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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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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

A DSSM network for inferring and prioritizing cell-type-specific regulons using single-cell RNA-seq data.

Yaxin Fan¹, Yichao Mei¹, Shengbao Bao¹

¹Hubei Key Laboratory of Agricultural Bioinformatics, College of Informatics, Huazhong Agricultural University, Wuhan, 430070, China.

BMC Bioinformatics

|December 7, 2025

Summary

This study introduces Deep Structured Semantic Model for inferring Regulons (DSSMReg), a novel method for identifying cell-type-specific gene regulatory networks. DSSMReg accurately prioritizes regulons using single-cell transcriptome data, outperforming existing algorithms.

Keywords:

Cell type specificity Deep learning Deep structured semantic model Regulon scRNA-seq

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Single-cell RNA Sequencing and Analysis of Human Pancreatic Islets

Single-cell RNA Sequencing and Analysis of Human Pancreatic Islets

Published on: July 18, 2019

Area of Science:

Computational Biology
Genomics
Bioinformatics

Background:

Gene regulation relies on transcription factors and target genes forming cell-type-specific regulons.
Integrating diverse datasets like single-cell transcriptomes and ChIP-seq is challenging for identifying active regulons.
Understanding cell-type-specific regulons is crucial for deciphering cellular function and disease mechanisms.

Purpose of the Study:

To develop a computational model for accurate inference and prioritization of cell-type-specific regulons.
To address the challenges in integrating single-cell transcriptome and transcription factor motif data.
To provide a robust tool for analyzing gene regulatory networks across different cell types.

Main Methods:

A Deep Structured Semantic Model (DSSMReg) was developed.
DSSMReg maps transcription factors and target genes into a low-dimensional semantic space using single-cell transcriptome and motif data.
Cosine similarity calculates regulatory strength, and the AUCell algorithm ranks regulon importance.

Main Results:

DSSMReg demonstrated superior performance compared to five other gene regulatory inference algorithms, achieving high AUROC and AUPRC metrics.
The model successfully inferred cell-type-specific regulons from triple-negative breast cancer and hematopoietic stem cell datasets.
Regulons identified with high AUCell scores showed significant biological relevance.

Conclusions:

DSSMReg is a highly effective tool for inferring and prioritizing cell-type-specific regulons from single-cell RNA sequencing data.
The method provides valuable insights into gene regulatory mechanisms in various biological contexts.
The source code is publicly available for broader research application.