Gene Expression and Transcriptome Analysis
Comprehensive Gene Expression and Transcriptome Analysis Solutions
Transcriptome analysis experiments enable researchers to characterize transcriptional activity (coding and non-coding), focus on a subset of relevant target genes and transcripts, or profile thousands of genes at once to create a global picture of cell function. Gene expression analysis studies can provide a snapshot of actively expressed genes and transcripts under various conditions.
Next-generation sequencing (NGS) capabilities have shifted the scope of transcriptomics from the interrogation of a few genes at a time to the profiling of genome-wide gene expression levels in a single experiment. Find out how NGS-based RNA sequencing (RNA-Seq) compares to other common gene expression and transcript profiling methods, gene expression microarrays and qRT-PCR. Learn how to analyze gene expression and identify novel transcripts using RNA-Seq.
Benefits of gene expression profiling with RNA-Seq
Explore the advantages of NGS for analysis of gene expression, gene regulation, and methylation.
Download eBookCommon Gene Expression Analysis Methods
A variety of methods may be used to profile gene expression for select targets of interest and/or analyze the coding transcriptome, based on your study goals. Learn about the pros and cons of several key gene expression and transcriptome analysis methods.
Gene Expression Microarrays
Pros: Familiar workflow, high sample throughput for analysis of known genes and transcripts
Cons: Inability to detect novel transcripts; gene expression measurement is limited by background at the low end and signal saturation at the high end1
Learn About Arrays vs. RNA-SeqqRT-PCR
Pros: Familiar workflow, effective for low target numbers
Cons: Can only detect known sequences, low scalability
Learn About qRT-PCR vs. RNA-SeqRNA-Seq
Pros: Broad dynamic range, can be applied to any species, and can detect both known and novel features in a single assay1
Cons: May be less cost-effective when interrogating a limited number of samples for a small set of known transcript variants
Learn More About RNA-SeqHow to Analyze Gene Expression Using RNA-Seq
Learn how to analyze gene expression using the following NGS-based RNA-Seq methods:
- mRNA Sequencing: Discover alternative transcripts, gene fusions, and allele-specific expression patterns with a clear, comprehensive view of the coding transcriptome.
- Targeted RNA Sequencing: Select and sequence specific genes or transcripts of interest. Profile gene expression for dozens to thousands of targets simultaneously.
Gene Expression Patterns in Breast Cancer
In episode 41 of the Illumina Genomics Podcast, Dr. Ake Borg discusses classification of breast tumors based on patterns of gene expression, methylation, and other genomic alterations.
Whole-Transcriptome Analysis
Learn how to capture the broad effects of gene expression changes using whole-transcriptome analysis with total RNA sequencing (total RNA-Seq). This method detects both coding and multiple forms of noncoding RNA for a comprehensive view of the entire transcriptome.
Learn More About Total RNA-Seq
Mapping Neural Diversity with Single-Cell Transcriptome Sequencing
The ability to analyze gene expression signatures from individual cells is transforming the way neurons are classified.
Featured Gene Expression and Transcriptome Research
Uncovering Drug-Susceptible Tumorigenic Pathways
Researchers use NGS-based RNA-Seq to profile biomarkers and analyze transcriptomic signatures of activated pathways in cancer samples.
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Precision Immunotherapies Using Tumor-Specific HLA Ligands
Annika Sonntag, PhD explains how using NGS helped her team obtain broader RNA data and measure exon-specific RNA expression.
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Searching for Gene Expression Profiles Associated with Cancer
Researchers discuss how they use RNA-Seq and other NGS methods to uncover cancer-associated gene expression biomarkers.
Read InterviewSpatial Transcriptomics
Unlock a New Spatial View of Multicellular Tissues
The Visium Spatial Gene Expression from 10x Genomics enables you to visualize tissue morphology overlaid with gene activity, revealing the spatial relationships between cells and how they contribute to tissue development, function, and disease state.
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Revealing the Tissue Architecture of Kidney Disease
This application note outlines an integrated workflow for spatially resolved transcriptomics and demonstrates the exceptional performance of the solution for profiling the heterogeneous pathology of diabetic kidney disease.
View App NoteGene Expression and Regulation in Plants
In this webinar, the speakers assess the effects of tissue heterogeneity on gene regulation in Arabidopsis thaliana. They characterize cell type-specific motif enrichments of large transcription factor families and examine link gene expression to changes in chromatin accessibility. Their approach provides an analytical framework to infer the gene regulatory networks that execute plant development.
