# GeneSpeed Beta Cell: An Online Genomics Data Repository and Analysis Resource Tailored for the Islet Cell Biologist

Authors: Nayeem Quayum; Alecksandr Kutchma; Suparna A. Sarkar; Kirstine Juhl; Gerard Gradwohl; Georg Mellitzer; John C. Hutton; Jan Jensen
Journal: Experimental Diabetes Research
 (2008)

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## Abstract

Objective. We here describe the development of a freely available online database resource, GeneSpeed Beta Cell, which has been created for the pancreatic islet and pancreatic developmental biology investigator community. Research Design and Methods. We have developed GeneSpeed Beta Cell as a separate component of the GeneSpeed database, providing a genomics-type data repository of pancreas and islet-relevant datasets interlinked with the domain-oriented GeneSpeed database. Results. GeneSpeed Beta Cell allows the query of multiple published and unpublished select genomics datasets in a simultaneous fashion (multiexperiment viewing) and is capable of defining intersection results from precomputed analysis of such datasets (multidimensional querying). Combined with the protein-domain categorization/assembly toolbox provided by the GeneSpeed database, the user is able to define spatial expression constraints of select gene lists in a relatively rigid fashion within the pancreatic expression space. We provide several demonstration case studies of relevance to islet cell biology and development of the pancreas that provide novel insight into islet biology. Conclusions. The combination of an exhaustive domain-based compilation of the transcriptome with gene array data of interest to the islet biologist affords novel methods for multidimensional querying between individual datasets in a rapid fashion, presently not available elsewhere.


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## Body

## 1. Introduction

Genomics is playing a growing role
in almost any biological experimentation. Based on presently available
commercial expression array technologies, an investigator is given almost
full-genome coverage of transcriptional changes that provides for novel methods
for gene identification and validation. However, exhaustive data mining from
genomics datasets is cumbersome, and to a large extent is outside the expertise
of the individual experimenter. The greatest strength in genomics data analysis
stems from multidimensional analysis, as such orthogonal comparison can bring
out biologically relevant information not extractable from the individual
datasets alone. However, such multidimensional querying is often advised
against, as individual genomics experiments are performed in different
laboratories, using dissimilar methodologies. Such array data should not be
uploaded and analyzed concomitantly in the available software data analysis
programs commonly used. Prudent analysis of multiexperimental results would
therefore call for individual data analysis of experimental sets, and only
parse for intersections/exclusions within the resulting gene lists. This is
possible through genomics analysis platforms using separate gene list saving.
However, the process is burdened by the fact that the analysis of a relevant
dataset for orthogonal querying requires the identification of the existence of
the data, upload, normalization, and scaling of individual DNA chip scan files
and thereafter selecting and executing a valid analysis for the particular
dataset, followed by results storage. In practice, this is time consuming, and
too overwhelming, for most biologists.In the islet and islet
developmental biology research fields, a continuously growing set of public
genomics data is becoming available. Also, initial problems in both genomics
chip design and experimental execution are gradually being overcome. It is
therefore appreciated that a large, generally untapped resource is provided by
genomics analyses performed in islet-focused laboratories around the world. Two
online databases, T1dbase [1] and EpconDb [2], contain genome data
repository components within their sites. However, they do not provide advanced
multiexperimental querying options that would allow generation of gene lists
between experiments. Acknowledging this, we set forth to create a resource that
would consolidate diabetes-research relevant genomics data and allow rapid
multidimensional analysis between such datasets. To do this, we created an
online genomics data repository, which we term GeneSpeed Beta Cell. This was
developed as an additional component of the GeneSpeed resource [3], see
Figure 1. GeneSpeed Beta
Cell (http://genespeed.ccf.org/betaCell/) contains two forms of
data: normalized and similarly scaled genomics data relevant for the islet or pancreatic
developmental biologist. Secondly, it contains precalculated analyses, which include pairwise
and self-organizing neural network clustering results applied to relevant data
series. On the analysis side, GeneSpeed Beta Cell provides “My Gene Workspace”
where gene list overlap can be evaluated. It also provides access to any search
parameter in the GeneSpeed environment, including precalculated data on tissue
specificity (Shannon entropy) and wide-tissue
batch expression queries. Together, the unified environment within the
GeneSpeed database provides for some unique capacities not found elsewhere. We
here describe the use of GeneSpeed Beta Cell by addressing a set of novel, and biologically
relevant, questions appealing to the islet biologist.Figure 1
GeneSpeed and GeneSpeed Beta Cell comparison.


## 2. Genespeed Beta Cell Database Structure and Navigation

### 2.1. Multiexperimental Viewing

“GeneSpeed Beta Cell” is a gene
array data repository linked to the GeneSpeed environment. For a more detailed
description of the domain-based gene categorization afforded by GeneSpeed,
please refer to [3] and the online background and
tutorials. GeneSpeed Beta Cell consists of a central “experiment selector page”
(Figure 2), listing experiments by relevance to embryonic development, adult
islet studies, adult whole pancreas studies, experiments using cell lines, and
solid tumor data. Currently, the site is being expanded with gene chip data of
developing nonpancreatic endoderm. For each group, experiments are separated
into human and mouse studies. At current, Affymetrix-type data is supported,
given that the body of relevant genomics data is the largest on this particular
platform; but as the parent GeneSpeed database is not platform-specific, we
have also made it capable of operating with the Illumina BeadChip type datasets.
All current Affymetrix-type datasets in the repository were obtained as
unnormalized raw cel files, and were normalized using MAS5.0 using identical
settings and similarly scaled for cross-experimental comparisons (see methods).
The available data can be viewed through multiexperimental viewing. Any saved
gene list can be viewed for any of the available datasets. This is a fast and
convenient way to display the normalized expression values of defined gene
lists between independent experiments performed in different laboratories. The
resulting display page is constructed to facilitate horizontal glancing of expression
values, while maintaining the individuality of experiments. As there is no
limit as to the number of genes shown or number of experiments selected, the
resulting page view can be quite large. To assist the identification of the
respective column (tissue type/experimental condition) and row (gene symbol), a
hovering tool supplying this information was implemented. Also, for quick
analysis of the gene ID, each cell is hyperlinked to the respective Unigene
page for that gene. If a gene within a gene list does not contain a respective
probeset, the cell content is displayed as N.A. The multiexperiment viewer
facilitates table sorting based on each component in selected datasets. This
provides, for example, quickly arranging genes in a larger gene list according
to expression levels for any tissue/condition selected (e.g., Figure 3 shows a
list of homeodomain-class genes sorted based on expression at the E12.5
gestational time point in pancreatic development).Figure 2
Web-page view of the GeneSpeed Beta Cell experiment selection window.Figure 3
Example output page of homeodomain-class
genes expressed in pancreatic development from E12.5 to E18.5. The list was
ordered based on expression levels in E12.5 pancreas. As examples, Mrg1 and
Adnp are expressed at high levels throughout pancreatic development. Several
genes such as Pitx2 and multiple Hox-family members decrease abruptly after
E14.5, and are only expressed significantly during early organ development.


