Discovering novel SNPs that are correlated with patient outcome in a Singaporean cancer patient cohort treated with gemcitabine-based chemotherapy.

BACKGROUND: Single Nucleotide Polymorphisms (SNPs) can influence patient outcome such as drug response and toxicity after drug intervention. The purpose of this study is to develop a systematic pathway approach to accurately and efficiently predict novel non-synonymous SNPs (nsSNPs) that could be causative to gemcitabine-based chemotherapy treatment outcome in Singaporean non-small cell lung cancer (NSCLC) patients. METHODS: Using a pathway approach that incorporates comprehensive protein-protein interaction data to systematically extend the gemcitabine pharmacologic pathway, we identified 77 related nsSNPs, common in the Singaporean population. After that, we used five computational criteria to prioritize the SNPs based on their importance for protein function. We specifically selected and screened six candidate SNPs in a patient cohort with NSCLC treated with gemcitabine-based chemotherapy. RESULT: We performed survival analysis followed by hematologic toxicity analyses and found that three of six candidate SNPs are significantly correlated with the patient outcome (P < 0.05) i.e. ABCG2 Q141K (rs2231142), SLC29A3 S158F (rs780668) and POLR2A N764K (rs2228130). CONCLUSIONS: Our computational SNP candidate enrichment workflow approach was able to identify several high confidence biomarkers predictive for personalized drug treatment outcome while providing a rationale for a molecular mechanism of the SNP effect. TRIAL REGISTRATION: NCT00695994. Registered 10 June, 2008 'retrospectively registered'.

The 3rd DBCLS BioHackathon: improving life science data integration with Semantic Web technologies.

BACKGROUND: BioHackathon 2010 was the third in a series of meetings hosted by the Database Center for Life Sciences (DBCLS) in Tokyo, Japan. The overall goal of the BioHackathon series is to improve the quality and accessibility of life science research data on the Web by bringing together representatives from public databases, analytical tool providers, and cyber-infrastructure researchers to jointly tackle important challenges in the area of in silico biological research. RESULTS: The theme of BioHackathon 2010 was the 'Semantic Web', and all attendees gathered with the shared goal of producing Semantic Web data from their respective resources, and/or consuming or interacting those data using their tools and interfaces. We discussed on topics including guidelines for designing semantic data and interoperability of resources. We consequently developed tools and clients for analysis and visualization. CONCLUSION: We provide a meeting report from BioHackathon 2010, in which we describe the discussions, decisions, and breakthroughs made as we moved towards compliance with Semantic Web technologies - from source provider, through middleware, to the end-consumer.

The 2nd DBCLS BioHackathon: interoperable bioinformatics Web services for integrated applications.

BACKGROUND: The interaction between biological researchers and the bioinformatics tools they use is still hampered by incomplete interoperability between such tools. To ensure interoperability initiatives are effectively deployed, end-user applications need to be aware of, and support, best practices and standards. Here, we report on an initiative in which software developers and genome biologists came together to explore and raise awareness of these issues: BioHackathon 2009. RESULTS: Developers in attendance came from diverse backgrounds, with experts in Web services, workflow tools, text mining and visualization. Genome biologists provided expertise and exemplar data from the domains of sequence and pathway analysis and glyco-informatics. One goal of the meeting was to evaluate the ability to address real world use cases in these domains using the tools that the developers represented. This resulted in i) a workflow to annotate 100,000 sequences from an invertebrate species; ii) an integrated system for analysis of the transcription factor binding sites (TFBSs) enriched based on differential gene expression data obtained from a microarray experiment; iii) a workflow to enumerate putative physical protein interactions among enzymes in a metabolic pathway using protein structure data; iv) a workflow to analyze glyco-gene-related diseases by searching for human homologs of glyco-genes in other species, such as fruit flies, and retrieving their phenotype-annotated SNPs. CONCLUSIONS: Beyond deriving prototype solutions for each use-case, a second major purpose of the BioHackathon was to highlight areas of insufficiency. We discuss the issues raised by our exploration of the problem/solution space, concluding that there are still problems with the way Web services are modeled and annotated, including: i) the absence of several useful data or analysis functions in the Web service "space"; ii) the lack of documentation of methods; iii) lack of compliance with the SOAP/WSDL specification among and between various programming-language libraries; and iv) incompatibility between various bioinformatics data formats. Although it was still difficult to solve real world problems posed to the developers by the biological researchers in attendance because of these problems, we note the promise of addressing these issues within a semantic framework.

Coincidence between transcriptome analyses on different microarray platforms using a parametric framework.

A parametric framework for the analysis of transcriptome data is demonstrated to yield coincident results when applied to data acquired using two different microarray platforms. Microarrays are widely employed to acquire transcriptome information, and several platforms of chips are currently in use. However, discrepancies among studies are frequently reported, particularly among those performed using different platforms, casting doubt on the reliability of collected data. The inconsistency among observations can be largely attributed to differences among the analytical frameworks employed for data analysis. The existing frameworks are based on different philosophies and yield different results, but all involve normalization against a standard determined from the data to be analyzed. In the present study, a parametric framework based on a strict model for normalization is applied to data acquired using several slide-glass-type chips and GeneChip. The model is based on a common statistical characteristic of microarray data, and each set of chip data is normalized on the basis of a linear relationship with this model. In the proposed framework, the expressional changes observed and genes selected are coincident between platforms, achieving superior universality of data compared to other frameworks.

OBIYagns: a grid-based biochemical simulator with a parameter estimator.

UNLABELLED: OBIYagns (yet another gene network simulator) is a biochemical system simulator that comprises a multiple-user Web-based graphical interface, an ordinary differential equation solver and a parameter estimators distributed over an open bioinformatics grid (OBIGrid). This grid-based biochemical simulation system can achieve high performance and provide a secure simulation environment for estimating kinetic parameters in an acceptable time period. OBIYagns can be applied to larger system biology-oriented simulation projects. AVAILABILITY: OBIYagns example models, methods and user guide are available at https://access.obigrid.org/yagns/ SUPPLEMENTARY INFORMATION: Please refer to Bioinformatics online.