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

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...

Proteomics

Proteomics

A proteome is the entire set of proteins that a cell type produces. We can study proteomes using the knowledge of genomes because genes code for mRNAs, and the mRNAs encode proteins. Although mRNA analysis is a step in the right direction, not all mRNAs are translated into proteins.
Proteomics is the study of proteomes' function. It involves the large-scale systematic study of the proteome to denote the protein complement expressed by a genome. Scientist Mark Wilkins coined the term...

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

You might also read

Related Articles

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

Sort by

Same author

Structural characterization and functional evaluation of deer sinew peptide-calcium chelate for intestinal calcium transport and osteogenic differentiation.

International journal of biological macromolecules·2026

Same author

Convergence of neuroinflammation across major neurotropic viral exposomes in AD and ADRD.

Journal of neuroinflammation·2026

Same author

An integrated single-cell and spatial proteotranscriptomics atlas of fibroblast-driven immunoregulation within the human adult oral cavity.

Cell press blue·2026

Same author

CD38⁺ endothelial remodeling marks spatially patterned vasculopathy in rapidly advancing periodontitis and peri-implantitis.

Nature communications·2026

Same author

E-PTES-S: Enhanced Trust Evaluation via Multidimensional Spatiotemporal Fusion and Variance-Based Stability Sequence Extraction in IoT Sensing Networks.

Sensors (Basel, Switzerland)·2026

Same author

The Potential of Plant-Derived Foods to Treat Glaucoma: A Review.

Nutrients·2026

Same journal

conMItion: an R package adjusting confounding factors for associations in multi-omics.

Bioinformatics (Oxford, England)·2026

Same journal

SpaMFG: a Spatial Multi-omics Integration Method based on Feature Grouping.

Bioinformatics (Oxford, England)·2026

Same journal

CSCN: Inference of Cell-Specific Causal Networks Using Single-Cell RNA-Seq Data.

Bioinformatics (Oxford, England)·2026

Same journal

Sparse CCA-Based Mediation Analysis with High-Dimensional Exposures and Mediators.

Bioinformatics (Oxford, England)·2026

Same journal

Enhancing Cross-Context Generalization in Drug Perturbation Prediction with a Multimodal Conditional Diffusion Framework.

Bioinformatics (Oxford, England)·2026

Same journal

Primer Design through Submodular Function Estimation.

Bioinformatics (Oxford, England)·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Jun 27, 2025

An Integrated Approach for Microprotein Identification and Sequence Analysis

An Integrated Approach for Microprotein Identification and Sequence Analysis

Published on: July 12, 2022

BTR: a bioinformatics tool recommendation system.

Ryan Green¹, Xufeng Qu², Jinze Liu²

¹Department of Computer Science, University of Cincinnati, Cincinnati 45219, United States.

Bioinformatics (Oxford, England)

|April 25, 2024

Summary

This summary is machine-generated.

This study introduces the Bioinformatics Tool Recommendation system (BTR), a deep learning model that helps researchers select appropriate computational tools for bioinformatics analysis pipelines, improving workflow development.

More Related Videos

A Practical Guide to Phylogenetics for Nonexperts

A Practical Guide to Phylogenetics for Nonexperts

Published on: February 5, 2014

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

Related Experiment Videos

Last Updated: Jun 27, 2025

An Integrated Approach for Microprotein Identification and Sequence Analysis

An Integrated Approach for Microprotein Identification and Sequence Analysis

Published on: July 12, 2022

A Practical Guide to Phylogenetics for Nonexperts

A Practical Guide to Phylogenetics for Nonexperts

Published on: February 5, 2014

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

Area of Science:

Bioinformatics
Computational Biology
Scientific Workflow Management

Background:

The rapid growth of bioinformatics necessitates efficient methods for constructing complex computational analysis pipelines.
Researchers often face challenges selecting appropriate tools due to the vast and evolving landscape of bioinformatics software.
Lack of domain expertise or unfamiliarity with specific fields can lead to suboptimal tool choices in workflow development.

Purpose of the Study:

To develop an automated system for recommending suitable bioinformatics tools for scientific analysis pipelines.
To address the challenges faced by researchers in selecting appropriate computational tools for workflow construction.
To enhance the efficiency and accuracy of bioinformatics workflow development.

Main Methods:

Developed the Bioinformatics Tool Recommendation system (BTR), a deep learning model.
Utilized graph neural networks to represent bioinformatics workflows as graphs, capturing contextual information.
Integrated natural language processing techniques to analyze tool descriptions for enhanced recommendation accuracy.

Main Results:

The BTR system demonstrated superior performance compared to the existing Galaxy tool recommendation system.
The model effectively recommends suitable tools by analyzing workflow context and tool descriptions.
Experimental results validate the potential of BTR to streamline the scientific workflow construction process.

Conclusions:

The Bioinformatics Tool Recommendation system (BTR) offers a promising solution for simplifying tool selection in bioinformatics.
Deep learning, particularly graph neural networks and NLP, can significantly improve the automation of bioinformatics workflow development.
BTR has the potential to empower both novice and expert researchers in building robust and efficient analysis pipelines.