### 2.2. Multidimensional Analysis in “My Gene Workspace”

To enhance online capabilities, we
developed tools for multidimensional analysis. The multidimensional analysis
tool operates within a “My Gene Workspace” environment (Figure4), which is
array-platform independent as it stores genes by Unigene identifier. “My Gene
Workspace” allows for temporary storage of gene lists, naming such, and
selecting individual lists to be combined using the Boolean operators AND or
OR. Hereby, intersections (AND), or additive combinations (OR) can be performed
on the selected gene lists, for further logical operations or visualized using
the multiexperimental viewer. Several means of populating the workspace is
possible. The user is provided with a “permanent list” account, in which work
between sessions can be saved. Lists can here be grouped according to project
name. Gene lists from the permanent account can be ported to the workspace or
gene family choices from a concurrent GeneSpeed query that can be directly imported. In addition,
gene list results from precalculated analyses based on the available datasets
can be added. The final option provides a highly useful method to dynamically aggregate
and compare results from individual experimental data that was not initially
designed for a combined analysis. Such comparisons can be highly scientifically
relevant, and examples are provided later.Figure 4
Web-page view of “My Gene Workspace” selection window.As this latter method is based on
precalculated analysis of available datasets, a certain level of a priori
choice has been necessary to implement, as all permutations of possible data analysis
could not be practically implemented. Consequently, depending on the underlying
experimental conditions, the precomputed analysis is restricted to pairwise
analysis (although multiple pairwise comparisons are often provided for a given
dataset), or a self-organizing cluster analysis (for series-type data such as
experimental time, drug concentration, or developmental time). Graphical
presentation of each analysis is provided to help the user gauge gene numbers
given the conditions chosen. For pairwise analysis, a volcano plot (plotting
significance (p-value) versus fold-change) of the pairwise analysis result is
shown. The default cutoff for gene selection is set at a false discovery rate
of 0.1, but can be changed to the user's preference. Similarly, the fold-change
range can be freely set, allowing the user to port, that is, >2-fold
upregulated genes in a given condition into the workspace. The graphical
presentation of cluster analyses contains a cluster number, and number of genes
within the cluster. The user is free to
select any number of clusters and port to the workspace. In this manner,
various experimental conditions can be continuously ported to the workspace,
and the experimental multi-intersectional analysis occurs there. There is no
limit to the number of gene lists present in the workspace. We should note that
for both the multidimensional viewing page, and for the multidimensional query
form in the workspace, individual datasets are always kept separate (viewer),
treated as such (query page), and are never pooled. Cross-experimental pooling
is not tolerable due to varying conditions in different laboratories during
data generation.


### 2.3. Current Experimental Content of  GeneSpeed Beta Cell

As the available datasets and analyses grow on a daily basis, users should visit the site for a list of
currently available datasets and analyses.


