Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

RNA-seq03:21

RNA-seq

10.4K
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...
10.4K

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

The history and function of a circular RNA.

Nature communications·2026
Same author

Accelerating Leigh syndrome drug discovery through deep learning screening in brain organoids.

Nature communications·2026
Same author

YAP1 Enhances Mesenchymal-Type Gene Expression in Human Adrenergic-Type Neuroblastoma Cells.

Cancers·2026
Same author

miRNA regulation in brain tissue space: the 3'UTR perspective.

RNA (New York, N.Y.)·2026
Same author

Personalized CRISPR knock-in cytokine gene therapy to remodel the tumor microenvironment and enhance CAR T cell therapy in solid tumors.

Nature communications·2025
Same author

The Puzzle of Genetic Stability and Chromosomal Copy Number Alterations for the Therapy of Ewing Sarcoma.

Cancers·2025
Same journal

NanoporeDB: A Structural Resource Of Multimeric Protein Nanopores For Single-Molecule Sensing.

GigaScience·2026
Same journal

From the Brain Cell Atlas to Precision Neurology: A review of the application of AI-driven multi-omics in brain science.

GigaScience·2026
Same journal

Comparison of Deep Learning Approaches for Extreme Low-SNR Image Restoration.

GigaScience·2026
Same journal

ScopeViewer: A Browser-Based Solution for Visualizing Large Biological Images.

GigaScience·2026
Same journal

ChatMDV: Reducing Technical Barriers in Bioinformatics Analysis using Large Language Models.

GigaScience·2026
Same journal

ClusterGraph: a new tool for visualisation and compression of multidimensional data.

GigaScience·2026
See all related articles

Related Experiment Video

Updated: Sep 4, 2025

Spatial Profiling of Protein and RNA Expression in Tissue: An Approach to Fine-Tune Virtual Microdissection
09:19

Spatial Profiling of Protein and RNA Expression in Tissue: An Approach to Fine-Tune Virtual Microdissection

Published on: July 6, 2022

5.0K

Spacemake: processing and analysis of large-scale spatial transcriptomics data.

Tamas Ryszard Sztanka-Toth1,2, Marvin Jens1, Nikos Karaiskos1

  • 1Systems Biology of Gene Regulatory Elements, Max-Delbrück-Center for Molecular Medicine in the Helmholtz Association (MDC), Berlin Institute for Medical Systems Biology (BIMSB), 10115 Berlin, Germany.

Gigascience
|July 19, 2022
PubMed
Summary
This summary is machine-generated.

Spacemake is a new, scalable pipeline for analyzing spatial transcriptomics data from various technologies. It offers a unified framework for reproducible analysis, integrating diverse datasets for enhanced gene discovery.

Keywords:
bioinformaticscomputational biologycomputational pipelinemodularityreproducibilityscalabilitysequence analysissingle-cell transcriptomicsspatial transcriptomicsworkflow

More Related Videos

Author Spotlight: Exploring Advanced Therapeutic Targets in Osteosarcoma Through Spatial Transcriptomics
07:43

Author Spotlight: Exploring Advanced Therapeutic Targets in Osteosarcoma Through Spatial Transcriptomics

Published on: May 3, 2024

3.2K
Mapping the Structure-Function Relationships of Disordered Oncogenic Transcription Factors Using Transcriptomic Analysis
09:58

Mapping the Structure-Function Relationships of Disordered Oncogenic Transcription Factors Using Transcriptomic Analysis

Published on: June 27, 2020

2.8K

Related Experiment Videos

Last Updated: Sep 4, 2025

Spatial Profiling of Protein and RNA Expression in Tissue: An Approach to Fine-Tune Virtual Microdissection
09:19

Spatial Profiling of Protein and RNA Expression in Tissue: An Approach to Fine-Tune Virtual Microdissection

Published on: July 6, 2022

5.0K
Author Spotlight: Exploring Advanced Therapeutic Targets in Osteosarcoma Through Spatial Transcriptomics
07:43

Author Spotlight: Exploring Advanced Therapeutic Targets in Osteosarcoma Through Spatial Transcriptomics

Published on: May 3, 2024

3.2K
Mapping the Structure-Function Relationships of Disordered Oncogenic Transcription Factors Using Transcriptomic Analysis
09:58

Mapping the Structure-Function Relationships of Disordered Oncogenic Transcription Factors Using Transcriptomic Analysis

Published on: June 27, 2020

2.8K

Area of Science:

  • Molecular Biology
  • Bioinformatics
  • Genomics

Background:

  • Spatial sequencing methods are vital for RNA biology, quantifying mRNA expression within tissue context.
  • Current analysis software lacks compatibility across different spatial transcriptomics technologies.
  • This incompatibility hinders reproducible data processing and integration.

Purpose of the Study:

  • To introduce spacemake, a unified and scalable computational pipeline for spatial transcriptomics data analysis.
  • To address the challenge of software incompatibility across diverse spatial transcriptomics platforms.
  • To enable robust, reproducible, and integrated analysis of spatial transcriptomics data.

Main Methods:

  • Developed spacemake using Snakemake and Python, emphasizing modularity, robustness, and scalability.
  • Designed spacemake to handle major spatial transcriptomics data sets and be configurable for new technologies.
  • Integrated novoSpaRc for combining spatial and single-cell transcriptomics data to improve gene detection.

Main Results:

  • Spacemake processes and analyzes multiple samples in parallel, even from different experimental methods.
  • The pipeline provides a unified framework for reproducible processing from raw data to analysis reports.
  • Spacemake includes modules for sample merging, saturation analysis, and long-read analysis, enhancing data utility.

Conclusions:

  • Spacemake offers a versatile solution for analyzing diverse spatial transcriptomics data.
  • The pipeline promotes reproducible research and facilitates integration with existing computational workflows.
  • Spacemake enhances gene discovery by enabling integrated analysis of spatial and single-cell transcriptomics data.