# Learning phylogenetic tree design and analysis with application

> **NIH NIH P20** · UNIVERSITY OF SOUTH DAKOTA · 2024 · $34,747

## Abstract

Project Summary
The exploration and comprehension of phylogenetic trees have emerged as fundamental
aspects of contemporary biological research. Phylogenetic trees offer significant insights
into the evolutionary interrelationships among organisms. Furthermore, they play a crucial
role in elucidating the spread of diseases, including the origin and evolution of pathogens,
the temporal and spatial distribution of prevalence, and the prediction of pathogen
transmission patterns. Additionally, phylogenetic trees facilitate the investigation of the
functional genomics of diverse species, including the emergence of novel body plans or
metabolic pathways, molecular adaptation, the evolution of morphological characteristics,
and demographic shifts in species that have recently diverged. Advances in sequencing
technologies have greatly enhanced the analysis of phylogenetic trees, enabling the
examination of extensive datasets, such as entire genomes.
The utilization of phylogenetic analysis use cases offers significant value by providing
researchers with a deeper understanding of the evolutionary progression of species and
their relationships. While our phylogenetic analysis framework is designed to incorporate
a generalized workflow, our primary focus will be on developing specific use cases
tailored to meet the needs of our user base within our cloud-based learning modules.
Because this learning module will be accessible through cloud computing, students will
be able to concentrate on phylogeny analysis without the need to install software or verify
software versions initially. Leveraging our use cases, we will expose learners to the
diverse applications of phylogeny in biomedical science. The use of "small" datasets will
ensure that cloud computing resources are not over-allocated. Additionally, users will
have the ability to upload other sample data, and our module will include a data controller
responsible for validating input and parameters, ensuring they conform to the specified
"acceptable" range before executing the workflow.
Impact: We will leverage our combined expertise to develop a Phylogeny Workflow Self-
Learning Module. This learning module will include instructional videos, an interactive
workflow implemented using Jupyter Notebook, and practical exercises that enable self-
learning with toy datasets. This resource will provide the educational community with a
valuable tool for understanding how biofilm impacts human health. The phylogeny
analysis workflow will be a versatile solution, allowing researchers to deploy it on various
platforms and apply it to a wide range of use cases.

## Key facts

- **NIH application ID:** 11036903
- **Project number:** 3P20GM103443-22S5
- **Recipient organization:** UNIVERSITY OF SOUTH DAKOTA
- **Principal Investigator:** Victor Chester Huber
- **Activity code:** P20 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $34,747
- **Award type:** 3
- **Project period:** 2001-09-24 → 2025-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/11036903

## Citation

> US National Institutes of Health, RePORTER application 11036903, Learning phylogenetic tree design and analysis with application (3P20GM103443-22S5). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/11036903. Licensed CC0.

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