### 2.4. GeneSpeed Beta Cell Use-Case Scenarios

Some biologically relevant use-case
scenarios for the islet cell biologist are described in the following. Each of
these is available also as online tutorials at GeneSpeed Beta Cell athttp://genespeed.ccf.org/betaCell/tutorial.jsp.
As for any bioinformatics-based method application, the results are provided as candidate gene lists, corresponding to genes/probesets fulfilling input
criteria. The further validation of such lists using noninformatics-based techniques is a
general requirement. In the following demonstrations, the end-result gene lists
are often supported by previous published data from other sources, hereby
providing the validation required for the particular demonstration scenarios.Compiling 1 (online tutorial 1).
We wish to address the issue of
defining islet-expressed transcription factor (TF) encoding genes. To do this,
we will utilize the predefined transcription factor categorization provided by
the GeneSpeed database, assemble a nonredundant list of TF encoding genes, and
find those reduced inNgn3 null pancreas. First, we select “new search,”
and desired species “mouse” from the drop-down menu. Next, we select “search by
transcription factor classification” within the GeneSpeed search options. As 5
major domain family groupings exist for the transcription factor type genes, we
will need to iterate the following procedure for each, but will here limit the
families to the “Basic,” “Beta-Scaffold,” and “HTH” superfamilies. These
families contain, for example, the leucine zipper, bHLH, and homeodomain
transcription factor families, but not the Zn-finger class. Selecting “Basic”
as the first type, we ctrl-select all the subfamily members of the basic TF
superfamily. Displaying the result provides 685 hits. These correspond to every
instance where the Unigene database of the mouse contains a homology hit for
any of the domain types associated the “basic” superfamily. However, as the
database has no preset lower E-score cutoff, several false positives exist in
this list (see discussion of how to set an E-score cutoff on the description
pages at GeneSpeed for a full explanation). To eliminate low-scoring similarity
hits, we set the E-score cutoff at E10-6, and redo the search. Now, a resulting
list of 167 genes is detected. We save these to the user account under an
arbitrary name (All_TFs). This process is repeated for the TF superfamilies
mentioned above, where the individual results is added to the All_TF's list,
consequently providing a list of >1600 individual Unigenes. These are next
imported into the “My Gene Workspace.” To extract genes unique to pancreatic
islets in the developing pancreas, we will take advantage of the available
dataset for Ngn3-null embryonic pancreas, which is listed under the experiment
listing page of GeneSpeed Beta Cell. A pair-wise analysis is provided comparing
E15.5 Wt and E15.5 Ngn3 Null pancreas. The Ngn3-deficient
pancreas is excellent to define endocrine-specificity, as the organ is devoid
of endocrine cells. Selecting genes upregulated >1.5 fold, P<.25,
a second list is imported into the workspace as Down_in_Ngn3. This list
contains 114 genes. Obtaining the intersection between the TF_ALL and Down_in_Ngn3 lists provide a total of 8 transcription factor encoding genes lost in Ngn3
mutant E15.5 pancreas: Ngn3, NeuroD, Isl1, Pax6, Arx, MafB, Nkx2.2, Insm1, and HIF1a.Multidimensional 2 (online tutorial 2).
We here will seek
to discover kinase-encoding genes that are enriched in either early or late
pancreatic development. A similar study has not been done before. To perform
this task, we first need to compile a list of all protein kinase-type genes in
the mouse transcriptome. Using a text-search for a gene known as a protein
kinase (e.g., insr), we obtain two hits:Insr and Insrr. Both of
these are receptor tyrosine kinases, and display the presence of the
Tyr_pkinase domain (IPR001245) with an E-score at 1E10−145. We also
note that the generic kinase domain (IPR000719) is detected in both at 1E10−24.
By checking the “InterPro
sub-search” box for the IPR001245 domain, and execute the search:
“refine by subsearch,” we obtain a nonredundant list of Unigene clusters having
similarity to the IPR001245 domain. This provides 480 hits, covering all
kinase-domain forms (S/T as well as Y-kinase types). To curate against low-similarity
hits, we manually set the E-score threshold at < 1E-6. The resulting list
contains bona-fide 432 kinase-containing genes, which we subsequently save as “Kinase_all”
to our account. Many of these genes represent genes with no previous annotation
as being of the kinase-domain containing type, and may not have been named yet.
Next, we wish to identify which of these kinase-encoding genes display a
downward trend during pancreatic development. To do this, we move to the
“search GeneSpeed Beta Cell,” and expand the “Embryonic studies” dataset tab.
Selecting the “kinetic series of mouse pancreatic development 1” precomputed
cluster analysis, we are provided with the results of a Kohonen's
self-organizing cluster analysis in a graphical format. Gene clusters with a
downward trend during pancreatic development are selected (cluster
3,4,5,8,9,15,20) and combined using the selection tool provided. The results
are saved as Genes_Trend_Down to the workspace. Within the workspace the
intersection between the Genes_Trend_Down and the Kinase_all lists are obtained using the Boolean operator AND. The resulting list contains
138 kinase-type genes. The list can be saved, or gene expression of the
particular genes can be displayed in some or all mouse array experiments in the
GeneSpeed Beta Cell database. The latter may provide important clues as to
tissue-specific expression of individual members. Finalizing this demonstration,
we wish to address the identity of kinase-encoding genes that are upregulated
over time in the developing pancreas. By repeating the above method for
kinase-type genes displaying upregulation (Cluster 6,11,16,17,21,22, generating
list Gene_Trend_Up), only 27 genes are identified. We can conclude that
more kinase signaling diversity exists prior to rather than after the secondary
transition in the mouse pancreas.Defining 3 (online tutorial 3).
This example uses the available
dataset on human tissues, as provided by the Novartis Genomics Institute (http://symatlas.gnf.org/SymAtlas/about.jsp).
A tissue set consisting of 79 human tissues and 61 different mouse tissues, mostly
adult solid organs, has been generated in duplicate using the Affymetrix GNF1
platform. To provide a measure of tissue expression selectivity, we adopted the
method of Shannon entropy determination, as previously described by Schug et
al. [4]. Shannon entropy provides quantitative measures of expression using a bit-rate scale.
For each gene, the Shannon entropy (Hgene)
defines the degree of ordered expression; as a rule, the lower the Hgene,
the fewer tissues in the total set express the gene in question. To identify
those tissues showing uniqueness in expression, the measure Qtissue can be used. Again, as a rule, the lower the Qtissue value, the more
specific the gene is expressed in that particular tissue. A rank order of the
lowest Qtissue values thus provides a list of those genes that have
the highest selectivity for the tissue in question. Shannon entropy computations were performed for all tissues in the above datasets. As
the human, but not the mouse, datasets contain array data for islets, the present
example is currently limited to human. From the GeneSpeed search query, we
select species “homo sapiens,” and thereafter “search by expression.” We select
the human GNF1A chip, and “calculated Shannon entropy” using the drop-down boxes. The following page contains selector boxes
for each tissue in the set. For pancreatic islets, we select a Qtissue value of <1.7×Hgene. The user should have experiment with the Qtissue setting; the lower values (approaching Hgene) provide smaller
numbers, but more tissue-specific genes. Relaxing the value towards a value of
2×Hgene provides more exhaustive, albeit less selectively
expressed genes. At Qislet at 1.7×Hgene, we identify 96
probesets (as more than one probeset may exist for each gene, the actual number
of genes identified is often slightly lower). Selecting to show all Shannon entropy values, we next copy the entire table
into Excel, in order to rank-order the hits. Not surprisingly, the
top of this list consists of Glucagon (GCG), Insulin (INS), IAPP,
but we also notice that not far from the top, some nonendocrine-type genes, such as PNLIPRP1 and CPA2 are present. The reason for this is due to exocrine
contamination. The majority of such genes are easily removed by eliminating all genes in which
Qpanc<Qislet. Ranking the resulting genes provides a
list of genes showing the highest selectivity for pancreatic islets compared to
78 other human tissues (the 30 top-ranked genes of this list is shown in Table
). The results include the complement of endocrine terminal products (Ins, Gcg, Sst, Ppy), four Reg-type genes
(Reg1b, Reg3g, Reg3a, Regl), several
well-known endocrine transcripts (Pcsk1 (PC1/3), Iapp, Slc30a8), secretogranins
(Scgb2a1, Scgn, Scg5, Scg2, Scg3),
and transcription factors known to function in the islet (FoxA2, Nkx2.2, Isl1).Table 1
Results, use-case scenario 3. Genes defined as most specific for
human islets as based on Shannon Entropy calculations.
GeneSymbolsEntropyHgQIsletsQPancreasHs.516494GCG1.321.654.22Hs.89832INS1.21.743.04Hs.46835IAPP1.952.335.63Hs.4158REG1B2.823.914.48Hs.447084REG3G3.614.467.89Hs.567312REG3A3.454.655.41Hs.584797REGL3.614.915.59Hs.12409SST45.159.27Hs.558368PPY4.285.3611.82Hs.78977PCSK14.315.7211.8Hs.97644SCGB2A14.175.8312.14Hs.204238LCN23.956.249.34Hs.612083NA4.236.517.43Hs.73923PNLIPRP14.696.577.05Hs.489786CFTR4.86.857.29Hs.116428SCGN5.036.899.34Hs.76452CRP5.187.218.66Hs.156540SCG54.927.6211.4Hs.53985GP25.237.717.72Hs.516726SCG24.968.312.7Hs.232618SCG35.128.4913.3Hs.534458CGI-385.028.5113.37Hs.125898GNAS5.648.612.81Hs.8364PDK45.728.6210.84Hs.592742ELL25.548.6312.26Hs.2256MMP75.78.711.14Hs.123072RAB3B5.138.7813.24Hs.155651FOXA25.458.959.62Hs.516922NKX2-25.459.1712.68Hs.558519ERO1LB5.639.1811.22Hs.491232SLC39A145.559.1810.11Hs.260720DNAJC125.699.2511.76Hs.82071CITED25.589.3613.37Hs.503733LOC6532755.889.3811.61Hs.203699GOLPH3L5.599.4111.95Hs.479602APBB25.769.4513.23Hs.109590GENX-34145.899.7913.27Hs.89655PTPRN6.019.8412.71Hs.532270SLC30A85.979.8712.33Hs.505ISL169.911.7Pituitary 4 (online tutorial 4).
It is known that neuroendocrine
cell types shares certain characteristics related to production and release of
secreted products. The pituitary and islets are highly enriched in cells
producing polypeptide hormones. Using Shannon entropy, we will here ask what are the genes that may be in common between pituitary and
pancreatic islets and not expressed widely elsewhere. Similar to above,
we select Shannon entropy query in GeneSpeed,
and input a slightly relaxedQtissue value of 1.8 for both pituitary
and pancreatic islets. Individually, QIslet<1.8×Hg and Qpituitary<1.8×Hg identify 292 and 222
probesets, respectively. The intersection is 21 probesets, corresponding to 19
individual genes (Table , 3 probesets for GNAS, guanine nucleotide
binding protein were identified). Three genes encode known granule-type
proteins (ChgrA, secretogranin 2 (SCG2), secretogranin 5 (SCG5)).
Two transcription factors are found: InsM1 and ZNF91. The proprotein convertase
subtilisin/kexin type-1 inhibitor (PCSKN1) and the peptidylglycine
alpha-amidating monooxygenase are also present. Other products include CACNA1F (Calcium channel, voltage-dependent, alpha 1F), CNGA3 (Cyclic nucleotide
gated channel alpha 3), the transmembrane protein TMEM30 as well as
several uncharacterized genes. Many of these genes represent expected hits, and
show the value of combining parameters such as tissue uniqueness and
overlapping gene expression to derive a meaningful candidate repertoire for
further scrutiny.Table 2
Results, use-case scenario 4. Genes specific for both pancreatic islets and pituitary.
Unigene IDSymbolGene name1Hs.6790DNAJB9DnaJ (Hsp40) homolog, subfamily B, member 92Hs.156540SCG5Secretogranin V (7B2 protein)3Hs.516726SCG2Secretogranin II (chromogranin C)4Hs.389378MON2MON2 homolog (S. cerevisiae)5Hs.150793CHGAChromogranin A (parathyroid secretory protein 1)6Hs.152944LOH11CR2ALoss of heterozygosity, 11, chromosomal region 2, gene A7Hs.631626ZNF91Zinc finger protein 918Hs.89584INSM1Insulinoma-associated 19Hs.234785CNGA3Cyclic nucleotide gated channel alpha 310Hs.632799CACNA1FCalcium channel, voltage-dependent, alpha 1F subunit11Hs.369430PAMPeptidylglycine alpha-amidating monooxygenase12Hs.146180TMEM30BTransmembrane protein 30B13Hs.125898GNASGNAS complex locus14Hs.459183ALPK3Alpha-kinase 315Hs.87295FAM18BFamily with sequence similarity 18, member B16Hs.522640PCSK1NProprotein convertase subtilisin/kexin type-1 inhibitor17Hs.496542RNF128Ring finger protein 12818Hs.444459C9orf135Chromosome 9 open reading frame 13519Hs.503733LOC653275Similar to cryptic/cripto


