Reactome: a knowledgebase of biological pathways
G. Joshi-Tope1,*, M. Gillespie1,3, I. Vastrik2, P. D’Eustachio1,4, E. Schmidt2, B. de Bono2,
B. Jassal2, G.R. Gopinath1, G.R. Wu1, L. Matthews1, S. Lewis5, E. Birney2and L. Stein1
1Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA,2European Bioinformatics Institute, Hinxton,
Cambridge, UK,3St Johns University, NY, USA,4New York University School of Medicine, NY, USA and
5University of California, Berkeley, CA, USA
Received August 19, 2004; Revised and Accepted October 6, 2004
Reactome, located at http://www.reactome.org is a
curated, peer-reviewed resource of human biological
the complete set of possible reactions constitutes its
reactome. The basic unit of the Reactome databaseis
a reaction; reactions are then grouped into causal
chains to form pathways. The Reactome data model
allows us to represent many diverse processes in the
human system, including the pathways of intermedi-
ary metabolism, regulatory pathways, and signal
transduction, and high-level processes, such as the
cell cycle. Reactome provides a qualitative frame-
work, on which quantitative data can be super-
imposed. Tools have been developed to facilitate
custom data entry and annotation by expert bio-
logists, and to allow visualization and exploration of
the finished dataset as an interactive process map.
Although our primary curational domain is pathways
from Homo sapiens, we regularly create electronic
via putative orthologs, thus making Reactome relev-
ant to model organism research communities. The
database is publicly available under open source
terms, which allows both its content and its software
infrastructure to be freely used and redistributed.
Although sequencing of the human genome has proved to be a
powerful tool for understanding biology, analysis of the gen-
ome has underscored the difficulty of deriving from it the
higher principles of biology. Further, studying whole tran-
scriptional profiles and cataloging protein–protein interactions
has yielded much valuable biological information. Yet it
remains difficult, often impossible, to make the leap from
the genome or proteome to the physiology of an organism,
an organ, a tissue or even a single cell.
The informationthat describes genes,their protein products,
and the biological processes in which they are involved is
scattered over several databases, the primary research liter-
ature and other publications. The inability to manipulate this
knowledge computationally is most keenly felt in the analysis
of high-throughput data. When a researcher looks at a set of
microarray expression results, can he/she reliably notice that
the dozens of up-regulated genes include all the componentsof
the phosphodiesterase signal pathway? When a researcher
identifies four quantitative trait loci for brittle bones in rats,
will he/she realize that these four genomic regions all contain
components of a named developmental pathway? The Reac-
tome database was created to provide an integrated view of
biological processes, which links such gene products and can
be systematically mined by using bioinformatics applications.
We curate well-established information about biological pro-
cesses, and usually defer the curation of contentious data for
curation to a later date, when clear supporting evidence may
The primary curational domain for Reactome includes path-
ways from Homo sapiens, except when there are gaps in
human data. Also, we regularly create electronic projections
of human pathways onto other organisms via putative ortho-
logs, thus making Reactome relevant to model organism
research communities. Both Reactome content and software
is publicly available under open source terms. Reactome
supersedes an earlier project, the Genome Knowledgebase (1).
DESIGN AND IMPLEMENTATION
We first describe the Reactome user interface and then its
underlying data model and data entry and curation processes.
*To whom correspondence should be addressed. Tel: +1 516 367 6904; Fax: +1 516 367 6851; Email: firstname.lastname@example.org
The online version of this article has been published under an open access model. Users are entitled to use, reproduce, disseminate, or display the open access
version of this article for non-commercial purposes provided that: the original authorship is properly and fully attributed; the Journal and Oxford University Press
only in part or as a derivative work this must be clearly indicated. For commercial re-use permissions, please contact email@example.com.
