# Subtractive assembly approaches for inferring disease-associated microbial genes and pathways from microbiome sequencing data

> **NIH NIH R01** · TRUSTEES OF INDIANA UNIVERSITY · 2020 · $270,268

## Abstract

Summary
Successful translational applications of microbiome research rely on computational tools
that can effectively detect microbial markers that are associated with diseases, and
provide explanations to the associations. We propose to develop subtractive assembly
approaches to microbiome sequencing data analysis, aiming to identity microbial genes
and pathways that are associated with diseases. The advantages of using subtractive
assembly approaches include: 1) they significantly reduce the complexity of the fragment
assembly problem by focusing only on the potential difference (genes and genomes) at
the initial (instead of the final) stage of the comparative analysis pipeline, and 2) they
improve the assemblies of differential genes, which are important inputs for building
predictive models for disease diagnosis and characterization of treatment efficacy. We
will apply our new tools to analyzing disease-associated microbiomes including those
associated with type II diabetes, liver cancer, inflammatory bowel disease (IBD) and
those known to be related to the efficacy of cancer immunotherapy.

## Key facts

- **NIH application ID:** 9816616
- **Project number:** 5R01AI143254-02
- **Recipient organization:** TRUSTEES OF INDIANA UNIVERSITY
- **Principal Investigator:** Yuzhen Ye
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $270,268
- **Award type:** 5
- **Project period:** 2018-11-05 → 2022-10-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9816616

## Citation

> US National Institutes of Health, RePORTER application 9816616, Subtractive assembly approaches for inferring disease-associated microbial genes and pathways from microbiome sequencing data (5R01AI143254-02). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/9816616. Licensed CC0.

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