## 2.1. Multiexperimental Viewing

“GeneSpeed Beta Cell” is a gene
array data repository linked to the GeneSpeed environment. For a more detailed
description of the domain-based gene categorization afforded by GeneSpeed,
please refer to [3] and the online background and
tutorials. GeneSpeed Beta Cell consists of a central “experiment selector page”
(Figure 2), listing experiments by relevance to embryonic development, adult
islet studies, adult whole pancreas studies, experiments using cell lines, and
solid tumor data. Currently, the site is being expanded with gene chip data of
developing nonpancreatic endoderm. For each group, experiments are separated
into human and mouse studies. At current, Affymetrix-type data is supported,
given that the body of relevant genomics data is the largest on this particular
platform; but as the parent GeneSpeed database is not platform-specific, we
have also made it capable of operating with the Illumina BeadChip type datasets.
All current Affymetrix-type datasets in the repository were obtained as
unnormalized raw cel files, and were normalized using MAS5.0 using identical
settings and similarly scaled for cross-experimental comparisons (see methods).
The available data can be viewed through multiexperimental viewing. Any saved
gene list can be viewed for any of the available datasets. This is a fast and
convenient way to display the normalized expression values of defined gene
lists between independent experiments performed in different laboratories. The
resulting display page is constructed to facilitate horizontal glancing of expression
values, while maintaining the individuality of experiments. As there is no
limit as to the number of genes shown or number of experiments selected, the
resulting page view can be quite large. To assist the identification of the
respective column (tissue type/experimental condition) and row (gene symbol), a
hovering tool supplying this information was implemented. Also, for quick
analysis of the gene ID, each cell is hyperlinked to the respective Unigene
page for that gene. If a gene within a gene list does not contain a respective
probeset, the cell content is displayed as N.A. The multiexperiment viewer
facilitates table sorting based on each component in selected datasets. This
provides, for example, quickly arranging genes in a larger gene list according
to expression levels for any tissue/condition selected (e.g., Figure 3 shows a
list of homeodomain-class genes sorted based on expression at the E12.5
gestational time point in pancreatic development).Figure 2
Web-page view of the GeneSpeed Beta Cell experiment selection window.Figure 3
Example output page of homeodomain-class
genes expressed in pancreatic development from E12.5 to E18.5. The list was
ordered based on expression levels in E12.5 pancreas. As examples, Mrg1 and
Adnp are expressed at high levels throughout pancreatic development. Several
genes such as Pitx2 and multiple Hox-family members decrease abruptly after
E14.5, and are only expressed significantly during early organ development.


