R spider: a network-based analysis of gene lists by
combining signaling and metabolic pathways from
Reactome and KEGG databases
Alexey V. Antonov1,*, Esther E. Schmidt2, Sabine Dietmann1, Maria Krestyaninova2and
1Helmholtz Zentrum Mu ¨nchen - German Research Center for Environmental Health (GmbH), Institute for
Bioinformatics and Systems Biology, Ingolsta ¨dter Landstraße 1, D-85764 Neuherberg, Germany and
2European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK
Received January 29, 2010; Revised April 26, 2010; Accepted May 14, 2010
R spider is a web-based tool for the analysis of a
gene list using the systematic knowledge of core
accumulated in the Reactome and KEGG databases.
R spider implements a network-based statistical
framework, which provides a global understanding
of gene relations in the supplied gene list, and fully
exploits the Reactome and KEGG knowledge bases.
R spider provides a user-friendly dialog-driven web
interface for several model organisms and supports
most available gene identifiers. R spider is freely
High-throughput technologies enable biological research-
ers to study hundreds or thousands of genes simultaneous-
ly. Genes or proteins are detected that are differentially
expressed or co-expressed across varying cellular condi-
underlying biological mechanisms based on experimental-
ly derived gene/protein lists remains a non-trivial task for
biologists. In 2002, a computerized analysis approach
using the Gene Ontology (GO) was proposed to deal
with this issue (1,2). Currently, there are over 25 tools
performing this type of analysis with some variations
(3–13). More recently, computational methods seek to in-
terpret or at least visualize the pathway context of the
experimentally derived genes (14–17). In this respect, one
recently in (17,18) which goes beyond gene pairs and
fully captures the topology of signaling pathways by
propagating the perturbations measured at gene levels
through the entire pathway. However, the development
of rigorous statistical methods for global network infer-
ence has been a challenging task.
Recently, we have introduced a network-based compu-
tational framework for the interpretation of gene/protein
lists derived from high-throughput studies (19,20). Our
commonly employed methods for enrichment analysis
(21) by providing network models that unite genes from
different pathways into a single connected network. A
Monte Carlo procedure was employed to estimate the sig-
nificance of the inferred models, thus providing a rigorous
quantitative statistical control (22). A web-based tool,
KEGG spider (19), was introduced that exploits the
network-based methodology for the exploration of meta-
bolic reactions accumulated in the KEGG database (23).
It was demonstrated that KEGG spider provides deeper
insight into the genomic basis of metabolism variations in
comparison to other tools (19).
Although being a powerful tool, KEGG spider is
limited only to metabolism-related genes which cover
<10% of the human genome (about 1100 genes). It is
clear that many other important cellular processes, such
as regulatory and signaling pathways remain uncovered
by the inferred network models. On the other hand, the
Reactome knowledgebase (24,25) is a dynamically ex-
panding project, which provides high quality expert-
authored, peer-reviewed knowledge of human reactions
and pathways, covering 3916 human proteins (as of
release 30). To provide experimentalists with an efficient
web-based tool for the analysis of high-throughput data
using Reactome knowledge, we have developed R spider,
which implements the network-based methodology and
exploits thedata accumulated
knowledgebase to the full extent. R spider unites both
*To whom correspondence should be addressed. Tel:+49 89 3187 2788; Fax:+49 (0) 89 3187 3585; Email: email@example.com
Nucleic Acids Research, 2010, Vol. 38, Web Server issuePublished online 2 June 2010
? The Author(s) 2010. Published by Oxford University Press.
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/
by-nc/2.5), which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
Reactome and KEGG knowledge databases covering
proteins from signaling and metabolism pathways.
We would like to point out that there are other signal-
ing and metabolic databases available in the public
domainlike themanually curated
or inferred data (26) or (27). R spider has the op-
tion to switchbetween Reactome&KEGG,
Curated pathways (http://pid.nci.nih.gov/) and BioCarta
MATERIALS AND METHODS
A global Reactome protein network
authored, peer-reviewed knowledgebase of human reac-
tions and pathways. We used a file in tab-delimited
format which specifies protein–protein interaction pairs
The meaning of ‘interaction’ is quite broad: two protein
sequences occur in the same complex or they occur in the
same or neighbouring reaction(s). For the human genome,
the global Reactome protein network covers about 3700
proteins (including proteins from non-human species that
interact with human proteins) involved in approximately
83000 unique pairwise interactions (based on release 30).
