Transcriptomics has become a cornerstone omics technique in modern biological research. While novel single-cell and spatial approaches offer unprecedented layers of resolution, bulk RNA sequencing remains an essential tool for addressing diverse biological questions. Consequently, an ever-growing wealth of transcriptomics data is populating publicly accessible databases. In reference-based transcriptomics, there is typically a fraction of sequencing reads that fails to align to the target organism and is routinely discarded as noise or contamination. However, this unmapped fraction often harbours valuable microbial information. Furthermore, while many microbiome studies rely on DNA-based approaches for precise taxonomic identification, applying meta-transcriptomics to leverage incidental microbial transcripts within larger samples can provide a critical proxy for investigating molecular functions and biological pathways that are active in situ.
Here, conforming to the reusability aspect of the FAIR principles, we implemented a workflow to mine both novel and previously published transcriptomics datasets that did not originally target microorganisms. As a proof of concept, we aimed to uncover active, unexpected microbiota that remain undiscovered but hold biological relevance. We also benchmarked k-mer- and marker gene-based approaches, demonstrating the need to carefully select tools and databases while fine-tuning default parameters.
Despite technical limitations inherent to original experimental designs, RNA extraction, and library preparation methods optimized for other purposes, we consistently uncovered valuable microbiota-related insights, particularly regarding host-pathogen interactions and vector-borne diseases. This work highlights the immense potential of data reusability to drive meta-transcriptomic discovery across a variety of biological systems and fields where bulk transcriptomics is already routinely applied.

Co-authors: Laura Carmen Terron Camero, Eduardo Andres-Leon, Hubert Rehrauer