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

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 proteomics...

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

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

Nature cancer·2026

Same author

Publisher Correction: Single-cell epigenetic and transcriptomic states across the continuum of monoclonal B cell lymphocytosis to chronic lymphocytic leukemia.

Genome biology·2026

Same author

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

Nature cancer·2026

Same author

Dissecting the Tumor Microenvironment to Identify Biomarkers of Outcome to Anti-PD-1 Therapy in Clear Cell Renal Cell Carcinoma: analyses of the HCRN GU16-260 trial.

Clinical cancer research : an official journal of the American Association for Cancer Research·2026

Same author

Unfinished Business in Chronic Lymphocytic Leukemia: Translational and Clinical Priorities for a Cure.

Blood·2026

Same author

Single-cell epigenetic and transcriptomic states across the continuum of monoclonal B cell lymphocytosis to chronic lymphocytic leukemia.

Genome biology·2026

Same journal

Mapping the 3D Chromosome Organization of a Biosynthetic Gene Cluster by Capture Hi-C (CHi-C).

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

Same journal

Mapping the 3D Chromosome Organization of Streptomyces by Hi-C.

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

Same journal

CUT&Tag Epigenomic Profiling of Biosynthetic Gene Clusters in Arabidopsis thaliana.

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

Same journal

Rhizobium rhizogenes-Mediated Hairy Root Transformation Protocol for Lotus japonicus and Other Legumes.

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

Same journal

Characterization of Bioactive Saponins from Sea Cucumbers.

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

Same journal

Methods for Functional Validation of Terpenoid Metabolic Clusters in Nicotiana benthamiana and Aspergillus oryzae.

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

See all related articles

Search research articles

Related Experiment Video

Updated: Jun 4, 2026

Extracellular Protein Microarray Technology for High Throughput Detection of Low Affinity Receptor-Ligand Interactions

Extracellular Protein Microarray Technology for High Throughput Detection of Low Affinity Receptor-Ligand Interactions

Published on: January 7, 2019

Data processing and analysis for protein microarrays.

David S DeLuca¹, Ovidiu Marina, Surajit Ray

¹Cancer Vaccine Center, Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA.

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

|March 4, 2011

Summary

This summary is machine-generated.

This study details essential bioinformatics algorithms for analyzing high-throughput protein microarray data. It focuses on methods like concentration dependent analysis (CDA) for robust biological insights.

More Related Videos

Processing the Loblolly Pine PtGen2 cDNA Microarray

Processing the Loblolly Pine PtGen2 cDNA Microarray

Published on: March 20, 2009

Probing High-density Functional Protein Microarrays to Detect Protein-protein Interactions

Probing High-density Functional Protein Microarrays to Detect Protein-protein Interactions

Published on: August 2, 2015

Related Experiment Videos

Last Updated: Jun 4, 2026

Extracellular Protein Microarray Technology for High Throughput Detection of Low Affinity Receptor-Ligand Interactions

Extracellular Protein Microarray Technology for High Throughput Detection of Low Affinity Receptor-Ligand Interactions

Published on: January 7, 2019

Processing the Loblolly Pine PtGen2 cDNA Microarray

Processing the Loblolly Pine PtGen2 cDNA Microarray

Published on: March 20, 2009

Probing High-density Functional Protein Microarrays to Detect Protein-protein Interactions

Probing High-density Functional Protein Microarrays to Detect Protein-protein Interactions

Published on: August 2, 2015

Area of Science:

Proteomics
Bioinformatics
Biotechnology

Background:

Protein microarrays generate substantial proteomics data for research and diagnostics.
Effective interpretation requires advanced bioinformatics and statistical analysis.

Purpose of the Study:

To describe essential algorithms for processing protein microarray data.
To present available tools and a step-by-step approach for data analysis.
To discuss fundamental and practical considerations in protein microarray analysis.

Main Methods:

Spot-finding algorithms for image analysis.
Statistical calculations including Z score, Significance Analysis of Microarrays (SAM), and Concentration Dependent Analysis (CDA).
Template for a step-by-step analysis workflow centered on CDA.

Main Results:

Essential algorithms for protein microarray data processing are outlined.
Available tools for analysis are described.
A practical, step-by-step approach using CDA is provided.

Conclusions:

Accurate analysis of protein microarray data is crucial for biological discovery.
Bioinformatics tools and statistical methods, particularly CDA, are vital for extracting meaningful conclusions.
The described methods and template facilitate robust protein microarray data interpretation.