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It is well known that information contained in a protein sequence is what allows it to fold into its three-dimensional shape, which performs a specific function. It has been possible for some time to search for proteins with similar sequences, using bioinformatics tools such as BLAST. But it is also known that proteins with similar, or even the same sequence can adopt different structures and vice-versa. With this in mind, we look to use a method called Rosetta-HMMER to perform conformationally specific sequence searches in order to exploit this property of proteins. This method involves the use of Rosetta to redesign protein structures to fit a specified α-carbon backbone, and then uses HMMER to generate a sequence profile. This profile can then be used to query for sequences able to adopt the specified backbone structure. These collected sequences can then be aligned for the purpose of performing statistical coupling analysis. We have used this Rosetta-HMMER method in conjunction with available structures of the α7 acetylcholine receptor to show that distinct sequence profiles generated from different conformations of the same protein are capable of retrieving unique sets of natural sequences when used as a query. We have also shown that when these unique sets of natural sequences are used to perform statistical coupling analysis, different residues are identified as statistically coupled, potentially generating insight into residues that have more potential importance for one backbone conformation over another.

A growing body of evidence generated from observational data has demonstrated the potential to influence decision-making and improve patient outcomes. For observational evidence to be actionable, however, it must be generated reliably and in a timely manner. Large distributed observational data networks enable research on diverse patient populations at scale and develop new sound methods to improve reproducibility and robustness of real-world evidence. Nevertheless, the problems of generalizability, portability and scalability persist and compound. As analytical methods only partially address bias, reliable observational research (especially in networks) must address the bias at the design stage (i.e., pre-analysis bias) including the strategies for identifying patients of interest and defining comparators. This thesis synthesizes and enumerates a set of challenges to addressing pre-analysis bias in observational studies and presents mixed-methods approaches and informatics solutions for overcoming a number of those obstacles. We develop frameworks, methods and tools for scalable and reliable phenotyping including data source granularity estimation, comprehensive concept set selection, index date specification, and structured data-based patient review for phenotype evaluation. We cover the research on potential bias in the unexposed comparator definition including systematic background rates estimation and interpretation, and definition and evaluation of the unexposed comparator. We propose that the use of standardized approaches and methods as described in this thesis not only improves reliability but also increases responsiveness of observational evidence. To test this hypothesis, we designed and piloted a Data Consult Service - a service that generates new on-demand evidence at the bedside. We demonstrate that it is feasible to generate reliable evidence to address clinicians’ information needs in a robust and timely fashion and provide our analysis of the current limitations and future steps needed to scale such a service.

Background: Studying the infant gut microbiome during the period of solid food introduction may provide valuable insight into gut colonization, microbial evolution, and the ecological role of bacterial metabolic pathways in microbial succession. However, since infant gut microbial communities are made of bacterial genera with high relative abundance, within-genus and within-species diversity, the efficacy of current computational tools in elucidating strain-specific differences is not known. Methods: 34 infant gut metagenomic samples were simulated with the CAMI-Simulator, using 16S rRNA gene profiles from subjects of the Baby & Mi study as a reference. Raw simulated reads were trimmed, assembled, and binned into metagenome-assembled genomes (MAGs) using mg_workflow, a Snakemake-based pipeline of current metagenomic analysis protocols. Results were compared to gold-standard references in order to benchmark the success of current computational methods in retrieving strain-level MAGs from the gut, and in predicting bacterial carbohydrate active enzymes. Real metagenomic samples from the Baby, Food & Mi cohort were processed through the bfm_mg_flow pipeline to study the taxonomic and metabolic changes in the infant gut microbiome during the solid food introduction period. Post-pipeline analyses were conducted in R. Results: Misassemblies were significantly impacted by sample community composition, including Shannon diversity, number of strains in the sample, and relative abundance of the most dominant strain. MAG completeness, contamination, quality, and reference coverage were significantly impacted by choice of assembly software, and choice of single- or co-sample assembly. Different assemblies yielded different MAGs from the same samples. Reference coverage of MAGs recovered from co-assemblies were lower than for those from single assemblies and CAZyme predictions were more accurate from MetaSPAdes than from MEGAHIT assemblies at both the assembly-level and the MAG-level. Based on these results, we propose the MetAGenomic PIpelinE (MAGPIE), with recommendations for ensemble methods for assembly, binning, and gene predictions. Using these methods, we identified changes in microbial community composition before and after solid food introduction in real Baby & Mi infant gut samples. These changes included an increase in bacteria that can digest a wide variety of carbohydrates, such as Bacteroides, and a decrease in Bifidobacterium. Conclusions: In this study, we characterized the current state of tools for genome-resolved metagenomics, and contributed a framework to tailor metagenomic data analysis for the unique composition of the infant gut microbiome. We further used this framework to study bacterial metabolism in the infant gut microbiome before and after the introduction of solid foods. / Thesis / Master of Science (MSc) / Solid food introduction to the infant diet brings new glycans to the gut environment, driving the selection of bacteria that are able to digest these compounds. Studying the gut microbiome during this timepoint is essential to deciphering how and when beneficial bacteria colonize, how they evolve, and how the infant gut matures to an adult-like state. A widely used method to characterize microbial identity and metabolic function in the gut is metagenomic sequencing. However, dominant bacterial genera in the infant gut often have multiple closely related species and strains, making it difficult to decipher the essential metabolic differences between them. In this study, we simulated an infant gut metagenomic dataset to understand how the structure of the infant gut impacts commonly used metagenomic tools, and to quantify the quality of genomes and metabolic predictions at the end of common metagenomic analyses. We found that gut microbial community composition and metagenomic assembler choice both impact the quality of final genomes retrieved from the data, and the accuracy of metabolic gene predictions. Based on these results, we make several recommendations to use ensemble methods to improve metagenomic data analysis, and additionally propose a metagenomic pipeline to analyze infant gut data over the period of solid food introduction.

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