Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Genomics

Genomics

Genomics is the science of genomes: it is the study of all the genetic material of an organism. In humans, the genome consists of information carried in 23 pairs of chromosomes in the nucleus, as well as mitochondrial DNA. In genomics, both coding and non-coding DNA is sequenced and analyzed. Genomics allows a better understanding of all living things, their evolution, and their diversity. It has a myriad of uses: for example, to build phylogenetic trees, to improve productivity and...

Evolutionary Relationships through Genome Comparisons

Evolutionary Relationships through Genome Comparisons

Genome comparison is one of the excellent ways to interpret the evolutionary relationships between organisms. The basic principle of genome comparison is that if two species share a common feature, it is likely encoded by the DNA sequence conserved between both species. The advent of genome sequencing technologies in the late 20th century enabled scientists to understand the concept of conservation of domains between species and helped them to deduce evolutionary relationships across diverse...

Statistical Software for Data Analysis and Clinical Trials

Statistical Software for Data Analysis and Clinical Trials

Statistical software is pivotal in data analysis and clinical trials by providing tools to analyze data, draw conclusions, and make predictions. These software packages range from simple data management applications to complex analytical platforms, supporting various statistical tests, models, and simulation techniques. Their significance lies in their ability to handle vast amounts of data with precision and efficiency, enabling researchers to validate hypotheses, identify trends, and make...

Genome Annotation and Assembly

Genome Annotation and Assembly

The genome refers to all of the genetic material in an organism. It can range from a few million base pairs in microbial cells to several billion base pairs in many eukaryotic organisms. Genome assembly refers to the process of taking the DNA sequencing data and putting it all back together in a correct order to create a close representation of the original genome. This is followed by the identification of functional elements on the newly assembled genome, a process called genome annotation.

Synthetic Biology

Synthetic Biology

Synthetic biology is an interdisciplinary science that involves using principles from disciplines such as engineering, molecular biology, cell biology, and systems biology. It involves remodeling existing organisms from nature or constructing completely new synthetic organisms for applications such as protein or enzyme production, bioremediation, value-added macromolecule production, and the addition of desirable traits to crops, to name a few.
Golden rice
Golden rice is a genetically modified...

You might also read

Related Articles

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

Sort by

Same author

<i>MetaHarmonizer</i> : robust biomedical metadata harmonization and a contamination control for inflated LLM performance on public benchmarks.

bioRxiv : the preprint server for biology·2026

Same author

A pivotal Wnt antagonist role promoting digit joint specification by constraining Wnt activity.

Nature communications·2026

Same author

Large-scale manual curation and harmonization of metadata from metagenomic and cancer genomic repositories: challenges and solutions.

Database : the journal of biological databases and curation·2026

Same author

omicsGMF: a multi-tool for dimensionality reduction, batch correction and imputation in bulk- and single-cell proteomics.

Nature communications·2026

Same author

Stochastic gradient descent estimation of generalized matrix factorization models with application to single-cell RNA sequencing data.

Biostatistics (Oxford, England)·2026

Same author

Switch-like methylation of functional pathways distinguishes COPD and idiopathic pulmonary fibrosis.

medRxiv : the preprint server for health sciences·2025

Same journal

Isolation of Mesenchymal Stem Cell-Derived Extracellular Vesicles.

Methods in molecular biology (Clifton, N.J.)·2026

Same journal

Modeling Melanoma Immune Surveillance by CAR-T Cells in Human Skin Organoids.

Methods in molecular biology (Clifton, N.J.)·2026

Same journal

Stepwise Optimization of a Matrigel-Based In Vitro Angiogenesis Assay for Reproducible and Quantifiable 2D-Tube Formation Using HUVECs.

Methods in molecular biology (Clifton, N.J.)·2026

Same journal

Quantifying Mechanical Properties of Fresh Ovarian Tissue with Optical Brillouin Microscopy.

Methods in molecular biology (Clifton, N.J.)·2026

Same journal

3D Chromatin Architecture During Early Development: New Methods and New Findings.