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Differential Expression Analysis
To understand disease mechanisms and cell development, researchers frequently investigate differential expression in specific tissues, during development, or in response to varying conditions. RNA-Seq has been shown to detect a higher percentage of differentially expressed genes compared to expression arrays, especially genes with low abundance.1
BaseSpace Sequence Hub, our genomics cloud computing environment, offers a variety of user-friendly tools, including the BaseSpace RNA-Seq Differential Expression App. This app helps researchers perform differential gene expression analysis on RNA-Seq data for a variety of species.
Single-Cell Transcriptome Analysis of Endometrial Tissue
This study presents a pipeline for endometrial single-cell gene expression profiling. RNA-Seq was used to identify differentially expressed genes in biopsies vs. cultured individual cells.
Gene Panel and Array Finder
Identify sequencing panels or microarrays that target your genes of interest. Search panels by gene, or find genotyping arrays by genomic location, variant ID, or species.
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Featured Products
NextSeq 2000 System
Groundbreaking benchtop sequencers allow you to explore new discoveries across a variety of current and emerging applications, with higher efficiency and fewer restraints.
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TruSeq RNA Exome
Provides a reproducible, economical solution for sequencing RNA from FFPE tissues and other low-quality samples. Accuracy from as little as 10 ng total RNA.
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Ampliseq for Illumina
AmpliSeq chemistry offers a highly multiplexed PCR-based workflow for a few to hundreds of targets in a single run.
Learn MoreRelated Gene Expression Analysis Products
AmpliSeq for Illumina Transcriptome Human Gene Expression Panel
Targeted research panel that measures expression levels of >20,000 human RefSeq genes.
View ProductDRAGEN Bio-IT Platform
Provides accurate, ultra-rapid secondary analysis of sequencing data, including RNA-Seq data.
View ProductNextSeq 550 System
This benchtop sequencer enables whole-genome, transcriptome, and targeted resequencing plus microarray scanning, with tunable output and high data quality.
View ProductRelated Solutions
Cancer Gene Expression Profiling
Analyzing gene expression and transcriptome changes with RNA sequencing can help researchers understand tumor classification and progression. Learn more about cancer RNA-Seq.
Microbial Transcriptome Analysis
Bacterial, viral, and other microbial RNA-Seq experiments enable annotation and quantification of comprehensive microbial transcripts. Learn more about microbial RNA-Seq.
Complex Disease Research
Gene expression and transcriptome profiling studies can help researchers better understand neurological, immunological, and other complex diseases on a molecular level. Learn more about complex disease genomics.
Drug Response Biomarker Studies
Find out how to utilize RNA-Seq to discover and profile RNA-based drug response biomarkers. Access resources designed to help researchers adopt this application. Learn more about drug response RNA biomarker analysis.
Introduction to Spatial Transcriptomics
Explore the benefits, approaches, and research emerging from spatial transcriptomics, a comprehensive roadmap of transcriptional activity within intact tissue sections. Learn more about spatial transcriptomics.
Measuring Gene Expression From Single Cells
Highly sensitive ultra-low-input and single-cell RNA-Seq methods enable researchers to explore the biology of individual cells in complex tissues and understand cellular subpopulation responses to environmental cues. Explore Single-Cell RNA Seq.
The HumanHT-12 v4 Expression BeadChip gene expression array has been discontinued. We recommend our RNA-Seq solutions as an alternative. Illumina remains committed to providing you with high-quality support and service.
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Additional Resources
Multiomic Interrogation of the Immune System using 10x Genomics libraries
This protocol enables simultaneous profiling of gene expression, BCRs, TCRs, antigen specificity, and cell surface proteins
RNA-Seq Data Analysis
User-friendly software tools simplify mRNA-Seq data analysis for biologists, regardless of bioinformatics experience.
Explore the Transcriptome with Single‑Cell Resolution
This technical note outlines a protocol for scrnA-Seq using Chromium Single Cell Gene Expression from. 10x Genomics on illumina platforms. This method uses.
Methods Guide
All the information you need, from BeadChips to library preparation to sequencer selection and analysis. Select the best tools for your lab.
Correlated Expression of Protein and RNA with BioLegend
Optimized BEN-Seq method for simultaneous bulk protein and gene expression profiling using Illumina NGS
Titration of TotalSeq-A Universal Cocktail Antibodies in Bulk RNA-Seq Reveals Key Expression Differences
Optimized use of BioLegend TotalSeq antibodies for simultaneous detection of cell surface proteins and RNA transcripts in cell populations
Analyze Gene Expression in Single Cells
Highly sensitive RNA-Seq methods enable gene expression analysis of very low-input samples, even single cells.
References
- Zhao S, Fung-Leung WP, Bittner A, and Ngo K, Liu X. Comparison of RNA-Seq and microarray in transcriptome profiling of activated T cells. PLoS One. 2014;16;9(1):e78644.