(http://www.reactome.org/) is anexpert-
A global metabolic gene network
The KEGG database is a collection of chemical structure
(reactant pairs). A detailed description of the procedure
used to construct a global metabolic gene network can be
found in ref. (19). The resulting global metabolic gene
network links by edges any two genes that are associated
with reactions sharing common compounds (from the
main reaction pair). For the human genome, the global
metabolic gene network covers about 1100 genes involved
in approximately 15000 unique pairwise interactions.
Integral reference network
To unite both networks, the Reactome protein network
was transformed into a gene network. As in many cases,
several proteins map to the same gene, the resulting gene
network has a smaller number of nodes and edges. Once
both KEGG and Reactome networks have the same type
of node identifiers, they can be united. For the human
genome, the resulting integral network covers about
3700 genes involved in approximately 50000 unique
pairwise gene interactions.
Network inference procedure and statistical treatment
Detailed information on the network inference and the
P-values can be found in our previously published
Initially, the genes from the input list are mapped to the
global reference network. At this point, all nodes from the
input list are disconnected. In the first step, all pairs of
nodes with distance 1 are connected by edges and
connected subnetworks are extracted. The subnetwork
with the maximal number of nodes is referred to as an
inferred network model D1. In the second step, the dis-
connected nodes from the input list with distance 2 are
connected by edges. The subnetwork with the maximal
number of input nodes is inferred and referred to as
network model D2. In the next step, the disconnected
nodes from the input list with distance 3 are connected
by edges and a network model D3 (a subnetwork with
the maximal number of input nodes) is inferred. Models
D2 and D3 incorporate nodes that are not from the input
list but are added to connect input nodes in the network
model. We refer to these added nodes as intermediate or
Let us assume that we have N genes from the input list
to be mapped to the reference network. Next, we refer to
the value N as the size of the input list. We infer the
network models D1, D2, D3. Let us denote S1, S2, S3
to be the number of input nodes in the inferred network
models. We also refer to S1, S2, S3 as the sizes of the
respective models D1, D2, D3. Given the number of
mapped genes from the input list (N), we consider the
sizes of the models (values S1, S2, S3) as statistics. We
have to estimate the probability to get models of the
same or larger sizes from a randomly generated gene list
which has N genes mapped to the reference network.
To generate the background distributions BD1, BD2,
BD3 we repeat the following simulation procedure k
times, where k specifies the upper significance level. A
random gene list Lj of size N (equal to the size of the
input list) is generated by sampling genes from global
gene network. Index j=1...k specifies each of the k
random simulations. The network inference procedure
described above is applied to the random list Lj and the
network models D1, D2, D3 are inferred. Let us denote
the size (the number of input genes) of the inferred models
D1, D2, D3 for the random list Lj as R1j, R2j, R3j. Thus,
after repeating the simulation procedure k times, we get
the background distribution R1j (j=1... k) for models
D1, the background distribution R2j (j=1... k) for
models D2, andthebackground
(j=1... k) for models D3.
To estimate significance of the inferred network model
D1 for the input gene list, the value S1 is compared with
the distribution R1j. Let n be the number of values from
the distribution R1j that are equal or greater than S1. The
estimate of P (P-value) of the inferred network model D1
is computed as P=(n+1)/k. In the same way the P-values
for the model D2 and D3 are estimated.
Statistical treatment plays an important role for the
quality control of inferred models. It is clear that given
a gene list and a reference network, one can always infer
some model, which will cover all genes from the list by
relaxing the number of possible intermediate genes. There
is a very simple test for any similar tool: the tool must be
able to recognize a random gene list and return on average
insignificant P-values for the random case. In 20 submis-
sions of different randomly generated gene lists on average
only 1 case is expected to be significant at the level of 0.05
(1/20). The estimate of the P-value provided by the Monte
Carlo procedure corresponds exactly to the definition of
Nucleic AcidsResearch, 2010,Vol. 38,Web Server issue W79
P-value: the probability to get a model of the same quality
for a random gene list.
