# Sequencing Technology Core

> **NIH NIH U19** · BAYLOR COLLEGE OF MEDICINE · 2021 · $1,139,925

## Abstract

PROJECT SUMMARY:
Advances in high-throughput next generation sequencing (NGS) have enabled the rapid accumulation of
pathogen-derived genomes for mining candidate virulence and antibiotic resistance genes, drug and vaccine
targets, and many other genes of interest. These technologies also have facilitated studies of the microbiome
and host susceptibility genetics. Advances in long-read sequencing contribute to higher quality genome
assemblies and facilitate comparisons of closely related bacteria. RNA sequencing analyses complement
whole genome sequencing data by providing additional insight into host-pathogen interactions, specifically a
global view of the host cell responses to infections and regulation of pathogen-encoded virulence genes at
multiple time-points during an infection. More recently, new approaches, such as single cell RNA sequencing,
have enabled a more discrete examination of the cellular response to infection and the transduction of signals
from an infected cell to adjacent cells. Applications of specialized molecular libraries (e.g. Hi-C and 10X
Genomics) and minimal-infrastructure long-read DNA sequencing (e.g. Oxford Nanopore) show promise to
produce high quality genome assemblies more effectively and efficiently, perhaps even from contaminated
samples for which the target organism may be one of several bacteria present. In this GCID program, the
Sequencing and Technology (ST) Core will leverage existing large-scale NGS infrastructure to evaluate the
merits of new technologies and determine the most cost effective and expedient means to generate high
quality genome assemblies for target pathogens. The experimental models developed in the four research
projects (bacteria, virus, fungus, and parasite) will provide maximum utility to assess the contribution of these
new technologies while simultaneously addressing questions related to infectious disease genomics using
proven methods

## Key facts

- **NIH application ID:** 10160779
- **Project number:** 5U19AI144297-03
- **Recipient organization:** BAYLOR COLLEGE OF MEDICINE
- **Principal Investigator:** Donna Muzny
- **Activity code:** U19 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $1,139,925
- **Award type:** 5
- **Project period:** 2019-04-15 → 2024-03-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10160779

## Citation

> US National Institutes of Health, RePORTER application 10160779, Sequencing Technology Core (5U19AI144297-03). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10160779. Licensed CC0.

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