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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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A Computational Pipeline for Intergenic/Intragenic Enhancer RNA Quantification in Mouse Embryonic Stem Cells

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stPipe: a flexible and streamlined R/Bioconductor pipeline for preprocessing sequencing-based spatial transcriptomics

Yang Xu^1,2, Callum J Sargeant¹, Yue You^3,4

¹The Walter and Eliza Hall Institute of Medical Research, Parkville, VIC 3052, Australia.

NAR Genomics and Bioinformatics

|November 24, 2025

Summary

This summary is machine-generated.

stPipe is a new R/Bioconductor package that offers a unified solution for preprocessing spatial transcriptomics (sST) data from various sequencing platforms. This tool simplifies data analysis and enables robust methods benchmarking for improved spatial biology research.

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Area of Science:

Genomics
Bioinformatics
Computational Biology

Background:

Sequencing-based spatial transcriptomics (sST) technologies like 10× Visium, Slide-seq, and Stereo-seq are rapidly advancing.
Diverse platforms present challenges in data preprocessing and standardization for downstream analysis.

Purpose of the Study:

To introduce stPipe, a comprehensive, modular, and open-source preprocessing pipeline for sST data.
To provide a unified solution for handling data from various mainstream sST platforms.

Main Methods:

stPipe is implemented as an R/Bioconductor package.
It processes raw FASTQ files into spatially resolved gene count matrices.
It includes quality control metrics and standardized data storage for downstream compatibility.

Main Results:

stPipe facilitates uniform preprocessing of sST data from different platforms.
The pipeline simplifies methods benchmarking using reference datasets like cadasSTre and SpatialBenchVisium.
Enables easier comparison of different sST technologies and analysis tools.

Conclusions:

stPipe addresses the need for simplified, standardized preprocessing of sST data.
It enhances the comparability of results across diverse spatial transcriptomics platforms.
Facilitates robust benchmarking and downstream analysis in spatial biology research.