Motivation: Given the abundance of genome sequencing and omics data, an opprtunity and challenge in bioinformatics relates to data mining and visualization. The majority of current bioinformatics visualizations are implemented either as multi-tier web server applications that require significant maintenance effort, or as client software that presumes technical expertise for installation. Here we present the Visual Omics Explorer (VOE), a cross-platform data visualization portal that is implemented using only HTML and Javascript code. VOE is a standalone software that can be loaded offline on the web browser from a local copy of the code, or over the internet without any dependency other than distributing the code through a file sharing service. VOE can interactively display genomics, transcriptomics, epigenomics and metagenomics data stored either locally or retrieved from cloud storage services, and runs on both desktop computers and mobile devices.
Availability And Implementation: VOE is accessible at http://bcil.github.io/VOE/ CONTACT: agbiotec@gmail.com
Supplementary Information: Supplementary data are available at Bioinformatics online.
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http://dx.doi.org/10.1093/bioinformatics/btw119 | DOI Listing |
J Transl Med
January 2025
Department of Ophthalmology, Renmin Hospital of Wuhan University, Jiefang Road, Wuhan, Hubei, 430060, China.
Diabetic retinopathy is a major ocular complication of diabetes, characterized by progressive retinal microvascular damage and significant visual impairment in working-age adults. Traditional bulk RNA sequencing offers overall gene expression profiles but does not account for cellular heterogeneity. Single-cell RNA sequencing overcomes this limitation by providing transcriptomic data at the individual cell level and distinguishing novel cell subtypes, developmental trajectories, and intercellular communications.
View Article and Find Full Text PDFInt J Chron Obstruct Pulmon Dis
January 2025
Department of Radiology, The First Affiliated Hospital of Huzhou University, Huzhou, Zhejiang, People's Republic of China.
Objective: There is increasing evidence that chronic obstructive pulmonary disease (COPD) is associated with coronary heart disease (CHD). In this study, we provide valuable insights in the field by examining the evolution of the relationship between COPD and CHD over the past 20 years.
Methods: A comprehensive computer search was conducted in the Web of Science (WOS) core dataset, covering literature on COPD combined with CHD from January 1, 2005, to August 20, 2024.
Cancer Discov
January 2025
Bioscience, Early Oncology, AstraZeneca, Cambridge, United Kingdom.
Understanding tumor heterogeneity is a major challenge that was recognized as one of the first Cancer Grand Challenges, with a call to provide solutions to visualize tumor heterogeneity. The Rosetta team took on this challenge, exploiting advances in spatial-omics approaches centered around mass spectrometry imaging to map tumor heterogeneity at the cellular and molecular scales with different levels of resolution. See related article by Bressan et al.
View Article and Find Full Text PDFBioinformatics
January 2025
Department of Pathology and Department of Immunobiology, Yale School of Medicine.
Summary: With the increased reliance on multi-omics data for bulk and single cell analyses, the availability of robust approaches to perform unsupervised learning for clustering, visualization, and feature selection is imperative. We introduce nipalsMCIA, an implementation of multiple co-inertia analysis (MCIA) for joint dimensionality reduction that solves the objective function using an extension to Non-linear Iterative Partial Least Squares (NIPALS). We applied nipalsMCIA to both bulk and single cell datasets and observed significant speed-up over other implementations for data with a large sample size and/or feature dimension.
View Article and Find Full Text PDFBMC Bioinformatics
January 2025
LBAI, UMR1227, Univ Brest, Inserm, Laboratory of Immunology, CHU Brest, Brest, France.
Background: Interpreting biological system changes requires interpreting vast amounts of multi-omics data. While user-friendly tools exist for single-omics analysis, integrating multiple omics still requires bioinformatics expertise, limiting accessibility for the broader scientific community.
Results: BiomiX tackles the bottleneck in high-throughput omics data analysis, enabling efficient and integrated analysis of multiomics data obtained from two cohorts.
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