Dataset Information

CMAP: Complement Map Database.

ABSTRACT:

Summary

The human complement system is increasingly perceived as an intricate protein network of effectors, inhibitors and regulators that drives critical processes in health and disease and extensively communicates with associated physiological pathways ranging from immunity and inflammation to homeostasis and development. A steady stream of experimental data reveals new fascinating connections at a rapid pace; although opening unique opportunities for research discoveries, the comprehensiveness and large diversity of experimental methods, nomenclatures and publication sources renders it highly challenging to keep up with the essential findings. With the Complement Map Database (CMAP), we have created a novel and easily accessible research tool to assist the complement community and scientists from related disciplines in exploring the complement network and discovering new connections.

Availability

http://www.complement.us/cmap.

Contact

lambris@upenn.edu

Supplementary information

Supplementary data are available at Bioinformatics online.

SUBMITTER: Yang K

PROVIDER: S-EPMC3702248 | biostudies-literature | 2013 Jul

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

CMAP: Complement Map Database.

Yang Kun K Dinasarapu Ashok R AR Reis Edimara S ES Deangelis Robert A RA Ricklin Daniel D Subramaniam Shankar S Lambris John D JD

Bioinformatics (Oxford, England) 20130509 14

<h4>Summary</h4>The human complement system is increasingly perceived as an intricate protein network of effectors, inhibitors and regulators that drives critical processes in health and disease and extensively communicates with associated physiological pathways ranging from immunity and inflammation to homeostasis and development. A steady stream of experimental data reveals new fascinating connections at a rapid pace; although opening unique opportunities for research discoveries, the comprehe ...[more]

PMID: 23661693

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Project description:BackgroundThe Connectivity Map (CMAP) database, an important public data source for drug repositioning, archives gene expression profiles from cancer cell lines treated with and without bioactive small molecules. However, there are only one or two technical replicates for each cell line under one treatment condition. For such small-scale data, current fold-changes-based methods lack statistical control in identifying differentially expressed genes (DEGs) in treated cells. Especially, one-to-one comparison may result in too many drug-irrelevant DEGs due to random experimental factors. To tackle this problem, CMAP adopts a pattern-matching strategy to build "connection" between disease signatures and gene expression changes associated with drug treatments. However, many drug-irrelevant genes may blur the "connection" if all the genes are used instead of pre-selected DEGs induced by drug treatments.MethodsWe applied OneComp, a customized version of RankComp, to identify DEGs in such small-scale cell line datasets. For a cell line, a list of gene pairs with stable relative expression orderings (REOs) were identified in a large collection of control cell samples measured in different experiments and they formed the background stable REOs. When applying OneComp to a small-scale cell line dataset, the background stable REOs were customized by filtering out the gene pairs with reversal REOs in the control samples of the analyzed dataset.ResultsIn simulated data, the consistency scores of overlapping genes between DEGs identified by OneComp and SAM were all higher than 99%, while the consistency score of the DEGs solely identified by OneComp was 96.85% according to the observed expression difference method. The usefulness of OneComp was exemplified in drug repositioning by identifying phenformin and metformin related genes using small-scale cell line datasets which helped to support them as a potential anti-tumor drug for non-small-cell lung carcinoma, while the pattern-matching strategy adopted by CMAP missed the two connections. The implementation of OneComp is available at https://github.com/pathint/reoa .ConclusionsOneComp performed well in both the simulated and real data. It is useful in drug repositioning studies by helping to find hidden "connections" between drugs and diseases.

Project description:BackgroundDrug repositioning is a cost-efficient and time-saving process to drug development compared to traditional techniques. A systematic method to drug repositioning is to identify candidate drug's gene expression profiles on target disease models and determine how similar these profiles are to approved drugs. Databases such as the CMAP have been developed recently to help with systematic drug repositioning.MethodsTo overcome the limitation of connectivity maps on data coverage, we constructed a comprehensive in silico drug-protein connectivity map called DMAP, which contains directed drug-to-protein effects and effect scores. The drug-to-protein effect scores are compiled from all database entries between the drug and protein have been previously observed and provide a confidence measure on the quality of such drug-to-protein effects.ResultsIn DMAP, we have compiled the direct effects between 24,121 PubChem Compound ID (CID), which were mapped from 289,571 chemical entities recognized from public literature, and 5,196 reviewed Uniprot proteins. DMAP compiles a total of 438,004 chemical-to-protein effect relationships. Compared to CMAP, DMAP shows an increase of 221 folds in the number of chemicals and 1.92 fold in the number of ATC codes. Furthermore, by overlapping DMAP chemicals with the approved drugs with known indications from the TTD database and literature, we obtained 982 drugs and 622 diseases; meanwhile, we only obtained 394 drugs with known indication from CMAP. To validate the feasibility of applying new DMAP for systematic drug repositioning, we compared the performance of DMAP and the well-known CMAP database on two popular computational techniques: drug-drug-similarity-based method with leave-one-out validation and Kolmogorov-Smirnov scoring based method. In drug-drug-similarity-based method, the drug repositioning prediction using DMAP achieved an Area-Under-Curve (AUC) score of 0.82, compared with that using CMAP, AUC = 0.64. For Kolmogorov-Smirnov scoring based method, with DMAP, we were able to retrieve several drug indications which could not be retrieved using CMAP. DMAP data can be queried using the existing C2MAP server or downloaded freely at: http://bio.informatics.iupui.edu/cmapsConclusionsReliable measurements of how drug affect disease-related proteins are critical to ongoing drug development in the genome medicine era. We demonstrated that DMAP can help drug development professionals assess drug-to-protein relationship data and improve chances of success for systematic drug repositioning efforts.

Dataset Information

CMAP: Complement Map Database.

Summary

Availability

Contact

Supplementary information

Publications

CMAP: Complement Map Database.

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