Enrichment of the reactome and KEGG canonical
To compute enrichment of canonical Reactome and
KEGG pathways, we also employed the Monte Carlo pro-
cedure. In this case, we randomly draw k genes (the
number of genes in the input list) 100 times from the set
of all genes (or from the background set of genes supplied
by the user) and each time we estimate P-value based on
the hypergeometric distribution for the best (whatever)
pathway. Thus, we got a distribution of size 100 of the
best P-values for a random drawing of k genes which we
compare with the P-value for the best (whatever) pathway
related to our original list. The estimate of the adjusted
P-value by Monte Carlo procedure is given by the share of
random simulations where the best P-value was equal or
superior (less) than the P-value for the best (whatever)
pathways related to our original gene list.
rspider) is a freely available web-based tool that imple-
ments a pathway-free statistical framework for the inter-
pretation of gene lists from high-throughput studies.
R spider is available for several model organisms (Mus
Drosophila melanogaster). In addition, R spider has the
option to switch to the other available in the public
domain signaling pathway databases, Nature Curated
pathways (http://pid.nci.nih.gov/) and BioCarta (www
R spider has a simple, user-friendly interface. As input it
accepts several types of gene or protein identifiers, such as
identifiers from ‘Entrez Gene’ (29), ‘UniProt/Swiss-Prot’
(30), ‘Hugo Gene Symbols’, ‘UniGene’, ‘Ensembl’ (31),
‘RefSeq’ (32) and ‘Affymetrix’ (33). As output, the user
obtains network models (D1, D2, D3), where (1, 2, 3) in-
dicates the maximal distance between any two input genes
to be considered as ‘connected’ in the output model. The
network model (D1, D2 or D3) represent a connected
subnetwork with the maximal number of input genes. R
spider provides a report on the statistical significance of
the inferred network models (D1, D2, D3), as well as a
catalog of the enriched Reactome or KEGG pathways.
For each model (D1, D2, D3), a link is provided to
obtain a graphical visualization. The visualization is per-
formed by the Medusa package (34). We would like to
point out that online visualization capabilities are
limited. For this reason, we recommend to download the
inferred network models as text files (links are provided on
the visualization page) and to use freely available
packages (Cytoscape, Meduza) for network visualization.
Using these programs the users can produce high-quality
In the graphical output, input genes are represented by
Intermediate genes are represented by triangles and
specified by Entrez Gene Symbols. Compounds are repre-
sented by circles and specified by compound names (if the
length of the name exceeds 10 digits then the compound
KEGG id is used). Different colors are used to specify
canonical Reactome or KEGG pathways. In general, up
to 11 of the most representative pathways (in terms of the
number of genes in the model, both input and intermedi-
ate genes are counted) are coloured. In most cases, a gene
can be associated to multiple pathways. For this reason, R
spider implements a strict hierarchical procedure for gene
coloring. First, pathways are ordered in respect to the
number of genes that are present in the model from any
given pathway. The most representative pathway will be
colored in red. Colored genes (red) are excluded and
pathways are reordered considering only the remaining
genes. The next most represented pathway will be
colored in blue. Colored genes (red and blue) are
excluded and pathways are reordered considering only
the remaining genes. The procedure will continue until
13 pathways will be colored or there will be no pathway
which covers at least two genes. Therefore, colors have a
strict hierarchy: red, blue, green and so on. The number
before the color indicates the hierarchy order (Figure 1). It
is evident that some red genes may also belong to the blue
(green and so on) pathway, but not vice versa.
by the inputgene Ids.
Table: interaction context
For each gene in the reported model, R spider provides the
full interaction context. This information is summarized in
the table ‘Interacting Pairs’. In the case of Reactome, there
‘indirect_complex’, ‘reaction’ or ‘neighbouring_reaction’.
In the case of the KEGG database, interactions represent
either a compound (connected genes are assigned to dif-
ferent reactions utilizing the same compound) or, rarely,
by a reaction ID (both connected genes catalyze the same
metabolic reaction). The edge can be supported by several
different interactions, all of which will be reported, and
corresponding links to the source data are provided.
We present at our website (http://mips.helmholtz-muenchen
.de/proj/rspider/example.html) several hundred examples of
analyses by R spider of gene lists, which were automatically
extracted by text mining from proteomics studies in various
biological contexts (36). Here, we present one example in
detail to demonstrate the potential benefit of our tool.
Currently, many clinical studies are designed to reveal
possible pathogenic mechanisms and novel therapeutic
targets for complex diseases with specific phenotypes.
