MOTIVATION: Prediction and identification of core promoter elements and transcription factor binding sites is essential for understanding the mechanism of transcription initiation and deciphering the biological activity of a specific locus. Thus, there is a need for an up-to-date tool to detect and curate core promoter elements/motifs in any provided nucleotide sequences. RESULTS: Here, we introduce ElemeNT 2023-a new and enhanced version of the Elements Navigation Tool, which provides novel capabilities for assessing evolutionary conservation and for readily evaluating the quality of high-throughput transcription start site (TSS) datasets, leveraging preferential motif positioning. ElemeNT 2023 is accessible both as a fast web-based tool and via command line (no coding skills are required to run the tool). While this tool is focused on core promoter elements, it can also be used for searching any user-defined motif, including sequence-specific DNA binding sites. Furthermore, ElemeNT's CORE database, which contains predicted core promoter elements around annotated TSSs, is now expanded to cover 10 species, ranging from worms to human. In this applications note, we describe the new workflow and demonstrate a case study using ElemeNT 2023 for core promoter composition analysis of diverse species, revealing motif prevalence and highlighting evolutionary insights. We discuss how this tool facilitates the exploration of uncharted transcriptomic data, appraises TSS quality, and aids in designing synthetic promoters for gene expression optimization. Taken together, ElemeNT 2023 empowers researchers with comprehensive tools for meticulous analysis of sequence elements and gene expression strategies. AVAILABILITY AND IMPLEMENTATION: ElemeNT 2023 is freely available at https://www.juven-gershonlab.org/resources/element-v2023/. The source code and command line version of ElemeNT 2023 are available at https://github.com/OritAdato/ElemeNT. No coding skills are required to run the tool.

Horizontal gene transfer (HGT), the transfer of genetic material between organisms, is crucial for genetic innovation and the evolution of genome architecture. Existing HGT detection algorithms rely on a strong phylogenetic signal distinguishing the transferred sequence from ancestral (vertically derived) genes in its recipient genome. Detecting HGT between closely related species or strains is challenging, as the phylogenetic signal is usually weak and the nucleotide composition is normally nearly identical. Nevertheless, there is a great importance in detecting HGT between congeneric species or strains, especially in clinical microbiology, where understanding the emergence of new virulent and drug-resistant strains is crucial, and often time-sensitive. We developed a novel, self-contained technique named Near HGT, based on the synteny index, to measure the divergence of a gene from its native genomic environment and used it to identify candidate HGT events between closely related strains. The method confirms candidate transferred genes based on the constant relative mutability (CRM). Using CRM, the algorithm assigns a confidence score based on "unusual" sequence divergence. A gene exhibiting exceptional deviations according to both synteny and mutability criteria, is considered a validated HGT product. We first employed the technique to a set of three E. coli strains and detected several highly probable horizontally acquired genes. We then compared the method to existing HGT detection tools using a larger strain data set. When combined with additional approaches our new algorithm provides richer picture and brings us closer to the goal of detecting all newly acquired genes in a particular strain.