# Targeted long-read sequencing sample preparation using Cas9 nucleases

> **NIH NIH R44** · SAGE SCIENCE, INC. · 2020 · $796,860

## Abstract

Project Summary
DNA sequencing is increasingly being used in clinical research in genetic disease and oncology. Currently
most clinical sequencing is carried out using short-read targeted sequencing methods to detect mutations in
protein-coding regions of the genome. However, short-read sequencing methods are not well suited to
detection of alterations involving DNA segments greater than 100 base pairs in length, nor can they detect the
arrangement of sequence polymorphisms that are more than a few hundred bases apart on a chromosome.
Such long-range genomic analyses are becoming increasingly important, and new long-read sequencing
technologies have been developed that can address these technical problems. However, the new long-read
sequencing methods are roughly 10-fold more expensive than commonly-used short-read sequencing
methods. Our proposal seeks to develop an instrument system that can isolate specific long genomic DNA
fragments (100,000 to 1 million base pairs in length) from biological samples, and thereby provide a new
economical approach for targeted long-read sequencing sample preparation. The proposed system is intended
for robust, high sample throughput, walk-away automated processing in high volume genome centers and
clinical laboratories.

## Key facts

- **NIH application ID:** 9980971
- **Project number:** 5R44HG010438-03
- **Recipient organization:** SAGE SCIENCE, INC.
- **Principal Investigator:** TRUETT C BOLES
- **Activity code:** R44 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $796,860
- **Award type:** 5
- **Project period:** 2018-11-20 → 2021-04-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9980971

## Citation

> US National Institutes of Health, RePORTER application 9980971, Targeted long-read sequencing sample preparation using Cas9 nucleases (5R44HG010438-03). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/9980971. Licensed CC0.

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