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

Regulated mRNA Transport02:22

Regulated mRNA Transport

2.8K
2.8K
Ribosome Profiling02:24

Ribosome Profiling

3.5K
Ribosome profiling or ribo-sequencing is a deep sequencing technique that produces a snapshot of active translation in a cell. It selectively sequences the mRNAs protected by ribosomes to get an insight into a cell’s translation landscape at any given point in time.
Applications of ribosome profiling
Ribosome profiling has many applications, including in vivo monitoring of translation inside a particular organ or tissue type and quantifying new protein synthesis levels.
The technique...
3.5K
Genome Size and the Evolution of New Genes03:21

Genome Size and the Evolution of New Genes

2.4K
2.4K

You might also read

Related Articles

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

Sort by
Same author

Ventricular fibrillation dynamics reveal regional asymmetry in resilience to cardiac arrest and predict clinical outcome.

Cardiovascular research·2026
Same author

Molecular subtypes in pancreatic cancer: from academic promise to clinical reality.

Molecular cancer·2026
Same author

EFFECTS OF TORSO IMPEDANCE ON IN SILICO VOLTAGE MAPPING OF CARDIAC DIPOLES OF ROTORS.

Annual Modeling and Simulation Conference (ANNSIM). Annual Modeling and Simulation Conference (Online)·2026
Same author

Selective genome editing of amplified oncogenes triggers immunogenic cell death and tumor remodeling.

Molecular cancer·2025
Same author

Discovering Genetic Variants in Hypertrophic Cardiomyopathy With Multiple Machine Learning Techniques.

IEEE transactions on computational biology and bioinformatics·2025
Same author

Reference for Electrocardiographic Imaging-Based T-Wave Alternans Estimation.

IEEE access : practical innovations, open solutions·2025
Same journal

An epigenetic clock for chronological age estimation in East Asian populations.

NAR genomics and bioinformatics·2026
Same journal

The role of ATF4 in neurons under mitochondrial stress.

NAR genomics and bioinformatics·2026
Same journal

Distinct repeat architecture landscapes in the proteomes of protozoan parasites.

NAR genomics and bioinformatics·2026
Same journal

Long non-coding RNA triplex-dependent regulation of melanoma gene networks.

NAR genomics and bioinformatics·2026
Same journal

Challenges in predicting chromatin accessibility differences between species.

NAR genomics and bioinformatics·2026
Same journal

Power-law penalties correct distance bias in single-cell co-accessibility and deep-learning chromatin interaction predictions.

NAR genomics and bioinformatics·2026
See all related articles

Related Experiment Video

Updated: Jun 5, 2025

High-Throughput Transcriptome Analysis for Investigating Host-Pathogen Interactions
14:58

High-Throughput Transcriptome Analysis for Investigating Host-Pathogen Interactions

Published on: March 5, 2022

4.1K

Pymportx: facilitating next-generation transcriptomics analysis in Python.

Paula Pena González1,2, Dafne Lozano-Paredes3, José Luis Rojo-Álvarez3

  • 1Molecular Oncology Group, Biosanitary Research Institute, Faculty of Experimental Sciences, Francisco de Vitoria University (UFV), Ctra. Pozuelo-Majadahonda Km. 1800 28223 Pozuelo de Alarcón, Madrid, Spain.

NAR Genomics and Bioinformatics
|December 5, 2024
PubMed
Summary
This summary is machine-generated.

A new Python package, Pymportx, enables efficient importation of gene expression data, bridging a gap for transcriptomics researchers. This tool enhances cross-platform bioinformatics interoperability and data analysis within the Python ecosystem.

More Related Videos

A Fast and Quantitative Method for Post-translational Modification and Variant Enabled Mapping of Peptides to Genomes
09:10

A Fast and Quantitative Method for Post-translational Modification and Variant Enabled Mapping of Peptides to Genomes

Published on: May 22, 2018

9.1K
Polysome Purification from Soybean Symbiotic Nodules
07:02

Polysome Purification from Soybean Symbiotic Nodules

Published on: July 1, 2022

1.5K

Related Experiment Videos

Last Updated: Jun 5, 2025

High-Throughput Transcriptome Analysis for Investigating Host-Pathogen Interactions
14:58

High-Throughput Transcriptome Analysis for Investigating Host-Pathogen Interactions

Published on: March 5, 2022

4.1K
A Fast and Quantitative Method for Post-translational Modification and Variant Enabled Mapping of Peptides to Genomes
09:10

A Fast and Quantitative Method for Post-translational Modification and Variant Enabled Mapping of Peptides to Genomes

Published on: May 22, 2018

9.1K
Polysome Purification from Soybean Symbiotic Nodules
07:02

Polysome Purification from Soybean Symbiotic Nodules

Published on: July 1, 2022

1.5K

Area of Science:

  • Bioinformatics
  • Computational Biology
  • Transcriptomics

Background:

  • Efficient importation of quantified gene expression data is crucial for transcriptomics analysis.
  • The R package Tximport facilitates data integration from various quantification tools.
  • A lack of similar tools in Python limited cross-platform bioinformatics interoperability.

Purpose of the Study:

  • Introduce Pymportx, a Python adaptation of Tximport.
  • Replicate and extend Tximport's functionalities for the Python ecosystem.
  • Enhance gene expression data processing speed and integration.

Main Methods:

  • Developed Pymportx as a Python package.
  • Ensured compatibility with existing Python bioinformatics tools.
  • Supported new data formats for gene expression data.

Main Results:

  • Pymportx replicates and extends Tximport's core functionalities.
  • The package improves processing speed and data integration within Python.
  • New data formats and enhanced analysis tools are supported.

Conclusions:

  • Pymportx provides a vital tool for Python-based transcriptomics.
  • Facilitates seamless data workflows across R and Python environments.
  • Promotes interdisciplinary collaboration and advanced bioinformatics analyses.