Nucleic Acids Research, 2007, Vol. 35, Web Server issue W21–W26 doi:10.1093/nar/gkm298 iHOP web services Jose´ M. Ferna´ ndez1,*, Robert Hoffmann2 and Alfonso Valencia1 1Structural Biology and Biocomputing Program, CNIO and 2Decentralized Information Group, Computer Science and Artificial Intelligence Laboratory, MIT. Received February 5, 2007; Revised April 5, 2007; Accepted April 12, 2007 ABSTRACT iHOP provides fast, accurate, comprehensive, and up-to-date summary information on more than 80 000 biological molecules by automatically extracting key sentences from millions of PubMed documents. Its intuitive user interface and naviga- tion scheme have made iHOP extremely successful among biologists, counting more than 500 000 visits per month (iHOP access statistics: http://www. ihop-net.org/UniPub/iHOP/info/logs/). Here we describe a public programmatic API that enables the integration of main iHOP functionalities in bioinformatic programs and workflows. INTRODUCTION iHOP (1) (iHOP literature server, http://www.ihop. net.org) allows researchers to explore a network of gene and protein interactions by directly navigating the pool of published scientiﬁc literature. Rather than providing long lists of entire abstracts upon keyword searches, iHOP selectively retrieves information that is speciﬁc to genes and proteins and summarizes their interactions and functions. The system adds value by ﬁltering and ranking extracted sentences according to signiﬁcance, impact factor, date of publication and syntax. iHOP web content is pre-compiled and generated in a multi-step process to annotate biomedical texts with gene and protein names, chemical compounds and MeSH terms. This annotation task is computationally expensive because of the sheer number of entities, but more importantly, hindered by a high semantic overloading of abbreviations and synonyms in biomedicine. The contin- uous development and optimization of heuristics and machine learning algorithms to improve entity detection and synonym disambiguation is therefore a central eﬀort in the maintenance of iHOP. Given the complexity and eﬀort that goes into the development and maintenance of a text-mining pipeline, it makes sense to build upon the existing infrastructure of iHOP rather than reinventing the wheel. Already numerous online resources are linking to iHOP and novel tools are emerging which are based on the iHOP resource, e.g. iHOPerator (2). The iHOP web service API has already been tested in selected projects over the last 2 years and is made publicly available now. Although on any biocomputing facility APIs are not as visible to the end user, they are very important for the diﬀerent omics, which usually depend on powerful data set analysis. Those powerful analysis run distributed workﬂows, which have to semantically integrate the results from diverse biocomputing facilities and data sources. Other large-scale biocomputing facilities provide environments such as NCBI Entrez (3) (Entrez CGI services, http://www.ncbi. nlm.nih.gov/entrez/query/static/eutils_help.html; Entrez SOAP services, http://www.ncbi.nlm.nih.gov/entrez/ query/static/esoap_help.html) or EBI WS (4) (EBI SOAP services, http://www.ebi.ac.uk/Tools/webservices/). METHODS To make the iHOP programmatic interface remotely accessible and integrable on workﬂows in a way that is neutral to programming languages and vendor indepen- dent, we decided to implement the public API in the form of web services (5). Three popular web service API models have been implemented for iHOP: the REST model (6) (Wikipedia description of REST, http://en.wikipedia.org/ wiki/Representational_State_Transfer), which the DAS (7) protocol follows; SOAP þ WSDL, which is based on WSDL document description and uses SOAP messages and XML for messaging; BioMOBY (8), which is focused on bioinformatic workﬂow building (9). Table 1 contains a brief description of these API models. All three API implementations are based on a common internal library and common XML schemas to facilitate maintenance and future developments. MOBY implementation required additional eﬀorts to integrate iHOP web services into the MOBY ontology. The schema design was driven by the iHOP function- alities that are directly useful for bioinformatical workﬂows (Figures 1 and 2). Table 2 contains a brief description of these functionalities, with their inputs and outputs. A key issue in the development was the design of an XML schema rich enough to describe and integrate *To whom correspondence should be addressed. Tel: þ34 917 328 000; Fax: þ34 912 246 976; Email: Jmfernandez@cnio.es 2007 The Author(s) This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/ by-nc/2.0/uk/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.