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Related Concept Videos

RNA-seq03:21

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. 
Before the discovery of RNA-seq, microarray-based methods and Sanger sequencing were used for transcriptome analysis. However, while...
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Related Experiment Video

Updated: Jul 11, 2025

Author Spotlight: Vascular Tissue Dissociation and Exploring Single-Cell Subclusters for Targeted Therapy
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scAN1.0: A reproducible and standardized pipeline for processing 10X single cell RNAseq data.

Maxime Lepetit1, Mirela Diana Ilie2,3, Marie Chanal2

  • 1ENS de Lyon, CNRS UMR 5239, Laboratory of Biology and Modelling of the Cell, Lyon, France.

In Silico Biology
|November 6, 2023
PubMed
Summary

We developed scAN1.0, a reproducible and modular pipeline for single-cell RNA sequencing data analysis. This tool ensures interoperability across institutions, enhancing single-cell transcriptomics research.

Keywords:
Pipelineannotationdata analysismappingsingle cell transcriptomics

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Area of Science:

  • Computational Biology
  • Genomics
  • Bioinformatics

Background:

  • Single cell transcriptomics is rapidly advancing, necessitating robust data analysis solutions.
  • Existing pipelines often lack reproducibility, modularity, and interoperability.
  • There is a growing demand for standardized and adaptable single-cell RNA sequencing (scRNA-seq) analysis tools.

Purpose of the Study:

  • To introduce scAN1.0, a novel processing pipeline for 10X single-cell RNA sequencing data.
  • To provide a reproducible, modular, and interoperable solution for scRNA-seq data analysis.
  • To demonstrate the pipeline's utility in evaluating specific analysis steps, such as data mapping.

Main Methods:

  • Development of scAN1.0 using the Nextflow Domain Specific Language 2 (DSL2).
  • Implementation of a modular pipeline design for flexible integration of analysis modules.
  • Application of scAN1.0 to two distinct scRNA-seq datasets: human pituitary tumor and murine CD8 T cells.

Main Results:

  • scAN1.0 is demonstrated to be executable on diverse computational systems.
  • The pipeline's modularity facilitates the evaluation of different analysis components.
  • The study showcases scAN1.0's capability to assess the impact of the mapping step in scRNA-seq analysis.

Conclusions:

  • scAN1.0 offers a versatile and reliable platform for analyzing 10X single-cell RNA sequencing data.
  • The pipeline addresses the need for reproducible and interoperable computational tools in transcriptomics.
  • scAN1.0 supports in-depth examination of analysis steps, contributing to more robust biological insights.