# Realizing the potential of long-read sequencing technology

> **NIH NIH R35** · UNIVERSITY OF CALIFORNIA SANTA CRUZ · 2024 · $443,585

## Abstract

Project Summary
Long-read sequencing in the form of Pacific Biosciences (PacBio) and Oxford Nanopore Technologies (ONT)
have upended the preconceived notion of the capabilities of DNA sequencing applications. However, the
molecular biology and computational methods available for long-read sequencing still lag behind the rich
ecosystem of methods available for short- read technology in terms of both capability and usability. Because of
this, the potential of long-read sequencing methods to benefit biomedical research remains largely unrealized.
To address this shortfall, my lab will continue our work on developing and using methods that push the
capabilities of long-read sequencing technology. First, we will generate complete isoform-level tissue and cell-
type transcriptomes for human and mouse which will be invaluable to the biomedical research community
investigating gene and isoform expression using RNA-seq. In addition, access to these transcriptomes will
strongly benefit assays that rely on prior knowledge of which isoforms are expressed at what level in any
particular cell-type or tissue. Second, we will develop an easy-to-use, ultra-accurate, and read-length agnostic
sequencing method which will democratize the use of high-throughput sequencing technology and thereby
increase the diversity of the genomics workforce by enabling a much larger number of less well funded labs to
perform high-quality high-throughput DNA sequencing assays.

## Key facts

- **NIH application ID:** 10842983
- **Project number:** 2R35GM133569-06
- **Recipient organization:** UNIVERSITY OF CALIFORNIA SANTA CRUZ
- **Principal Investigator:** Christopher Vollmers
- **Activity code:** R35 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $443,585
- **Award type:** 2
- **Project period:** 2019-08-01 → 2029-07-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10842983

## Citation

> US National Institutes of Health, RePORTER application 10842983, Realizing the potential of long-read sequencing technology (2R35GM133569-06). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10842983. Licensed CC0.

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