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RNA-seq

RNA-seq

RNA sequencing, or RNA-Seq, is a high-throughput sequencing technology used to study the transcriptome of a cell. Transcriptomics helps to interpret the functional elements of a genome and identify the molecular constituents of an organism. Additionally, it also helps in understanding the development of an organism and the occurrence of diseases.
Before the discovery of RNA-seq, microarray-based methods and Sanger sequencing were used for transcriptome analysis. However, while...

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Updated: Jul 9, 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

Modeling fragment counts improves single-cell ATAC-seq analysis.

Laura D Martens^1,2,3, David S Fischer^2,4, Vicente A Yépez¹

¹School of Computation, Information and Technology, Technical University of Munich, Garching, Germany.

|December 4, 2023

Summary

This summary is machine-generated.

Binarizing single-cell ATAC sequencing data is unnecessary and does not improve analysis. Modeling fragment counts, not read counts, preserves crucial quantitative regulatory information for better insights.

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Area of Science:

Genomics
Molecular Biology
Bioinformatics

Background:

Single-cell ATAC sequencing (scATAC-seq) is widely used to study chromatin accessibility.
Regulatory regions are often represented by binarized data (open/closed chromatin).
Binarization simplifies data but may lose quantitative information.

Purpose of the Study:

To investigate the necessity and impact of binarization in scATAC-seq data analysis.
To determine if alternative data modeling approaches offer improvements.
To highlight the importance of quantitative information in scATAC-seq.

Main Methods:

Comparison of binarized versus non-binarized scATAC-seq data.
Evaluation of data modeling using fragment counts versus read counts.
Assessment of analytical outcomes including goodness of fit, clustering, cell type identification, and batch integration.

Main Results:

Binarization did not enhance goodness of fit, clustering, cell type identification, or batch integration.
Modeling fragment counts, rather than read counts, preserves quantitative regulatory information.
Read counts were found to be less informative than fragment counts for modeling.

Conclusions:

Binarization is an unnecessary step in scATAC-seq analysis.
Modeling fragment counts directly is recommended for preserving quantitative regulatory information.
This approach offers improved insights into chromatin accessibility at a single-cell level.