Biology subjects

Sovacool, K. L.

Publications and source records attributed to Sovacool, K. L..

5 recordsLinked to original sources

bioRxiv2022

Streamlined implementation of a machine learning model to classify screen relevant neoplasia using reference-based OTU clustering

The ability to use 16S rRNA gene sequence data to train machine learning classification models offers the opportunity diagnose patients based on the composition of their microbiome. In some applications the taxonomic resolution that provides the best models may require the use of de novo OTUs whose composition changes when new data are added. We previously developed a new reference-based approach, OptiFit, that fits new sequence data to existing de novo OTUs without changing the composition of the original OTUs. While OptiFit produces OTUs that are as high quality as de novo OTUs, it is unclear whether this method for fitting new sequence data into existing OTUs will impact the performance of classification models relative to models trained and tested only using de novo OTUs. We used OptiFit to cluster sequences into existing OTUs and evaluated model performance in classifying a dataset containing samples from patients with and without colonic screen relevant neoplasia (SRN). We compared the performance of this model to standard methods including de novo and database-reference-based clustering. We found that using OptiFit performed as well or better in classifying SRNs. OptiFit can streamline the process of classifying new samples by avoiding the need to retrain models using reclustered sequences.

microbiology↗

bioRxiv2022

Intestinal inflammation reversibly alters the microbiota to drive susceptibility to Clostridioides difficile colonization in a mouse model of colitis

Susceptibility to Clostridioides difficile infection (CDI) typically follows the administration of antibiotics. Patients with inflammatory bowel disease (IBD) have increased incidence of CDI, even in the absence of antibiotic treatment. However, the mechanisms underlying this susceptibility are not well understood. To explore the intersection between CDI and IBD, we recently described a mouse model where colitis triggered by the murine gut bacterium, Helicobacter hepaticus, in IL-10-/- mice led to susceptibility to C. difficile colonization without antibiotic administration. The current work disentangles the relative contributions of inflammation and gut microbiota in colonization resistance to C. difficile in this model. We show that inflammation drives changes in microbiota composition, which leads to CDI susceptibility. Decreasing inflammation with an anti-p40 monoclonal antibody promotes a shift of the microbiota back toward a colonization-resistant state. Transferring microbiota from susceptible and resistant mice to germ-free animals transfers the susceptibility phenotype, supporting the primacy of the microbiota in colonization resistance. These findings shine light on the complex interactions between the host, microbiota, and C. difficile in the context of intestinal inflammation, and may form a basis for the development of strategies to prevent or treat CDI in IBD patients. ImportancePatients with inflammatory bowel disease (IBD) have an increased risk of developing C. difficile infection (CDI), even in the absence of antibiotic treatment. Yet, mechanisms regulating C. difficile colonization in IBD patients remain unclear. Here, we use an antibiotic-independent mouse model to demonstrate that intestinal inflammation alters microbiota composition to permit C. difficile colonization in mice with colitis. Notably, treating inflammation with an anti-p40 monoclonal antibody, a clinically relevant IBD therapeutic, restores microbiota-mediated colonization resistance to the pathogen. Through microbiota transfer experiments in germ-free mice, we confirm that the microbiota shaped in the setting of IBD is the primary driver of susceptibility to C. diffiicile colonization. Collectively, our findings provide insight into CDI pathogenesis in the context of intestinal inflammation, which may inform methods to manage infection in IBD patients. More broadly, this work advances our understanding of mechanisms by which the host-microbiota interface modulates colonization resistance to C. difficile.

