Published online 16 October 2007 Nucleic Acids Research, 2008, Vol. 36, Database issue D69–D76 doi:10.1093/nar/gkm804 CMGSDB: integrating heterogeneous Caenorhabditis elegans data sources using compositional data mining Amrita Pati1,*, Ying Jin1, Karsten Klage 2 , Richard F. Helm2, Lenwood S. Heath1 and Naren Ramakrishnan1 1Department of Computer Science and 2Department of Biochemistry, Virginia Tech, Blacksburg, VA 24061, USA Received August 15, 2007; Revised September 16, 2007; Accepted September 17, 2007 ABSTRACT CMGSDB (Database for Computational Modeling of Gene Silencing) is an integration of heterogeneous data sources about Caenorhabditis elegans with capabilities for compositional data mining (CDM) across diverse domains. Besides gene, protein and functional annotations, CMGSDB currently unifies information about 531 RNAi phenotypes obtained from heterogeneous databases using a hierarchical scheme. A phenotype browser at the CMGSDB website serves this hierarchy and relates pheno- types to other biological entities. The application of CDM to CMGSDB produces ‘chains’ of relationships in the data by finding two-way connections between sets of biological entities. Chains can, for example, relate the knock down of a set of genes during an RNAi experiment to the disruption of a pathway or specific gene expression through another set of genes not directly related to the former set. The web interface for CMGSDB is available at https://bioinformatics.cs.vt.edu/cmgs/CMGSDB/, and serves individual biological entity information as well as details of all chains computed by CDM. INTRODUCTION The availability of high-throughput screens has opened up awareness of the importance of data integration to reveal useful biological insight. For instance, the study of even a focused aspect of cellular activity, such as gene action, now beneﬁts from multiple high-throughput data acquisi- tion technologies, such as microarrays, genome-wide deletion screens and RNAi assays. While enormous quan- tities of data are available, it remains a major challenge to construe meaningful biological evidence from this data that explains, for example, the role of a biological pathway, the eﬀects of a SNP on disease phenotypes or the regulatory networks or metabolic pathways underlying a cellular state. Two major factors make this process harder. First, high-throughput experiments for a given genome are performed by independent groups of researchers that develop their own naming conventions and schemes for information storage and retrieval. This makes it diﬃcult for scientists to utilize ‘all’ available data for a genome to draw inferences. Second, even if such integration is accomplished, the possibility of linking data across sources is often restricted to individual entities, such as genes or proteins; it is diﬃcult to track ‘sets’ of entities, which is the more natural way to interact with such databases. As a case in point, consider the possibilities of integ- ration opened up by the availability of RNAi screens. Post-transcriptional gene silencing via RNAi was ﬁrst described in the nematode Caenorhabditis elegans (1), and is presently utilized for a variety of functional genomics experiments using RNAi assays. Although Wormbase serves as a centralized repository for C. elegans data, the sources of RNAi experiments in C. elegans are many, their data representation formats are varied and some information is lost while integrating them into the Wormbase (2) schema. Here, we present CMGSDB, a database for computa- tional models in gene silencing, where the following goals have been achieved. We have integrated genome annota- tion data, gene expression data, protein interaction data, gene regulation data, GO (Gene Ontology) annotation data and RNAi data for C. elegans into a centralized schema. RNAi experiments and phenotypes have been integrated from independent research groups into a single schema. A common hierarchical structure has been designed to organize the phenotypes from diﬀerent sources. The hierarchy is available in the form of a web browser. Compositional data mining (CDM) (3) is used to identify relationships among sets of entities across the database schema, where these sets are mined automa- tically and not deﬁned a priori. A detailed web interface that reports all the data and the patterns computed *To whom correspondence should be addressed. Tel: +1 540 231 7857; Fax: +1 540 231 7040; Email: apati@vt.edu 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.