## 2.2. Multidimensional Analysis in “My Gene Workspace”

To enhance online capabilities, we
developed tools for multidimensional analysis. The multidimensional analysis
tool operates within a “My Gene Workspace” environment (Figure4), which is
array-platform independent as it stores genes by Unigene identifier. “My Gene
Workspace” allows for temporary storage of gene lists, naming such, and
selecting individual lists to be combined using the Boolean operators AND or
OR. Hereby, intersections (AND), or additive combinations (OR) can be performed
on the selected gene lists, for further logical operations or visualized using
the multiexperimental viewer. Several means of populating the workspace is
possible. The user is provided with a “permanent list” account, in which work
between sessions can be saved. Lists can here be grouped according to project
name. Gene lists from the permanent account can be ported to the workspace or
gene family choices from a concurrent GeneSpeed query that can be directly imported. In addition,
gene list results from precalculated analyses based on the available datasets
can be added. The final option provides a highly useful method to dynamically aggregate
and compare results from individual experimental data that was not initially
designed for a combined analysis. Such comparisons can be highly scientifically
relevant, and examples are provided later.Figure 4
Web-page view of “My Gene Workspace” selection window.As this latter method is based on
precalculated analysis of available datasets, a certain level of a priori
choice has been necessary to implement, as all permutations of possible data analysis
could not be practically implemented. Consequently, depending on the underlying
experimental conditions, the precomputed analysis is restricted to pairwise
analysis (although multiple pairwise comparisons are often provided for a given
dataset), or a self-organizing cluster analysis (for series-type data such as
experimental time, drug concentration, or developmental time). Graphical
presentation of each analysis is provided to help the user gauge gene numbers
given the conditions chosen. For pairwise analysis, a volcano plot (plotting
significance (p-value) versus fold-change) of the pairwise analysis result is
shown. The default cutoff for gene selection is set at a false discovery rate
of 0.1, but can be changed to the user's preference. Similarly, the fold-change
range can be freely set, allowing the user to port, that is, >2-fold
upregulated genes in a given condition into the workspace. The graphical
presentation of cluster analyses contains a cluster number, and number of genes
within the cluster. The user is free to
select any number of clusters and port to the workspace. In this manner,
various experimental conditions can be continuously ported to the workspace,
and the experimental multi-intersectional analysis occurs there. There is no
limit to the number of gene lists present in the workspace. We should note that
for both the multidimensional viewing page, and for the multidimensional query
form in the workspace, individual datasets are always kept separate (viewer),
treated as such (query page), and are never pooled. Cross-experimental pooling
is not tolerable due to varying conditions in different laboratories during
data generation.


## 2.3. Current Experimental Content of  GeneSpeed Beta Cell

As the available datasets and analyses grow on a daily basis, users should visit the site for a list of
currently available datasets and analyses.


