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RNA-seq

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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.
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Related Experiment Video

Updated: May 6, 2026

Leveraging CyVerse Resources for De Novo Comparative Transcriptomics of Underserved Non-model Organisms

Leveraging CyVerse Resources for De Novo Comparative Transcriptomics of Underserved Non-model Organisms

Published on: May 9, 2017

SCSEQ: A web tool for analyzing single-cell RNA-seq data.

Shiyu Du¹, Pengcheng Sun¹, Li Shen²

¹Qingdao Institute of Software, College of Computer Science and Technology, China University of Petroleum (East China), Shandong Key Laboratory of Intelligent Oil & Gas Industrial Software, Qingdao 266580, China.

|May 5, 2026

Summary

This summary is machine-generated.

SCSEQ is a user-friendly web platform for analyzing single-cell RNA sequencing data, simplifying complex bioinformatics workflows for researchers without programming expertise. It offers comprehensive tools for data processing, analysis, and visualization, ensuring reliable results for biological studies.

Keywords:

data analysis platform machine learning single-cell RNA sequencing web-based tool

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Published on: May 9, 2017

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Targeted RNA Sequencing Assay to Characterize Gene Expression and Genomic Alterations

Targeted RNA Sequencing Assay to Characterize Gene Expression and Genomic Alterations

Published on: August 4, 2016

Area of Science:

Bioinformatics
Genomics
Computational Biology

Background:

Single-cell RNA sequencing (scRNA-seq) is crucial for understanding cellular heterogeneity.
High-throughput sequencing generates vast, complex datasets challenging for researchers.
Effective data processing and analysis are critical for scRNA-seq studies.

Purpose of the Study:

To develop an accessible bioinformatics platform for scRNA-seq data analysis.
To empower researchers without programming expertise to process and analyze sequencing data.
To provide a comprehensive and user-friendly solution for single-cell transcriptome analysis.

Main Methods:

Developed SCSEQ, an interactive web-based bioinformatics platform.
Integrated a comprehensive workflow: preprocessing, normalization, clustering, dimension reduction, differential expression, cell type identification.
Included downstream analyses: gene enrichment, transcription factor analysis, cell-cell communication, CNV detection, trajectory inference, pan-cancer analysis.

Main Results:

SCSEQ offers a complete analysis pipeline from data preprocessing to downstream tasks.
The platform supports various input formats and provides graphical/tabular outputs.
User-friendly interface with detailed parameters and dynamic interactions enhances usability.
Comprehensive manuals aid parameter configuration and workflow execution.

Conclusions:

SCSEQ provides an intuitive and convenient solution for single-cell transcriptome data analysis.
The platform successfully performed full-process analyses on real-world data.
SCSEQ demonstrates practical applicability for reliable results in biological research.