The pedant genome database Dmitrij Frishman
The PEDANT genome database
*, Martin Mokrejs1
, Denis Kosykh
, Gabi Kastenmu¨ller1
, Igor Zubrzycki
, Christian Gruber2
, Birgitta Geier
, Andreas Kaps2
, Kaj Albermann
, Andreas Volz2
, Christian Wagner
, Matthias Fellenberg2
, Klaus Heumann
and Hans-Werner Mewes1,3
Institute for Bioinformatics, GSF - National Research Center for Environment and Health, Ingolsta¨dter Landstraße 1,85764 Neueherberg, Germany,
Biomax Informatics AG, Lochhamer Straße 11, 82152 Martinsried, Germany and3
Department of Genome-oriented Bioinformatics, Wissenschaftszentrum Weihenstephan, Technische Universita¨t
Mu¨nchen, 85350 Freising, Germany
Received August 13, 2002; Revised and Accepted September 12, 2002
The PEDANT genome database (http://pedant.gsf.de)
provides exhaustive automatic analysis of genomic
sequences by a large variety of established bioinfor-
matics tools through a comprehensive Web-based
user interface. One hundred and seventy seven
completely sequenced and unﬁnished genomes
have been processed so far, including large eukar-
yotic genomes (mouse, human) published recently.
In this contribution, we describe the current status of
the PEDANT database and novel analytical features
added to the PEDANT server in 2002. Those include:
(i) integration with the BioRS
data retrieval systemwhich allows fast text queries, (ii) pre-computed
sequence clusters in each complete genome, (iii) a
comprehensive set of tools for genome comparison,
including genome comparison tables and protein
function prediction based on genomic context, and
protein–protein interaction (PPI) networks based on experi-
mental data. The availability of functional and
structural predictions for 650 000 genomic proteins
in well organized form makes PEDANT a useful
OVERVIEW AND STATUS OF THE PEDANT
DATABASE IN 2003
When the ﬁrst version of the PEDANT genome database was
launched in 1996 (1) it provided a computational analysis of
the ﬁve ﬁrst completely sequenced genomes available at that
time using a limited set of algorithms and with results stored
as static HTML pages. In the past seven years, the PEDANT
genome analysis software has matured (2): it is now based on
an efﬁcient relational database schema compatible with both
TM and OracleTM
database management systems,
employs a broad range of modern bioinformatics methods to
analyze sequence data, and offers an extensive user interface.
In parallel, the database content was explosively growing
following the fast pace of genome sequencing projects.
However, the main concept of the database has not changed
since the ﬁrst day of its existence. Since in-depth manual
annotation of all genomic sequences pouring into the
databases is virtually impossible our goal has been to provide
and structural characterization
publicly available genomes by automatic means in a timelyfashion. Being fully aware of the pitfalls of automatic
sequence analysis (3) we use reasonably stringent recognition
parameters to avoid excessive false positive rates, and at the
same time not only provide search and prediction results in
digested form, but also store the raw output of bioinformatics
methods, enabling the annotator or the biologist using the
database to make his own judgement on the signiﬁcance of
the results presented.
At the time of writing the total of 177 genomes are
available on-line. The database consists of three major
1. Genomes which undergo careful in-depth analysis by the
MIPS biologists using the subsystem for manual annotation
available in the PEDANT software suite. This section
currently includes Neurospora crassa, Thermoplasma
acidophilum, and Arabidopsis thaliana.
2. Completely sequenced and published genomes. The main
source of sequence data for this section, including DNA
contigs and ORF nomenclature, is the genomes division of
GenBank (4), although in some cases we obtain data directly
from sequencing centres. Whenever possible we use data
manually curated by NCBI staff (ftp://ftp.ncbi.nih.gov/
genomes/Bacteria). If a curated version is not avail-
able, original data as submitted by the authors (ftp://
ftp.ncbi.nih.gov/genbank/genomes/Bacteria) is processed.
This section contains 5 eukaryotic, 84 eubacterial, and 16
*To whom correspondence should be addressed. Tel:
þ49 89 31874201; Fax: þ49 89 31873585; Email: email@example.com
2003 Oxford University PressNucleic Acids Research, 2003, Vol. 31, No. 1
3. Unﬁnished genomic sequences. Gene prediction is con-
ducted by ORPHEUS (5) in a completely automatic
fashion, usually allowing for large overlaps between
ORFs. This leads to many over-predicted ORFs, but
ensures that fewer real ORFs are missed. In many cases,
the PEDANT database is the only source of annotation for
such datasets. In recent time, this section of the database
was growing slower then before because we chose to
commit our processing capacity to the quickly growing
number of completely sequenced genomes recently pub-
lished, including all publicly available eukaryotic genomes.