## 2.4. GeneSpeed Beta Cell Use-Case Scenarios

Some biologically relevant use-case
scenarios for the islet cell biologist are described in the following. Each of
these is available also as online tutorials at GeneSpeed Beta Cell athttp://genespeed.ccf.org/betaCell/tutorial.jsp.
As for any bioinformatics-based method application, the results are provided as candidate gene lists, corresponding to genes/probesets fulfilling input
criteria. The further validation of such lists using noninformatics-based techniques is a
general requirement. In the following demonstrations, the end-result gene lists
are often supported by previous published data from other sources, hereby
providing the validation required for the particular demonstration scenarios.Compiling 1 (online tutorial 1).
We wish to address the issue of
defining islet-expressed transcription factor (TF) encoding genes. To do this,
we will utilize the predefined transcription factor categorization provided by
the GeneSpeed database, assemble a nonredundant list of TF encoding genes, and
find those reduced inNgn3 null pancreas. First, we select “new search,”
and desired species “mouse” from the drop-down menu. Next, we select “search by
transcription factor classification” within the GeneSpeed search options. As 5
major domain family groupings exist for the transcription factor type genes, we
will need to iterate the following procedure for each, but will here limit the
families to the “Basic,” “Beta-Scaffold,” and “HTH” superfamilies. These
families contain, for example, the leucine zipper, bHLH, and homeodomain
transcription factor families, but not the Zn-finger class. Selecting “Basic”
as the first type, we ctrl-select all the subfamily members of the basic TF
superfamily. Displaying the result provides 685 hits. These correspond to every
instance where the Unigene database of the mouse contains a homology hit for
any of the domain types associated the “basic” superfamily. However, as the
database has no preset lower E-score cutoff, several false positives exist in
this list (see discussion of how to set an E-score cutoff on the description
pages at GeneSpeed for a full explanation). To eliminate low-scoring similarity
hits, we set the E-score cutoff at E10-6, and redo the search. Now, a resulting
list of 167 genes is detected. We save these to the user account under an
arbitrary name (All_TFs). This process is repeated for the TF superfamilies
mentioned above, where the individual results is added to the All_TF's list,
consequently providing a list of >1600 individual Unigenes. These are next
imported into the “My Gene Workspace.” To extract genes unique to pancreatic
islets in the developing pancreas, we will take advantage of the available
dataset for Ngn3-null embryonic pancreas, which is listed under the experiment
listing page of GeneSpeed Beta Cell. A pair-wise analysis is provided comparing
E15.5 Wt and E15.5 Ngn3 Null pancreas. The Ngn3-deficient
pancreas is excellent to define endocrine-specificity, as the organ is devoid
of endocrine cells. Selecting genes upregulated >1.5 fold, P<.25,
a second list is imported into the workspace as Down_in_Ngn3. This list
contains 114 genes. Obtaining the intersection between the TF_ALL and Down_in_Ngn3 lists provide a total of 8 transcription factor encoding genes lost in Ngn3
mutant E15.5 pancreas: Ngn3, NeuroD, Isl1, Pax6, Arx, MafB, Nkx2.2, Insm1, and HIF1a.Multidimensional 2 (online tutorial 2).
We here will seek
to discover kinase-encoding genes that are enriched in either early or late
pancreatic development. A similar study has not been done before. To perform
this task, we first need to compile a list of all protein kinase-type genes in
the mouse transcriptome. Using a text-search for a gene known as a protein
kinase (e.g., insr), we obtain two hits:Insr and Insrr. Both of
these are receptor tyrosine kinases, and display the presence of the
Tyr_pkinase domain (IPR001245) with an E-score at 1E10−145. We also
note that the generic kinase domain (IPR000719) is detected in both at 1E10−24.
By checking the “InterPro
sub-search” box for the IPR001245 domain, and execute the search:
“refine by subsearch,” we obtain a nonredundant list of Unigene clusters having
similarity to the IPR001245 domain. This provides 480 hits, covering all
kinase-domain forms (S/T as well as Y-kinase types). To curate against low-similarity
hits, we manually set the E-score threshold at < 1E-6. The resulting list
contains bona-fide 432 kinase-containing genes, which we subsequently save as “Kinase_all”
to our account. Many of these genes represent genes with no previous annotation
as being of the kinase-domain containing type, and may not have been named yet.
Next, we wish to identify which of these kinase-encoding genes display a
downward trend during pancreatic development. To do this, we move to the
“search GeneSpeed Beta Cell,” and expand the “Embryonic studies” dataset tab.
Selecting the “kinetic series of mouse pancreatic development 1” precomputed
cluster analysis, we are provided with the results of a Kohonen's
self-organizing cluster analysis in a graphical format. Gene clusters with a
downward trend during pancreatic development are selected (cluster
3,4,5,8,9,15,20) and combined using the selection tool provided. The results
are saved as Genes_Trend_Down to the workspace. Within the workspace the
intersection between the Genes_Trend_Down and the Kinase_all lists are obtained using the Boolean operator AND. The resulting list contains
138 kinase-type genes. The list can be saved, or gene expression of the
particular genes can be displayed in some or all mouse array experiments in the
GeneSpeed Beta Cell database. The latter may provide important clues as to
tissue-specific expression of individual members. Finalizing this demonstration,
we wish to address the identity of kinase-encoding genes that are upregulated
over time in the developing pancreas. By repeating the above method for
kinase-type genes displaying upregulation (Cluster 6,11,16,17,21,22, generating
list Gene_Trend_Up), only 27 genes are identified. We can conclude that
more kinase signaling diversity exists prior to rather than after the secondary
transition in the mouse pancreas.Defining 3 (online tutorial 3).
This example uses the available
dataset on human tissues, as provided by the Novartis Genomics Institute (http://symatlas.gnf.org/SymAtlas/about.jsp).
A tissue set consisting of 79 human tissues and 61 different mouse tissues, mostly
adult solid organs, has been generated in duplicate using the Affymetrix GNF1
platform. To provide a measure of tissue expression selectivity, we adopted the
method of Shannon entropy determination, as previously described by Schug et
al. [4]. Shannon entropy provides quantitative measures of expression using a bit-rate scale.
For each gene, the Shannon entropy (Hgene)
defines the degree of ordered expression; as a rule, the lower the Hgene,
the fewer tissues in the total set express the gene in question. To identify
those tissues showing uniqueness in expression, the measure Qtissue can be used. Again, as a rule, the lower the Qtissue value, the more
specific the gene is expressed in that particular tissue. A rank order of the
lowest Qtissue values thus provides a list of those genes that have
the highest selectivity for the tissue in question. Shannon entropy computations were performed for all tissues in the above datasets. As
the human, but not the mouse, datasets contain array data for islets, the present
example is currently limited to human. From the GeneSpeed search query, we
select species “homo sapiens,” and thereafter “search by expression.” We select
the human GNF1A chip, and “calculated Shannon entropy” using the drop-down boxes. The following page contains selector boxes
for each tissue in the set. For pancreatic islets, we select a Qtissue value of <1.7×Hgene. The user should have experiment with the Qtissue setting; the lower values (approaching Hgene) provide smaller
numbers, but more tissue-specific genes. Relaxing the value towards a value of
2×Hgene provides more exhaustive, albeit less selectively
expressed genes. At Qislet at 1.7×Hgene, we identify 96
probesets (as more than one probeset may exist for each gene, the actual number
of genes identified is often slightly lower). Selecting to show all Shannon entropy values, we next copy the entire table
into Excel, in order to rank-order the hits. Not surprisingly, the
top of this list consists of Glucagon (GCG), Insulin (INS), IAPP,
but we also notice that not far from the top, some nonendocrine-type genes, such as PNLIPRP1 and CPA2 are present. The reason for this is due to exocrine
contamination. The majority of such genes are easily removed by eliminating all genes in which
Qpanc<Qislet. Ranking the resulting genes provides a
list of genes showing the highest selectivity for pancreatic islets compared to
78 other human tissues (the 30 top-ranked genes of this list is shown in Table
). The results include the complement of endocrine terminal products (Ins, Gcg, Sst, Ppy), four Reg-type genes
(Reg1b, Reg3g, Reg3a, Regl), several
well-known endocrine transcripts (Pcsk1 (PC1/3), Iapp, Slc30a8), secretogranins
(Scgb2a1, Scgn, Scg5, Scg2, Scg3),
and transcription factors known to function in the islet (FoxA2, Nkx2.2, Isl1).Table 1
Results, use-case scenario 3. Genes defined as most specific for
human islets as based on Shannon Entropy calculations.
GeneSymbolsEntropyHgQIsletsQPancreasHs.516494GCG1.321.654.22Hs.89832INS1.21.743.04Hs.46835IAPP1.952.335.63Hs.4158REG1B2.823.914.48Hs.447084REG3G3.614.467.89Hs.567312REG3A3.454.655.41Hs.584797REGL3.614.915.59Hs.12409SST45.159.27Hs.558368PPY4.285.3611.82Hs.78977PCSK14.315.7211.8Hs.97644SCGB2A14.175.8312.14Hs.204238LCN23.956.249.34Hs.612083NA4.236.517.43Hs.73923PNLIPRP14.696.577.05Hs.489786CFTR4.86.857.29Hs.116428SCGN5.036.899.34Hs.76452CRP5.187.218.66Hs.156540SCG54.927.6211.4Hs.53985GP25.237.717.72Hs.516726SCG24.968.312.7Hs.232618SCG35.128.4913.3Hs.534458CGI-385.028.5113.37Hs.125898GNAS5.648.612.81Hs.8364PDK45.728.6210.84Hs.592742ELL25.548.6312.26Hs.2256MMP75.78.711.14Hs.123072RAB3B5.138.7813.24Hs.155651FOXA25.458.959.62Hs.516922NKX2-25.459.1712.68Hs.558519ERO1LB5.639.1811.22Hs.491232SLC39A145.559.1810.11Hs.260720DNAJC125.699.2511.76Hs.82071CITED25.589.3613.37Hs.503733LOC6532755.889.3811.61Hs.203699GOLPH3L5.599.4111.95Hs.479602APBB25.769.4513.23Hs.109590GENX-34145.899.7913.27Hs.89655PTPRN6.019.8412.71Hs.532270SLC30A85.979.8712.33Hs.505ISL169.911.7Pituitary 4 (online tutorial 4).
It is known that neuroendocrine
cell types shares certain characteristics related to production and release of
secreted products. The pituitary and islets are highly enriched in cells
producing polypeptide hormones. Using Shannon entropy, we will here ask what are the genes that may be in common between pituitary and
pancreatic islets and not expressed widely elsewhere. Similar to above,
we select Shannon entropy query in GeneSpeed,
and input a slightly relaxedQtissue value of 1.8 for both pituitary
and pancreatic islets. Individually, QIslet<1.8×Hg and Qpituitary<1.8×Hg identify 292 and 222
probesets, respectively. The intersection is 21 probesets, corresponding to 19
individual genes (Table , 3 probesets for GNAS, guanine nucleotide
binding protein were identified). Three genes encode known granule-type
proteins (ChgrA, secretogranin 2 (SCG2), secretogranin 5 (SCG5)).
Two transcription factors are found: InsM1 and ZNF91. The proprotein convertase
subtilisin/kexin type-1 inhibitor (PCSKN1) and the peptidylglycine
alpha-amidating monooxygenase are also present. Other products include CACNA1F (Calcium channel, voltage-dependent, alpha 1F), CNGA3 (Cyclic nucleotide
gated channel alpha 3), the transmembrane protein TMEM30 as well as
several uncharacterized genes. Many of these genes represent expected hits, and
show the value of combining parameters such as tissue uniqueness and
overlapping gene expression to derive a meaningful candidate repertoire for
further scrutiny.Table 2
Results, use-case scenario 4. Genes specific for both pancreatic islets and pituitary.
Unigene IDSymbolGene name1Hs.6790DNAJB9DnaJ (Hsp40) homolog, subfamily B, member 92Hs.156540SCG5Secretogranin V (7B2 protein)3Hs.516726SCG2Secretogranin II (chromogranin C)4Hs.389378MON2MON2 homolog (S. cerevisiae)5Hs.150793CHGAChromogranin A (parathyroid secretory protein 1)6Hs.152944LOH11CR2ALoss of heterozygosity, 11, chromosomal region 2, gene A7Hs.631626ZNF91Zinc finger protein 918Hs.89584INSM1Insulinoma-associated 19Hs.234785CNGA3Cyclic nucleotide gated channel alpha 310Hs.632799CACNA1FCalcium channel, voltage-dependent, alpha 1F subunit11Hs.369430PAMPeptidylglycine alpha-amidating monooxygenase12Hs.146180TMEM30BTransmembrane protein 30B13Hs.125898GNASGNAS complex locus14Hs.459183ALPK3Alpha-kinase 315Hs.87295FAM18BFamily with sequence similarity 18, member B16Hs.522640PCSK1NProprotein convertase subtilisin/kexin type-1 inhibitor17Hs.496542RNF128Ring finger protein 12818Hs.444459C9orf135Chromosome 9 open reading frame 13519Hs.503733LOC653275Similar to cryptic/cripto


