Current strategies to improve graft outcome following kidney transplantation consider information at the human leukocyte antigen (HLA) loci. Cell surface antigens, in addition to HLA, may serve as the stimuli as well as the targets for the anti-allograft immune response and influence long-term graft outcomes. We therefore performed exome sequencing of DNA from kidney graft recipients and their living donors and estimated all possible cell surface antigens mismatches for a given donor/recipient pair by computing the number of amino acid mismatches in trans-membrane proteins. We designated this tally as the allogenomics mismatch score (AMS). We examined the association between the AMS and post-transplant estimated glomerular filtration rate (eGFR) using mixed models, considering transplants from three independent cohorts (a total of 53 donor-recipient pairs, 106 exomes, and 239 eGFR measurements). We found that the AMS has a significant effect on eGFR (mixed model, effect size across the entire range of the score: -19.4 [-37.7, -1.1], P = 0.0042, χ2 = 8.1919, d.f. = 1) that is independent of the HLA-A, B, DR matching, donor age, and time post-transplantation. The AMS effect is consistent across the three independent cohorts studied and similar to the strong effect size of donor age. Taken together, these results show that the AMS, a novel tool to quantify amino acid mismatches in trans-membrane proteins in individual donor/recipient pair, is a strong, robust predictor of long-term graft function in kidney transplant recipients.

UNLABELLED: High-throughput data can be used in conjunction with clinical information to develop predictive models. Automating the process of developing, evaluating and testing such predictive models on different datasets would minimize operator errors and facilitate the comparison of different modeling approaches on the same dataset. Complete automation would also yield unambiguous documentation of the process followed to develop each model. We present the BDVal suite of programs that fully automate the construction of predictive classification models from high-throughput data and generate detailed reports about the model construction process. We have used BDVal to construct models from microarray and proteomics data, as well as from DNA-methylation datasets. The programs are designed for scalability and support the construction of thousands of alternative models from a given dataset and prediction task. AVAILABILITY AND IMPLEMENTATION: The BDVal programs are implemented in Java, provided under the GNU General Public License and freely available at http://bdval.campagnelab.org.

SUMMARY: Rapid expansion of available data about G Protein Coupled Receptor (GPCR) dimers/oligomers over the past few years requires an effective system to organize this information electronically. Based on an ontology derived from a community dialog involving colleagues using experimental and computational methodologies, we developed the GPCR-Oligomerization Knowledge Base (GPCR-OKB). GPCR-OKB is a system that supports browsing and searching for GPCR oligomer data. Such data were manually derived from the literature. While focused on GPCR oligomers, GPCR-OKB is seamlessly connected to GPCRDB, facilitating the correlation of information about GPCR protomers and oligomers. AVAILABILITY AND IMPLEMENTATION: The GPCR-OKB web application is freely available at http://www.gpcr-okb.org

BACKGROUND: The evaluation of information retrieval techniques has traditionally relied on human judges to determine which documents are relevant to a query and which are not. This protocol is used in the Text Retrieval Evaluation Conference (TREC), organized annually for the past 15 years, to support the unbiased evaluation of novel information retrieval approaches. The TREC Genomics Track has recently been introduced to measure the performance of information retrieval for biomedical applications. RESULTS: We describe two protocols for evaluating biomedical information retrieval techniques without human relevance judgments. We call these protocols No Title Evaluation (NT Evaluation). The first protocol measures performance for focused searches, where only one relevant document exists for each query. The second protocol measures performance for queries expected to have potentially many relevant documents per query (high-recall searches). Both protocols take advantage of the clear separation of titles and abstracts found in Medline. We compare the performance obtained with these evaluation protocols to results obtained by reusing the relevance judgments produced in the 2004 and 2005 TREC Genomics Track and observe significant correlations between performance rankings generated by our approach and TREC. Spearman's correlation coefficients in the range of 0.79-0.92 are observed comparing bpref measured with NT Evaluation or with TREC evaluations. For comparison, coefficients in the range 0.86-0.94 can be observed when evaluating the same set of methods with data from two independent TREC Genomics Track evaluations. We discuss the advantages of NT Evaluation over the TRels and the data fusion evaluation protocols introduced recently. CONCLUSION: Our results suggest that the NT Evaluation protocols described here could be used to optimize some search engine parameters before human evaluation. Further research is needed to determine if NT Evaluation or variants of these protocols can fully substitute for human evaluations.

BACKGROUND: G Protein-Coupled Receptors (GPCRs) are a large and diverse family of membrane proteins whose members participate in the regulation of most cellular and physiological processes and therefore represent key pharmacological targets. Although several bioinformatics resources support research on GPCRs, most of these have been designed based on the traditional assumption that monomeric GPCRs constitute the functional receptor unit. The increase in the frequency and number of reports about GPCR dimerization/oligomerization and the implication of oligomerization in receptor function makes necessary the ability to store and access information about GPCR dimers/oligomers electronically. RESULTS: We present here the requirements and ontology (the information scheme to describe oligomers and associated concepts and their relationships) for an information system that can manage the elements of information needed to describe comprehensively the phenomena of both homo- and hetero-oligomerization of GPCRs. The comprehensive information management scheme that we plan to use for the development of an intuitive and user-friendly GPCR-Oligomerization Knowledge Base (GPCR-OKB) is the result of a community dialog involving experimental and computational colleagues working on GPCRs. CONCLUSION: Our long term goal is to disseminate to the scientific community organized, curated, and detailed information about GPCR dimerization/oligomerization and its related structural context. This information will be reported as close to the data as possible so the user can make his own judgment on the conclusions drawn for a particular study. The requirements and ontology described here will facilitate the development of future information systems for GPCR oligomers that contain both computational and experimental information about GPCR oligomerization. This information is freely accessible at http://www.gpcr-okb.org.

