Sex-Based Precision Medicine Research Core

Mission: The overarching goal of the Sex-Based Precision Medicine Research Core (SPMRC) is to promote and support sex-based precision medicine (SPM) research at the Tulane Center of Biomedical Research Excellence

in Sex-Based Precision Medicine (COBRE-SPM).

OUR DATA

We have at our disposal multiple datasets which we can assist in getting your hands on and working with:

GEO and SRA

NCBI Gene Expression Omnibus (GEO) is a public, MIAME-compliant functional genomics data repository that archives high-throughput microarray, next-generation sequencing (RNA-seq, single-cell/spatial transcriptomics, ChIP-seq, ATAC-seq), and other functional genomic experimental data. It archives functional genomic data across four key entity types: 4,348 curated DataSets (GDS), 291,715 original study Series (GSE), 28,692 technological Platforms (GPL), and 8,635,078 individual biological Samples (GSM). Together, these entities structurally categorize high-throughput datasets to enable systematic querying and reproducible re-analysis.

Sequence Read Archive (SRA) data is the largest publicly available repository of high throughput sequencing data. The archive accepts data from all branches of life as well as metagenomic and environmental surveys. SRA stores raw sequencing data and alignment information to enhance reproducibility and facilitate new discoveries through data analysis. It is available through multiple cloud providers and NCBI servers.

All of Us Research Program
The All of Us Research Program's dataset contains data from over 700,000 participants, including genetic sequencing, procedure and diagnosis codes, medications, and survey responses. This data is available on the All of Us Researcher Workbench.

eMERGE Network

The eMERGE Network's data features data from 105,000 participants from various areas across the US. These data include genetic sequencing linked to demographics, diagnosis and procedure codes, and a limited number of medications. This data is available on Tulane's local High Performance Computing Cluster, Cypress.

YOUR DATA

We can also assist you in analyzing and managing your own multi-omics data:

1) high-throughput microarray, next-generation sequencing (whole genome/exome-seq, RNA-seq, single-cell/spatial transcriptomics, ChIP-seq, ATAC-seq);

2) other functional genomic experimental data, e.g., mass spec proteomics data.

3) Clinical data, including phenotypic, demographic, and medical metadata.

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Meet the SPMRC Team

SPMRC Team

Xiaojiang Xu, PhD
Director, Sex-Based Precision Medicine Research Core

Dr. Xu is an Associate Professor in the Department of Pathology and Laboratory Medicine at the Tulane University School of Medicine and serves as the Director of the Pathology Bioinformatics and AI Research Center. His research focuses on developing novel bioinformatics algorithms and computational tools for the analysis of large-scale genomic and multi-omics datasets, with particular expertise in high-definition (HD) spatial transcriptomics. His laboratory develops and applies advanced computational methods to analyze diverse high-throughput omics data, including bulk RNA-seq, ATAC-seq, ChIP-seq, single-cell multi-omics datasets (scRNA-seq, scATAC-seq, CITE-seq, TCR/BCR-seq, and Perturb-seq), spatial transcriptomics, and proteomics. Through these efforts, his group aims to advance the understanding of disease mechanisms and accelerate the discovery of clinically relevant biomarkers and therapeutic targets.

xxu17@tulane.edu

Haoyang Liang, MS, PhD Candidate

Haoyang Liang is a fourth-year PhD student with a BS and an MS in molecular biology. His research applies computational and statistical methods to high-throughput transcriptomic data. He works across bulk RNA-seq, ATAC-seq, single-cell/single-nucleus RNA-seq, and spatial transcriptomics, with an emphasis on integrating these modalities to resolve cell-state heterogeneity and its spatial organization within tissue. His methodological interests include cross-dataset and cross-species integration, trajectory and pseudotime inference, gene regulatory network reconstruction and in silico perturbation, matrix factorization approaches to gene program discovery, functional enrichment and pathway analysis, and cell–cell communication inference.

hliang4@tulane.edu

Fengxue Li, MS, PhD Candidate

Fengxue is a fourth-year Ph.D. candidate in Biostatistics at the Tulane University School of Public Health and Tropical Medicine, with a master's in biostatistics from UCLA. Areas of expertise include statistical methodology for missing data, longitudinal data analysis, survival analysis, and machine learning. Fengxue also has extensive hands-on experience with omics data, including single-cell and spatial transcriptomics, DNA methylation, and metabolomics. Within the core, Fengxue supports investigators with study design, power calculation, statistical analysis, and omics data analysis, with a particular interest in sex-based differences in disease risk and treatment response.

fli10@tulane.edu

Kaili Sun, MS, PhD candidate

Kaili is a third-year Biomedical Sciences Ph.D. student with a background in statistics, with additional strengths in statistical analysis, machine learning, deep learning, and applied AI. His prior research focused on bioinformatics, where he engineered a customized scRNA-seq analysis pipeline tailored to his lab's research needs. His current work applies statistical, machine learning, and deep learning approaches to multi-omics integration and other complex biological data, bridging the gap between computational methods and biological interpretation. Moving forward, he looks to integrate more of his statistical and AI background into his research. Outside of research, Kaili enjoys playing badminton and exploring everyday applications of AI.

Ksun4@tulane.edu

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