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View additional product information for GeneChip™ Human Gene 1.0 ST Array - FAQs (901087, 901086, 901085)
34 product FAQs found
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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Please refer to the Microarray Reagent Guide for Arrays and Expression Kits to match the correct reagents your array.
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Pseudogene databases were not included in the design of expression arrays.
Labeled material can be stored for 2 weeks at -20 degrees C.
The protocols for the Gene 1.0 ST and Exon 1.0 ST arrays are the same until the fragmentation and labeling steps.
Depending on the input amount, please refer to the GeneChip WT Pico Reagent Kit or the GeneChip WT PLUS Reagent Kit for the protocol:
- WT Pico: 50 - 500ng of total RNA
- WT PLUS: ≥ 100pg of total RNA (approx. 10 cells)
From the hybridization step onwards, the differences in protocol are due to the format of the arrays. The Exon array is a 49 format array, whereas the Gene 1.0 ST array is a 169 format array.
For the fluidics step the following scripts should be used depending upon which array you are using:
- Gene 1.0 St array: FS450_0001
- Exon 1.0 St array: FS450_0007
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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.
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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.
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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.
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If a sample appears “on the line”, it is best to leave the sample in the experiment for analysis and simply flag it as questionable. During the initial analysis, treat all samples uniformly. Once candidate genes have been identified, review how the questionable sample changes data relative to the other replicates within the sample. It is always possible to remove a sample later in the analysis workflows.
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Pos_vs_Neg_AUC is a good first-pass metric. A value below 0.7 is an indicator that sample problems may exist. However, a value greater than 0.7 does not guarantee that the sample is good.
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Users will benefit from using the SST-RMA when comparing data with those processed using the RMA summarization only. RMA is the suggested analysis pipeline to use when doing QC on FFPE or degraded samples. For downstream analysis of FFPE or degraded samples, SST-RMA is the suggested analysis pipeline to use.
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When filtering by fold-change cutoffs, expect a larger number of differentially expressed genes compared to RMA. When comparing the number of differentially expressed genes (defined by fold-change filters) to other methods, such as RT-PCR and RNA-Seq, expect this number to align more with these methods when using SST-RMA. There is no impact on sensitivity or specificity of data. SST-RMA was designed to address comparability to other technologies. The same experimental design recommendations still apply when designing an expression study with microarrays.
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Historically, microarrays were perceived to underestimate fold-change values when compared to other methods such as RT-PCR. For customers who are filtering using fold-change cutoff, SST-RMA addresses this “fold-change compression” by applying a GC correction and also by transforming the microarray data signal to a similar signal space of other methods in a pre-processing step prior to RMA. No changes have been made to RMA. SST-RMA is the default algorithm for Human Transcriptome Arrays and Clariom D Human, Mouse, and Rat assays.
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Library files are on each array type product page. Alternatively, library files can be installed directly through TAC 4.0 by going to the 'Preferences' tab and clicking the 'Download Library Files' button located at the top left (NetAffx account required).
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Due to the amount of memory that TAC needs to operate, we STRONGLY recommend that you DO NOT install TAC 4.0 on production AGCC computers being used for scanning and operating fluidics systems.
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Both .CEL and .CHP files are accepted in TAC 4.0 Software. The exception will be previous *.exon*. CHP files and *.alt-splice*. CHP files. These two types of CHP files are no longer supported in TAC 4.0. .ARR files are recommended as they contain sample attribute information. The .CHP file is generated by TAC 4.0 in the normalization and summarization process.
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TAC 4.0 is a new software that combines many of the features of Expression Console 1.4 with the biological analysis capabilities of TAC into one workflow. Some of the new features include:
- Perform QC and statistical analysis in one software
- Ability to analyze 1000 samples
- Integration of LIMMA package (LInear Modeling for MicroArrays package) into TAC 4.0 to analyze complex data
- Perform batch effect adjustment (new)
- Perform repeated measure analysis (improved in TAC 4.0)
- Perform random factor analysis (new)
- Perform real covariate analysis (new)
- Ability to perform Exploratory Grouping Analysis (EGA)
- Integration of Event Pointer algorithm for Alternative Splicing Analysis
- Improved visualization with Splicing Viewer
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Expression array CEL files are processed and analyzed in TAC 4.0 Software. This is a free download from the Thermo Fisher website for processing CEL files.
