GeneChip™ Human Gene 2.1 ST Array Plate
Applied Biosystems™

GeneChip™ Human Gene 2.1 ST Array Plate

The Human Gene 2.1 ST 16-Array Plate and Trays provide the most accurate, sensitive, and comprehensive measurement of protein codingMás información
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Número de catálogoNúmero de arrays
90213724 matrices
90213616 matrices
90213896 matrices
Número de catálogo 902137
Precio (EUR)
-
Número de arrays:
24 matrices
The Human Gene 2.1 ST 16-Array Plate and Trays provide the most accurate, sensitive, and comprehensive measurement of protein coding and long intergenic non-coding RNA transcripts.

Comprehensive design
Keeping pace with the research community's understanding of the transcriptome, we have designed whole-transcript arrays that include probes to measure both messenger (mRNA) and long intergenic non-coding RNA transcripts (lincRNA). These whole-transcript array designs provide a complete expression profile of mRNA as well as the intermediary lincRNA transcripts that impact the mRNA expression profile.

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 10,000 transcripts (>200 bases) 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
   - >30,000 coding transcripts
   - 11,000 long intergenic non-coding transcripts
• Reproducible: Intra-lot correlation coefficient = 0.99

Content profile
Since the design of the Human Gene 1.1 ST Array Plates, there have been a massive number of new 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, the Human Gene 2.1 ST Array Plates was designed. To supplement the lincRNA data contained in RefSeq, we used sequences and transcripts from lncRNA db (www.lncrnadb.com) and Broad Institute, Human Body Map lincRNAs and TUCP (transcripts of uncertain coding potential) catalog (http://www.broadinstitute.org/genome_bio/human_lincrnas/).
For Research Use Only. Not for use in diagnostic procedures.
Especificaciones
IncluyeProbe
Línea de productosGeneChip
Cantidad1 x 24 array plate
TipoHuman Gene 2.1 ST Array Plate
matriz, red, conjuntoTranscriptome Profiling
FormatoArray Plate and Trays
Número de arrays24 matrices
EspecieHumano
Unit SizeEach

Preguntas frecuentes

What is contained in the tab-delimited format of the GeneChip probe sequence download file?

The tab-delimited probe sequence file contains the following information:
-Probe Set Name
-Probe X: The X coordinate of the probe sequence on the GeneChip probe array.
-Probe Y: The Y coordinate of the probe sequence on the GeneChip probe array.
-Probe Interrogation Position: The base position on the consensus/exemplar sequence where the central base of the probe aligns, which is the 13th base of a 25mer probe.
-Probe Sequence: The 25-base perfect match sequence.
-Target Strandedness: The sense/antisense orientation of the target sequence that can hybridize with the probe sequence.

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.