# Extend and improve the functional annotation tools dbNSFP and WGSA

> **NIH NIH R03** · UNIVERSITY OF SOUTH FLORIDA · 2020 · $74,750

## Abstract

PROJECT SUMMARY
Whole exome sequencing (WES) and whole genome sequencing (WGS) have increasingly been used to
identify variants, genes and regulatory regions that are associated with human diseases. As a result, we are
witnessing a tsunami of DNA sequence data from both healthy human subjects and those with Mendelian or
complex diseases. Identifying variants that are causal of a disease or associated with disease risks from a
huge amount of DNA variants identified in sequencing is like looking for a needle in a hay stack. To accomplish
this daunting task, investigators have relied on functional annotation to filter or prioritize variants based on
our current knowledge or prediction models. We previously developed the dbNSFP database with
deleteriousness prediction scores of all possible missense mutations in humans, as well as the WGS
annotator (WGSA) software to facilitate functional annotation for both coding and non-coding variants which
current contains > 1.5 Tb (compressed) resource data. These software tools are widely used by worldwide
investigators. In the proposed study, to extend and harden our functional annotation tools and resources for
handling the rapidly-increasing amount of WES and WGS data. Specifically, we will extend and improve the
functional annotation resources of dbNSFP and WGSA, and improve the speed, user-interface and
dissemination approach of dbNSFP and WGSA. Successful completion of these aims will accelerate the
progress to study newly discovered variants for their involvement in human disease in the era of big data and
precision medicine. This contribution will also benefit the human genomics and human biomedical sciences
in general because DNA sequence analyses have become the essential approach in those areas and DNA
variant functional annotation will certainly help us to understand and interpret the functions of the variants.

## Key facts

- **NIH application ID:** 9952585
- **Project number:** 1R03HG011075-01
- **Recipient organization:** UNIVERSITY OF SOUTH FLORIDA
- **Principal Investigator:** XIAOMING LIU
- **Activity code:** R03 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $74,750
- **Award type:** 1
- **Project period:** 2020-05-15 → 2022-04-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9952585

## Citation

> US National Institutes of Health, RePORTER application 9952585, Extend and improve the functional annotation tools dbNSFP and WGSA (1R03HG011075-01). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/9952585. Licensed CC0.

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