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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: Jul 21, 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

MosaiCatcher v2: a single-cell structural variations detection and analysis reference framework based on Strand-seq.

Thomas Weber¹, Marco Raffaele Cosenza¹, Jan Korbel^1,2

¹European Molecular Biology Laboratory, Genome Biology Unit, Heidelberg, Germany.

Biorxiv : the Preprint Server for Biology

|July 28, 2023

Summary

This summary is machine-generated.

MosaiCatcher v2 is a new workflow for detecting structural variations (SVs) in single cells using Strand sequencing. This tool enhances reproducibility and enables advanced genomic analysis in human genetics research.

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Last Updated: Jul 21, 2025

Detection of Rare Genomic Variants from Pooled Sequencing Using SPLINTER

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Published on: June 23, 2012

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Published on: March 12, 2021

Area of Science:

Genomics
Computational Biology
Single-cell analysis

Background:

Single-cell DNA template strand sequencing (Strand-seq) is a powerful technique for genomic analysis, including haplotype phasing and structural variation (SV) detection.
Accurate and reproducible SV detection at the single-cell level is crucial for understanding genomic heterogeneity and evolution.

Approach:

MosaiCatcher v2 is presented as a standardized workflow and reference framework for single-cell SV detection using Strand-seq data.
It incorporates an automated upstream Quality Control (QC) and assembly sub-workflow with multistep normalization.
The framework integrates scNOVA for SV functional characterization and ArbiGent for SV genotyping, offering platform portability and a user-friendly web report.

Key Points:

MosaiCatcher v2 provides automated QC, normalization, and assembly for Strand-seq data.
Integration with scNOVA and ArbiGent modules enhances SV characterization and genotyping capabilities.
The workflow is designed for platform portability and generates shareable web reports for user-friendly analysis.

Conclusions:

MosaiCatcher v2 significantly improves the reproducibility of computational processing for Strand-seq data.
This standardized framework facilitates the use of Strand-seq in production environments for human genetics and single-cell genomics.
The enhanced functionalities enable more robust and comprehensive single-cell SV detection.