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Related Experiment Video

Updated: May 29, 2026

Rup (RNA-seq Usability Assessment Pipeline) - Quality Control for Bulk RNA-seq Experiments in Eukaryotes

Rup (RNA-seq Usability Assessment Pipeline) - Quality Control for Bulk RNA-seq Experiments in Eukaryotes

Published on: November 7, 2025

The impact of transcriptome assembly algorithms on downstream quantification in RNA-seq data analysis.

Zeming Tan¹, Jiahao Li¹, Enfeng Qi²

¹Research Center for Mathematics and Interdisciplinary Sciences, Frontiers Science Center for Nonlinear Expectations (Ministry of Education), Shandong University, 72 Binhai Road, Jimo, Qingdao, 266237, China.

Briefings in Bioinformatics

|May 27, 2026

Summary

This summary is machine-generated.

Choosing the right transcriptome assembly algorithm is key for accurate RNA-seq quantification. The HISAT2 and StringTie2 pipeline offers the most stable and effective results for RNA sequencing data analysis.

Keywords:

DEG analysis RNA-seq transcriptome assembly transcriptome quantification

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AQRNA-seq for Quantifying Small RNAs

AQRNA-seq for Quantifying Small RNAs

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Last Updated: May 29, 2026

Rup (RNA-seq Usability Assessment Pipeline) - Quality Control for Bulk RNA-seq Experiments in Eukaryotes

Rup (RNA-seq Usability Assessment Pipeline) - Quality Control for Bulk RNA-seq Experiments in Eukaryotes

Published on: November 7, 2025

AQRNA-seq for Quantifying Small RNAs

AQRNA-seq for Quantifying Small RNAs

Published on: February 2, 2024

Area of Science:

Bioinformatics
Computational Biology
Genomics

Background:

Accurate transcriptome assembly and quantification are essential for RNA sequencing (RNA-seq) differential expression analysis.
Transcriptome assembly directly impacts quantification outcomes, influencing downstream biological interpretations.

Purpose of the Study:

To evaluate the impact of different transcriptome assembly algorithms on quantification results in next-generation RNA-seq data analysis.
To assess the quality and stability of commonly used assemblers (StringTie2, Scallop, Cufflinks) using simulated and real RNA-seq datasets.

Main Methods:

Comparative analysis of StringTie2, Scallop, and Cufflinks for transcriptome assembly.
Evaluation using both simulated RNA-seq data and real RNA-seq datasets.
Assessment of assembly quality and stability concerning quantification outcomes.

Main Results:

The pipeline combining HISAT2 (for alignment) and StringTie2 (for assembly) demonstrated the most effective and stable performance.
Simulated RNA-seq data does not fully represent the complexity of real biological data.
Long, low-expression transcripts and short transcripts present challenges for accurate assembly and quantification.

Conclusions:

The choice of transcriptome assembly significantly affects RNA-seq quantification accuracy.
The HISAT2-StringTie2 pipeline is recommended for robust RNA-seq analysis.
Future software development should address challenges associated with assembling and quantifying extreme transcript types.