Circular RNA (circRNA) is an emerging class of RNA molecules attracting researchers due to its potential for serving as markers for diagnosis, prognosis, or therapeutic targets of cancer, cardiovascular, and autoimmune diseases. Current methods for detection of circRNA from RNA ...
Read More »Single-cell analysis support the use of PDAC organoids as a clinically relevant model for in vitro studies of tumor heterogeneity
Pancreatic ductal adenocarcinoma (PDAC) is projected to be the second leading cause of cancer mortality by 2030. Bulk transcriptomic analyses have distinguished ‘classical’ from ‘basal-like’ tumors with...
Read More »lncRNA Regulation of IFNɣ and Its Effect on Checkpoint Inhibitor Expression Enhances Understanding of Therapeutic Cancer Target
In this paper, a cross-institutional team led by the Harvey N. Cushing Neuro-oncology Laboratories (HCNL) at Harvard, explains how checkpoint inhibitors...
Read More »Detection of atherosclerosis by small RNA-sequencing analysis of extracellular vesicle enriched serum samples
Atherosclerosis can occur throughout the arterial vascular system and lead to various diseases. Early diagnosis of atherosclerotic processes and of individual disease patterns would be more likely to be...
Read More »Isoform variability is an important source of latent information in RNA-seq data that can be used to improve clinical prediction models.
Most predictive models based on gene expression data do not leverage information related to gene splicing, despite the fact that splicing is a fundamental feature of eukaryotic gene expression. Cigarette smoking is an important environmental risk factor for many diseases, ...
Read More »Transcriptome-wide RNA-protein interaction predictions
RNA molecules can fold into complex structures and interact with trans-acting factors to control their biology. Recent methods have been focused on developing novel tools to measure RNA structure...
Read More »tuMap – high-resolution quantitative comparison of cancer samples
Researchers at the Technion’s Rappaport Faculty of Medicine have developed an innovative algorithm that detects an uninterrupted common denominator..
Read More »DUBStepR – a scalable correlation-based feature selection method for accurately clustering single-cell data
Feature selection (marker gene selection) is widely believed to improve clustering accuracy, and is thus a key component of single cell clustering pipelines. Existing feature selection methods perform...
Read More »Neuroscientists roll out first comprehensive atlas of brain cells
The BRAIN Initiative Cell Census Network (BICCN) — created in 2017 — endeavors to map all the different cell types throughout the brain, which consists of more than 160 billion individual cells, both neurons and support cells called glia. The ...
Read More »scRNA-Seq can define host and viral transcriptional activity at the site of infection
Influenza A virus (IAV) and SARS-CoV-2 are pandemic viruses causing millions of deaths, yet their clinical manifestations are distinctly different...
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