Ines Hellmann

The paper identifies and compares orthologous cell types across primate species using scRNA-seq data from embryoid bodies, finding limited transferability of marker genes between species.

CroCoNet quantitatively compares gene regulatory networks across species, identifying conserved and diverged modules, including POU5F1's rewired network in great apes.

The paper investigates how pleiotropy affects the evolution of cis-regulatory elements, finding that more pleiotropic elements show greater conservation in function despite lower sequence conservation.

The paper profiles the transcriptome of striatal subregions in mice during learning stages to identify molecular changes and provides a web tool for exploring the data.

This study evaluates background noise in single-cell RNA-seq data and assesses methods for its removal, finding that CellBender performs best for marker gene detection.

Prime-seq is a bulk RNA-seq method that is four-fold more cost-efficient than TruSeq while maintaining equivalent library complexity, measurement accuracy, and statistical power.

This study evaluates scRNA-seq pipelines by simulating five library protocols and nine DE setups, finding that normalization and library prep have the largest impact on analysis outcomes.

The paper introduces mcSCRB-seq, a new scRNA-seq protocol that improves sensitivity and efficiency through optimized experimental conditions.

zUMIs processes RNA-seq data with UMIs and BCs, improving gene detection and cluster resolution by analyzing intronic reads.

This study quantifies biases in cross-species RNA-seq analyses and provides guidance for reliable expression quantification among closely related species.

powsimR simulates and evaluates differential expression in bulk and single-cell RNA-seq data to perform power analysis for experimental design and comparison.

This study compares five scRNA-seq protocols using mouse embryonic stem cells, finding Smart-seq2 most sensitive and SCRB-seq/Drop-seq most cost-efficient for differential gene detection.

This study evaluates how RNA-seq amplification affects differential expression analysis, finding that duplicate read removal does not improve accuracy but UMI-based protocols enhance detection power.