Browsing by Subject "Computational Biology"

Now showing 1 - 3 of 3
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    Evaluating the information content of human microbiomes
    (2022-03) Hillmann, Benjamin
    Microbes vastly outnumber all other organisms on earth and are integral to many aspects of the ecological fitness of the earth’s soils, oceans, animals, and plants. Unfortunately, most of the microbes in these communities cannot be cultured, so to observe these communities’ biological functions, we must study their DNA. After a researcher sequences a microbial community, they utilize informatics methods to correlate the taxonomic and functional profiles to their traits of interest. However, these methods assume that the underlying taxonomic and functional profiling are accurate. If procedures are developed to identify the profiles of a community more accurately, the increased precision will enable higher power testing of hypotheses and detection of these communities’ causal roles. We propose novel, accurate, and data-efficient methods for taxonomic and functional profiles in shotgun metagenomic datasets.
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    Integrated analysis of genomic data for inferring gene regulatory networks.
    (2009-04) Zare Sangederazi, Hossein
    As genomic technology and sequencing projects continue to advance, more emphasis needs to be put on data analysis, while addressing the issue of how best to extract information from diverse data sets. For example, functional annotation of new genes can no longer depends only on sequence analysis, but requires integration of additional sources of information including phylogeny, gene expression, protein interaction, metabolic and regulatory networks. Therefore, new biological discoveries will depend strongly on our ability to combine these diverse data sets. We demonstrate how information from gene expression, regulatory sequence patterns and location data can be combined to discover regulatory modules and to construct gene transcriptional regulatory networks. In the context of modeling regulatory sequences, we propose a higher order probabilistic model to efficiently discriminate between the binding sites of a transcription factor and non-specific DNA sequences. Moreover, a model-based algorithm is developed, which integrates gene expression data, modeled by mixtures of Gaussian, with the regulatory sequence patterns for clustering of functionally related genes. For the construction of the gene regulatory network, we introduce the concept of Gene-Regulon association in contrast to Gene-Gene interaction. Unlike Gene-Gene interaction methods, where the mRNA levels of the regulators play the important role, Gene-Regulon methods rely on the activity profiles of the transcription factors. These activity profiles, in the absence of their direct measurements, are estimated concurrently via a computational model. We develop a model selection algorithm, which is capable of capturing the activity profile of a transcription factor from the transcriptional activity of its target genes. In addition, we present a data driven approach based on nonlinear kernel embedding for capturing the nonlinear correlation and geometric connectivity pattern in gene expression data. We apply these methods for integrating gene expression and interaction data to construct a network of transcriptional regulation in Escherichia coli (E. coli).
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    The relationship of gut microbiota in standard and overweight children, before and after probiotic administration
    (2019) Linhardt, Carter A; Clayton, Jonathan B; Hoops, Suzie; Amin-Nordin, Syafinaz; Knights, Dan
    The usage of probiotic foods and supplements has been widely considered part of a healthy diet by supplementing the gut microbiome with beneficial bacteria. Although the usage of probiotics is a common dietary accessory, there is limited reproducible evidence showing bacterial colonization, thus limiting long term effectiveness. We administered Yakult, a commercial probiotic composed of Lactobacillus paracasei strain Shirota, to overweight and standard weight school children in Malaysia. Using a crossover intervention study design, two groups of school children were administered the probiotic supplement or continued their typical diet in sequential 5-week intervention periods, separated by a 5-week washout period. Fecal samples were collected every five weeks over the course of the 15-week study period. The gut microbiome of each subject was analyzed using 16S rRNA gene sequencing. We observed significant differences in Lachnospiraceae, Coproccus, Roseburia, Pyramidobacter, and Bacteroides ovatus between weight classes. However, differences in overall microbiome diversity between weight classes were not found to be significant. Subjects clustered according to their relative abundance of well-known genera Bacteroides and Prevotella, regardless of age, gender, or weight class. Overall, individual-to-individual variation overshadowed trends in gut microbiome composition associated with probiotic administration.