BACKGROUND: Gene expression data are a rich source of information about the transcriptional dis-regulation of genes in cancer. Genes that display differential regulation in cancer are a subtype of cancer biomarkers. RESULTS: We present an approach to mine expressed sequence tags to discover cancer biomarkers. A false discovery rate analysis suggests that the approach generates less than 22% false discoveries when applied to combined human and mouse whole genome screens. With this approach, we identify the 200 genes most consistently differentially expressed in cancer (called HM200) and proceed to characterize these genes. When used for prediction in a variety of cancer classification tasks (in 24 independent cancer microarray datasets, 59 classifications total), we show that HM200 and the shorter gene list HM100 are very competitive cancer biomarker sets. Indeed, when compared to 13 published cancer marker gene lists, HM200 achieves the best or second best classification performance in 79% of the classifications considered. CONCLUSION: These results indicate the existence of at least one general cancer marker set whose predictive value spans several tumor types and classification types. Our comparison with other marker gene lists shows that HM200 markers are mostly novel cancer markers. We also identify the previously published Pomeroy-400 list as another general cancer marker set. Strikingly, Pomeroy-400 has 27 genes in common with HM200. Our data suggest that a core set of genes are responsive to the deregulation of pathways involved in tumorigenesis in a variety of tumor types and that these genes could serve as transcriptional cancer markers in applications of clinical interest. Finally, our study suggests new strategies to select and evaluate cancer biomarkers in microarray studies.

BACKGROUND: The majority of information in the biological literature resides in full text articles, instead of abstracts. Yet, abstracts remain the focus of many publicly available literature data mining tools. Most literature mining tools rely on pre-existing lexicons of biological names, often extracted from curated gene or protein databases. This is a limitation, because such databases have low coverage of the many name variants which are used to refer to biological entities in the literature. RESULTS: We present an approach to recognize named entities in full text. The approach collects high frequency terms in an article, and uses support vector machines (SVM) to identify biological entity names. It is also computationally efficient and robust to noise commonly found in full text material. We use the method to create a protein name dictionary from a set of 80,528 full text articles. Only 8.3% of the names in this dictionary match SwissProt description lines. We assess the quality of the dictionary by studying its protein name recognition performance in full text. CONCLUSION: This dictionary term lookup method compares favourably to other published methods, supporting the significance of our direct extraction approach. The method is strong in recognizing name variants not found in SwissProt.

BACKGROUND: Complex biological database systems have become key computational tools used daily by scientists and researchers. Many of these systems must be capable of executing on multiple different hardware and software configurations and are also often made available to users via the Internet. We have used the Java Data Object (JDO) persistence technology to develop the database layer of such a system known as the SigPath information management system. SigPath is an example of a complex biological database that needs to store various types of information connected by many relationships. RESULTS: Using this system as an example, we perform a critical evaluation of current JDO technology; discuss the suitability of the JDO standard to achieve portability, scalability and performance. We show that JDO supports portability of the SigPath system from a relational database backend to an object database backend and achieves acceptable scalability. To answer the performance question, we have created the SigPath JDO application benchmark that we distribute under the Gnu General Public License. This benchmark can be used as an example of using JDO technology to create a complex biological database and makes it possible for vendors and users of the technology to evaluate the performance of other JDO implementations for similar applications. CONCLUSIONS: The SigPath JDO benchmark and our discussion of JDO technology in the context of biological databases will be useful to bioinformaticians who design new complex biological databases and aim to create systems that can be ported easily to a variety of database backends.

SUMMARY: Residue-based diagrams of proteins are graphical representations that can be used in protein information systems. These diagrams make it possible to visually integrate different types of biological information. The approach has been used successfully for membrane proteins. We developed the Residue-based diagram generator to (i) make it possible to generate residue-based diagrams of proteins in a batch mode that is compatible with the needs of information system curators, (ii) allow the generation of residue-based diagrams for families of soluble proteins or domains. AVAILABILITY: Licensed. Royalty free licenses are granted to non-profit institutions for educational and research purposes. http://icb.mssm.edu/crt/RbDg/index.xml

Abstract Motivation: Performing sequence alignment operations from a different program than the original sequence alignment code, and/or through a network connection, is often required. Interactive alignment editors and large-scale biological data analysis are common examples where such a flexibility is important. Interoperability between the alignment engine and the client should be obtained regardless of the architectures and programming languages of the server and client. Results: Clustalnet, a Clustal alignment CORBA server is described, which was developed on the basis of Clustalw. This server brings the robustness of the algorithms and implementations of Clustal to a new level of reuse. A Clustalnet server object can be accessed from a program, transparently through the network. We present interfaces to perform the alignment operations and to control these operations via immutable contexts. The interfaces that select the contexts do not depend on the nature of the operation to be performed, making the design modular. The IDL interfaces presented here are not specific to Clustal and can be implemented on top of different sequence alignment algorithm implementations. Availability: This implementation is freely available for non-commercial use for a variety of architectures. Clustalnet can be downloaded from http://transport.physbio.mssm.edu/clustalnet. Contact: [email protected] Supplementary Information: Alignment encoding description and full IDL in the documentation, with the distribution.