This section contains 15 eukaryotic, 51 eubacterial, and 3
Among the most significant recent additions to the database is
mouse genome data obtained from http://genome.cse.ucsc.edu.
The mouse database contains 20 chromosome contigs with
37 793 genes predicted using the Fgenesh
For each of the roughly 650 000 protein sequences processed
so far the following pre-computed analyses are available:
(A) Protein function
BLAST (6) similarity searches against the completenon-redundant protein sequence database.
Motif searches against the Pfam (7), BLOCKS (8), andPROSITE (9) databases. InterPro (10) calculations are
Predictions of cellular roles and functions based on high-stringency BLAST searches against protein sequences
with manually assigned functional categories according
to the FunCat Functional Catalogue developed by MIPS
and Biomax Informatics AG. The FunCat catalogue
covers a broad range of biological concepts, including
cellular processes, systemic physiology, development
and anatomy for prokaryotes and unicellular eukaryotes,
plants and animals. In addition, genomes annotated with
other vocabularies (such as Gene Ontology) can be
mapped to FunCat annotations and thus integrated into
the similarity search, as already done for the genomes of
Drosophila melanogaster and Caenorhabditis elegans.
At present, we use proteins with manually assigned
functional categories of the following species: plant
A.thaliana, fungi Saccharomyces cerevisiae, eubacter-
ium Listeria monocytogenes EGD and archaebacterium
T.acidophilum. More species-speciﬁc catalogues are in
preparation and will be available shortly (e.g. bacteria
Bacillus subtilis, Helicobacter pylori, N. crassa).
Similarity-based predictions of enzyme nomenclature(EC numbers).
Similarity-based extraction of keywords and super-family assignments from the PIR-International sequence
Assignment of sequence to known clusters of ortholo-gous groups [COGS, (12)].
(B) Protein structure
Sensitive similarity-based identiﬁcation of known 3Dstructures and structural domains. For this purpose, we
are using the IMPALA software (13) which allows
comparison of each gene product with a collection of
position speciﬁc scoring matrices, or proﬁle library,
representing sequences with known three dimensional
structure from the PDB database (14) and sequences of
structural domains from the SCOP database (15). CATH
(16) domain predictions are being currently added to the
TMHMM software (17).
Identiﬁcation of local low similarity regions and entirenon-globular domains based on the SEG algorithm (18).
Prediction of coiled coil motifs (19).
Prediction of protein structural classes (all-a, all-b, a/b).
In some cases, further analyses may be available. For example,
for cDNA collections we conduct BLASTN searches against
relevant taxonomic subdivisions of the EMBL database (20).
Several additional methods to predict protein features, such as
localization or presence of signal peptides are implemented,
but not systematically used due to high error rates.
Perhaps the most characteristic feature of the PEDANT user
interface, available since its conception, is the automatic
assignment of gene products to various functional and
structural categories. There are two types of such categories:
Individual categories, such as sequences with homologues.Selecting this category immediately leads to the list of
sequences possessing a BLAST hit, sorted by signiﬁcance.
Further categories of this type are: sequences without
homology, non-identical closest homologues, sequences
with predicted transmembrane segments, coiled coils, low
complexity and non-globular regions.
Group categories, such as sequence and structure motifs.Selecting such category ﬁrst leads to the list of all groups
of a given type actually identiﬁed in a particular genome. In
a second step, the user selects an item of interest, e.g.,
a Pfam domain, and gets the list of sequences that are
predicted to possess this domain. Categories of this type
are: Pfam, BLOCKS, and PROSITE motifs, functional
categories, EC numbers, PIR keywords and superfamilies,
SCOP and CATH domains, COGs, as well as sequence
clusters (see below). In addition, BLAST similarity hits are
classiﬁed based on their taxonomic origin; additional
kingdom, phylum, class, and species—allow the user to
obtain the lists of respective taxonomic divisions and then
select sequences that have at least one BLAST hit in a given
In addition, the following searches can be performed
interactively against protein sequences as well as DNA
sequences or ORFs and contigs of a particular genome:
BLAST search with a user query sequence
Sequence pattern search using the PROSITE regular
As soon as an ORF of interest has been selected from a given
category or based on an interactive search, an integrated,
hyperlinked protein report is provided showing analysis results
according to dynamically set thresholds. All evidence available
is summarized in the report, including a number of calculated
parameters, such as molecular weight, pI value, position of
the ORF on the contig, homology-derived data, as well as
Nucleic Acids Research, 2003, Vol. 31, No. 1
predicted structural features. A navigation toolbar in the upper
part of the report page allows access to the protein and DNA
sequence of a given ORF and the raw results of individual
computational methods. Those are also equipped with Web
links and can be used as reference for further manual
annotation. An advanced DNA viewer represents contigs in
graphical form and allows one to navigate, zoom, produce six-
frame translation, and show DNA features such as restriction
sites and genetic elements (genes, ORFs, exons, tRNAs, etc.).
