Just before the Thanksgiving holiday break, Mangul Lab at USC released our second preprint—a review of the diagnostic and therapeutic potential of metagenomics.
Several members of our group—Caitlin Loeffler (Bioinformatics Analyst), Lana Martin (Project Specialist), Yutong Chang (Master’s student), Jeremy Rotman (alumni)—collaborated with Keylie Gibson, Ian Toma, and Keith Crandall of George Washington University; Joseph Zackular of University of Pennsylvania; and David Koslicki of Penn State to produce a review of clinical metagenomics that is designed to be useful for any researcher regardless of their background in computer science or genomics.
Our review explores the potential for development of clinical applications based on current technology in metagenomics sequencing and analysis. During the past decade, rapid advancements in sequencing technologies have enabled the study of human-associated microbiomes at an unprecedented scale.
Metagenomics is emerging as a clinical tool to identify the agents of infection, track the spread of diseases, and surveil potential pathogens. Examples of existing metagenomics-based clinical applications include tools that enable identification of:
- strains of Mycobacterium tuberculosis (TB)
- infectious agents in cases of encephalitis
- biomarkers for Inflammatory Bowel Disease (IBD)
Yet, despite advances in high-throughput sequencing technologies and bioinformatics algorithms, metagenomics still has limitations barring mass clinical acceptance and broad implementation. Algorithms currently struggle to balance sensitivity and specificity of discovered biomarkers, genomic reference databases are incomplete, and studies are frequently not reproducible.
Once these hurdles are overcome, clinical metagenomics will be able to inform doctors of the best, targeted treatment for their patients and provide early detection of disease. Our review summarizes 11 of the most recent clinical metagenomics studies and discusses their commonalities and unique features. We include a lay-friendly overview of metagenomics methods with a discussion of computational challenges and limitations.