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

DNA Isolation

DNA Isolation

DNA isolation protocols can be fast and straightforward or complex and time-consuming depending on the type and quality of DNA required for further processing. For example, plasmid DNA extraction is a bit more complicated than genomic DNA extraction because of the need for an appropriate lysis method to separate plasmid DNA from gDNA during isolation. However, for specific applications, such as long-range DNA sequencing that require a good yield of high- quality DNA samples, we need to follow...

You might also read

Related Articles

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

Sort by

Same author

scCOSMIX: A Mixed-Effects Framework for Differential Coexpression and Transcriptional Interactions Modeling in Single-Cell RNA-Seq.

Statistics in medicine·2025

Same author

Time-coexpress: temporal trajectory modeling of dynamic gene co-expression patterns using single-cell transcriptomics data.

BMC bioinformatics·2025

Same author

Multi-omics Integrative Analysis for Incomplete Data Using Weighted <i>p</i>-Value Adjustment Approaches.

Journal of agricultural, biological, and environmental statistics·2025

Same author

TIME-CoExpress: Temporal Trajectory Modeling of Dynamic Gene Co-expression Patterns Using Single-Cell Transcriptomics Data.

bioRxiv : the preprint server for biology·2025

Same author

Sex Differences in Aging-related Myocardial Stiffening Quantitatively Measured with MR Elastography.

Radiology. Cardiothoracic imaging·2024

Same author

Genome-wide search algorithms for identifying dynamic gene co-expression via Bayesian variable selection.

Statistics in medicine·2023

Same journal

OpenIMC: an open-source platform for analyzing single-cell and spatial proteomics by imaging mass cytometry.

BMC bioinformatics·2026

Same journal

NAP: an open source pipeline for cross-domain microbiome profiling using Nanopore sequencing-derived amplicon data.

BMC bioinformatics·2026

Same journal

SurvGME: an R package for survival analysis with graphical and measurement error models.

BMC bioinformatics·2026

Same journal

SimMapNet: a Bayesian framework for gene regulatory network inference using gene ontology similarities as external hint.

BMC bioinformatics·2026

Same journal

Dual channel drug-drug interactions extraction based on cross attention.

BMC bioinformatics·2026

Same journal

FeSseqdb: a curated sequence-level database and interpretable machine learning framework for identifying iron-sulfur proteins.

BMC bioinformatics·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Apr 20, 2026

Author Spotlight: Comprehensive Epigenetic Analysis for Investigating Human Cellular Plasticity and Environmental Adaptation Using Immunofluorescence Assays

Author Spotlight: Comprehensive Epigenetic Analysis for Investigating Human Cellular Plasticity and Environmental Adaptation Using Immunofluorescence Assays

Published on: June 28, 2024

An efficient algorithm to explore liquid association on a genome-wide scale.

Tina Gunderson¹, Yen-Yi Ho²

¹Division of Biostatistics, School of Public Health, University of Minnesota, 420 Delaware St. S.E., MMC 303, Minneapolis, 55455, MN, USA. gunderson.tina@gmail.com.

BMC Bioinformatics

|November 29, 2014

Summary

This summary is machine-generated.

A new algorithm efficiently screens gene expression data for liquid association (LA), identifying gene triplets involved in fundamental yeast processes like energy regulation and sporulation with high accuracy.

More Related Videos

An Ultrahigh-throughput Microfluidic Platform for Single-cell Genome Sequencing

An Ultrahigh-throughput Microfluidic Platform for Single-cell Genome Sequencing

Published on: May 23, 2018

A Simple Fractionated Extraction Method for the Comprehensive Analysis of Metabolites, Lipids, and Proteins from a Single Sample

A Simple Fractionated Extraction Method for the Comprehensive Analysis of Metabolites, Lipids, and Proteins from a Single Sample

Published on: June 1, 2017

Related Experiment Videos

Last Updated: Apr 20, 2026

Author Spotlight: Comprehensive Epigenetic Analysis for Investigating Human Cellular Plasticity and Environmental Adaptation Using Immunofluorescence Assays

Author Spotlight: Comprehensive Epigenetic Analysis for Investigating Human Cellular Plasticity and Environmental Adaptation Using Immunofluorescence Assays

Published on: June 28, 2024

An Ultrahigh-throughput Microfluidic Platform for Single-cell Genome Sequencing

An Ultrahigh-throughput Microfluidic Platform for Single-cell Genome Sequencing

Published on: May 23, 2018

A Simple Fractionated Extraction Method for the Comprehensive Analysis of Metabolites, Lipids, and Proteins from a Single Sample

A Simple Fractionated Extraction Method for the Comprehensive Analysis of Metabolites, Lipids, and Proteins from a Single Sample

Published on: June 1, 2017

Area of Science:

Genomics
Bioinformatics
Systems Biology

Background:

Publicly available gene expression data enables systemic studies of gene interactions.
Liquid association (LA) measures how gene coexpression varies with a controller gene's expression.
Genome-wide LA estimation is challenging and resource-intensive.

Purpose of the Study:

To develop a novel, efficient screening algorithm for genome-wide LA estimation.
To apply the algorithm to Saccharomyces cerevisiae data for identifying significant gene triplets.
To facilitate large-scale analysis of gene regulatory networks.

Main Methods:

Developed a fast screening algorithm for LA estimation.
Applied the algorithm to a Saccharomyces cerevisiae gene expression dataset.
Utilized exploratory gene set enrichment analysis.

Main Results:

>99.8% agreement between the screening algorithm and exhaustive search on a data subset.
81-93% reduction in run time compared to exhaustive methods.
Identified significant gene triplets in yeast cell-cycle pathways, with controller genes linked to energy regulation, transportation, and sporulation.

Conclusions:

A novel and efficient algorithm for genome-wide LA exploration was developed.
The algorithm successfully identified biologically relevant gene triplets in yeast.
A software package, fastLiquidAssociation, is available for public use.