The protein viewer visualizes information about similarity to
entries in the protein databases used and predicted protein
features, e.g. sequence motifs and secondary structure
elements. This is especially useful for judging on the domain
structure of the homology hits.
The public PEDANT database server has been upgraded in
terms of CPU speed, RAM memory and disk space. In order to
improve the performance of the public MySQL database
server, a separate server is utilized to conduct computations
and prepare the data. When newly created datasets pass
extensive quality tests and a substantial number of new
databases have been accumulated, a new release of the
PEDANT database is made. At the time of writing the version
of the database is 1.0.2.
SEARCHING AND DATA MINING IN THE PEDANT
GENOME DATABASE USING THE BioRS
INTEGRATION AND RETRIEVAL SYSTEMIn order to enable users to take full advantage of the exhaustive
genome annotation available in the PEDANT database, fast
and efﬁcient data mining and search capabilities must be
provided. However, given the enormous amount of pre-
computed bioinformatics analyses stored in MySQL tables
this requirement is not easy to meet. Although MySQL is
arguably the fastest relational database currently available a
simple text search for the word ‘kinase’ in only one 500 mB
table containing BLAST results for the A.thaliana genome
takes more than a minute to complete, and composite queries
in such large datasets are all but impossible.
To enhance the data-mining capabilities of the PEDANT
Genome Database its latest release has been integrated with the
BioRS Integration and Retrieval System developed by Biomax
Informatics AG (www.biomax.de). The BioRS system is able
to integrate and search ﬂat-ﬁle databases as well as relational
databases (at present, MySQL, Oracle and DB2). Additional
index data structures are generated, allowing queries to be
processed on the index for enhanced query performance. The
original data source is accessed only when the user requests
the entire entry or when indexing is performed. Because the
open Common Object Request Broker Architecture (CORBA)
is used as platform-independent middleware, indexing and
querying processes can be distributed over as many CPUs as
are available, facilitating timely updates of the indices.
The PEDANT GUI now provides an HTML-based search
form which allows one to specify complex search terms (using
wildcards) and apply them selectively to different parts of the
annotation, e.g. to search only in Pfam motifs, functional
categories or known 3D structures. Several instances of such
pairs of attributes and search values are provided and can be
combined by Boolean operators. Additional criteria for
searching include sequence length, number of transmembrane
regions, pI range and percentage of low complexity sequence.
After clicking the ‘Search’ button, a CGI program is initiated
to translate the values of the HTML search form into the
BioRS Query Language. The query is executed by the BioRS
core using search daemons and the results are returned to the
PEDANT client which then generates an HTML-based table
including hyperlinks to the corresponding protein reports.
Due to the use of pre-calculated indices search results are
returned essentially instantly, allowing interactive exploration
of the information contained in the PEDANT database. For
example, a search for A.thaliana proteins having the word
‘transcription’ in functional categories, the word ‘ﬂoral’ in
BLAST search results, the word ‘mads’ anywhere in the
annotation, and pI in the range from 4 to 8 ﬁnds 12 hits in the
11 gB annotation of the genome in just a few seconds.
SEQUENCE CLUSTERING AND PARALOGOUS
One of the important aspects of genome annotation involves
evaluation of gene duplication and the analysis of paralogous
gene families. Within each completely sequenced genome we
conduct an all against all comparison of proteins by PSI-
BLAST, with low complexity sequence regions masked.