microbiology↗

bioRxiv2021

OptiFit: an improved method for fitting amplicon sequences to existing OTUs

Assigning amplicon sequences to operational taxonomic units (OTUs) is often an important step in characterizing the composition of microbial communities across large datasets. OptiClust, a de novo OTU clustering method, has been shown to produce higher quality OTU assignments than other methods and at comparable or faster speeds. A notable difference between de novo clustering and database-dependent reference clustering methods is that OTU assignments from de novo methods may change when new sequences are added to a dataset. However, in some cases one may wish to incorporate new samples into a previously clustered dataset without performing clustering again on all sequences, such as when comparing across datasets or deploying machine learning models where OTUs are features. Existing reference-based clustering methods produce consistent OTUs, but they only consider the similarity of each query sequence to a single reference sequence in an OTU, thus resulting in OTU assignments that are significantly worse than those generated by de novo methods. To provide an efficient and robust method to fit amplicon sequence data to existing OTUs, we developed the OptiFit algorithm. Inspired by OptiClust, OptiFit considers the similarity of all pairs of reference and query sequences in an OTU to produce OTUs of the best possible quality. We tested OptiFit using four microbiome datasets with two different strategies: by clustering to an external reference database or by splitting the dataset into a reference and query set and clustering the query sequences to the reference set after clustering it using OptiClust. The result is an improved implementation of closed and open-reference clustering. OptiFit produces OTUs of similar quality as OptiClust and at faster speeds when using the split dataset strategy, although the OTU quality and processing speed depends on the database chosen when using the external database strategy. OptiFit provides a suitable option for users who require consistent OTU assignments at the same quality afforded by de novo clustering methods. ImportanceAdvancements in DNA sequencing technology have allowed researchers to affordably generate millions of sequence reads from microorganisms in diverse environments. Efficient and robust software tools are needed to assign microbial sequences into taxonomic groups for characterization and comparison of communities. The OptiClust algorithm produces high quality groups by comparing sequences to each other, but the assignments can change when new sequences are added to a dataset, making it difficult to compare different studies. Other approaches assign sequences to groups by comparing them to sequences in a reference database to produce consistent assignments, but the quality of the groups produced is reduced compared to OptiClust. We developed OptiFit, a new reference-based algorithm that produces consistent yet high quality assignments like OptiClust. OptiFit allows researchers to compare microbial communities across different studies or add new data to existing studies without sacrificing the quality of the group assignments.

microbiology↗

bioRxiv2021

Teaching Python for Data Science: Collaborative development of a modular & interactive curriculum

We are bioinformatics trainees at the University of Michigan who started a local chapter of Girls Who Code to provide a fun and supportive environment for high school women to learn the power of coding. Our goal was to cover basic coding topics and data science concepts through live coding and hands-on practice. However, we could not find a resource that exactly met our needs. Therefore, over the past three years, we have developed a curriculum and instructional format using Jupyter notebooks to effectively teach introductory Python for data science. This method, inspired by The Carpentries organization, uses bite-sized lessons followed by independent practice time to reinforce coding concepts, and culminates in a data science capstone project using real-world data. We believe our open curriculum is a valuable resource to the wider education community and hope that educators will use and improve our lessons, practice problems, and teaching best practices. Anyone can contribute to our educational materials on GitHub.

scientific communication and education↗

bioRxiv2021

Developing and deploying an integrated workshop curriculum teaching computational skills for reproducible research

Inspired by well-established material and pedagogy provided by The Carpentries (Wilson 2016), we developed a two-day workshop curriculum that teaches introductory R programming for managing, analyzing, plotting and reporting data using packages from the tidyverse (Wickham et al. 2019), the Unix shell, version control with git, and GitHub. While the official Software Carpentry curriculum is comprehensive, we found that it contains too much content for a two-day workshop. We also felt that the independent nature of the lessons left learners confused about how to integrate the newly acquired programming skills in their own work. Thus, we developed a new curriculum that aims to teach novices how to implement reproducible research principles in their own data analysis. The curriculum integrates live coding lessons with individual-level and group-based practice exercises, and also serves as a succinct resource that learners can reference both during and after the workshop. Moreover, it lowers the entry barrier for new instructors as they do not have to develop their own teaching materials or sift through extensive content. We developed this curriculum during a two-day sprint, successfully used it to host a two-day virtual workshop with almost 40 participants, and updated the material based on instructor and learner feedback. We hope that our new curriculum will prove useful to future instructors interested in teaching workshops with similar learning objectives.

scientific communication and education↗