Structure-primed embedding on the transcription factor manifold enables transparent model architectures for gene regulatory network and latent activity inference.

Background: Modeling of gene regulatory networks (GRNs) is limited due to a lack of direct measurements of genome-wide transcription factor activity (TFA) making it difficult to separate covariance and regulatory interactions. Inference of regulatory interactions and TFA requires aggregation of complementary evidence. Estimating TFA explicitly is problematic as it disconnects GRN inference and TFA estimation and is unable to account for, for example, contextual transcription factor-transcription factor interactions, and other higher order features.

Structure primed embedding on the transcription factor manifold enables transparent model architectures for gene regulatory network and latent activity inference.

The modeling of gene regulatory networks (GRNs) is limited due to a lack of direct measurements of regulatory features in genome-wide screens. Most GRN inference methods are therefore forced to model relationships between regulatory genes and their targets with expression as a proxy for the upstream independent features, complicating validation and predictions produced by modeling frameworks. Separating covariance and regulatory influence requires aggregation of independent and complementary sets of evidence, such as transcription factor (TF) binding and target gene expression.

Optimal tuning of weighted kNN- and diffusion-based methods for denoising single cell genomics data.

The analysis of single-cell genomics data presents several statistical challenges, and extensive efforts have been made to produce methods for the analysis of this data that impute missing values, address sampling issues and quantify and correct for noise. In spite of such efforts, no consensus on best practices has been established and all current approaches vary substantially based on the available data and empirical tests. The k-Nearest Neighbor Graph (kNN-G) is often used to infer the identities of, and relationships between, cells and is the basis of many widely used dimensionality-reduction and projection methods.

Evolutionary recruitment of flexible Esrp-dependent splicing programs into diverse embryonic morphogenetic processes.

Epithelial-mesenchymal interactions are crucial for the development of numerous animal structures. Thus, unraveling how molecular tools are recruited in different lineages to control interplays between these tissues is key to understanding morphogenetic evolution. Here, we study Esrp genes, which regulate extensive splicing programs and are essential for mammalian organogenesis.

Fibroblast growth factor signalling controls nervous system patterning and pigment cell formation in Ciona intestinalis.

During the development of the central nervous system (CNS), combinations of transcription factors and signalling molecules orchestrate patterning, specification and differentiation of neural cell types. In vertebrates, three types of melanin-containing pigment cells, exert a variety of functional roles including visual perception. Here we analysed the mechanisms underlying pigment cell specification within the CNS of a simple chordate, the ascidian Ciona intestinalis.