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activate projects page
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‎_pages/projects.md‎

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layout: page
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title: projects
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permalink: /projects/
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description: A growing collection of your cool projects.
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nav: false
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description: Treangen lab projects
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nav: true
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nav_order: 2
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display_categories: [work, fun]
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display_categories: [software, datasets, analysis]
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horizontal: false
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---
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‎_projects/bakdrive.md‎

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---
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layout: page
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title: Bakdrive
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description: A novel approach to inferring microbial interactions across multiple microbiome samples
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img:
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importance: 4
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category: software
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---
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# Optional external URL for project (replaces project detail page).
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# external_link: "https://www.iarpa.gov/index.php/research-programs/fun-gcat"
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# image:
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# caption: Bakdrive pipeline
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# focal_point: Smart
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# links:
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# url_code: "https://gitlab.com/treangenlab/bakdrive"
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# url_pdf: "https://www.biorxiv.org/content/10.1101/2021.09.24.461746v1.full.pdf"
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# url_slides: ""
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# url_video: ""
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# Slides (optional).
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# Associate this project with Markdown slides.
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# Simply enter your slide deck's filename without extension.
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# E.g. `slides = "example-slides"` references `content/slides/example-slides.md`.
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# Otherwise, set `slides = ""`.
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# slides: bakdrive
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<iframe src="https://docs.google.com/presentation/d/e/2PACX-1vS9ydjWtiIZejkaW0fA5gjJnNmZR1p_IUw3Uwze6cg7RGLHQjOxIzmXoo9ptCODaS8mOzDXX-qTwGM_/embed?start=true&loop=true&delayms=5000" frameborder="0" width="720" height="434" allowfullscreen="true" mozallowfullscreen="true" webkitallowfullscreen="true"></iframe>
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## Background
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Interactions among microbes within microbial communities have been shown to play crucial roles in human health. In spite of recent progress, low-level knowledge of bacteria driving microbial interactions within microbiomes remains unknown, limiting our ability to fully understand and control microbial communities. In this study, we present a novel approach for identifying driver species within microbiomes. Bakdrive infers ecological networks of given metagenomic sequencing samples and identifies minimum sets of driver species using control theory. Bakdrive has three key innovations in this space: (i) it leverages inherent information from metagenomic sequencing samples to identify driver species, (ii) it explicitly takes host-specific variation into consideration, and (iii) it does not require a known ecological network. In extensive simulated data, we demonstrate identifying driver species identified from healthy donor samples and introducing them to the disease samples, we can restore the gut microbiome in recurrent Clostridioides difficile infection patients to a healthy state. We also applied Bakdrive to two real datasets, rCDI and Crohn’s disease patients, uncovering driver species consistent with previous work. In summary, Bakdrive provides a novel approach for teasing apart microbial interactions. Bakdrive is open-source and available at https://gitlab.com/treangenlab/bakdrive
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‎_projects/emu.md‎

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---
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layout: page
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title: Emu
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description: Species-Level Microbial Community Profiling for Full-Length Nanopore 16S Reads
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img:
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importance: 4
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category: software
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---
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# Optional external URL for project (replaces project detail page).
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# external_link: "https://www.iarpa.gov/index.php/research-programs/fun-gcat"
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# image:
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# caption: Photo by rawpixel on Unsplash
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# focal_point: Smart
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# links:
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# url_code: "https://gitlab.com/treangenlab/emu"
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# url_pdf: "https://www.biorxiv.org/content/10.1101/2021.05.02.442339v1.abstract"
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# url_slides: ""
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# url_video: ""
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# Slides (optional).
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# Associate this project with Markdown slides.
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# Simply enter your slide deck's filename without extension.
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# E.g. `slides = "example-slides"` references `content/slides/example-slides.md`.
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# Otherwise, set `slides = ""`.
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# slides: ""
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---
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## Background
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16S rRNA based analysis is the established standard for elucidating microbial community composition. While short read 16S analyses are largely confined to genus-level resolution at best since only a portion of the gene is sequenced, full-length 16S sequences have the potential to provide species-level accuracy. However, existing taxonomic identification algorithms are not optimized for the increased read length and error rate of long-read data. Here we present Emu, a novel approach that employs an expectation-maximization (EM) algorithm to generate taxonomic abundance profiles from full-length 16S rRNA reads. Results produced from one simulated data set and two mock communities prove Emu capable of accurate microbial community profiling while obtaining fewer false positives and false negatives than alternative methods. Additionally, we illustrate a real-world application of our new software by comparing clinical sample composition estimates generated by an established whole-genome shotgun sequencing workflow to those returned by full-length 16S sequences processed with Emu.
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## Collaborators
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- Alona Tyshaieva (Univ of Dusseldorf)
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- Dr. Alex Dilthey (Univ of Dusseldorf)
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- Dr. Sonia Villapol (Houston Methodist)
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- Dr. Tor Savidge (Texas Childrens)
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- Dr. Qinglong Wu (Texas Childrens)
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