ª 2005, the authors
Nucleic Acids Research, Vol. 33, Database issue ª Oxford University Press 2005; all rights reserved
Nucleic Acids Research, 2005, Vol. 33, Database issue
the reactions contained within the database. Each reaction is
represented as an arrow. Reactions that are causally or tem-
porally related in a clustered set of reactions are arranged in a
head-to-tail manner; thin gray lines indicate similar such links
among reactions in different pathways. We have clustered the
reactions to create recognizable patterns so that a researcher
quickly learns to recognize each pathway’s distinguishable
appearance. This is useful both as a navigational tool to
keep the biologist oriented as he/she moves around the
resource, and also as a data-mining tool using the ‘skypainter’
facility, described in detail later.
As a researcher mouses over the reaction map, the relevant
topic headers in the lower index panel are highlighted. The
index panel lists a growing number of high-level processes
among the six organisms for which events have been curated
(human, Hsa) or inferred (mouse, Mmu; rat, Rno; puffer fish,
Fru; zebra fish, Dre; and chicken, Gga). Each high-level pro-
cess links to a chapter-like collection of pathways and reac-
tions. A color-coding system allows the researcher to
distinguish between reactions that have been curated from
direct evidence in the literature from those that have been
inferred via orthology in other species.
A researcher can navigate Reactome either by clicking on
the reaction map, by selecting a topic of interest from a hier-
archical table of contents, or via several structured database
searches. As the researcher follows the path down into the
resource he/she is presented with increasing levels of detail
associated with the pathway: its constituent reactions, the
participating complexes and macromolecules and the relation-
ships among the pathways in the several species covered by
Reactome. At each level, there is information designed for
human browsing, such as text summaries of the pathway and
hand-drawn diagrams of key events, as well as machine-
readable information. As an example of the latter, Reactome
makes it easy to obtain a list of all UniProt (2) accession
numbers corresponding to proteins that participate in any
phase of the cell cycle, or even in the cell cycle as a whole.
A typical top-down browse path is shown in Supplementary
Reactome can also be used from the bottom up. Genomic
databases provide comprehensive lists of genes and their pro-
tein products, but often with limited functional annotation.
Reactome can be used to fill this gap for individual genes.
A typical scenario occurs when a researcher encounters an
unfamiliar UniProt protein, for example, a protein with the
accession number Q14676 (Supplementary Material 6). Via
agreement with UniProt, proteins whose functions have been
annotated in Reactome have a link to the Reactome database.
However, Q14676 is an entry in the TrEMBL database and
only limited functional annotation is available for this protein.
The link to Gene Ontology (GO) (3) only tells the user that this
protein is intracellular, and is inferred by electronic annota-
tion. On the other hand, the link to Reactome takes the
researcher to a page that summarizes all the reactions and
pathways in which the protein, a modification of the protein
or a complex involving the protein, participates in (Supple-
mentary Material 7), along with relevant literature citations
and the module author’s commentary (Supplementary
Material 8). This immediately places the protein into its
biological context in a richer and more useful manner than
the simpler GO process annotation might provide. Similar to
the UniProt links, Reactome also has reciprocal links to GO,
Ensembl (4) and Entrez Gene (http://www.ncbi.nlm.nih.gov/
entrez/query. fcgi?db=Protein) databases.
Reactome features a full text search of the database as well
as an advanced search. Advanced searches can be conducted
across any Reactome data type and can be restricted by up to
four properties. An example search is shown in Figure 2.
The Reactome ‘skypainter’ tool allows researchers to
upload a list of gene or protein identifiers in order to colorize
the reaction map in a number of ways. An example of the
usage of this tool is shown in Figure 3, where the set of genes
associated with human disease, the morbid map from Online
Mendelian Inheritance in Man (OMIM) (5) (http://www.
ncbi.nlm.nih.gov/omim/), has been overlaid onto the reaction
map. The resulting graphic representation instantly shows that
human disease genes are not arranged haphazardly, but instead
cluster in certain key pathways. For example, ovarian cancer
genes map onto the DNA damage checkpoint pathway, while
those implicated in breast cancer can be found in the DNA
double strand break repair pathway. Almost half the reactions
in the reaction map are relevant to disease phenotypes.
