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.
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.
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 end1Learn About Arrays vs. RNA-Seq
Pros: Familiar workflow, effective for low target numbers
Cons: Can only detect known sequences, low scalabilityLearn About qRT-PCR vs. RNA-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 variantsLearn More About RNA-Seq
Learn how to analyze gene expression using the following NGS-based RNA-Seq methods:
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
Researchers use NGS-based RNA-Seq to profile biomarkers and analyze transcriptomic signatures of activated pathways in cancer samples.Read Interview
Annika Sonntag, PhD explains how using NGS helped her team obtain broader RNA data and measure exon-specific RNA expression.Read Interview
Researchers discuss how they use RNA-Seq and other NGS methods to uncover cancer-associated gene expression biomarkers.Read Interview
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.View Webinar
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.
Targeted research panel that measures expression levels of >20,000 human RefSeq genes.View Product
Provides accurate, ultra-rapid secondary analysis of sequencing data, including RNA-Seq data.View Product
This benchtop sequencer enables whole-genome, transcriptome, and targeted resequencing plus microarray scanning, with tunable output and high data quality.View Product
Analyzing gene expression and transcriptome changes with RNA sequencing can help researchers understand tumor classification and progression. Learn more about cancer RNA-Seq.
Bacterial, viral, and other microbial RNA-Seq experiments enable annotation and quantification of comprehensive microbial transcripts. Learn more about microbial RNA-Seq.
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.
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.