Methods in molecular biology (Clifton, N.J.)·2026

Same journal

Metabolic Plasticity in Embryogenesis Throughout the Lens of NAD<sup></sup>.

Methods in molecular biology (Clifton, N.J.)·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Sep 12, 2025

Performing Data Mining And Integrative Analysis Of Biomarker in Breast Cancer Using Multiple Publicly Accessible Databases

Performing Data Mining And Integrative Analysis Of Biomarker in Breast Cancer Using Multiple Publicly Accessible Databases

Published on: May 17, 2019

Bioconductor's Computational Ecosystem for Genomic Data Science in Cancer.

Marcel Ramos¹, Lori Shepherd², Nathan C Sheffield³

¹City University of New York School of Public Health, New York, NY, USA.

Methods in Molecular Biology (Clifton, N.J.)

|August 8, 2025

Summary

This summary is machine-generated.

The Bioconductor project offers extensive genomic data science resources for cancer research, including over 2,000 packages and 100,000 resources. It supports researchers with curated data, annotation tools, and training for cancer genomics.

Keywords:

Cancer genomics data structures epigenomics mutations ontology open source software spatial transcriptomics transcriptomics

More Related Videos

Integration of Bioinformatics Approaches and Experimental Validations to Understand the Role of Notch Signaling in Ovarian Cancer

Integration of Bioinformatics Approaches and Experimental Validations to Understand the Role of Notch Signaling in Ovarian Cancer

Published on: January 12, 2020

Cancer-Associated Fibroblasts from Mouse Mammary Tumors as Tools for Molecular and Computational Studies

Cancer-Associated Fibroblasts from Mouse Mammary Tumors as Tools for Molecular and Computational Studies

Published on: July 3, 2025

Related Experiment Videos

Last Updated: Sep 12, 2025

Performing Data Mining And Integrative Analysis Of Biomarker in Breast Cancer Using Multiple Publicly Accessible Databases

Performing Data Mining And Integrative Analysis Of Biomarker in Breast Cancer Using Multiple Publicly Accessible Databases

Published on: May 17, 2019

Integration of Bioinformatics Approaches and Experimental Validations to Understand the Role of Notch Signaling in Ovarian Cancer

Integration of Bioinformatics Approaches and Experimental Validations to Understand the Role of Notch Signaling in Ovarian Cancer

Published on: January 12, 2020

Cancer-Associated Fibroblasts from Mouse Mammary Tumors as Tools for Molecular and Computational Studies

Cancer-Associated Fibroblasts from Mouse Mammary Tumors as Tools for Molecular and Computational Studies

Published on: July 3, 2025

Area of Science:

Genomic data science
Computational biology
Cancer research

Background:

The Bioconductor project has become a vital resource in genomic data science.
It provides a vast ecosystem of software packages and data resources.
Its impact on cancer genomics is evidenced by numerous citations and data usage.

Purpose of the Study:

To provide an overview of Bioconductor's cancer genomics resources.
To highlight the project's principles and contributions to cancer data science.
To guide researchers in utilizing Bioconductor for cancer genomics analysis.

Main Methods:

Overview of Bioconductor project principles.
Exploration of curated cancer genomics data (e.g., TCGA).
Review of genomic annotation and ontology resources relevant to cancer.
Survey of analytical workflows for cancer genomics.
Discussion of resource integration and workforce training.

Main Results:

Bioconductor offers over 2,000 packages and 100,000 annotation/experiment resources.
The platform facilitates exploration of datasets like TCGA.
Resources cover genomic annotation, ontologies, and analytical workflows for cancer.
Training and support are provided for methods developers and researchers.
Tools are maintained and accessible in local or cloud environments.

Conclusions:

Bioconductor is a mature and impactful platform for cancer genomic data science.
It provides comprehensive resources, workflows, and training to support the research community.
The project continuously evolves to meet the needs of cancer genomics research.