The basic unit of Reactome is the reaction. A reaction is any
event that converts inputs to outputs, where inputs and outputs
are physical entities such as small molecules, proteins, lipids
or nucleotides, or complexes of these. This definition of
reaction is broad enough to encompass classical biochemical
reactions, such as the phosphorylation of glucose to glucose-
6-phosphate, as well as less conventional types of reactions,
such as binding, dissociation, complex formation, transloca-
inputs and outputs, a reaction will include information on the
species, sub-cellular location, and critically, the experimental
Figure 1. The Reaction map shows the reactions annotated in Reactome. The reaction clusters of top-level processes are shown.
Nucleic Acids Research, 2005, Vol. 33, Database issueD429
evidence for the reaction, typically taking the form of one or
more literature citations. Other attributes of reactions include a
catalyst activity, when appropriate, as well as information
on their regulation. Reactions are then grouped into pathways
that take into account their temporal relationships and
interdependencies. Pathways in Reactome are useful group-
ings of reactions, and can contain sequential reactions, parallel
reactionsorreactions ordered inacycle. Further,pathwayscan
nest; pathways can have other pathways as their components,
and can be sequential or parallel.
Many reactions are involved in the transformation of a
physical entity from one state to another. For example, a
carbohydrate transport reaction may move an extracellular
sugar molecule into the cytosol. Reactome explicitly annotates
such states by representing extracellular and cytosolic glucose
as separate entries. Another example of the explicit annotation
of states of a molecule is the p53 protein, which is represented
by three distinct entities in Reactome: native p53, p53 phos-
phorylated at Ser15 and p53 phosphorylated at Ser20. This
allows the distinct biological activities of each of these p53
Figure 3. The OMIM Morbid Map of the Human Genome lists all genes whose mutant forms are causally associated with human disease. Each Reactome event
in which, one or more such gene products are involved as input, catalyst or regulator is shown in red. Some examples of diseases that map to Reactome reactions
D430 Nucleic Acids Research, 2005, Vol. 33, Database issue
states to be described unambiguously. These multiple states
are derived from a single Reference Entity, which contains
information on the polypeptide sequence of p53 as well as
cross-references to the Uniprot, Entrez Gene and Ensembl
The Reactome project is careful in using unambiguous,
well-known identifiers whenever possible. In addition to
links between reference entities and the protein and gene
databases, Reactome links small molecules to ChEBI
(http://www.ebi.ac.uk/chebi/), catalyst activities to the GO
molecular function ontology, and sub-cellular locations to
facilitate the integration of Reactome reactions and pathways
with other bioinformatics Web resources. The data model also
allows for statements about generic physical entities such
as ‘any tRNA’ in order to avoid creating families of reactions
that differ only by the particular species of tRNA that it
Reactomeisorganized likeanonline journal. Theeditors,after
consultation with the scientific advisory board, select a series
of topics to annotate, and then invite bench biologists to author
database ‘modules’ that are roughly the same scope and
amount of work as a minireview. Our authors are typically
at the faculty level, but run the gamut from postdoctoral fel-
lows to tenured professors.
Authors create their modules using the Reactome Author
Tool—a desktop application written in Java. This tool hides
most of the complexities of the Reactome data model behind a
graphical front end, whose major features are an interactive,
user-friendly pathway editor and a task list pane that enforces
consistency and completeness on the module. After the author
has completed a module, it is handed over to a Reactome
curator, who refines the annotation using a more sophisticated
set of software applications. Reactome curators are full-time
staff members who combine a broad knowledge of biology
(most are PhD-level biologists) with a good understanding
of bioinformatics and knowledge engineering. The curator’s
job is to ensure that the module is complete and internally
After curation, the module appears on a private website for
inspection by peer reviewers. Like the primary authors, peer
reviewers are faculty-level biologists with expertise in the
relevant field of biology. The authors and/or curators remedy
any inconsistencies, omissions or errors discovered by the peer
reviewers, and then the module is made available to the public.
