GeneChip™ Mouse Gene 2.0 ST Array
GeneChip™ Mouse Gene 2.0 ST Array
Applied Biosystems™

GeneChip™ Mouse Gene 2.0 ST Array

The GeneChip™ Mouse Gene 2.0 ST Array is a whole-transcript array that includes probes to measure both messenger (mRNA) andRead more
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Catalog NumberNumber of Arrays
9021186 arrays
9025002 arrays
90211930 arrays
Catalog number 902118
Price (USD)
3,995.00
Each
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Number of Arrays:
6 arrays
Recurring order eligible. Learn more »
Price (USD)
3,995.00
Each
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The GeneChip™ Mouse Gene 2.0 ST Array is a whole-transcript array that includes probes to measure both messenger (mRNA) and long intergenic non-coding RNA transcripts (lincRNA). This whole-transcript array design provides a complete expression profile of mRNA as well as the intermediary lincRNA transcripts that impact the mRNA expression profile.

Since the design of the GeneChip Mouse Gene 1.0 ST Array, there has been a substantial increase in the structural and functional understanding of the mouse genome. This increase in knowledge includes the identification of a large number of long intergenic lincRNA that have been identified by the research community. In order to provide the research community with a tool that can measure the differential expression of this exciting class of RNA transcripts, we designed the GeneChip Mouse Gene 2.0 ST Array. To supplement the lincRNA data contained in RefSeq, we used sequence and transcript data from lncRNA db (http://lncrnadb.com/).

Comprehensive design
Research over the past 20 years has predominantly focused on protein-coding messenger RNA transcripts and their role in cellular processes, such as disease and development. Recently researchers have identified more that 2,000 transcripts (>200 bases) in the mouse genome with little or no protein coding potential. Only a small fraction of these non-coding RNAs has functional annotations to date. However, there is ample evidence that differential expression of lincRNAs plays an important role in the genesis and progression of disease and that aberrant expression of these molecules have also been linked to cancer. Recent advancements in transcriptome profiling provided evidence of the association of lincRNAs in diverse range of cellular functions:
• Regulation of mRNA transcription
• Regulation of mRNA post-transcriptional modifications
• Occlusion/recruitment of transcription factor binding
• Activation and transportation of transcription factors
• Interaction with accessory proteins
• Guide protein complexes to locations in the genome

Key benefits
• Comprehensive coverage provides the best opportunity to discover interesting biology
   - >28,000 coding transcripts
   - >7,000 non-coding (include ˜2,000) long intergenic non-coding transcripts
• Measure alternative splicing events/transcript variants with probes designed to maximize exon coverage
• Reproducible: Intra-lot correlation coefficient = 0.99
For Research Use Only. Not for use in diagnostic procedures.
Specifications
TypeMouse Gene 2.0 ST Array
ArrayTranscriptome Profiling
Number of Arrays6 arrays
FormatArray Cartridge
SpeciesMouse
Product LineGeneChip™
Quantity6 arrays
Unit SizeEach
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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.

Are pseudogene databases included in the design of expression arrays?

Pseudogene databases were not included in the design of expression arrays.

How long can I store labeled cDNA when working with expression microarrays?

Labeled material can be stored for 2 weeks at -20 degrees C.

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.

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