## 3. Discussion

Of the current available places for
genomics data reposition, the NCBI GEO (gene expression omnibus, [5]) is presently the most
exhaustive. The development of GEO proceeds to include data analysis of public
array-type experiments, which also include those deposited on islets, or
developing pancreas. The tools are currently limited to analyses performed
within individual experiments, and data results cannot be ported between experiments.
However, no other resource exists with a similar exhaustive compilation of DNA microarray-type datasets,
and as such, GEO represents a growing and increasingly important pillar for
array data compilation. In contrast to the more universal user-base that GEO
seeks to cover, certain resources have also been made available and dedicated
to the islet community. T1Dbase (http://www.t1dbase.org/)
was specifically developed to catalogue information on the genetics of type-I
diabetes, and contains extensive information on candidate gene regions [1]. It also contains a
microarray repository and a recently developed Gene Atlas search
function, aimed at providing a rapid visualization of gene expression in
islets. The strength of the environment lies in the use of Gaggle [6], which is a Java-based
communicator interface to several bioinformatics tools. However, to use this
requires a significant knowledge of the Gaggle-implemented tools such as the
TIGR tmev (http://www.tm4.org/mev.html) or R
(http://www.r-project.org/),
notwithstanding a rather complicated data upload scheme. Despite its strength,
this may therefore represent a time-consuming and intellectual barrier to most
biologists using the resource irregularly. Another comparable resource, the
EpconDb (http://www.cbil.upenn.edu/epcondb42/)
[2], originally generated by the
Endocrine Pancreas Consortium and funded through the NIH Beta Cell Biology
Consortium (http://www.betacell.org/), also
provides microarray chip repository support. Recently, precalculated analysis
results for select experiments are also provided. The structure of the EpConDb
resource centers on the GUS (genome unified schema), which includes the DOTS
database. DOTS shares significant similarities to the NCBI-devised Unigene EST
database, but extend to include splice site data, as well as promoter
definition.The GeneSpeed Beta
Cell site seeks to complement these resources on particularly two fronts: to
provide more extensive orthogonal analysis between array experiments and to
provide a functional gene list operator workspace, which neither the T1dbase
nor Epcondb sites allow. To achieve the former, we focused on providing a
larger degree of relevant precomputed analyses of array experiments providing
these in an easy-to-query format. To achieve the latter, we developed a gene
list workspace that would allow for platform-to-platform compatibility using
the common Unigene denominator, which is the nexus of the GeneSpeed MySQL
database. The current version of the database provides certain features not
found elsewhere, some of which has been addressed through the demonstration
cases. Yet, the database is a currently developing structure that in its
present form is useful, but easily imagined improved. Therefore, we are
currently focusing on key aspects for the further development of the GeneSpeed
environment. These include the identification of additional relevant microarray
experiments; filling out “missing links” by performing stop-gap-type microarray
experiments for populating critical, but missing, areas of the pancreatic
expression space; improving the search and query formats for user-friendliness;
and finally developing an export/import interface for pathway analysis programs
such as Ingenuity Pathway Analysis (IPA).The usefulness of GeneSpeed
Beta Cell database is dependent on the amount of available genomics data
content. A linear increase in number of available datasets and accompanying precomputed
analyses translates into an exponentially growing set of query combinations.
There are obvious gaps in the available datasets, as multiple null mutations
have been created for several key developmental regulators during pancreatic
development, and several mutant models resulting in diabetes due to beta-cell
dysfunction have also been reported, all of which would represent valuable data
in the present environment. Therefore, we are asking the islet research
community to share available datasets for multidimensional analysis. Also, we
will continue to upload publicly available datasets from the GEO environment.For a wet-biology laboratory like our own, the present
incarnation of the database has provided means of moving forward in otherwise
difficult-to-execute bioinformatics-based questions. We hope that the same
appreciation may pioneer gene identification challenges in other laboratories
hereby helping the diabetes research community.