Topics are also on a schedule for a rolling review every two
years, in order to keep the data current; the modules also get
augmented with new data during such a review.
Non-human pathways in Reactome
Although Reactome is primarily concerned with curation and
presentation of human processes, it is impossible to make
assertions about human biology without reference to experi-
mental work on non-human species. Non-human pathways
appear in Reactome via two routes. The first route occurs
when a non-human reaction is curated in order to provide
indirect evidence for an implied equivalent human reaction.
We prefer that our authors document reactions by using experi-
ments performed on human systems (e.g. tissue culture). In
many cases, however, a reaction is well described in yeast
or frog, and only inferred in humans based on the finding of
putative human orthologs for the proteins that participate in the
reaction. In this case, the author creates a reaction for yeast or
creates a ‘deduced’ reaction in human whose indirect evidence
is the presence of the equivalent reaction in the model organ-
ism. By strictly separating direct and indirect evidence, we try
to maintain clear chains of evidence and to leverage the power
of comparative genomics without creating erroneous pathways
in which the proteins of one species appear to interact with the
proteins of another. Interactions between proteins from differ-
ent species are only relevant while annotating inter-species
relationships, such as the relationship between hosts and para-
sites or between symbionts, for example.
The second route by which non-human pathways appear in
Reactome occurs just prior to each release, when we electro-
nically project curated pathways from human onto each of the
vertebrate species contained within the Ensembl Compara
database (6). We build reactions in the non-human vertebrate
where we can establish one-to-one mapping for putative ortho-
logs based on mutual best hits between humans and a non-
human vertebrate for the inputs or the catalysts of the reaction.
This allows us to predict events in rat, mouse, fugu, zebra fish
Since the first announced release in January 2003, Reactome
has grown to cover almost 10% of the human proteins in
UniProt, and cites 894 literature references as supporting
evidence for the reactions and pathways annotated in the
The number of proteins from the different species covered
in Reactome, and the number of complexes, reactions and
pathways they participate in, are listed in Table 1.
Currently, at version 10, we cover the following biological
processes: cell cycle and its checkpoints, repair and replication
of DNA, gene expression including the transcription by the
three nuclear RNA polymerases, processing of the mRNA and
its translation to protein, metabolism of sugars, ethanol, amino
acids, nucleotides and lipids, the tricarboxylic acid cycle, and
insulin receptor activation and recycling. We have completed
a two-year rolling review of modules on DNA replication, and
Table 1. Reactome holdings
aProteins include entries referenced by UniProt and by Ensembl gene
bData from humans is curated, whereas most of the data from the other
organisms is from electronic event prediction.
Nucleic Acids Research, 2005, Vol. 33, Database issue D431
we are currently in the process of similarly reviewing the Download full-text
modules on protein translation.
DISCUSSION AND FUTURE DIRECTIONS
The Reactome project is an attempt to capture all reactions and
pathways thought to occur in humans. This is, however, only a
small part of the story. In any cell or tissue type, only a small
percentage of the whole genome is expressed, and therefore
the full repertoire of the human reactome is never active
simultaneously in any single cell or developmental stage.
Knowledge of stage and tissue-specific expression patterns,
along with kinetic data such as reaction rates and binding
constants, are the key to creating quantitative models of phy-
siology. Reactome does not attempt to capture stage or tissue-
specific expression data, but defers this task to other databases
that are capturing the results of high-throughput expression
of possible reactions which, when combined with expression
and enzyme kinetic data, provides the infrastructure for quan-
titative models. In order to facilitate this type of data integra-
tion, we are working to make Reactome data available in a
variety of standard formats, including BioPAX, SBML and
PSI-MI. This will also enable data exchange with other path-
way databases, such as the Cycs (7), KEGG (8) and amaze (9),
and molecular interaction databases, such as BIND (10) and
The next data release, version 11, will cover apoptosis,
including the death receptor signaling pathways, and the
Bcl2 pathways, as well as pathways involved in hemostasis.
