Investigating susceptibility to tuberculosis using functional genomics approaches

A major goal of human genetics is to characterize the role of genetic variation on complex, polygenic phenotypes. With the discovery from genome-wide association studies (GWAS) that many associated variants have a small effect size and are located in non-coding regions of the genome, there has been a large effort to collect functional genomics data. The hope is that a better understanding of how the genome functions in diverse developmental states and environments will provide insight into the context-specific activity of associated non-coding variants. My research applies this paradigm to the complex phenotype of susceptibility to develop tuberculosis (TB). It has been estimated that 10% of individuals infected with Mycobacterium tuberculosis (MTB) progress to active disease. Despite being heritable, very few genetic variants have been associated with susceptibility to TB. For my studies, I use RNA sequencing (RNA-seq) to interrogate genome-wide transcript levels in in vitro cellular models. In Chapter 2, I use a joint Bayesian model to identify genes which are differentially expressed in macrophages only after infection with MTB and related mycobacteria, but not other bacterial pathogens. In Chapter 3, I build a support vector machine model to classify individuals as susceptible or resistant to TB based on the gene expression levels in their dendritic cells. In Chapter 4, I characterize the technical variation introduced by batch processing of single-cell RNA-seq (scRNA-seq) and propose an effective study design that accounts for technical variation while minimizing replication. In addition to providing insight into the genes important for the innate immune response to MTB infection, my work is informative for the design and analysis of future functional genomics experiments. (Note: Supplementary tables are provided in a .zip file available online. Captions for the tables are provided within the dissertation.

Creating and sharing reproducible research code the workflowr way

Making scientific analyses reproducible, well documented, and easily shareable is crucial to maximizing their impact and ensuring that others can build on them. However, accomplishing these goals is not easy, requiring careful attention to organization, workflow, and familiarity with tools that are not a regular part of every scientist's toolbox. We have developed an R package, workflowr, to help all scientists, regardless of background, overcome these challenges. Workflowr aims to instill a particular "workflow" — a sequence of steps to be repeated and integrated into research practice — that helps make projects more reproducible and accessible.This workflow integrates four key elements: (1) version control (via Git); (2) literate programming (via R Markdown); (3) automatic checks and safeguards that improve code reproducibility; and (4) sharing code and results via a browsable website. These features exploit powerful existing tools, whose mastery would take considerable study. However, the workflowr interface is simple enough that novice users can quickly enjoy its many benefits. By simply following the workflowr "workflow", R users can create projects whose results, figures, and development history are easily accessible on a static website — thereby conveniently shareable with collaborators by sending them a URL — and accompanied by source code and reproducibility safeguards. The workflowr R package is open source and available on CRAN, with full documentation and source code available at https://github.com/jdblischak/workflowr

A Quick Introduction to Version Control with Git and GitHub.

<p>A Quick Introduction to Version Control with Git and GitHub</p

A Quick Introduction to Version Control with Git and GitHub

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Mycobacterial infection induces a specific human innate immune response

International audienceThe innate immune system provides the first response to infection and is now recognized to be partially pathogen-specific. Mycobacterium tuberculosis (MTB) is able to subvert the innate immune response and survive inside macrophages. Curiously, only 5-10% of otherwise healthy individuals infected with MTB develop active tuberculosis (TB). We do not yet understand the genetic basis underlying this individual-specific susceptibility. Moreover, we still do not know which properties of the innate immune response are specific to MTB infection. To identify immune responses that are specific to MTB, we infected macrophages with eight different bacteria, including different MTB strains and related mycobacteria, and studied their transcriptional response. We identified a novel subset of genes whose regulation was affected specifically by infection with mycobacteria. This subset includes genes involved in phagosome maturation, superoxide production, response to vitamin D, macrophage chemotaxis, and sialic acid synthesis. We suggest that genetic variants that affect the function or regulation of these genes should be considered candidate loci for explaining TB susceptibility

Working with both a local and remote repository as a single user.

<p>(A) On your computer, you commit to a Git repository (commit d75es). (B) On GitHub, you create a new repository called thesis. This repository is currently empty and not linked to the repo on your local machine. (C) The command git remote add connects your local repository to your remote repository. The remote repository is still empty, however, because you have not pushed any content to it. (D) You send all the local commits to the remote repository using the command git push. Only files that have been committed will appear in the remote repository. (E) You repeat several more rounds of updating scripts and committing on your local computer (commit f658t and then commit xv871). You have not yet pushed these commits to the remote repository, so only the previously pushed commit is in the remote repo (commit d75es). (F) To bring the remote repository up to date with your local repository, you git push the two new commits to the remote repository. The local and remote repositories now contain the same files and commit histories.</p