Clariom™ D Pico Assay, human
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Applied Biosystems™

Clariom™ D Pico Assay, human

Accelerate your biomarker discovery from deep within the transcriptome with Clariom D Pico Assays for human samples, the next generationRead more
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Catalog NumberNumber of Arrays
90292530 arrays
90292412 arrays
Catalog number 902925
Price (USD)
-
Number of Arrays:
30 arrays
Accelerate your biomarker discovery from deep within the transcriptome with Clariom D Pico Assays for human samples, the next generation of transcriptome-level expression profiling tools. Human Clariom D Pico Assays provide a highly detailed view of the transcriptome and offer the fastest path to the results you need for your research. Clariom D Pico Assays allow translational research scientists to generate high-fidelity biomarker signatures quickly and easily. Based on industry-leading microarray technology, the novel Clariom D Pico Assay design provides the most intricate transcriptome-wide gene- and exon-level expression profiles, including the ability to detect alternative splicing events of coding and long non-coding (lnc)RNA, in a single three-day experiment.

Expand your potential to discover novel, informative biomarkers
The number of known transcribed genes has expanded rapidly in recent years, providing more sources for actionable biomarkers, such as transcript variants and lncRNA, that can be used for clinical utility and advancing our understanding of disease mechanisms. Such biomarkers can be missed by lengthy, complex, and costly sequencing and targeted expression approaches, leading to irreproducible signatures and wasted time and money.

With full coverage of the transcribed genome including all known coding and non-coding splice variants, compatibility with clinical sample types, and flexible data analysis software, Clariom D Pico assays are the premier tools for translational researchers performing complex expression biomarker discovery studies and wanting the fastest path to robust, clinically relevant, and actionable results.

Get all the data you need
• Rapidly identify complex disease signatures from >540,000 transcripts sourced from the largest number of public databases, the most comprehensive coverage of the human transcriptome available, to ensure biomarkers are not missed.
• Confidently detect genes, exons, and alternative splicing events that give rise to coding RNA and lncRNA isoforms.
• Detect rare and low-expressing transcripts otherwise not detected by common sequencing approaches.
• Go from data to insight in minutes with intuitive, highly visual, free analysis software.

When you have precious samples, get it right the first time.
• Generate robust expression profiles from as little as 100 pg of total RNA—as few as 10 cells.
• Utilize RNA from various sample types including blood, cells, and fresh/fresh-frozen or FFPE tissues.
• Preserve sample integrity and reduce data variability with an assay that does not require a globin or rRNA removal step.

Clariom D solutions are available in a single sample (cartridge array) format for use on the GeneChip™ 3000 instrument system and include reagents and fast, simple Transcriptome Analysis Console (TAC) software to analyze and visualize global expression patterns of genes, exons, pathways, and alternative splicing events.

Get the coverage you require, the reproducibility you need, and the insights you want to act on your discoveries.
For Research Use Only. Not for use in diagnostic procedures.
Specifications
TypeD Pico Assay
ArrayTranscriptome Profiling
Number of Arrays30 arrays
FormatArray Cartridge
SpeciesHuman
Product LineApplied Biosystems™
Shipping ConditionApproved for shipment at Room Temperature or on Wet or Dry Ice
Unit SizeEach
Contents & Storage
• Control HeLA RNA, store at -20°C
• Hybridization Control Kit, store at -20°C
• WT Pico Amplification Kit, Module 1, store at -20°C
• WT Pico Amplification Kit, Module 2, store at -20°C
• WT Pico Amplification Kit, Module 3, store at 4°C
• Clariom D 30 Arrays, human, store at 4°C

Frequently asked questions (FAQs)

What reagent kit should I use with my array?

Please refer to the Microarray Reagent Guide for Arrays and Expression Kits to match the correct reagents your array.

Find additional tips, troubleshooting help, and resources within our Microarray Analysis Support Center.

What is an Event Score in TAC 4.0 Software?

TAC 4.0 includes two algorithms for identifying alternative splicing events: the TAC 2.0 algorithm and the new EventPointer. Algorithmic determination of alternate splicing remains a challenging problem. TAC 4.0 supports two different approaches that have different sets of strengths and weaknesses. After considerable testing, the new TAC 4.0 “'Event Score” leverages both previous TAC 2.0 event estimation score and Event Pointer p-value and sorts the most likely alternative splicing events to the top. Of course, the TAC 2.0 event score and EventPointer p-values remain individually available.

Find additional tips, troubleshooting help, and resources within our Microarray Analysis Support Center.

What are the new software components of TAC 4.0?

LIMMA: LIMMA stands for Linear Models for MicroArray data. It is an R/Bioconductor software package that provides an integrated solution for analyzing data from gene expression experiments. It contains rich features for handling complex experimental designs and for information borrowing to overcome the problem of small sample sizes. Over the past decade, LIMMA has been a popular choice for gene discovery through differential expression analyses of microarray data. There are ˜8000 citations using LIMMA and Affymetrix arrays. The TAC 4.0 interface exposes the core differential expression analysis functionality including real covariates and random factors. In addition, the interface simplifies the creation of the design and contrast matrices that specify the experimental design and comparisons for the analysis.

Batch Effect Adjustment: Batch effects are systematic changes in microarray sample intensities that reflect changes in the assay sometimes found in different batches. These effects occur more commonly in larger studies in which all of the samples cannot be processed at the same time. TAC 4.0 enables the interface to the ComBat batch adjustment algorithm, which can remove the batch effects from the signals.

EventPointer: EventPointer is a Bioconductor package that identifies alternative splicing events in microarray data. TAC 4.0 incorporates an interface to this package.

Exploratory Grouping Analysis: Exploratory Grouping Analysis (EGA) is an interface to a set of R packages that offer the ability to examine the relationships between multiple microarray samples. While the scientist typically has a preconceived idea regarding the classification of the samples in an experiment, the resulting data often show additional substructure due to unexpected biological differences or batch effects. The EGA interface enables the identification of this substructure. Biological differences can be further explored using LIMMA differential expression analysis. Batch effects can be removed using ComBat to prevent them from obscuring the biology of interest.

Find additional tips, troubleshooting help, and resources within our Microarray Analysis Support Center.

If I have TAC 3.1 .TAC files (TAC analysis files), can I load these into TAC 4.0 Software or will I need to reanalyze?

TAC 3.1 .TAC files cannot be opened in TAC 4.0 Software. Studies will need to be reprocessed in TAC 4.0. The new analysis can be run from .CEL files or .CHP files.

Find additional tips, troubleshooting help, and resources within our Microarray Analysis Support Center.

In TAC 4.0 Software, can I measure the quality of a single hybridization without the rest of the experiment?

We do not recommend this. In large-scale expression experiments using similar sample types, researchers are likely to develop their own single-array guidelines on what metric values are predictive of high- or poor-quality samples. However, these guidelines are likely to be dependent on sample type and we are unable to recommend such guidelines for all possible situations. Note that the trend toward favoring model-based signal estimation algorithms (for all microarray experiments even beyond the Thermo Fisher platform) makes single-array quality determination very difficult due to the necessity of simultaneously analyzing multiple arrays to calculate signal estimates.

Find additional tips, troubleshooting help, and resources within our Microarray Analysis Support Center.