Here are the minimum requirements:
Microsoft Windows 7 and 10 (64 bit) Professional
CPU: 2.83 GHz
Memory: 8 GB RAM
Hard drive: 150 GB free hard disk space
Web browser: Internet Explorer 11 or greater and Microsoft Edge
Here is the recommended hardware:
Microsoft Windows 7 and 10 (64 bit) Professional
CPU: Intel Pentium 4X 2.83 GHz processor (Quad Core)
Memory: 16 GB RAM
Hard drive: 150 GB free hard disk space
Web browser: Internet Explorer 11 or greater and Microsoft Edge
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You should re-analyze your data if comparing with RT-PCR or RNA-Seq data using fold change values for filtering.
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Based on mitomap, approximately 87 exons from the mitochondrial transcriptome are represented on this array.
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Approximately 190 unprocessed human microRNA sequences from the Sanger MicroRNA Registry are represented on this array. Although the probe sets are present, the current WT Sense Target Labeling Assay has not been tested or optimized to efficiently label the very small RNA molecules. Therefore, the utility of the system to measure microRNA expression is uncertain at this moment.
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-Information about the annotation types supporting each probe set
-Genome coordinates for all PSRs, probe sets, and probes
-Various quality information about the probe and probe sets (i.e., number of overlapping probes in a probe set)
-Some biological annotation (i.e., gene names)
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A variety of controls are included on the Exon Array, designed specifically to facilitate the application of genome-wide exon-level expression profiling. These controls include:
Intron controls--for approximately 100 genes with relatively constitutive expression, both exon-based and intron-based probe sets were tiled. The intron/exon normalization control probe sets can be used to monitor contamination from genomic DNA, hnRNA, as well as to provide a baseline for experiment quality control.
Hybridization controls--bioB, vbioC, bioD and cre
Poly-A RNA controls--lys, dap, phe, and thr
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A significant amount of research was dedicated to developing optimal algorithms for generating the design to both ensure comprehensive coverage of putative exons and also prevent over-fragmentation. Many of these design criteria are discussed in the Genechip Exon Array Design Technical Note (https://tools.thermofisher.com/content/sfs/brochures/exon_array_design_technote.pdf), such as restricting exon fragmentation to only those ESTs with consensus splice sites.
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All annotation sources were considered. In cases where both strong support (i.e., aligned RefSeq mRNA sequences) and poor support (i.e., GENSCAN predictions) exist, both were used. Thus, a gene locus that contains a RefSeq mRNA will frequently contain probe sets representing not only the RefSeq exons, but also a number of other exons supported by other annotation sources. Slightly over half of the array is supported only by a single source and many of these are EST- or GENACAN- based annotations. See the GeneChip Exon Array Design Technical Note (https://tools.thermofisher.com/content/sfs/brochures/exon_array_design_technote.pdf) for a more comprehensive discussion of the content on the array.
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The majority of the splicing changes occur because of alternative exon usage or exon skipping, which will be captured with this type of array design. Fine modifications such as alternative 5' or 3' splice site usage with shifts of less than 25 bases will not be captured by this design.
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In many cases the Genechip probes do overlap quite heavily. Multiple probes, although overlapping, are utilized as they still provide separate physical measures of expression and add to the robustness of the platform. Care was taken to ensure that probes from the same probe set, however similar in sequence composition, are spatially separated on the array. In addition, the annotation library file contains information regarding how many independent probes there are, as well as the number of nonoverlapping probes, in each probe set. Independent probes are defined as those that have less or equal to 13 base overlaps with other probes in the probe set. This information provides additional insight and may be helpful to users when interpreting unexpected results.
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About 90% of the exon-based probe sets contain 4 probes for each PSR. The remaining 10% are roughly equally split between 3, 2, and 1 probes for each probe set.
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PSR is the smallest unit on the exon array for expression profiling and each PSR is represented by an individual probe set. In some cases, each PSR is also an exon; in other cases, due to variation in overlapping exon structures, the PSR can be a subset of the true biological exon. As a result, alternatively spliced exons from the same gene may overlap (i.e., alternative donor or acceptor site); however, PSRs have the property that they do not overlap each other in the genome space, except if annotations change with a newer version of the genome assemblies. In cases where multiple annotations infer different exon structures, that one exon cluster (a group of overlapping exons) will be divided into multiple PSRs. Therefore, in the final design, there are approximately 1,000,000 exon clusters represented by approximately 1,400,000 PSRs.
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Median is 123 bases, and minimum is 25 bases.
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DABG stands for ''detection above background'' and is a detection metric generated by comparing Perfect Match probes to a distribution of background probes. This comparison yields a p-value which is then combined into a probe set level p-value using the Fischer equation. PLIER stands for ''Probe Logarithmic Intensity Error'' and is a model-based signal estimator which benefits from multi-array analysis.
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The .dat files are approximately 750 MB in size, and the binary .cel files are approximately 60 MB in size.
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