# Improving metagenomic analysis with novel algorithms and technologies

> **NIH NIH R35** · WEILL MEDICAL COLL OF CORNELL UNIV · 2022 · $421,783

## Abstract

SUMMARY/ABSTRACT
Microbiome research through sequencing is becoming increasingly important for clinical studies.
The human commensal microbiomes have been shown to have a wide variety of potential health
impacts. However, our ability to genetically assay microbes is still limited. Microbiomes are
extremely complex and standard short-read sequencing technologies often do not provide
sufficient basis for the recovery of relevant genes and organisms.
Low-input and low-cost linked-read DNA sequencing technologies, such as the 10x Genomics
chromium system, have recently emerged with unprecedented promise for de novo assembly of
whole genome or metagenome samples. These technologies employ a novel molecular
barcoding technique which offers long-range information over standard high-throughput short
read, next-generation sequencing, while still at reasonable reagent and low-costs. We plan to
develop several innovative novel algorithms to fully leverage barcoded reads in a fast manner to
improve several integral and challenging applications, in particular: improving metagenome
assembly and leveraging the increased sensitivity to low abundance genomic information in
order to identify clinically relevant and potentially pathogenic organisms that can inform clinical
decisions.
All our proposed methods and computational tools will be made freely available with extensive
documentations for the community to use. To ensure the utility of our methods we plan to
extensively apply them to a wide range of research and clinical shotgun metagenome data sets,
in my laboratory and through various established local, external and industrial collaborations.
We also plan to collect control samples and sequence them using multiple platforms (Illumina,
10x Genomics, Loop Genomics Read Cloud, UTS TELL-SEQ, Oxford Nanopore) for
benchmarking. We will also use our proposed methods to improve the detection and
classification of low abundance organisms in clinical samples. We will launch two pilot projects
in collaborations with our Department of Pathology and Hospital for Special Surgery (HSS).
Successful completion of this project will provide fast and scalable computational methods that
can be applied to large-scale data sets.

## Key facts

- **NIH application ID:** 10438845
- **Project number:** 5R35GM138152-03
- **Recipient organization:** WEILL MEDICAL COLL OF CORNELL UNIV
- **Principal Investigator:** Iman Hajirasouliha
- **Activity code:** R35 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $421,783
- **Award type:** 5
- **Project period:** 2020-09-01 → 2025-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10438845

## Citation

> US National Institutes of Health, RePORTER application 10438845, Improving metagenomic analysis with novel algorithms and technologies (5R35GM138152-03). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10438845. Licensed CC0.

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