Sequences possessing sufﬁcient degree of similarity in a
reciprocal fashion (BLAST similarity score greater than
45 bits) are joined into single-linkage groups. In cases where
reciprocal BLAST comparisons produce only one local
alignment between two sequences in each direction, this hit
is made symmetrical by taking into account only the longer
alignment. Additionally, results of sensitive recognition of
Pfam domains through HMMER searches (21) are taken into
account. If two or more proteins in a genome display similarity
to the same Pfam domain with a signiﬁcant E-value (typically
0.001), it may be safely assumed that the corresponding
protein sequence spans are similar to each other, even if
BLAST fails to recognize such relationships. Correspondingly,
by selecting the ‘sequence clusters’ category on the PEDANT
launch panel the user is presented with a list of sequence
clusters found in the given genome, with the number of
sequences in each cluster and the cluster name indicated. The
latter is automatically derived from the description lines of the
cluster sequences, with informative description lines given
priority over those containing the words ‘unknown’, ‘putative’,
and the like. For each cluster the list of sequences can be
displayed. In addition, a graphical representation of the cluster
is available in form of a circular diagram, visualizing the
structure of the BLAST and Pfam hits as well as the structural
information available for the cluster proteins (22).
Starting from the year 2002 an exhaustive all-on-all BLAST
comparison of all protein sequences in completely sequenced
genomes is conducted for each major release of the PEDANT
database; the current version encompasses 165 000 proteins in
70 genomes. After selecting the ‘intergenome comparison’
Nucleic Acids Research, 2003, Vol. 31, No. 1
category on the launch panel the user may choose up to 10
genomes to be compared and obtain a table of similarity
relationships between a query genome and the selected target
genomes. Similarity hits are coloured according to their
BLAST score and equipped with links to respective genome
datasets. In addition, on each report page of proteins involved
in the cross-genome comparison a link ‘compare genomes
starting from this gene’ appears, leading to the appropriate
page of the genome comparison table. Such table is a very
convenient tool for quickly assessing the distribution of a given
gene across selected representatives of main taxonomic groups
or most important model organisms. Since chromosomal
coordinates of genes are also provided it is also possible to
estimate the conservation of genomic context around a given
gene of interest.
For more in-depth exploration of gene context we have
developed a novel computational method called SNAP
[Similarity-Neighbourhood APproach; (23)]. A Similarity-
Neighbourhood Graph (SN-Graph) is built that involves chains
of alternating S- and N-relationships. The former represent
BLAST similarity hits between putative orthologues in
different genomes while the latter involve neighbouring genes
on the same genome. An SN-Graph can thus be thought of as a
walk across many genomes which begins with a particular
gene in genome A and proceeds to its orthologue in genome B.
The walk then continues to encompass a given number of
neighbours of this orthologue on each side. Subsequently,
orthologues of these neighbours are found in other genomes,
their neighbours identiﬁed, and so on. Closed paths on an SN-
graph, that we call SN-cycles, are strongly non-random and
have the tendency to join functionally related genes involved in
the same biochemical process. A specialized Web server,
Snapper, has been developed which allows one to submit a
protein sequence for a SNAP analysis [http://pedant.gsf.de/
snapper; (24)]. This server takes full advantage of the
PEDANT functional annotation and provides links to
PEDANT entries. Conversely, a Snapper session can be
launched from any PEDANT database report page by pressing
the ‘submit this sequence for SNAP analysis’ button.
Yet another way to establish functional links between gene
products in a similarity-free fashion is through phylogenetic
proﬁling which involves ﬁnding genes with correlated
occurrence in different genomes (25). We have incorporated
a feature-rich implementation of this method (Wong et al., in
preparation) into the PEDANT server. In this case, too, the user
can invoke a proﬁling analysis for a gene of interest directly
from the PEDANT report page.
PROTEIN – PROTEIN INTERACTIONS
Another novel feature of the PEDANT database introduced in
2002 is the incorporation of the data on protein–protein
interactions (PPI). The information is directly imported from
the MIPS PPI catalogue [(26); http://mips.gsf.de/proj/yeast/
CYGD/interaction] which currently describes the total of
13 842 interactions for 4033 proteins from the S.cerevisiae
genome. In particular, the catalogue includes the following two
components: (i) the original PPI catalogue which was being
built by a group of MIPS biologists since 1997 based on
careful analysis of yeast literature (27). This ‘classical’ part of
the catalogue contains information on 1889 proteins involved
in 4924 interactions, classiﬁed into physical and genetic
interactions, and (ii) recently published data from large-scale
two-hybrid experiments [e.g., (28)]. After clicking on the
category ‘protein–protein interactions’ on the PEDANT launch
panel the user is presented with a list of individual experiments
(for convenience the ‘classic’ catalogue is treated as one
experiment although data come from hundreds of different
publications). For each experiment, a table of interactions
between pairs of ORFs is shown, interlinked to the
corresponding protein reports. In addition, individual disjoint
PPI networks can be delineated and visualized using a
graphical Java applet. Direct incorporation of PPI data into
PEDANT facilitates its efﬁcient exploration in the context of
functional annotation (29). At present, this feature is only
available for the S.cerevisiae genome; data on other organisms
will be added in the future.
