name and number of citations. Web of Science and Scopus databases was used for systematic data collection and all text data of the publications included in the study were evaluated with VOSviewer software. These analyses were performed using text mining and data visualization methods (bubble maps and ot her graphical) to ensure the accuracy and reliability of the study. In this article, information is given about 173 articles taken from the WOS and Scopus databases and 8706 citations to these articles. The average number of citations per article was calculated as 51 and the H index as 28. Since 2013, both the number of articles and the number of citations have increased. Most of the articles (60%) are devoted to mathematical biology, biochemical, genetic inheritance. The United States of America is the country that publishes the most (47%) articles on this subject. Most of the articles (59%) were published by publishers such as Springer Nature, Oxford University Press, Elsevier, Wiley. Most of the articles (91%) are in SCI-Expanded category. The findings of our study show that many researchers are active in the field of genomic data analysis and that the researches in this field are increasing. This bibliometric analysis reveals global trends and important work in the field of genomic data analysis and provides important insights into the future directions of research in this field. Since this analysis only aims to identify trends and trends in the literature, it is not intended to evaluate the applicability of this topic. [ABSTRACT FROM AUTHOR]

Background: With the wide-spreading of public repositories of NGS processed data, the availability of user-friendly and effective tools for data exploration, analysis and visualization is becoming very relevant. These tools enable interactive analytics, an exploratory approach for the seamless "sense-making" of data through on-the-fly integration of analysis and visualization phases, suggested not only for evaluating processing results, but also for designing and adapting NGS data analysis pipelines. Results: This paper presents abstractions for supporting the early analysis of NGS processed data and their implementation in an associated tool, named GenoMetric Space Explorer (GeMSE). This tool serves the needs of the GenoMetric Query Language, an innovative cloud-based system for computing complex queries over heterogeneous processed data. It can also be used starting from any text files in standard BED, BroadPeak, NarrowPeak, GTF, or general tab-delimited format, containing numerical features of genomic regions; metadata can be provided as text files in tab-delimited attribute-value format. GeMSE allows interactive analytics, consisting of on-the-fly cycling among steps of data exploration, analysis and visualization that help biologists and bioinformaticians in making sense of heterogeneous genomic datasets. By means of an explorative interaction support, users can trace past activities and quickly recover their results, seamlessly going backward and forward in the analysis steps and comparative visualizations of heatmaps. Conclusions: GeMSE effective application and practical usefulness is demonstrated through significant use cases of biological interest. GeMSE is available at http://www.bioinformatics.deib.polimi.it/GeMSE/, and its source code is available at https://github.com/Genometric/GeMSE under GPLv3 open-source license. [ABSTRACT FROM AUTHOR]

• Phyllosticta citricarpa is a fungal pathogen causing citrus black spot (CBS). • Whole genome sequencing data were generated for 12 populations from eight countries. • Three analysis strategies were employed to infer phylogeographic distribution. • Dissemination pathways among the populations are proposed. • This represents the largest whole genome sequencing survey of P. citricarpa to date. Phyllosticta citricarpa is a fungal pathogen causing citrus black spot (CBS). As a regulated pest in some countries, the presence of the pathogen limits the export of fruit and is therefore of agricultural and economic importance. In this study, we used high throughput sequencing data to infer the global phylogeographic distribution of this pathogen, including 71 isolates from eight countries, Argentina, Australia, Brazil, China, Cuba, Eswatini, South Africa and the United States of America. We assembled draft genomes and used a pairwise read mapping approach for the detection and enumeration of variants between isolates. We performed SSR marker discovery based on the assembled genome with the best assembly statistics, and generated genotype profiles for all isolates with 1987 SSR markers in silico. Furthermore, we identified 32,560 SNPs relative to a reference sequence followed by population genetic analyses based on the three datasets; pairwise variant counts, SSR genotypes and SNP genotypes. All three analysis approaches gave similar overall results. Possible pathways of dissemination among the populations from China, Australia, southern Africa and the Americas are postulated. The Chinese population is the most diverse, and is genetically the furthest removed from all other populations, and is therefore considered the closest to the origin of the pathogen. Isolates from Australia, Eswatini and the South African province Mpumalanga are closely associated and clustered together with those from Argentina and Brazil. The Eastern Cape, North West, and KwaZulu-Natal populations in South Africa grouped in another cluster, while isolates from Limpopo are distributed between the two aforementioned clusters. Southern African populations showed a close relationship to populations in North America, and could be a possible source of P. citricarpa populations that are now found in North America. This study represents the largest whole genome sequencing survey of P. citricarpa to date and provides a more comprehensive assessment of the population genetic diversity and connectivity of P. citricarpa from different geographic origins. This information could further assist in a better understanding of the epidemiology of the CBS pathogen, its long-distance dispersal and dissemination pathways, and can be used to refine phytosanitary regulations and management programmes for the disease. [ABSTRACT FROM AUTHOR]

Background: Dynamic visual exploration of detailed pathway information can help researchers digest and interpret complex mechanisms and genomic datasets. Results: ChiBE is a free, open-source software tool for visualizing, querying, and analyzing human biological pathways in BioPAX format. The recently released version 2 can search for neighborhoods, paths between molecules, and common regulators/targets of molecules, on large integrated cellular networks in the Pathway Commons database as well as in local BioPAX models. Resulting networks can be automatically laid out for visualization using a graphically rich, process-centric notation. Profiling data from the cBioPortal for Cancer Genomics and expression data from the Gene Expression Omnibus can be overlaid on these networks. Conclusions: ChiBE’s new capabilities are organized around a genomics-oriented workflow and offer a unique comprehensive pathway analysis solution for genomics researchers. [ABSTRACT FROM AUTHOR]

Abstract: Random forests (RF) is a popular tree-based ensemble machine learning tool that is highly data adaptive, applies to “large p, small n” problems, and is able to account for correlation as well as interactions among features. This makes RF particularly appealing for high-dimensional genomic data analysis. In this article, we systematically review the applications and recent progresses of RF for genomic data, including prediction and classification, variable selection, pathway analysis, genetic association and epistasis detection, and unsupervised learning. [Copyright &y& Elsevier]

We consider the choice of an optimal sample size for multiple-comparison problems. The motivating application is the choice of the number of microarray experiments to be carried out when learning about differential gene expression. However, the approach is valid in any application that involves multiple comparisons in a large number of hypothesis tests. We discuss two decision problems in the context of this setup: the sample size selection and the decision about the multiple comparisons. We adopt a decision-theoretic approach, using loss functions that combine the competing goals of discovering as many differentially expressed genes as possible, while keeping the number of false discoveries manageable. For consistency, we use the same loss function for both decisions. The decision rule that emerges for the multiple-comparison problem takes the exact form of the rules proposed in the recent literature to control the posterior expected false-discovery rate. For the sample size selection, we combine the expected utility argument with an additional sensitivity analysis, reporting the conditional expected utilities and conditioning on assumed levels of the true differential expression. We recognize the resulting diagnostic as a form of statistical power facilitating interpretation and communication. As a sampling model for observed gene expression densities across genes and arrays, we use a variation of a hierarchical gamma/gamma model. But the discussion of the decision problem is independent of the chosen probability model. The approach is valid for any model that includes positive prior probabilities for the null hypotheses in the multiple comparisons and that allows for efficient marginal and posterior simulation, possibly by dependent Markov chain Monte Carlo simulation. [ABSTRACT FROM AUTHOR]