Siddharth Chauhan

This paper uses pangenome decomposition to reveal lineage-specific immune-evasion and virulence architectures in group A Streptococcus, showing that virulence factors are organized into distinct modules.

The paper develops a workflow to reconstruct dynamic transcriptional regulatory networks from public gene expression data using Bacillus subtilis as a case study.

The paper analyzes the pangenome of Escherichia coli using 2,377 genomes to define accessory genes and rare genomic variation across phylogroups.

The paper analyzes the alleleome of the Lactobacillaceae family's core pangenome to understand strain diversity and identify phylogroup-specific variants.

This study combines ALE and WT variants to explore sequence solutions for industrial tolerance in E. coli, finding that WT variants can provide greater benefits and help identify feasible genome design variables.

This study performs a pangenome analysis of the Lactobacillaceae family, revealing correlations between pangenome openness, transposon abundance, and species-specific genomic features.

This study uses machine learning to analyze RNA-Seq data from multiple Salmonella Typhimurium strains, revealing transcriptional modules and regulatory patterns across the pan-genome.

This study uses transcriptomic data to construct a regulatory network of Pseudomonas aeruginosa, revealing roles of efflux pumps and media composition in antibiotic responses.

The paper uses machine learning to analyze M. tuberculosis RNA-seq data, identifying gene modules and their roles in metabolism, stress response, and infection.

The paper uses machine learning to uncover a transcriptional regulatory network in Sulfolobus acidocaldarius, identifying new gene regulators and regulatory modules.

Memote, open-source Python software, runs a community-maintained, partly scored test suite (consistency, biomass, annotations, optional experimental data) on SBML-encoded genome-scale metabolic models, once or through continuous integration.