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Bioinformatics pipeline for the systematic mining genomic and proteomic variation linked to rare diseases: The example of monogenic diabetes.
PLoS One
April 2024
Mohn Center for Diabetes Precision Medicine, Department of Clinical Science, University of Bergen, Bergen, Norway.
Monogenic diabetes is characterized as a group of diseases caused by rare variants in single genes. Like for other rare diseases, multiple genes have been linked to monogenic diabetes with different measures of pathogenicity, but the information on the genes and variants is not unified among different resources, making it challenging to process them informatically. We have developed an automated pipeline for collecting and harmonizing data on genetic variants linked to monogenic diabetes.
View Article and Find Full Text PDFAmbient RNAs removal of cortex-specific snRNA-seq reveals Apoe microglia/macrophage after deeper cerebral hypoperfusion in mice.
J Neuroinflammation
June 2023
Department of Vascular Surgery, Huashan Hospital of Fudan University, Shanghai, People's Republic of China.
Background: Ambient RNAs contamination in single-nuclei RNA sequencing (snRNA-seq) is a challenging problem, but the consequences of ambient RNAs contamination of damaged and/or diseased tissues are poorly understood. Cognitive impairments and white/gray matter injuries are characteristic of deeper cerebral hypoperfusion mouse models induced by bilateral carotid artery stenosis (BCAS), but the molecular mechanisms still need to be further explored. More importantly, the BCAS mice can also offer an excellent model to examine the signatures of ambient RNAs contamination in damaged tissues when performing snRNA-seq.
View Article and Find Full Text PDFBioinformatic Searching for Optimal RNA Targets of Dimeric Compounds Informs Design of a MicroRNA-27a Inhibitor.
ACS Chem Biol
January 2022
Department of Chemistry, The Scripps Research Institute, Jupiter, Florida 33458, United States.
Various studies have shown that selective molecular recognition of RNA targets by small molecules in cells, although challenging, is indeed possible. One facile strategy to enhance selectivity and potency is binding two or more sites within an RNA simultaneously with a single molecule. To simplify the identification of targets amenable to such a strategy, we informatically mined all human microRNA (miRNA) precursors to identify those with two proximal noncanonically paired sites.
View Article and Find Full Text PDFCharacterization of Diabetic and Non-Diabetic Foot Ulcers Using Single-Cell RNA-Sequencing.
Micromachines (Basel)
August 2020
Department of Surgery, Division of Plastic and Reconstructive Surgery, Stanford University School of Medicine, Stanford, CA 94305, USA.
Recent advances in high-throughput single-cell sequencing technologies have led to their increasingly widespread adoption for clinical applications. However, challenges associated with tissue viability, cell yield, and delayed time-to-capture have created unique obstacles for data processing. Chronic wounds, in particular, represent some of the most difficult target specimens, due to the significant amount of fibrinous debris, extracellular matrix components, and non-viable cells inherent in tissue routinely obtained from debridement.
View Article and Find Full Text PDFRNA-Bloom enables reference-free and reference-guided sequence assembly for single-cell transcriptomes.
Genome Res
August 2020
Canada's Michael Smith Genome Sciences Centre, BC Cancer, Vancouver, British Columbia, Canada V5Z 4S6.
Despite the rapid advance in single-cell RNA sequencing (scRNA-seq) technologies within the last decade, single-cell transcriptome analysis workflows have primarily used gene expression data while isoform sequence analysis at the single-cell level still remains fairly limited. Detection and discovery of isoforms in single cells is difficult because of the inherent technical shortcomings of scRNA-seq data, and existing transcriptome assembly methods are mainly designed for bulk RNA samples. To address this challenge, we developed RNA-Bloom, an assembly algorithm that leverages the rich information content aggregated from multiple single-cell transcriptomes to reconstruct cell-specific isoforms.
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