Metatranscriptomics: A Tool for Clinical Metagenomics

• Published in: Omics (Larchmont, N.Y.) Vol. 28; no. 8; p. 394
• Main Authors: Tyagi, Shivani, Katara, Pramod
• Format: Journal Article
• Language: English
• Published: United States 01.08.2024
• Subjects: Computational Biology - methods; Gene Expression Profiling - methods; Humans; Metagenome - genetics; Metagenomics - methods; Microbiota - genetics; RNA, Ribosomal, 16S - genetics; Transcriptome - genetics; metatranscriptome; ecology; microbiome; metagenomics; bioinformatics; clinical omics
• ISSN: 1557-8100
• DOI: 10.1089/omi.2024.0130

In the field of bioinformatics, amplicon sequencing of 16S rRNA genes has long been used to investigate community membership and taxonomic abundance in microbiome studies. As we can observe, shotgun metagenomics has become the dominant method in this field. This is largely owing to advancements in sequencing technology, which now allow for random sequencing of the entire genetic content of a microbiome. Furthermore, this method allows profiling both genes and the microbiome's membership. Although these methods have provided extensive insights into various microbiomes, they solely assess the existence of organisms or genes, without determining their active role within the microbiome. Microbiome scholarship now includes metatranscriptomics to decipher how a community of microorganisms responds to changing environmental conditions over a period of time. Metagenomic studies identify the microbes that make up a community but metatranscriptomics explores the diversity of active genes within that community, understanding their expression profile and observing how these genes respond to changes in environmental conditions. This expert review article offers a critical examination of the computational metatranscriptomics tools for studying the transcriptomes of microbial communities. First, we unpack the reasons behind the need for community transcriptomics. Second, we explore the prospects and challenges of metatranscriptomic workflows, starting with isolation and sequencing of the RNA community, then moving on to bioinformatics approaches for quantifying RNA features, and statistical techniques for detecting differential expression in a community. Finally, we discuss strengths and shortcomings in relation to other microbiome analysis approaches, pipelines, use cases and limitations, and contextualize metatranscriptomics as a tool for clinical metagenomics.