# System and Methods for Analysis of Bacterial Transcriptomes

> **NIH NIH R15** · WELLESLEY COLLEGE · 2020 · $391,722

## Abstract

PROJECT SUMMARY
Regulatory RNA genes pervade bacteria. Our understanding of these noncoding genes has
increased dramatically in recent years, thanks, in part, to advances in high-throughput
sequencing technology. High-throughput sequencing technology enables, among other things,
experiments that produce massive amounts of data about RNA transcripts in bacteria. However,
processing the large resulting data sets from high-throughput sequencing experiments can be a
bottleneck in biological and medical research studies, partly because existing methods are
insufficient for analyzing these data sets from bacteria.
 This project aims to develop new algorithms for correcting errors in the large sets of data
generated from bacterial high-throughput sequencing experiments. Further, a computational
system will be designed for managing and analyzing the sequencing data, with the aim of
systematically annotating evinced transcripts. Finally, since many RNA genes in bacteria act as
regulators of other transcripts, novel methods will be developed to identify the interactions
between these noncoding RNAs and their regulatory targets. The methods developed will be
applied and evaluated in several different bacterial systems.

## Key facts

- **NIH application ID:** 10026210
- **Project number:** 2R15GM102755-03
- **Recipient organization:** WELLESLEY COLLEGE
- **Principal Investigator:** BRIAN TJADEN
- **Activity code:** R15 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $391,722
- **Award type:** 2
- **Project period:** 2013-06-03 → 2024-02-29

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10026210

## Citation

> US National Institutes of Health, RePORTER application 10026210, System and Methods for Analysis of Bacterial Transcriptomes (2R15GM102755-03). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10026210. Licensed CC0.

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