Probiotics are live microorganisms that, when consumed in adequate amounts, exert health benefits on the host by regulating intestinal and extraintestinal homeostasis. Common probiotic microorganisms include lactic acid bacteria (LAB), yeasts, and Bacillus species. Here, we present Probio-ichnos, the first manually curated, literature-based database that collects and comprehensively presents information on the microbial strains exhibiting in vitro probiotic characteristics (i.e., resistance to acid and bile, attachment to host epithelia, as well as antimicrobial, immunomodulatory, antiproliferative, and antioxidant activity), derived from human, animal or plant microbiota, fermented dairy or non-dairy food products, and environmental sources. Employing a rigorous methodology, we conducted a systematic search of the PubMed database utilizing the keyword ‘probiotic’ within the abstracts or titles, resulting in a total of 27,715 studies. Upon further manual filtering, 2207 studies presenting in vitro experiments and elucidating strain-specific probiotic attributes were collected and used for data extraction. The Probio-ichnos database consists of 12,993 entries on the in vitro probiotic characteristics of 11,202 distinct strains belonging to 470 species and 143 genera. Data are presented using a binary categorization approach for the presence of probiotic attributes according to the authors’ conclusions. Additionally, information about the availability of the whole-genome sequence (WGS) of strains is included in the database. Overall, the Probio-ichnos database aims to streamline the navigation of the available literature to facilitate targeted validation and comparative investigation of the probiotic properties of the microbial strains.

MicroRNAs (miRNAs) are small non-coding RNAs (~22 nts) that are considered central post-transcriptional regulators of gene expression and key components in many pathological conditions. Next-Generation Sequencing (NGS) technologies have led to inexpensive, massive data production, revolutionizing every research aspect in the fields of biology and medicine. Particularly, small RNA-Seq (sRNA-Seq) enables small non-coding RNA quantification on a high-throughput scale, providing a closer look into the expression profiles of these crucial regulators within the cell. Here, we present DIANA-microRNA-Analysis-Pipeline (DIANA-mAP), a fully automated computational pipeline that allows the user to perform miRNA NGS data analysis from raw sRNA-Seq libraries to quantification and Differential Expression Analysis in an easy, scalable, efficient, and intuitive way. Emphasis has been given to data pre-processing, an early, critical step in the analysis for the robustness of the final results and conclusions. Through modularity, parallelizability and customization, DIANA-mAP produces high quality expression results, reports and graphs for downstream data mining and statistical analysis. In an extended evaluation, the tool outperforms similar tools providing pre-processing without any adapter knowledge. Closing, DIANA-mAP is a freely available tool. It is available dockerized with no dependency installations or standalone, accompanied by an installation manual through Github.

Small open reading frames (sORFs; <300 nucleotides or <100 amino acids) are widespread across all genomes, and an increasing variety of them appear to be translating from non-genic regions. Over the past few decades, peptides produced from sORFs have been identified as functional in various organisms, from bacteria to humans. Despite recent advances in next-generation sequencing and proteomics, accurate annotation and classification of sORFs remain a rate-limiting step toward reliable and high-throughput detection of small proteins from non-genic regions. Additionally, the cost of computational methods utilizing machine learning is lower than that of biological experiments, and they can be employed to detect sORFs, laying the groundwork for biological experiments. We present D-sORF, a machine-learning framework that integrates the statistical nucleotide context and motif information around the start codon to predict coding sORFs. D-sORF scores directly for coding identity and requires only the underlying genomic sequence, without incorporating parameters such as the conservation, which, in the case of sORFs, may increase the dispersion of scores within the significantly less conserved non-genic regions. D-sORF achieves 94.74% precision and 92.37% accuracy for small ORFs (using the 99 nt medium length window). When D-sORF is applied to sORFs associated with ribosomes, the identification of transcripts producing peptides (annotated by the Ensembl IDs) is similar to or superior to experimental methodologies based on ribosome-sequencing (Ribo-Seq) profiling. In parallel, the recognition of putative negative data, such as the intron-containing transcripts that associate with ribosomes, remains remarkably low, indicating that D-sORF could be efficiently applied to filter out false-positive sORFs from Ribo-Seq data because of the non-productive ribosomal binding or noise inherent in these protocols.

Anaerobic digestion (AD) is an essential biotechnology for sustainable waste management and bioenergy production, driven by a complex microbial ecosystem. However, the stability and efficiency of AD reactors are frequently challenged by microbial shifts resulting from operational disturbances. This study employs microbiome-oriented data mining and bioinformatics approaches to analyze microbial community dynamics in an anaerobic digestion reactor under steady-state operation, failure, and restoration phases. Data were extracted from a biogas plant and used to evaluate a system failure, providing crucial insights into microbial shifts associated with operational instability. A custom-built microbiome-oriented database, μ2Gas, was developed to integrate sequencing data with physicochemical monitoring results, enabling structured data storage, retrieval, and correlation analysis. This database was instrumental in identifying key microbial taxa responsible for reactor destabilization and in predicting failure events based on microbial and physicochemical trends. Through high-throughput 16S rRNA gene sequencing and advanced bioinformatics analysis, microbial shifts were correlated with key operational parameters such as pH, volatile fatty acids (VFAs), and ammonia concentrations. Results indicate that methanogenic archaea, particularly Methanosarcina, experienced a sharp decline during reactor failure, followed by a gradual recovery, demonstrating their sensitivity to environmental stressors. Additionally, the balance between Firmicutes and Actinobacteria was identified as a crucial determinant of reactor stability, with Firmicutes recovering in tandem with system restoration. Feedstock composition was found to play a significant role in microbial shifts, with seasonal variability influencing community composition and reactor performance. Machine learning models applied to the database suggested the potential for predictive analytics in anticipating system failures. By leveraging microbial abundance patterns, these models were able to identify early warning signs of instability. Restoration efforts, including feedstock adjustments and operational parameter optimization, successfully reinstated core microbial communities essential for biogas production. This study underscores the critical role of microbiome monitoring in anaerobic digestion and highlights the potential of bioinformatics tools for enhancing reactor resilience. Future research should focus on real-time microbiome tracking, machine learning-driven predictive maintenance, and adaptive operational strategies to further improve AD efficiency and biogas yield.