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

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Published on: March 12, 2021

EcTracker: Tracking and elucidating ectopic expression leveraging large-scale scRNA-seq studies.

Vishakha Gautam¹, Aayushi Mittal¹, Siddhant Kalra¹

¹Indraprastha Institute of Information Technology, Delhi, India.

Briefings in Bioinformatics

|June 29, 2021

Summary

This summary is machine-generated.

EcTracker is a new web server for analyzing single-cell RNA sequencing data. It helps identify cell types and ectopically expressed genes, aiding in understanding cellular identity and regulatory networks.

Keywords:

AUCell Seurat ectopic expression shiny single-cell web server

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Area of Science:

Genomics
Computational Biology
Molecular Biology

Background:

Genomic alterations can activate normally silent genes, posing challenges for transcript detection.
Accurate identification of these transcripts requires knowledge of physiological gene expression levels.

Purpose of the Study:

To introduce EcTracker, an R-/Shiny-based web server for single-cell data analysis.
To enable quantitative and qualitative assessments of cell-specific and ectopically expressed genes.
To facilitate regulon analysis for identifying key transcriptional factors.

Main Methods:

Developed an R-/Shiny-based web server named EcTracker.
Implemented functionalities for assessing bona fide and ectopic gene expression in single-cell RNA sequencing datasets.
Integrated regulon analysis for identifying regulatory transcriptional factors.
Reanalyzed a CRISPR interference (CRISPRi) dataset of human embryonic stem cells.

Main Results:

EcTracker enables quantitative and qualitative assessments of gene expression in single-cell data.
Identified a specific gene signature enrichment in SMAD2 knockout cells from a human embryonic stem cell differentiation dataset.
Demonstrated EcTracker's processing speed, modularity, interactive interface, and comprehensiveness.
Successfully decoded cellular identities and identified ectopically expressed genes and their regulatory networks.

Conclusions:

EcTracker provides an integrative, end-to-end platform for single-cell data analysis.
The tool facilitates the decoding of cellular identities and the identification of ectopically expressed genes.
EcTracker offers a novel dimension for analyzing single-cell datasets by revealing regulatory networks.