#35 · Systems biology
Inferring kinetics and time labels from nascent, untimed, and mistimed transcriptomic data
The clearest thread here is inference of time-linked quantities that the readout does not directly provide. A moments-based framework uses nascent RNA data to derive closed-form moment expressions for inferring transcriptional kinetics, with model selection validated within the framework. A supervised Bayesian method re-annotates time labels in transcriptomics experiments. The remaining items are network and atlas resources built from compendia of microarray and RNA-seq data, including GEAR across 83 conditions, YeastCoDEGNet from 143 yeast experiments, and GRAsp from 18 A. fumigatus datasets. These resources do not infer time directly, so the shared element is temporal inference only in part. What is new is recovery of kinetic parameters from nascent RNA and temporal labels from existing expression data.
Published papers
SGCRNA: spectral clustering-guided co-expression network analysis without scale-free constraints for multi-omic data.
SGCRNA is a Julia tool for co-expression network analysis that avoids WGCNA's scale-free assumption and tuning requirements, addressing slope neglect in…
Uncovering coordinated pathway interactions through gene co-differential expression in yeast.
YeastCoDEGNet: a co-differential expression network from 143 curated yeast microarray experiments, linking genes and pathway modules.
A network-based model of Aspergillus fumigatus elucidates regulators of development and defensive natural products of an opportunistic pathogen.
GRAsp, a network-based gene regulatory resource built from 18 public A. fumigatus RNA-seq datasets, recovers known pathways and validates new regulators of…
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