The Se ´ zary syndrome, for example, is associated with
the aggressive cutaneous T-cell lymphoma/leukemia. In
a study by Vermeer et al. (37), a high-resolution array-
based comparative genomic hybridization was performed
on malignant T cells from 20 patients to reveal highly
W80Nucleic Acids Research, 2010,Vol. 38,Web Server issue
recurrent genetic alterations typical for the Se ´ zary
syndrome. Minimal common regions with copy number
alteration occurring in at least 35% of patients were
reported, which comprised in total about 360 candidate
genes (see Table 1 in ref. 37).
Only 22 of these genes are mapped to KEGG metabolic
pathways. Thus, for comparison, an analysis by KEGG
spider reports that the inferred network model is not sig-
nificant (P=?0.1). On the contrary, consideration of the
integral reference network that unites both Reactome and
KEGG data provides more interesting insights into the
possible molecular mechanisms behind genes with copy
number alteration in the Se ´ zary syndrome. In this case,
92 out of the 360 genes are mapped to the integral
network. Network model D3, which allows up to two
missing genes between any two input genes, connects 74
out of the 92 mapped candidate genes into a single
non-interrupted network. The model is statistically signifi-
cant (P<0.01). R spider randomly sampled 92 genes from
the set of 3700 human genes that constitute the integral
reference network for 1000 times; and in 993 cases, the size
of the resulting network model D3 was less than 74 genes.
Thus, the significance of the model is about 0.01.
R spider provides graphical models. The network model
D3 for the considered example, which covers 74 genes
(P<0.01), is presented in Figure 1. Proteins from the
input list are indicated by rectangles, intermediate
proteins by triangles, and chemical compounds are
indicated by circles. The colours are used to specify
Reactome and KEGG canonical pathways.
In comparison to other available pathway analyses
tools, R spider provides a global vision of gene functional
relations. For example, submission to Onto-express (17)
results in reporting of several (?10) enriched pathways
with possibility to visualize them separately. This is cer-
tainly valuable information. However, the best model
(enriched pathway ‘Pathways in cancer’) covers 19 genes.
The relation between pathways as well as the role and
relation between genes that are not covered by enriched
pathways is not disclosed. Thus, in comparison to
Onto-express R spider demonstrates that genes residing
in regions which frequently have a copy number alteration
Figure 1. Network model D3 returned by R spider on submission of 360 candidate genes residing in regions with copy number alteration typical of
the Se ´ zary syndrome (37). Boxes represent input genes, triangles represent intermediate genes (genes that are added to connect two input genes, for
model D3 up to two intermediate genes are allowed between any two input genes), circles represent compounds which are common substrates or
products for both connected genes. Diamonds are used to specify the colour of canonical Reactome or KEGG pathways.
Nucleic AcidsResearch, 2010,Vol. 38,Web Server issueW81
in Se ´ zary syndrome are dependent although they belongs
to a wide spectrum of signaling and metabolic pathways.
In this case the user gets a newly created pathway which
covers 74 genes and actually runs through several canon-
ical Reactome and KEGG pathways.
Various modern genomics technologies result in gene lists.
A better understanding of the biological mechanisms,
which unite the identified genes, can give clues to a
better understanding of the phenomena under study.
R spider provides a possibility to actively exploit the
knowledge of biological processes of various natures
accumulated in the Reactome knowledgebase and metab-
olism related processes in the KEGG database to decipher
the mechanisms behind experimentally derived gene lists.
A pathway-free statistical framework combined with the
most advanced publicly available databases for pathways
and reactions makes R spider a very attractive tool for
interpretation of genomics data.
We thank Philip Wong for helpful discussions.
Funding for open access charge: European Bioinformatics
development of Reactome is supported by a grant from
the US National Institutes of Health (P41 HG003751)
and EU grant LSHG-CT-2005-518254 ‘‘ENFIN’’.
Genome Campus; The
Conflict of interest statement. None declared.
1. Draghici,S., Khatri,P., Martins,R.P., Ostermeier,G.C. and
Krawetz,S.A. (2003) Global functional profiling of gene
expression. Genomics, 81, 98–104.
