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Bioinformatics Core aims to provide accurate analysis and interpretation of various types of data to help researchers increase their knowledge, productivity, and competitiveness in securing research funding. 

Areas of Service 

We analyze and interpret single-cell sequence data as well as bulk sequence data, primarily provided by our Genomics core. Our bioinformatics of sequencing data includes RNA-seq, ATAC-seq, and spatial transcriptomics. We also provide DNA methylomics, metabolomics, proteomics, integrative multiomics, and predictive modeling and feature selection using machine learning algorithms. 


For analysis of 10x Genomics-based data, we use analysis pipelines provided by 10X Genomics Cell Ranger, Cell Ranger-ARC (for multiome analysis), Space Ranger, and visualization functions of the Loupe Browser. We also use open-source third-party tools, such as the R Seurat and Monocle packages. Spatial transcriptomics, named Method of the Year by Nature in 2020, combines single-cell RNA-seq data with tissue-specific spatial information of individual single cells. The spatial resolution technique, provided by our Spatial Multiomics core, can be applied to other omics, such as DNA methylomics and proteomics. For processing and analysis of large single-cell datasets, we use Tulane’s High Performance Computing system (Cypress). For traditional bulk RNA-seq, we use R DESeq2 and limma-voom packages. 


For detailed information about our Bioinformatics core and project consultation, please contact:

Md Mehedi Hasan, PhD

Postdoctoral Researcher

Dept. of Medicine

Tulane Center for Aging



Sangkyu Kim, PhD 

Bioinformatics Core Director

Dept. of Medicine Tulane Center for Aging