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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: Sep 17, 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

scGT:integration algorithm for single-cell RNA-seq and ATAC-seq based on graph transformer.

Yunjing Qi¹, Yulong Kan¹, Jing Qi^1,2

¹School of Mathematics, Harbin Institute of Technology, Harbin 150000, China.

Bioinformatics (Oxford, England)

|June 29, 2025

Summary

This summary is machine-generated.

scGT, a novel Graph Transformer model, effectively integrates multi-omics single-cell data by utilizing correlation features. This approach enhances label transfer accuracy and preserves biological variation in large datasets.

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Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues

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Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues

Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues

Published on: January 10, 2019

Area of Science:

Computational Biology
Genomics
Single-cell Analysis

Background:

Multi-omics analysis of single cells is crucial for understanding gene regulatory dynamics.
Current integration algorithms struggle with discrepancies between omics data, often ignoring crucial correlation features.

Purpose of the Study:

To develop a novel model, scGT, for harmonizing multi-omics single-cell data.
To improve the accuracy of label transfer and data integration in large-scale single-cell atlases.

Main Methods:

scGT employs a Graph Transformer architecture to leverage correlation features within raw single-cell RNA-seq and ATAC-seq datasets.
The model constructs robust graph structures to harmonize multi-omics representations.

Main Results:

scGT demonstrates superior performance in label transfer compared to state-of-the-art methods.
The model effectively integrates large datasets, including those with millions of cells, while preserving biological variation.

Conclusions:

scGT offers a powerful new approach for multi-omics single-cell data integration.
The method's ability to incorporate correlation features significantly improves integration accuracy and biological data preservation.