Analysis Pipelines

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 ...

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...

Researchers have created new algorithms to help meet the need for computational tools that can analyze and extract information from the increasingly large datasets that are being...

The advent of single-cell RNA sequencing (scRNA-seq) technologies has revolutionized transcriptomic studies. However, large-scale integrative analysis...

scArches uses transfer learning and parameter optimization to enable efficient, decentralized, iterative reference building and contextualization...

Advances in single-cell RNA-sequencing technology over the last decade have enabled exponential increases in throughput: datasets with over a...

Detection of somatic point mutations using patients sequencing data has many clinical applications, including the identification of cancer driver genes...

Human pluripotent stem cells (hPSCs) are known to acquire genetic aberrations during in vitro propagation. In addition to recurrent chromosomal...

Current bioinformatics workflows for PIWI-interacting RNA (piRNA) analysis focus primarily on germline-derived piRNAs and piRNA-clusters...