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 to analyze gene expression and identify novel transcripts using NGS-based RNA sequencing (RNA-Seq) methods. Illumina offers comprehensive solutions for a broad range of sample types.
RNA-Seq provides a unique combination of transcriptome-wide coverage, sensitivity, and accuracy for a comprehensive view of gene expression changes. Illumina RNA-Seq methods provide precise measurement of strand orientation, uniform coverage, and high confidence mapping of alternate transcripts and gene fusions. Discover novel gene isoforms, profile gene expression for select targets of interest, analyze the coding transcriptome, and accurately measure transcript abundance and fold changes in expression.
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.
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 apps such as DESeq2 can help researchers perform differential gene expression analysis on RNA-Seq data for a wide variety of species.
Researchers use NGS-based RNA-Seq to profile biomarkers and analyze transcriptomic signatures of activated pathways in cancer samples.Read Interview
Researchers use whole-genome sequencing to predict cancer gene expression status based on nucleosome patterns, and RNA-Seq to confirm gene expression.Read Interview
Researchers use RNA-Seq and other NGS methods to uncover cancer-associated gene expression biomarkers.Read Interview
While gene expression microarrays are effective for identifying the expression of known genes and transcripts, they cannot detect previously unidentified transcripts. In contrast, RNA-Seq offers a comprehensive solution for analyzing gene expression, allowing researchers to detect both known and novel genes and transcripts in a single assay. Learn more about RNA-Seq advantages over arrays.
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.