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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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Genome comparison is one of the excellent ways to interpret the evolutionary relationships between organisms. The basic principle of genome comparison is that if two species share a common feature, it is likely encoded by the DNA sequence conserved between both species. The advent of genome sequencing technologies in the late 20th century enabled scientists to understand the concept of conservation of domains between species and helped them to deduce evolutionary relationships across diverse...

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Related Experiment Video

Updated: Jun 8, 2025

Author Spotlight: A Computational Pipeline for Analyzing Chimeric Noncoding RNA-Target RNA Interactions in High-Throughput Sequencing Data

Author Spotlight: A Computational Pipeline for Analyzing Chimeric Noncoding RNA-Target RNA Interactions in High-Throughput Sequencing Data

Published on: December 1, 2023

Graph contrastive learning as a versatile foundation for advanced scRNA-seq data analysis.

Zhenhao Zhang^1,2, Yuxi Liu³, Meichen Xiao¹

¹College of Life Sciences, Northwest A&F University, Yangling, 712100 Shaanxi, China.

Briefings in Bioinformatics

|November 1, 2024

Summary

This summary is machine-generated.

scSimGCL, a novel graph contrastive learning framework, generates high-quality representations for robust cell clustering in single-cell RNA sequencing (scRNA-seq) data. It enhances cell clustering performance and applicability across various algorithms.

Keywords:

cell clustering cell-cell graph graph contrastive learning scRNA-seq data self-supervised learning

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Novel Sequence Discovery by Subtractive Genomics

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Published on: January 25, 2019

Area of Science:

Computational biology
Genomics
Machine learning

Background:

Single-cell RNA sequencing (scRNA-seq) provides high-resolution gene expression data.
Cell clustering is crucial for scRNA-seq analysis but faces challenges like high dimensionality and dropout values.
Existing deep learning models improve clustering but lack a simple, effective representation learning framework.

Purpose of the Study:

To develop scSimGCL, a novel framework for self-supervised pretraining of graph neural networks.
To generate high-quality cell representations for robust scRNA-seq data clustering.
To enhance the performance and general applicability of cell clustering.

Main Methods:

Proposed scSimGCL framework based on graph contrastive learning.
Incorporated cell-cell graph structure and contrastive learning for representation enhancement.
Utilized self-supervised pretraining of graph neural networks.

Main Results:

scSimGCL demonstrated superior performance on simulated and real scRNA-seq datasets.
Clustering assignment analysis confirmed the general applicability of scSimGCL with state-of-the-art algorithms.
Ablation studies and hyperparameter analysis validated the network architecture's efficacy and robustness.

Conclusions:

scSimGCL provides a robust framework for learning high-quality representations essential for effective cell clustering.
The framework enhances scRNA-seq data analysis and can be adopted by practitioners.
Source code is publicly available for broader use and development.