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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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Updated: Jun 28, 2025

Detection of Rare Genomic Variants from Pooled Sequencing Using SPLINTER

Detection of Rare Genomic Variants from Pooled Sequencing Using SPLINTER

Published on: June 23, 2012

Split Pool Ligation-based Single-cell Transcriptome sequencing (SPLiT-seq) data processing pipeline comparison.

Lucas Kuijpers¹, Bastian Hornung², Mirjam C G N van den Hout-van Vroonhoven²

¹Department of Cell Biology, Erasmus University Medical Center Rotterdam (Erasmus MC), Wytemaweg 80, Rotterdam, 3015CN, The Netherlands. l.kuijpers@erasmusmc.nl.

|April 12, 2024

Summary

This summary is machine-generated.

We compared eight bioinformatic pipelines for processing SPLiT-seq (split-pool ligation-based transcriptome sequencing) data. STARsolo and splitpipe are recommended for efficient single-cell transcriptome analysis due to their performance and reliable results.

Keywords:

Combinatorial barcoding Data-preprocessing SPLiT-seq Single cell RNA sequencing Split-pool barcoding

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Published on: November 11, 2014

Area of Science:

Single-cell biology
Genomics
Bioinformatics

Background:

Single-cell sequencing enables molecular analysis at high resolution.
Single-cell transcriptome sequencing is a prevalent technique.
SPLiT-seq (split-pool ligation-based transcriptome sequencing) utilizes combinatorial barcoding for transcriptome analysis.

Purpose of the Study:

To compare eight bioinformatic pipelines for processing SPLiT-seq data.
To evaluate computational performance, functionality, and impact on downstream analysis.
To guide users in selecting optimal tools for SPLiT-seq data processing.

Main Methods:

Comparative analysis of eight bioinformatic pipelines: alevin-fry splitp, LR-splitpipe, SCSit, splitpipe, splitpipeline, SPLiTseq-demultiplex, STARsolo, and zUMI.
Evaluation of data processing speed and accuracy for varying dataset sizes.
Assessment of downstream data processing outcomes.

Main Results:

STARsolo, splitpipe, and alevin-fry splitp efficiently process large datasets.
Most pipelines show similar cell barcode results on smaller datasets, except SPLiTseq-demultiplex and splitpipeline.
STARsolo and splitpipe exhibit highly similar performance; STARsolo requires additional coding for hexamer read collapsing.

Conclusions:

The choice of pipeline significantly impacts SPLiT-seq data processing efficiency and outcomes.
STARsolo and splitpipe are recommended for SPLiT-seq data analysis.
Users should consider pipeline prerequisites for efficient data processing.