Pangenome analysis reveals the genetic basis for taxonomic classification of the Lactobacillaceae family.

Lactobacillaceae represent a large family of important microbes that are foundational to the food industry. Many genome sequences of Lactobacillaceae strains are now available, enabling us to conduct a comprehensive pangenome analysis of this family. We collected 3591 high-quality genomes from public sources and found that: 1) they contained enough genomes for 26 species to perform a pangenomic analysis, 2) the normalized Heap's coefficient λ (a measure of pangenome openness) was found to have an average value of 0.

Advanced transcriptomic analysis reveals the role of efflux pumps and media composition in antibiotic responses of Pseudomonas aeruginosa.

Pseudomonas aeruginosa is an opportunistic pathogen and major cause of hospital-acquired infections. The virulence of P. aeruginosa is largely determined by its transcriptional regulatory network (TRN).

proChIPdb: a chromatin immunoprecipitation database for prokaryotic organisms.

The transcriptional regulatory network in prokaryotes controls global gene expression mostly through transcription factors (TFs), which are DNA-binding proteins. Chromatin immunoprecipitation (ChIP) with DNA sequencing methods can identify TF binding sites across the genome, providing a bottom-up, mechanistic understanding of how gene expression is regulated. ChIP provides indispensable evidence toward the goal of acquiring a comprehensive understanding of cellular adaptation and regulation, including condition-specificity.

Machine Learning Uncovers a Data-Driven Transcriptional Regulatory Network for the Crenarchaeal Thermoacidophile .

Dynamic cellular responses to environmental constraints are coordinated by the transcriptional regulatory network (TRN), which modulates gene expression. This network controls most fundamental cellular responses, including metabolism, motility, and stress responses. Here, we apply independent component analysis, an unsupervised machine learning approach, to 95 high-quality RNA-seq datasets and extract 45 independently modulated gene sets, or iModulons.