The rich set of structural and functional characteristics derived
for each protein as well as the high degree of automation and
advanced analytical features make the PEDANT database a
useful tool for structural genomics. In particular, PEDANT can
be used to facilitate the target selection process. Using the
sequence clustering results described above it is easy to judge
the domain structure of the protein families. Further, circular
diagrams visualize available structural information on each
cluster member (domains with known three-dimensional
structure, transmembrane regions). Based on these pre-
computed results we have created an efﬁcient target selection
tool called STRUDEL [STRucture DEtermination Logic;
(22)]. A Web-based interface for this tool allowing PEDANT
users to select structural targets of interest according to
speciﬁed criteria is currently being developed.
1. Frishman,D. and Mewes,H.W. (1997) PEDANTic genome analysis.
Trends Genet., 13, 415–416.
2. Frishman,D., Albermann,K., Hani,J., Heumann,K., Metanomski,A.,
Zollner,A. and Mewes,H.W. (2001) Functional and structural genomics
using PEDANT. Bioinformatics, 17, 44–57.
3. Galperin,M.Y. and Koonin,E.V. (1998) Sources of systematic error in
functional annotation of genomes: domain rearrangement, non-orthologous
gene displacement and operon disruption. In Silico. Biol., 1, 55–67.
4. Benson,D.A., Karsch-Mizrachi,I., Lipman,D.J., Ostell,J., Rapp,B.A. and
Wheeler,D.L. (2002) GenBank. Nucleic Acids Res., 30, 17–20.
5. Frishman,D., Mironov,A., Mewes,H.W. and Gelfand,M. (1998) Combining
diverse evidence for gene recognition in completely sequenced bacterial
genomes. Nucleic Acids Res., 26, 2941–2947.
6. Altschul,S.F., Madden,T.L., Schaffer,A.A., Zhang,J., Zhang,Z., Miller,W.
and Lipman,D.J. (1997) Gapped BLAST and PSI-BLAST: a new
generation of protein database search programs. Nucleic Acids Res., 25,
7. Bateman,A., Birney,E., Cerruti,L., Durbin,R., Etwiller,L., Eddy,S.R.,
Grifﬁths-Jones,S., Howe,K.L., Marshall,M. and Sonnhammer,E.L. (2002)
The Pfam protein families database. Nucleic Acids Res., 30, 276–280.
8. Henikoff,S., Henikoff,J.G. and Pietrokovski,S. (1999) Blocks
þ: a non-
redundant database of protein alignment blocks derived from multiple
compilations. Bioinformatics, 15, 471–479.
Nucleic Acids Research, 2003, Vol. 31, No. 1
9. Falquet,L., Pagni,M., Bucher,P., Hulo,N., Sigrist,C.J., Hofmann,K. and
Bairoch,A. (2002) The PROSITE database, its status in 2002. Nucleic
Acids Res., 30, 235–238.
10. Apweiler,R., Attwood,T.K., Bairoch,A., Bateman,A., Birney,E.,
Biswas,M., Bucher,P., Cerutti,L., Corpet,F., Croning,M.D. et al. (2001)
The InterPro database, an integrated documentation resource for
protein families, domains and functional sites. Nucleic Acids Res., 29,
11. Barker,W.C., Garavelli,J.S., Huang,H., McGarvey,P.B., Orcutt,B.C.,
Srinivasarao,G.Y., Xiao,C., Yeh,L.S., Ledley,R.S., Janda,J.F. et al. (2000)
The protein information resource (PIR). Nucleic Acids Res., 28, 41–44.
12. Tatusov,R.L., Natale,D.A., Garkavtsev,I.V., Tatusova,T.A.,
Shankavaram,U.T., Rao,B.S., Kiryutin,B., Galperin,M.Y., Fedorova,N.D.
and Koonin,E.V. (2001) The COG database: new developments in
phylogenetic classiﬁcation of proteins from complete genomes. Nucleic
Acids Res., 29, 22–28.