2. Khatri,P., Draghici,S., Ostermeier,G.C. and Krawetz,S.A. (2002)
Profiling gene expression using onto-express. Genomics, 79,
3. Subramanian,A., Tamayo,P., Mootha,V.K., Mukherjee,S.,
Ebert,B.L., Gillette,M.A., Paulovich,A., Pomeroy,S.L.,
Golub,T.R., Lander,E.S. et al. (2005) Gene set enrichment
analysis: a knowledge-based approach for interpreting
genome-wide expression profiles. Proc. Natl Acad. Sci. USA, 102,
4. Reimand,J., Kull,M., Peterson,H., Hansen,J. and Vilo,J. (2007)
g:Profiler—a web-based toolset for functional profiling of gene
lists from large-scale experiments. Nucleic Acids Res., 35,
5. Masseroli,M., Martucci,D. and Pinciroli,F. (2004) GFINDer:
Genome Function INtegrated Discoverer through dynamic
annotation, statistical analysis, and mining. Nucleic Acids Res.,
6. Martin,D., Brun,C., Remy,E., Mouren,P., Thieffry,D. and Jacq,B.
(2004) GOToolBox: functional analysis of gene datasets based on
Gene Ontology. Genome Biol., 5, R101.
7. Khatri,P., Voichita,C., Kattan,K., Ansari,N., Khatri,A.,
Georgescu,C., Tarca,A.L. and Draghici,S. (2007) Onto-Tools: new
additions and improvements in 2006. Nucleic Acids Res., 35,
8. Dietmann,S., Georgii,E., Antonov,A., Tsuda,K. and Mewes,H.W.
(2009) The DICS repository: module-assisted analysis of
disease-related gene lists. Bioinformatics, 25, 830–831.
9. Berriz,G.F., King,O.D., Bryant,B., Sander,C. and Roth,F.P.
(2003) Characterizing gene sets with FuncAssociate.
Bioinformatics, 19, 2502–2504.
10. Antonov,A.V., Schmidt,T., Wang,Y. and Mewes,H.W. (2008)
ProfCom: a web tool for profiling the complex functionality of
gene groups identified from high-throughput data. Nucleic Acids
Res., 36, W347–W351.
11. Antonov,A.V., Dietmann,S., Wong,P., Lutter,D. and Mewes,H.W.
(2009) GeneSet2miRNA: finding the signature of cooperative
miRNA activities in the gene lists. Nucleic Acids Res., 37,
12. Khatri,P., Bhavsar,P., Bawa,G. and Draghici,S. (2004)
Onto-Tools: an ensemble of web-accessible, ontology-based tools
for the functional design and interpretation of high-throughput
gene expression experiments. Nucleic Acids Res., 32, W449–W456.
13. Alexa,A., Rahnenfuhrer,J. and Lengauer,T. (2006) Improved
scoring of functional groups from gene expression data by
decorrelating GO graph structure 2. Bioinformatics, 22,
14. Adler,P., Reimand,J., Janes,J., Kolde,R., Peterson,H. and Vilo,J.
(2008) KEGGanim: pathway animations for high-throughput
data. Bioinformatics, 24, 588–590.
15. Reimand,J., Tooming,L., Peterson,H., Adler,P. and Vilo,J. (2008)
GraphWeb: mining heterogeneous biological networks for gene
modules with functional significance. Nucleic Acids Res., 36,
16. Berger,S.I., Posner,J.M. and Ma’ayan,A. (2007) Genes2Networks:
connecting lists of gene symbols using mammalian protein
interactions databases. BMC Bioinformatics, 8, 372.
17. Draghici,S., Khatri,P., Tarca,A.L., Amin,K., Done,A.,
Voichita,C., Georgescu,C. and Romero,R. (2007) A systems
biology approach for pathway level analysis. Genome Res., 17,
18. Tarca,A.L., Draghici,S., Khatri,P., Hassan,S.S., Mittal,P., Kim,J.S.,
Kim,C.J., Kusanovic,J.P. and Romero,R. (2009) A novel signaling
pathway impact analysis 1. Bioinformatics, 25, 75–82.
19. Antonov,A.V., Dietmann,S. and Mewes,H.W. (2008) KEGG
spider: interpretation of genomics data in the context of the
global gene metabolic network. Genome Biol., 9, R179.
20. Antonov,A.V., Dietmann,S., Rodchenkov,I. and Mewes,H.W.
(2009) PPI spider: a tool for the interpretation of proteomics data
in the context of protein-protein interaction networks. Proteomics,
21. Khatri,P. and Draghici,S. (2005) Ontological analysis of gene
expression data: current tools, limitations, and open problems.
