Arrays or RNA-Seq?
Choose the right tool for the job
Are you leveraging the appropriate technology for your research? The scientific community is now widely acknowledging that there are specific applications best served by arrays and others by RNA sequencing (RNA-Seq). Additionally, many see opportunities to harness the power of both technologies for expression studies.
Look beyond the gene
Expand your biomarker discovery universe
Due to the complexity of the transcriptome, scientists are now aware of the importance of expanding their scope beyond the gene level. Gaining attention as critical regulators of coding RNA and alternative splicing, lncRNA have been implicated in a wide range of diseases, opening up the possibility of their use as biomarkers and therapeutic targets.8 In addition, because an estimated 95% of human genes undergo alternative splicing,9,10 and the disruption of such events is known to be highly associated with many diseases,9 alternatively spliced variants are also promising candidates for biomarkers.11
A growing body of evidence has shown that identifying lncRNA and alternative splicing events can be very challenging with RNA-Seq.
- To reveal low-abundance transcripts and splice junctions, very deep sequencing is required— which is not cost effective.12
- Detection of alternative splicing events with RNA-Seq is challenging due to sampling noise, requiring >300 million reads providing only 80% confidence.1,3
- Due to significant biases introduced in library preparation, interpreting exon-level RNA-Seq results—especially when looking for alternative splicing events—should be done with caution.13
Go beyond gene-level with Clariom D solutions
Accuracy for RNA-Seq is read-depth dependent. Clariom D solutions are designed to deliver accurate results equivalent to two full lanes of RNA-Seq.
Choose Clariom D solutions
Simple workflow. Fast analysis. Cost effective.
Human, mouse, and rat assays from Applied Biosystems allow researchers to:
- Generate comprehensive datasets quickly across known pathways and genes, allowing time-consuming RNA-Seq experiments to be focused on discovery of unknown transcripts
- Perform global gene expression profiling with as little as 100 pg of RNA or 500 pg of degraded formalin-fixed, paraffin-embedded (FFPE) RNA— sample inputs which are not easily amenable to RNA-Seq
- Better quantify low-abundance transcripts7
- Validate complex RNA-Seq data quickly and easily
- Quickly and cost-effectively complete high-volume studies7
- Analyze data in minutes with free Transcriptome Analysis Console (TAC) software
Improve turnaround time. Data to insight in minutes.
- Identify genes, exons, and alternative splicing events
- Explore expression changes across networks of miRNA and target genes
- Visualize gene models with exon and junction signals
- Filter on genes and pathways of interest
- Link directly to multiple public databases
- View data in multiple formats—volcano and scatter plots; mRNA-miRNA interaction networks; chromosome summaries; hierarchical clustering and transcript isoform views; WikiPathways integration
Take gene expression studies further, faster
Combine sequencing with arrays
- SEQC/MAQC-III Consortium. A comprehensive assessment of RNA-Seq accuracy, reproducibility and information content by the Sequencing Quality Control Consortium.
Nature Biotechnology 32(9):903–914 (2014).
- Hou, R., et al. Impact of the next-generation sequencing data depth on various biological result inferences.
Science China Life Sciences 56(2):104–109 (2013).
- Liu, Y., et al. Evaluating the impact of sequencing depth on transcriptome profiling in human adipose.
PLoS One 8(6):e66883 (2013).
- Wang, X., et al. The long arm of long noncoding RNAs: Roles as sensors regulating gene transcriptional programs.
Cold Spring Harbor Perspectives in Biology 3(1):a003756 (2011).
- Necsulea, A., et al. The evolution of lncRNA repertoires and expression patterns in tetrapods.
Nature 505(7485):635–640 (2014).
- Mills, J. D., et al. Strand-specific RNA-Seq provides greater resolution of transcriptome profiling.
Current Genomics 14(3):173–181 (2013).
- Xu, W., et al. Human transcriptome array for high throughput clinical studies.
Proceedings of the National Academy of Sciences of the United States of America 108(9):3707–3712 (2011).
- Sanchez, Y., et al. Long non-coding RNAs: challenges for diagnosis and therapies.
Nucleic Acid Therapeutics 23(1):15–20 (2013).
- Wang, E. T., et al. Alternative isoform regulation in human tissue transcriptomes.
Nature 456(7221):470–476 (2008).
- Pan, Q., et al. Deep surveying of alternative splicing complexity in the human transcriptome by high-throughput sequencing.
Nature Genetics 40(12):1413–1415 (2008).
- Le, K. Q., et al. Alternative splicing as a biomarker and potential target for drug discovery.
Acta Pharmacologica Sinica 36(10):1212–1218 (2015).
- Li, S., et al. Multi-platform assessment of transcriptome profiling using RNA-Seq in the ABRF next-generation sequencing study.
Nature Biotechnology 32(9):915–925 (2014).
- Lahens, N. F., et al. IVT-seq reveals extreme bias in RNA sequencing.
Genome Biology 15(6):R86 (2014).
- Strandedness is preserved in the following sample preparation kits: GeneChip™ WT Pico Kit; GeneChip™ WT PLUS Reagent Kit; GeneChip™ 3’ IVT PLUS Reagent Kit; SensationPlus™ FFPE Amplification and 3’ IVT Labeling Kit.
- No rRNA or globin mRNA reduction required for the following sample preparation kits: GeneChip™ WT Pico Kit; GeneChip™ WT PLUS Reagent Kit; GeneChip™ IVT Pico Kit; SensationPlus™ FFPE Amplification and WT Labeling Kit; SensationPlus™ FFPE Amplification and 3’ IVT Labeling Kit. GeneChip™ 3’ IVT PLUS Reagent Kit requires globin mRNA reduction.
For Research Use Only. Not for use in diagnostic procedures.