The microarray-based analysis of gene expression has become a workhorse for biomedical research. Managing the amount and diversity of data that such experiments produce is a task that must be supported by appropriate software tools, which led to the creation of literally hundreds of systems. In consequence, choosing the right tool for a given project is difficult even for the expert. We report on the results of a survey encompassing 78 of such tools, of which 22 were inspected in detail and seven were tested hands-on. We report on our experiences with a focus on completeness of functionality, ease-of-use, and necessary effort for installation and maintenance. Thereby, our survey provides a valuable guideline for any project considering the use of a microarray data management system. It reveals which tasks are covered by mature tools and also shows that important requirements, especially in the area of integrated analysis of different experimental data, are not yet met satisfyingly by existing systems.
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http://dx.doi.org/10.1093/bib/bbr010 | DOI Listing |
Imaging-based spatial transcriptomics (ST) is evolving rapidly as a pivotal technology in studying the biology of tumors and their associated microenvironments. However, the strengths of the commercially available ST platforms in studying spatial biology have not been systematically evaluated using rigorously controlled experiments. In this study, we used serial 5-m sections of formalin-fixed, paraffin-embedded surgically resected lung adenocarcinoma and pleural mesothelioma tumor samples in tissue microarrays to compare the performance of the single cell ST platforms CosMx, MERFISH, and Xenium (uni/multi-modal) platforms in reference to bulk RNA sequencing, multiplex immunofluorescence, GeoMx Digital Spatial Profiler, and hematoxylin and eosin staining data for the same samples.
View Article and Find Full Text PDFBackground: Known pathogenic variants in Parkinson's disease (PD) contribute to disease development but have yet to be fully explored by arrays at scale.
Objectives: This study evaluated genotyping success of the NeuroBooster array (NBA) and determined the frequencies of pathogenic variants across ancestries.
Method: We analyzed the presence and allele frequency of 34 pathogenic variants in 28,710 PD cases, 9,614 other neurodegenerative disorder cases, and 15,821 controls across 11 ancestries within the Global Parkinson's Genetics Program dataset.
ACS Infect Dis
January 2025
Department of Microbiology, Genetics, and Immunology, Michigan State University, East Lansing, Michigan 48824, United States.
Group B (GBS) is a major cause of fetal and neonatal mortality worldwide. Many of the adverse effects of invasive GBS are associated with inflammation; therefore, understanding bacterial factors that promote inflammation is of critical importance. Membrane vesicles (MVs), which are produced by many bacteria, may modulate host inflammatory responses.
View Article and Find Full Text PDFKidney Dis (Basel)
November 2024
Department of Geriatric Urology, Xiangya International Medical Center, Xiangya Hospital, Central South University, Changsha, PR China.
Introduction: This study aims to explore the contribution of neutrophil extracellular traps (NETs) to kidney stones.
Methods: The microarray data from GSE73680 and bioinformatic analysis were applied to identify differentially expressed genes in patients with kidney stones. A rat model of kidney stones was established through ethylene glycol and ammonium chloride administration.
J Immunother Cancer
January 2025
National Translational Science Center for Molecular Medicine & Department of Cell Biology, Fourth Military Medical University, Xi'an, Shaanxi, China
Background: Clear cell renal cell carcinoma (ccRCC) is the most common histologic type of RCC. However, the spatial and functional heterogeneity of immunosuppressive cells and the mechanisms by which their interactions promote immunosuppression in the ccRCC have not been thoroughly investigated.
Methods: To further investigate the cellular and regional heterogeneity of ccRCC, we analyzed single-cell and spatial transcriptome RNA sequencing data from four patients, which were obtained from samples from multiple regions, including the tumor core, tumor-normal interface, and distal normal tissue.
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