Dr. Rob Knight is new to the Microbiome consortium and brings a wealth of expertise in analyzing microbiome datasets. He is a world-renowned microbiome researcher who develops computational and analytical methods to address the challenges that microbiome studies often face, particularly the inconsistencies across studies. The gut microbiome is highly sensitive to many factors that are difficult to control in every laboratory setting, leading to high variability across studies. Another challenge is whether findings about the mouse microbiome translate to the human microbiome.
Dr. Knight’s project will address these challenges and serve as a force multiplier for the other consortium teams. Each team has generated substantial datasets during the first funding cycle and will continue to generate them as they move into the second cycle. To analyze this data, they all employ similar analyses and techniques, which Dr. Knight will improve and refine. By applying his solutions and techniques across consortium teams, Dr. Knight will dramatically improve the consistency and translatability of the consortium’s findings.
Dr. Knight’s proposal has three goals. In the first, Dr. Knight will use existing microbiome samples from the consortium to perform quantitative long-read sequencing. Sequencing DNA requires breaking it into smaller segments and reassembling them as they are sequenced. In some cases, this results in information being lost. Long-read sequencing requires fewer breaks and improves sequencing reliability. Dr. Knight believes that applying long-read sequencing to microbiome analyses will improve the quality of genomic data produced in these studies. The second aim will then focus on developing methods to analyze mouse and human microbiome data, which can differ dramatically, to identify relevant similarities. Additionally, Dr. Knight aims to also combine data about the metabolism of the gut microbiomes of both mice and humans as part of this effort. By doing so, Dr. Knight believes he can develop useful, comprehensive pictures of gut microbiome changes across species and resolve otherwise contradictory results about the harm or benefit caused by certain bacterial populations. The final aim will be to compile several computational tools into a concise workflow that can be used by each consortium team and other researchers in the field. This will include some of the tools Dr. Knight is refining here, as well as previously constructed and published tools.
