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.
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
Precise results
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.
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One experiment; multiple layers of biology
Clariom D-human content is derived from sixteen databases, greatly exceeding the number used in most RNA-Seq analysis pipelines. This allows exploration of all known coding and non-coding genes, exons, and isoforms.
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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.
With simple and free TAC software you can:
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
SEQC/MAQC-III Consortium. A comprehensive assessment of RNA-Seq accuracy, reproducibility and information content by the Sequencing Quality Control Consortium. Nature Biotechnology32(9):903–914 (2014).
Hou, R., et al. Impact of the next-generation sequencing data depth on various biological result inferences. Science China Life Sciences56(2):104–109 (2013).
Liu, Y., et al. Evaluating the impact of sequencing depth on transcriptome profiling in human adipose. PLoS One8(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 Biology3(1):a003756 (2011).
Necsulea, A., et al. The evolution of lncRNA repertoires and expression patterns in tetrapods. Nature505(7485):635–640 (2014).
Mills, J. D., et al. Strand-specific RNA-Seq provides greater resolution of transcriptome profiling. Current Genomics14(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 America108(9):3707–3712 (2011).
Sanchez, Y., et al. Long non-coding RNAs: challenges for diagnosis and therapies. Nucleic Acid Therapeutics23(1):15–20 (2013).
Wang, E. T., et al. Alternative isoform regulation in human tissue transcriptomes. Nature456(7221):470–476 (2008).
Pan, Q., et al. Deep surveying of alternative splicing complexity in the human transcriptome by high-throughput sequencing. Nature Genetics40(12):1413–1415 (2008).
Le, K. Q., et al. Alternative splicing as a biomarker and potential target for drug discovery. Acta Pharmacologica Sinica36(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 Biotechnology32(9):915–925 (2014).
Lahens, N. F., et al. IVT-seq reveals extreme bias in RNA sequencing. Genome Biology15(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.