MOTIVATION: Genome annotation files (GFF3/GTF) are the standard for storing genomic feature data, yet their flexibility often results in formatting inconsistencies that create bottlenecks for downstream bioinformatics analyses. A robust, unified framework is required to parse, standardise, and validate these files to ensure interoperability and facilitate complex comparative genomic tasks. RESULTS: We present AEGIS (Annotation Extraction and Genomic Integration Suite), a comprehensive toolkit designed to parse, correct, and standardise genome annotations. Beyond quality control, AEGIS provides advanced modules for flexible feature extraction (e.g., coding sequences, promoters) and comparative genomic analysis. Uniquely, it integrates multiple lines of evidence, including sequence homology, synteny, and coordinate-based lift-overs, to assess gene model correspondence and infer orthology. We demonstrate the utility of AEGIS by quantifying complex structural changes between Arabidopsis annotation versions and identifying high-confidence orthologues across diverse plant genomes. AVAILABILITY: AEGIS is implemented in Python. Source code and documentation are freely available under the GPL-3 license at https://github.com/Tomsbiolab/aegis and as a Docker container at https://hub.docker.com/r/tomsbiolab/aegis. The package is also available on PyPI (pip install aegis-bio).

BACKGROUND: Exploring cellular responses to stimuli using extensive gene expression profiles has become a routine procedure performed on a daily basis. Raw and processed data from these studies are available on public databases but the opportunity to fully exploit such rich datasets is limited due to the large heterogeneity of data formats. In recent years, several approaches have been proposed to effectively integrate gene expression data for analysis and exploration at a broader level. Despite the different goals and approaches towards gene expression data integration, the first step is common to any proposed method: data acquisition. Although it is seemingly straightforward to extract valuable information from a set of downloaded files, things can rapidly get complicated, especially as the number of experiments grows. Transcriptomic datasets are deposited in public databases with little regard to data format and thus retrieving raw data might become a challenging task. While for RNA-seq experiments such problem is partially mitigated by the fact that raw reads are generally available on databases such as the NCBI SRA, for microarray experiments standards are not equally well established, or enforced during submission, and thus a multitude of data formats has emerged. RESULTS: COMMAND>_ is a specialized tool meant to simplify gene expression data acquisition. It is a flexible multi-user web-application that allows users to search and download gene expression experiments, extract only the relevant information from experiment files, re-annotate microarray platforms, and present data in a simple and coherent data model for subsequent analysis. CONCLUSIONS: COMMAND>_ facilitates the creation of local datasets of gene expression data coming from both microarray and RNA-seq experiments and may be a more efficient tool to build integrated gene expression compendia. COMMAND>_ is free and open-source software, including publicly available tutorials and documentation.

Reconstruction of the regulatory network is an important step in understanding how organisms control the expression of gene products and therefore phenotypes. Recent studies have pointed out the importance of regulatory network plasticity in bacterial adaptation and evolution. The evolution of such networks within and outside the species boundary is however still obscure. Sinorhizobium meliloti is an ideal species for such study, having three large replicons, many genomes available and a significant knowledge of its transcription factors (TF). Each replicon has a specific functional and evolutionary mark; which might also emerge from the analysis of their regulatory signatures. Here we have studied the plasticity of the regulatory network within and outside the S. meliloti species, looking for the presence of 41 TFs binding motifs in 51 strains and 5 related rhizobial species. We have detected a preference of several TFs for one of the three replicons, and the function of regulated genes was found to be in accordance with the overall replicon functional signature: house-keeping functions for the chromosome, metabolism for the chromid, symbiosis for the megaplasmid. This therefore suggests a replicon-specific wiring of the regulatory network in the S. meliloti species. At the same time a significant part of the predicted regulatory network is shared between the chromosome and the chromid, thus adding an additional layer by which the chromid integrates itself in the core genome. Furthermore, the regulatory network distance was found to be correlated with both promoter regions and accessory genome evolution inside the species, indicating that both pangenome compartments are involved in the regulatory network evolution. We also observed that genes which are not included in the species regulatory network are more likely to belong to the accessory genome, indicating that regulatory interactions should also be considered to predict gene conservation in bacterial pangenomes.