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.

Protein Networks

Protein Networks

Combinatorial Gene Control

Combinatorial Gene Control

Combinatorial gene control is the synergistic action of several transcriptional factors to regulate the expression of a single gene. The absence of one or more of these factors may lead to a significant difference in the level of gene expression or repression.
The expression of more than 30,000 genes is controlled by approximately 2000-3000 transcription factors. This is possible because a single transcription factor can recognize more than one regulatory sequence. The specificity in gene...

You might also read

Related Articles

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

Sort by

Same author

Immune cell landscape and metabolic gene activity distinguish triple negative from luminal A-like ductal carcinoma in situ.

Scientific reports·2026

Same author

NExON-Bayes: A Bayesian approach to network estimation informed by ordinal covariates.

Bioinformatics (Oxford, England)·2026

Same author

Correction: Lien et al. Sample Preparation Approach Influences PAM50 Risk of Recurrence Score in Early Breast Cancer. <i>Cancers</i> 2021, <i>13</i>, 6118.

Cancers·2026

Same author

Expression of Estrogen Receptor Alpha and Ki-67 in Synchronous Canine Mammary Tumors.

Journal of mammary gland biology and neoplasia·2026

Same author

Transcriptomic Profiling of Canine Mammary Tumours Reveals Significant Heterogeneity Between and Within Histological Classes.

Veterinary and comparative oncology·2026

Same author

Rank signaling drives basal cell-lineage infidelity leading to mammary tumorigenesis.

Nature communications·2026

Search research articles

Related Experiment Video

Updated: Oct 16, 2025

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

Tailored graphical lasso for data integration in gene network reconstruction.

Camilla Lingjærde¹, Tonje G Lien², Ørnulf Borgan³

¹MRC Biostatistics Unit, University of Cambridge, Forvie Site, Robinson Way, Cambridge, CB2 0SR, UK. camilla.lingjaerde@mrc-bsu.cam.ac.uk.

BMC Bioinformatics

|October 16, 2021

Summary

We developed a tailored graphical lasso method to improve gene interaction network inference using prior biological information of uncertain accuracy. This approach effectively integrates data, outperforming existing methods for high-dimensional genomics.

Keywords:

Cancer genomics Gene networks Genomics Graphical lasso High-dimensional inference Integrative analysis Multiomics Network models Protein–protein interaction networks Weighted graphical lasso

More Related Videos

Author Spotlight: Emerging Technologies and Advanced Tools for Decoding Metabolomics Data Analysis

Author Spotlight: Emerging Technologies and Advanced Tools for Decoding Metabolomics Data Analysis

Published on: November 10, 2023

JUMPn: A Streamlined Application for Protein Co-Expression Clustering and Network Analysis in Proteomics

JUMPn: A Streamlined Application for Protein Co-Expression Clustering and Network Analysis in Proteomics

Published on: October 19, 2021

Related Experiment Videos

Last Updated: Oct 16, 2025

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

Author Spotlight: Emerging Technologies and Advanced Tools for Decoding Metabolomics Data Analysis

Author Spotlight: Emerging Technologies and Advanced Tools for Decoding Metabolomics Data Analysis

Published on: November 10, 2023

JUMPn: A Streamlined Application for Protein Co-Expression Clustering and Network Analysis in Proteomics

JUMPn: A Streamlined Application for Protein Co-Expression Clustering and Network Analysis in Proteomics

Published on: October 19, 2021

Area of Science:

Genomics
Systems Biology
Bioinformatics

Background:

Gene interaction network identification is crucial in genomics.
Gaussian graphical models estimate networks from multiomic data via inverse covariance matrices.
High dimensionality poses challenges for traditional network inference methods, necessitating approaches like the graphical lasso.

Purpose of the Study:

To introduce a novel graphical lasso method, the tailored graphical lasso, for more effective integration of prior biological information with unknown accuracy.
To address limitations of the weighted graphical lasso, which can naively incorporate potentially misleading prior data.
To provide a flexible framework where the utility of prior information is data-driven.

Main Methods:

The tailored graphical lasso method is proposed, extending the graphical lasso and weighted graphical lasso.
An R package, tailoredGlasso, is developed for implementing the method.
The approach uses a data-determined parameter to weigh the influence of prior information.

Main Results:

The tailored graphical lasso outperforms unweighted and weighted graphical lasso methods in simulations and real multiomic data analysis.
It demonstrates superior performance across all evaluated metrics for network inference.
mRNA data is shown to provide highly valuable prior information for protein-protein interaction network construction.

Conclusions:

The tailored graphical lasso effectively utilizes accurate prior information without compromising accuracy if the prior is misleading.
This method offers a robust solution for network inference in high-dimensional biological data with uncertain prior knowledge.
It enhances the reliability and accuracy of biological network reconstruction.