Other topics currently under development include several
signaling pathways, mitosis, visual phototransduction and
In summary, Reactome provides high-quality curated sum-
maries of fundamental biological processes in humans in a
form that is as useful to students working on a single protein as
it is to bioinformaticists striving to make sense of large-scale
datasets. Reactomeprovides biologist-friendlyvisualizationof
biological pathways data, and is an open-source project.
Supplementary Material is available at NAR Online.
We are grateful to our external authors and peer-reviewers
who help us to maintain a high quality of data in Reactome.
The development of Reactome is supported by grants R01
HG002639 from the NHGRI at the US NIH and LSHG-CT-
2003-503269 from EU (6th Framework Programme) and a
subcontract from the NIH-funded Cell Migration Consortium.
1. Joshi-Tope,G., Vastrik,I., Gopinathrao,G., Matthews,L., Schmidt,E.,
Gillespie,M., D’Eustachio,P., Jassal,B., Lewis,S., Wu,G. et al. (2003)
The Genome Knowledgebase: a resource for biologists and
bioinformaticists. Cold Spring Harb. Symp. Quant. Biol., 68, 237–243.
2. Apweiler,R., Bairoch,A., Wu,C.H., Barker,W.C., Boeckmann,B.,
Ferro,S., Gasteiger,E., Huang,H., Lopez,R., Magrane,M. et al. (2004)
UniProt: the Universal Protein knowledgebase. Nucleic Acids Res.,
3. Harris,M.A., Clark,J., Ireland,A., Lomax,J., Ashburner,M., Foulger,R.,
Eilbeck,K., Lewis,S., Marshall,B., Mungall,C. et al. (2004) The Gene
Ontology (GO) database and informatics resource. Nucleic Acids Res.,
4. Birney,E., Andrews,D., Bevan,P., Caccamo,M., Cameron,G., Chen,Y.,
Clarke,L., Coates,G., Cox,T., Cuff,J. et al. (2004) Ensembl.
Nucleic Acids Res., 32, D468–D470.
5. McKusick,V.A. (1998) Mendelian Inheritance in Man. A Catalog of
Human Genes and Genetic Disorders, 12th edn. Johns Hopkins
University Press, Baltimore, MD.
6. Clamp,M., Andrews,D., Barker,D., Bevan,P., Cameron,G., Chen,Y.,
Clark,L., Cox,T., Cuff,J., Curwen,V. et al. (2003) Ensembl 2002:
accommodating comparative genomics. Nucleic Acids Res., 31,
7. Krieger,C.J., Zhang,P., Mueller,L.A., Wang,A., Paley,S., Arnaud,M.,
Pick,J., Rhee,S.Y. and Karp,P.D. (2004) MetaCyc: a multiorganism
database of metabolic pathways and enzymes. Nucleic Acids Res.,
8. Kanehisa,M., Goto,S., Kawashima,S., Okuno,Y. and Hattori,M. (2004)
The KEGG resource for deciphering the genome. Nucleic Acids Res.,
9. Lemer,C., Antezana,E., Couche,F., Fays,F., Santolaria,X., Janky,R.,
Deville,Y., Richelle,J. and Wodak,S.J. (2004) The aMAZE LightBench:
a web interface to a relational database of cellular processes.
Nucleic Acids Res., 32, D443–D448.
10. Bader,G.D., Betel,D. and Hogue,C.W. (2003) BIND: the Biomolecular
Interaction Network Database. Nucleic Acids Res., 31, 248–250.
11. Peri,S., Navarro,J.D., Kristiansen,T.Z., Amanchy,R., Surendranath,V.,
Muthusamy,B., Gandhi,T.K., Chandrika,K.N., Deshpande,N., Suresh,S.
et al. (2004) Human protein reference database as a discovery resource
for proteomics. Nucleic Acids Res., 32, D497–D501.
D432Nucleic Acids Research, 2005, Vol. 33, Database issue