13. Schaffer,A.A., Wolf,Y.I., Ponting,C.P., Koonin,E.V., Aravind,L. and
Altschul,S.F. (1999) IMPALA: matching a protein sequence against a
collection of PSI-BLAST- constructed position-speciﬁc score matrices.
Bioinformatics, 15, 1000–1011.
14. Berman,H.M., Westbrook,J., Feng,Z., Gilliland,G., Bhat,T.N., Weissig,H.,
Shindyalov,I.N. and Bourne,P.E. (2000) The Protein Data Bank. Nucleic
Acids Res., 28, 235–242.
15. Lo,C.L., Brenner,S.E., Hubbard,T.J., Chothia,C. and Murzin,A.G. (2002)
SCOP database in 2002: reﬁnements accommodate structural genomics.
Nucleic Acids Res., 30, 264–267.
16. Pearl,F.M., Martin,N., Bray,J.E., Buchan,D.W., Harrison,A.P., Lee,D.,
Reeves,G.A., Shepherd,A.J., Sillitoe,I., Todd,A.E. et al. (2001) A rapid
classiﬁcation protocol for the CATH Domain Database to support
structural genomics. Nucleic Acids Res., 29, 223–227.
17. Krogh,A., Larsson,B., von Heijne,G. and Sonnhammer,E.L. (2001)
Predicting transmembrane protein topology with a hidden Markov model:
application to complete genomes. J. Mol. Biol., 305, 567–580.
18. Wootton,J.C. and Federhen,S. (1993) Statistics of local complexity in amino
acid sequences and sequence databases. Comput. Chem., 17, 149–163.
19. Lupas,A., Van Dyke,M. and Stock,J. (1991) Predicting coiled coils from
protein sequences. Science, 252, 1162–1164.
20. Stoesser,G., Baker,W., van den,Broek,A., Camon,E., Garcia-Pastor,M.,
Kanz,C., Kulikova,T., Leinonen,R., Lin,Q., Lombard,V. et al. (2002)
The EMBL Nucleotide Sequence Database. Nucleic Acids Res., 30, 21–26.
21. Eddy,S.R. (1998) Proﬁle hidden Markov models. Bioinformatics,
22. Frishman,D. (2002) Knowledge-based selection of targets for structural
genomics. Protein Eng., 15, 169–183.
23. Kolesov,G., Mewes,H.W. and Frishman,D. (2001) Snapping up
functionally related genes based on context information: a colinearity-free
approach. J. Mol. Biol., 311, 639–656.
24. Kolesov,G., Mewes,H.W. and Frishman,D. (2002) SNAPper: gene order
predicts gene function. Bioinformatics, 18, 1017–1019.
25. Pellegrini,M., Marcotte,E.M., Thompson,M.J., Eisenberg,D. and Yeates,T.O.
(1999) Assigning protein functions by comparative genome analysis: protein
phylogenetic proﬁles. Proc. Natl Acad. Sci. USA, 96, 4285–4288.
26. Mewes,H.W., Frishman,D., Guldener,U., Mannhaupt,G., Mayer,K.,
Mokrejs,M., Morgenstern,B., Munsterkotter,M., Rudd,S. and Weil,B.
(2002) MIPS: a database for genomes and protein sequences. Nucleic Acids
Res., 30, 31–34.
27. Mewes,H.W., Albermann,K., Bahr,M., Frishman,D., Gleissner,A., Hani,J.,
Heumann,K., Kleine,K., Maierl,A., Oliver,S.G. et al. (1997) Overview of
the yeast genome. Nature, 387, 7–65.
28. Uetz,P., Giot,L., Cagney,G., Mansﬁeld,T.A., Judson,R.S., Knight,J.R.,
Lockshon,D., Narayan,V., Srinivasan,M., Pochart,P. et al. (2000) A
comprehensive analysis of protein-protein interactions in Saccharomyces
cerevisiae. Nature, 403, 623–627.
29. Fellenberg,M., Albermann,K., Zollner,A., Mewes,H.W. and Hani,J. (2000)
Integrative analysis of protein interaction data. Proc. Int. Conf. Intell. Syst.
Mol. Biol., 8, 152–161.Nucleic Acids Research, 2003, Vol. 31, No. 1 211
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