## 4. Methods

### 4.1. Genomics Data Incorporation and Analysis

The “GeneSpeed Beta Cell” environment was developed using the J2EE platform on a Linux
server. For Affymetrix-type genomics data, we compiled the CEL files (raw data)
associated with different experiments from different sources and normalized
them locally using MAS5.0 algorithm, using an identical scaling factor of 500,
to ensure optimal comparability in a cross-experimental setting.The microarray experiments
currently available can be grouped, and hence analyzed, according to
experimental design type. For time-series experiments (and drug-effect studies),
an SOM neural network clustering algorithm was applied. The number of clusters
selected is empirically based on individual results, selecting the minimal
number adequately describing the data complexity. A graphical presentation is
provided of the log-transformed expression averages of genes within the
cluster. Also, the total number of gene number contained/cluster is provided. R
and Bioconductor [7] were used to accomplish this
task.For multicomponent analysis, which also
includes single pair-wise analysis, ANOVA testing was performed. For
multiple-condition datasets, several pair-wise analyses are provided. These
results are depicted through volcano plots. A false discovery rate (FDR) test
correction on the ANOVA result at 10% significance is provided for each plot as
the defaultP-value setting. On the volcano plots, the boxed areas
outlining a 10% FDR corrected P-value and the −2 to +2 fold regions of
change are shown.


### 4.2. Account Functionality

There are two account types in
GeneSpeed: “guest” and “registered user.” In order to use the workspace
environment registration is required. A “registered user” can log back into
their account to gain access to saved studies. Establishing a registered
account is free and can be done on the GeneSpeed registration page (http://genespeed.ccf.org/loginReq.php).
An automated password will be sent to the newly registered user. The
confidentiality of all registration information is strictly maintained and we
will only use such information to notify our users of any disruptions or
modifications of the GeneSpeed service. At present, only registered users are
allowed to use the GeneSpeed Beta Cell database.The GeneSpeed account allows
registered users to save gene lists into a private account that is permanent
and may only be viewed by the owner. The “My Gene Workspace,” on the other
hand, maintains gene lists temporarily during the current login session; upon
logging out the content of the “workspace” will be deleted.


### 4.3. Functional Implementation of  “My Gene Workspace”

The “My Gene Workspace” logic was
developed using J2EE and sql-type queries. Facilitating cross-platform
comparisons, the workspace utilizes Unigene cluster Ids (UID). Consequently,
the probeset identification through the experimental analyses is translated
into corresponding UID upon transfer to the workspace. As a result, if more
than one probeset is detected for a given gene in the analysis, these probesets collapse into
the UID of that gene. Secondly, upon selection of the content of a gene list in
the workspace, followed by showing the content in the expression space, all probesets corresponding to the
selected Unigene will be displayed. To reduce ambiguities, we update the system
continuously upon the availability of updated mapping files from NetAffx Analysis Center
server. Given that the NCBI Unigene dataset is constantly evolving, updated
mapping to the most recent UID is done every 6 months.


## 4.1. Genomics Data Incorporation and Analysis

The “GeneSpeed Beta Cell” environment was developed using the J2EE platform on a Linux
server. For Affymetrix-type genomics data, we compiled the CEL files (raw data)
associated with different experiments from different sources and normalized
them locally using MAS5.0 algorithm, using an identical scaling factor of 500,
to ensure optimal comparability in a cross-experimental setting.The microarray experiments
currently available can be grouped, and hence analyzed, according to
experimental design type. For time-series experiments (and drug-effect studies),
an SOM neural network clustering algorithm was applied. The number of clusters
selected is empirically based on individual results, selecting the minimal
number adequately describing the data complexity. A graphical presentation is
provided of the log-transformed expression averages of genes within the
cluster. Also, the total number of gene number contained/cluster is provided. R
and Bioconductor [7] were used to accomplish this
task.For multicomponent analysis, which also
includes single pair-wise analysis, ANOVA testing was performed. For
multiple-condition datasets, several pair-wise analyses are provided. These
results are depicted through volcano plots. A false discovery rate (FDR) test
correction on the ANOVA result at 10% significance is provided for each plot as
the defaultP-value setting. On the volcano plots, the boxed areas
outlining a 10% FDR corrected P-value and the −2 to +2 fold regions of
change are shown.


## 4.2. Account Functionality

There are two account types in
GeneSpeed: “guest” and “registered user.” In order to use the workspace
environment registration is required. A “registered user” can log back into
their account to gain access to saved studies. Establishing a registered
account is free and can be done on the GeneSpeed registration page (http://genespeed.ccf.org/loginReq.php).
An automated password will be sent to the newly registered user. The
confidentiality of all registration information is strictly maintained and we
will only use such information to notify our users of any disruptions or
modifications of the GeneSpeed service. At present, only registered users are
allowed to use the GeneSpeed Beta Cell database.The GeneSpeed account allows
registered users to save gene lists into a private account that is permanent
and may only be viewed by the owner. The “My Gene Workspace,” on the other
hand, maintains gene lists temporarily during the current login session; upon
logging out the content of the “workspace” will be deleted.


## 4.3. Functional Implementation of  “My Gene Workspace”

The “My Gene Workspace” logic was
developed using J2EE and sql-type queries. Facilitating cross-platform
comparisons, the workspace utilizes Unigene cluster Ids (UID). Consequently,
the probeset identification through the experimental analyses is translated
into corresponding UID upon transfer to the workspace. As a result, if more
than one probeset is detected for a given gene in the analysis, these probesets collapse into
the UID of that gene. Secondly, upon selection of the content of a gene list in
the workspace, followed by showing the content in the expression space, all probesets corresponding to the
selected Unigene will be displayed. To reduce ambiguities, we update the system
continuously upon the availability of updated mapping files from NetAffx Analysis Center
server. Given that the NCBI Unigene dataset is constantly evolving, updated
mapping to the most recent UID is done every 6 months.



---

*Source: 312060-2008-09-09.xml*