Nico Trummer

TL;DR: NeEDL is the first application that demonstrates the potential of seamlessly integrated quantum computing techniques to accelerate biomedical research and detects higher-order EIs with unprecedented statistical and biological evidence, yielding unique insights into polygenic diseases and providing a basis for the development of improved risk scores and combination therapies.

TL;DR: TF-Prioritizer accepts ChIP-seq and RNA-seq data, as input and suggests TFs with differential activity, thus offering an understanding of genome-wide gene regulation, potential pathogenesis, and therapeutic targets in biomedical research.

TL;DR: The circRNA-sponging pipeline as mentioned in this paper identifies circRNAs via backplicing junctions detected in RNA-seq data, quantifies their expression values in relation to their linear counterparts spliced from the same gene, performs differential expression analysis, identifies and quantifies miRNA expression from miRNA-sequencing (miRNA-seq) data, predicts miRNA binding sites on circRNA, systematically investigates potential circRN-miRNA sponging events, creates a network of competing endogenous RNAs, and identifies potential circRNA biomarkers.

TL;DR: This study reveals that computational cell-type deconvolution and BCR/TCR methods using bulk RNA-seq analyses can supplement missing CBC data and offer insights into immune responses, disease severity, and pathogen-specific immunity, all achievable with a sequencing depth of 10 million reads per sample.