# Detection and annotation of structural variants from long-read sequencing

> **NIH NIH R01** · CHILDREN'S HOSP OF PHILADELPHIA · 2022 · $440,000

## Abstract

PROJECT SUMMARY
The overarching goal of this project is to develop a suite of computational tools to detect structural variants (SVs)
by long-read sequencing, and to facilitate their annotation and clinical interpretation. Although short-read
sequencing has been widely used in research and clinical settings, it has limited ability to identify SVs due to the
presence of repeat elements. It is known that pathogenic SVs might be missed by short-read sequencing,
potentially contributing to the low diagnostic rates (~30-40%) in clinical genome/exome sequencing. The lack of
reliable tools for clinical interpretation of SVs further limits our ability to identify mutations that contribute to
human diseases. To address these challenges, we will develop LinkedSV to detect SVs from linked-read
genome and exome sequencing data generated by the 10X Genomics platform, and develop LongSV to detect
SVs from PacBio and Nonopore long-read sequencing data. We will also develop LabelSV to analyze optical
mapping data from Bionano Genomics, and to characterize complex SVs by integrating kilobase-resolution SV
calls from optical mapping and base-resolution SV calls from sequencing platforms. Finally, based on our prior
development of ANNOVAR and InterVar tools, we will develop a computational method to facilitate clinical
interpretation of SVs. By integrating gene dosage sensitivity, mutation intolerance, and phenotype information,
this method helps clinical interpretation of candidate SVs on disease phenotypes. Taken together, our methods
will streamline the workflow for SV detection and variant interpretation. We will distribute and maintain
user-friendly software tools to implement the proposed SV detection methods, and to generate reproducible and
traceable results that conform to the current and future versions of ACMG (American College of Medical
Genetics and Genomics) / AMP (Association for Molecular Pathology) guidelines. We believe that our methods
will substantially improve SV detection, enable consistent interpretation of SVs, and facilitate the implementation
of genome-guided precision medicine.

## Key facts

- **NIH application ID:** 10378720
- **Project number:** 5R01GM132713-04
- **Recipient organization:** CHILDREN'S HOSP OF PHILADELPHIA
- **Principal Investigator:** Kai Wang
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $440,000
- **Award type:** 5
- **Project period:** 2019-06-01 → 2024-03-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10378720

## Citation

> US National Institutes of Health, RePORTER application 10378720, Detection and annotation of structural variants from long-read sequencing (5R01GM132713-04). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10378720. Licensed CC0.

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