Tyler Faits Successfully Defends His Thesis
Congratulations to Tyler Faits on the successful defense of their thesis, "The Evaluation, Application, and Expansion of 16S Amplicon Metagenomics" Great job Tyler!
To learn more about Tyler's thesis, read their abstract below:
Since the invention of high-throughput sequencing, the majority of experiments studying bacterial microbiomes have relied on the PCR amplification of all or part of the gene for the 16S rRNA subunit, which serves as a biomarker for identifying and quantifying the various taxa present in a microbiomic sample. Several computational methods exist for analyzing 16S amplicon based metagenomics, but the most commonly used bioinformatics tools are unable to produce quality genus-level or species-level taxonomic calls and may underestimate the degree to which such calls are possible. In this thesis, I have used 16S sequencing data from mock bacterial communities to evaluate the sensitivity and specificity of several bioinformatics pipelines and genomic reference libraries used for microbiome analyses, with a focus on measuring the accuracy of species-level taxonomic assignments of 16S amplicon reads. With the efficacy of these tools established, I then applied them in the analysis of data from two studies into human microbiomes. I evaluated the metagenomics analysis tools Qiime 2, Mothur, PathoScope 2, and Kraken, in conjunction with reference libraries from GreenGenes, Silva, Kraken, and RefSeq, using publicly available mock community data from several sources, comprising 137 samples spanning a range of taxonomic diversity, amplicon regions, and sequencing methods. PathoScope and Kraken, both tools designed for whole genome metagenomics, outperformed Qiime 2 and Mothur, which are theoretically specialized in 16S analyses. I used PathoScope 2 to analyze longitudinal 16S data from infants in Zambia, exploring the maturation of nasopharyngeal microbiomes in healthy infants, establishing a range of typical healthy taxonomic profiles, and identifying dysbiotic patterns which are associated with the development of severe lower respiratory tract infections in early childhood. With more data, these dysbiotic patterns may help identify infants at high risk of developing respiratory disease.
I used Qiime 2 to analyze 16S data from human subjects in a controlled dietary intervention study with a focus on dietary carbohydrate quality. I correlated alterations in the gut microbiome with various cardiometabolic risk factors, and identified increases in some butyrate-producing bacteria in response to complex carbohydrates. I also constructed a metatranscriptomics pipeline to analyze paired rRNA-depleted RNAseq data
Major Professor: W. Evan Johnson