Bioinformatics, 21, 3587–3595.
22. Westfall,P.N. and Young,S.S. (1993) Resampling-based Multiple
Testing: Examples and Methods for P-value Adjustment. John
Wiley & Sons, Inc, New York.
23. Ogata,H., Goto,S., Sato,K., Fujibuchi,W., Bono,H. and
Kanehisa,M. (1999) KEGG: Kyoto Encyclopedia of Genes and
Genomes. Nucleic Acids Res., 27, 29–34.
24. Matthews,L., Gopinath,G., Gillespie,M., Caudy,M., Croft,D.,
de,B.B., Garapati,P., Hemish,J., Hermjakob,H., Jassal,B. et al.
(2009) Reactome knowledgebase of human biological pathways
and processes. Nucleic Acids Res., 37, D619–D622.
25. Vastrik,I., D’Eustachio,P., Schmidt,E., Gopinath,G., Croft,D.,
de,B.B., Gillespie,M., Jassal,B., Lewis,S., Matthews,L. et al.
(2007) Reactome: a knowledge base of biologic pathways and
processes. Genome Biol., 8, R39.
26. Kitano,H. and Oda,K. (2006) Robustness trade-offs and
host-microbial symbiosis in the immune system4. Mol. Syst. Biol.,
27. Ma’ayan,A., Jenkins,S.L., Neves,S., Hasseldine,A., Grace,E.,
Dubin-Thaler,B., Eungdamrong,N.J., Weng,G., Ram,P.T.,
Rice,J.J. et al. (2005) Formation of regulatory patterns during
signal propagation in a Mammalian cellular network16. Science,
28. Antonov,A.V. and Mewes,H.W. (2006) BIOREL: the benchmark
resource to estimate the relevance of the gene networks. FEBS
Lett., 580, 844–848.
W82 Nucleic Acids Research, 2010,Vol. 38,Web Server issue
29. Wheeler,D.L., Barrett,T., Benson,D.A., Bryant,S.H., Canese,K., Download full-text
Chetvernin,V., Church,D.M., DiCuccio,M., Edgar,R., Federhen,S.
et al. (2006) Database resources of the National Center for
Biotechnology Information. Nucleic Acids Res., 34, D173–D180.
30. Boutet,E., Lieberherr,D., Tognolli,M., Schneider,M. and
Bairoch,A. (2007) UniProtKB/Swiss-Prot. Methods Mol. Biol.,
31. Hubbard,T.J., Aken,B.L., Beal,K., Ballester,B., Caccamo,M.,
Chen,Y., Clarke,L., Coates,G., Cunningham,F., Cutts,T. et al.
(2007) Ensembl 2007. Nucleic Acids Res., 35, D610–D617.
32. Pruitt,K.D., Tatusova,T. and Maglott,D.R. (2007) NCBI
reference sequences (RefSeq): a curated non-redundant sequence
database of genomes, transcripts and proteins. Nucleic Acids Res.,
33. Liu,G., Loraine,A.E., Shigeta,R., Cline,M., Cheng,J.,
Valmeekam,V., Sun,S., Kulp,D. and Siani-Rose,M.A. (2003)
NetAffx: Affymetrix probesets and annotations. Nucleic Acids
Res., 31, 82–86.
34. Hooper,S.D. and Bork,P. (2005) Medusa: a simple tool for
interaction graph analysis. Bioinformatics, 21, 4432–4433.
35. Shannon,P., Markiel,A., Ozier,O., Baliga,N.S., Wang,J.T.,
Ramage,D., Amin,N., Schwikowski,B. and Ideker,T. (2003)
Cytoscape: a software environment for integrated models of
biomolecular interaction networks. Genome Res., 13, 2498–2504.
36. Antonov,A.V., Dietmann,S., Wong,P., Igor,R. and Mewes,H.W.
(2009) PLIPS, an automatically collected database of protein lists
reported by proteomics studies. J. Proteome. Res., 8, 1193–1197.
37. Vermeer,M.H., van Doorn,R., Dijkman,R., Mao,X., Whittaker,S.,
van Voorst Varder,P.C., Gerritsen,M.J., Geerts,M.L., Gellrich,S.,
Soderberg,O. et al. (2008) Novel and highly recurrent
chromosomal alterations in Sezary syndrome. Cancer Res., 68,
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