GeneChip™ 果蝇基因 1.1 ST 阵列板

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.

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The NetAffx Analysis Center used to contain information and files regarding array content, probe sets, and functional annotations. However, the NetAffx Analysis Center has been retired. Much of the content provided by the NetAffx Analysis Center can now be accessed on the arrays’ thermofisher.com product pages or by contacting Technical Support (techsupport@thermofisher.com).

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The hybridization mix for PEG arrays is different from the hybridization mix for cartridges. The concentrations of the hybridization mix are different and in addition, DMSO is not added into the hybridization mix for PEG arrays because it can affect the glue that holds the PEG to the plate.

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.

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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.