Andrea Rau

TL;DR: This work focuses on a comprehensive comparison of seven recently proposed normalization methods for the differential analysis of RNA-seq data, with an emphasis on the use of varied real and simulated datasets involving different species and experimental designs to represent data characteristics commonly observed in practice.

TL;DR: This work proposes a data-driven method based on the Jaccard similarity index to calculate a filtering threshold for replicated RNA sequencing data, and demonstrates the effectiveness of the proposed method to correctly filter lowly expressed genes, leading to increased detection power for moderately to highly expressed genes.

TL;DR: In this article, the p-value combination techniques used for microarray meta-analyses can be used for the differential analysis of RNA-seq data from multiple related studies and compared to a negative binomial generalized linear model (GLM) including a fixed study effect on simulated data and real data on human melanoma cell lines.

TL;DR: First multi-species and multi-assay genome annotation results obtained by a FAANG project are reported, showing that conserved TAD boundaries had stronger insulation properties than species-specific ones and that the genomic distribution of orthologous genes in A/B compartments was significantly conserved across species.

TL;DR: This work has developed an iterative empirical Bayesian procedure with a Kalman filter that estimates the posterior distributions of network parameters and finds that the proposed method performs comparably on both model-based and data-based simulations in considerably less computational time.