# Discovering Novel Structural Genomic Rearrangements Using Deep Neural Networks

> **NIH NIH F31** · UNIVERSITY OF MICHIGAN AT ANN ARBOR · 2021 · $38,173

## Abstract

Abstract
Accurately detecting structural variation in the genome is a challenging task. Many approaches have been
developed over the last few decades, yet it is estimated that tens of thousands of variants are still being missed
in a given sample. Many of these variants are missed due to the limitations of using short-read sequencing to
identify large variants. Although many of these missed variants are located within complex regions of the
genome, it has been shown that some still have clinical relevance making their discovery important. New
platforms have been developed for sequencing the genome using long-reads and show promise for overcoming
many of these limitations creating the ability to identify the full spectrum of simple and complex structural variants.
Because this technology is relatively young, new computational approaches to support the analysis of long-read
sequencing data can aid in the discovery of these variants which are still being missed. In addition to detecting
novel variation in samples with long-read sequencing data, computational approaches can be developed to
leverage these novel variant calls to reanalyze the hundreds of thousands of short-read datasets currently
available. In this proposal, we plan to develop new computational approaches to identify novel structural variation
in the genome. In Aim 1, we will apply a recurrence approach to analyze long read sequencing datasets utilizing
deep neural networks. In Aim 2, we will develop a tool to derive profiles of structural variants predicted in long-
reads which can be used to identify and genotype structural variants calls in short read data-sets. Together,
these approaches will allow researchers to accurately characterize structural variation in both long and short-
read datasets.

## Key facts

- **NIH application ID:** 10128484
- **Project number:** 5F31HG010569-03
- **Recipient organization:** UNIVERSITY OF MICHIGAN AT ANN ARBOR
- **Principal Investigator:** Alexandra Marie Weber
- **Activity code:** F31 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $38,173
- **Award type:** 5
- **Project period:** 2019-04-01 → 2022-03-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10128484

## Citation

> US National Institutes of Health, RePORTER application 10128484, Discovering Novel Structural Genomic Rearrangements Using Deep Neural Networks (5F31HG010569-03). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10128484. Licensed CC0.

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