Methods

Bulk RNA sequencing provides the opportunity to understand biology at the whole transcriptome level without the prohibitive cost of single cell profiling. Advances in spatial transcriptomics enable to dissect tissue organization and function by genome-wide gene expressions. However, the readout ...

Gene selection in unannotated large single cell RNA sequencing (scRNA-seq) data is important and crucial step in the preliminary step of downstream...

Microdroplet single-cell ATAC-seq is widely used to measure chromatin accessibility, however, highly scalable and simple sample multiplexing procedures are not available. UT MD Anderson Cancer Center...

N6-methyladenosine (m6A) is the most prevalent modification of messenger RNA in mammals. To interrogate its functions and dynamics, there is a critical need to quantify m6A at three levels: site, gene and sample. Current approaches address these needs in a ...

NGS libraries from sRNA are commonly prepared by sequential ligation of adaptors to input RNA 3′ and 5′ ends, often with a gel purification step after each ligation step, followed by cDNA synthesis and PCR...

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 ...

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, ...

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...

Researchers at the Technion’s Rappaport Faculty of Medicine have developed an innovative algorithm that detects an uninterrupted common denominator..

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...