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

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.

DNA Microarrays

DNA Microarrays

Microarrays are high-throughput and relatively inexpensive assays that can be automated to analyze large quantities of data at a time. They are used in genome-wide studies to compare gene or protein expression under two varied conditions, such as healthy and diseased states. Microarrays consist of glass or silica slides on which probe molecules are covalently attached through surface functionalization. Most commonly, the slides are prepared through the chemisorption of silanes to silica...

You might also read

Related Articles

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

Sort by

Same author

ESR1 mutations and CDK4/6 inhibitor choice shape clonal selection and adaptive cell states during acquired resistance.

Genome medicine·2026

Same author

Publisher Correction: Tumor transcriptional state predicts survival in immune-checkpoint-blockade-treated glioblastoma.

Nature cancer·2026

Same author

Tumor transcriptional state predicts survival in immune-checkpoint-blockade-treated glioblastoma.

Nature cancer·2026

Same author

Myeloma Precursors Erode Durable Immunity: Results of the IMPACT study.

Research square·2026

Same author

A 15-layer multi-omics analysis of gastric cancer ecotypes provides therapeutic insights.

Cell reports. Medicine·2026

Same author

Clinical validation of a high-performance somatic exome sequencing assay: from target-enrichment strategy to variant calling.

NPJ genomic medicine·2026

Same journal

Zero-shot reconstruction of mutant spatial transcriptomes.

Patterns (New York, N.Y.)·2026

Same journal

Dendritic nonlinearities mitigate communication costs.

Patterns (New York, N.Y.)·2026

Same journal

Erratum: Agentic AI as a coordination paradigm in digital health and agri-food systems.

Patterns (New York, N.Y.)·2026

Same journal

Spacing effect improves generalization in biological and artificial systems.

Patterns (New York, N.Y.)·2026

Same journal

A multi-modal foundation model for brain disease diagnosis and medical imaging.

Patterns (New York, N.Y.)·2026

Same journal

DuoMod-Net: Logarithmic balancing and geometric refinement for imbalanced semi-supervised medical image segmentation.

Patterns (New York, N.Y.)·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Jun 7, 2025

Facilitating the Analysis of Immunological Data with Visual Analytic Techniques

Facilitating the Analysis of Immunological Data with Visual Analytic Techniques

Published on: January 2, 2011

AnnoMate: Exploring and annotating integrated molecular data through custom interactive visualizations.

Claudia Chu¹, Conor Messer¹, Samantha Van Seters¹

¹Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA.

Patterns (New York, N.Y.)

|November 21, 2024

Summary

This summary is machine-generated.

Manual review of large datasets is tedious. AnnoMate is a new Python package creating customizable dashboards to streamline data annotation and review for multi-omic and cancer genome studies.

Keywords:

AnnoMate Plotly annotations cancer genomics dashboards genomics manual review sequencing visualization

More Related Videos

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline

Published on: December 7, 2021

Modeling an Enzyme Active Site using Molecular Visualization Freeware

Modeling an Enzyme Active Site using Molecular Visualization Freeware

Published on: December 25, 2021

Related Experiment Videos

Last Updated: Jun 7, 2025

Facilitating the Analysis of Immunological Data with Visual Analytic Techniques

Facilitating the Analysis of Immunological Data with Visual Analytic Techniques

Published on: January 2, 2011

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline

Published on: December 7, 2021

Modeling an Enzyme Active Site using Molecular Visualization Freeware

Modeling an Enzyme Active Site using Molecular Visualization Freeware

Published on: December 25, 2021

Area of Science:

Bioinformatics
Computational Biology
Genomics

Background:

Manual data review is essential but time-consuming in large-scale biological studies.
Increasing data generation in multi-omic and cancer genomics necessitates efficient review processes.

Purpose of the Study:

To develop a flexible and modular framework for automating manual data review.
To create interactive and customizable dashboards for data annotation tasks.

Main Methods:

Developed AnnoMate, a Python package utilizing Plotly Dash for interactive dashboards.
Implemented an object-oriented framework for easy customization of review dashboards.
Applied the framework to create specific dashboards for cancer genome sequencing data review.

Main Results:

AnnoMate provides a modular and flexible solution for manual data review.
The package facilitates the creation of interactive, customizable dashboards.
Successfully implemented reviewer dashboards for common cancer genome sequencing tasks.

Conclusions:

AnnoMate offers a powerful tool to enhance efficiency and reduce errors in manual data review.
The framework supports the development of tailored solutions for diverse bioinformatic challenges.
Facilitates scalable analysis of large-scale multi-omic and cancer genomics data.