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Genomics

Genomics

Genomics is the science of genomes: it is the study of all the genetic material of an organism. In humans, the genome consists of information carried in 23 pairs of chromosomes in the nucleus, as well as mitochondrial DNA. In genomics, both coding and non-coding DNA is sequenced and analyzed. Genomics allows a better understanding of all living things, their evolution, and their diversity. It has a myriad of uses: for example, to build phylogenetic trees, to improve productivity and...

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Related Experiment Video

Updated: Jul 11, 2025

Multiplexed Analysis of Retinal Gene Expression and Chromatin Accessibility Using scRNA-Seq and scATAC-Seq

Multiplexed Analysis of Retinal Gene Expression and Chromatin Accessibility Using scRNA-Seq and scATAC-Seq

Published on: March 12, 2021

Mapping disease regulatory circuits at cell-type resolution from single-cell multiomics data.

Xi Chen^1,2, Yuan Wang³, Antonio Cappuccio⁴

¹Center for Computational Biology, Flatiron Institute, New York, NY, USA.

Nature Computational Science

|November 17, 2023

Summary

MAGICAL, a new computational method, maps gene regulatory circuits in single cells. It identified sepsis-related circuits in monocytes and epigenetic biomarkers distinguishing MRSA from MSSA infections.

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Last Updated: Jul 11, 2025

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Published on: March 12, 2021

Multiplexed Single Cell mRNA Sequencing Analysis of Mouse Embryonic Cells

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Area of Science:

Genomics
Computational Biology
Immunology

Background:

Understanding gene expression changes in disease requires cell-type resolution.
Chromatin remodeling plays a key role in disease pathogenesis.

Purpose of the Study:

To develop a computational approach for mapping gene regulatory circuits using multi-omic single-cell data.
To apply this method to identify sepsis-associated regulatory circuits and biomarkers.

Main Methods:

Developed MAGICAL (Multiome Accessibility Gene Integration Calling and Looping), a hierarchical Bayesian method.
Integrated paired single-cell RNA sequencing and single-cell ATAC sequencing data.
Applied MAGICAL to peripheral blood mononuclear cells from sepsis patients and controls.

Main Results:

MAGICAL accurately inferred regulatory circuits by modeling variations across cells and conditions.
Identified sepsis-associated regulatory circuits primarily in CD14 monocytes.
Discovered epigenetic circuit biomarkers distinguishing methicillin-resistant *Staphylococcus aureus* (MRSA) from methicillin-susceptible *S. aureus* (MSSA) infections.

Conclusions:

MAGICAL provides a powerful framework for dissecting cell-type-specific regulatory mechanisms in disease.
The identified biomarkers offer potential for differentiating MRSA from MSSA infections.
This approach advances the understanding of host responses to bacterial sepsis.