Joshua Xu

An FDA bioinformatics tool for microbial genomics research on molecular characterization of bacterial foodborne pathogens using microarrays

BackgroundAdvances in microbial genomics and bioinformatics are offering greater insights into the emergence and spread of foodborne pathogens in outbreak scenarios. The Food and Drug Administration (FDA) has developed a genomics tool, ArrayTrackTM, which provides extensive functionalities to manage, analyze, and interpret genomic data for mammalian species. ArrayTrackTM has been widely adopted by the research community and used for pharmacogenomics data review in the FDA’s Voluntary Genomics Data Submission program.ResultsArrayTrackTM has been extended to manage and analyze genomics data from bacterial pathogens of human, animal, and food origin. It was populated with bioinformatics data from public databases such as NCBI, Swiss-Prot, KEGG Pathway, and Gene Ontology to facilitate pathogen detection and characterization. ArrayTrackTM’s data processing and visualization tools were enhanced with analysis capabilities designed specifically for microbial genomics including flag-based hierarchical clustering analysis (HCA), flag concordance heat maps, and mixed scatter plots. These specific functionalities were evaluated on data generated from a custom Affymetrix array (FDA-ECSG) previously developed within the FDA. The FDA-ECSG array represents 32 complete genomes of Escherichia coli and Shigella. The new functions were also used to analyze microarray data focusing on antimicrobial resistance genes from Salmonella isolates in a poultry production environment using a universal antimicrobial resistance microarray developed by the United States Department of Agriculture (USDA).ConclusionThe application of ArrayTrackTM to different microarray platforms demonstrates its utility in microbial genomics research, and thus will improve the capabilities of the FDA to rapidly identify foodborne bacteria and their genetic traits (e.g., antimicrobial resistance, virulence, etc.) during outbreak investigations. ArrayTrackTM is free to use and available to public, private, and academic researchers at http://www.fda.gov/ArrayTrack.

An investigation of biomarkers derived from legacy microarray data for their utility in the RNA-seq era

BackgroundGene expression microarray has been the primary biomarker platform ubiquitously applied in biomedical research, resulting in enormous data, predictive models, and biomarkers accrued. Recently, RNA-seq has looked likely to replace microarrays, but there will be a period where both technologies co-exist. This raises two important questions: Can microarray-based models and biomarkers be directly applied to RNA-seq data? Can future RNA-seq-based predictive models and biomarkers be applied to microarray data to leverage past investment?ResultsWe systematically evaluated the transferability of predictive models and signature genes between microarray and RNA-seq using two large clinical data sets. The complexity of cross-platform sequence correspondence was considered in the analysis and examined using three human and two rat data sets, and three levels of mapping complexity were revealed. Three algorithms representing different modeling complexity were applied to the three levels of mappings for each of the eight binary endpoints and Cox regression was used to model survival times with expression data. In total, 240,096 predictive models were examined.ConclusionsSignature genes of predictive models are reciprocally transferable between microarray and RNA-seq data for model development, and microarray-based models can accurately predict RNA-seq-profiled samples; while RNA-seq-based models are less accurate in predicting microarray-profiled samples and are affected both by the choice of modeling algorithm and the gene mapping complexity. The results suggest continued usefulness of legacy microarray data and established microarray biomarkers and predictive models in the forthcoming RNA-seq era.

Two new ArrayTrack libraries for personalized biomedical research.

BackgroundRecent advances in high-throughput genotyping technology are paving the way for research in personalized medicine and nutrition. However, most of the genetic markers identified from association studies account for a small contribution to the total risk/benefit of the studied phenotypic trait. Testing whether the candidate genes identified by association studies are causal is critically important to the development of personalized medicine and nutrition. An efficient data mining strategy and a set of sophisticated tools are necessary to help better understand and utilize the findings from genetic association studies.DescriptionSNP (single nucleotide polymorphism) and QTL (quantitative trait locus) libraries were constructed and incorporated into ArrayTrack, with user-friendly interfaces and powerful search features. Data from several public repositories were collected in the SNP and QTL libraries and connected to other domain libraries (genes, proteins, metabolites, and pathways) in ArrayTrack. Linking the data sets within ArrayTrack allows searching of SNP and QTL data as well as their relationships to other biological molecules. The SNP library includes approximately 15 million human SNPs and their annotations, while the QTL library contains publically available QTLs identified in mouse, rat, and human. The QTL library was developed for finding the overlap between the map position of a candidate or metabolic gene and QTLs from these species. Two use cases were included to demonstrate the utility of these tools. The SNP and QTL libraries are freely available to the public through ArrayTrack at http://www.fda.gov/ArrayTrack.ConclusionsThese libraries developed in ArrayTrack contain comprehensive information on SNPs and QTLs and are further cross-linked to other libraries. Connecting domain specific knowledge is a cornerstone of systems biology strategies and allows for a better understanding of the genetic and biological context of the findings from genetic association studies.

FDA drug labeling: rich resources to facilitate precision medicine, drug safety, and regulatory science

Here, we provide a concise overview of US Food and Drug Administration (FDA) drug labeling, which details drug products, drug-drug interactions, adverse drug reactions (ADRs), and more. Labeling data have been collected over several decades by the FDA and are an important resource for regulatory research and decision making. However, navigating through this data is challenging. To aid such navigation, the FDALabel database was developed, which contains a set of approximately 80000 labeling data. The full-text searching capability of FDALabel and querying based on any combination of specific sections, document types, market categories, market date, and other labeling information makes it a powerful and attractive tool for a variety of applications. Here, we illustrate the utility of FDALabel using case scenarios in pharmacogenomics biomarkers and ADR studies.

NETBAGs: a network-based clustering approach with gene signatures for cancer subtyping analysis

AIM To evaluate gene signature and network-based approach for cancer subtyping and classification. MATERIALS & METHODS Here we introduced NETwork Based clustering Approach with Gene signatures (NETBAGs) algorithm, which clustered samples based on gene signatures and identified molecular markers based on their significantly expressed gene network profiles. RESULTS Applying NETBAGs to multiple independent breast cancer datasets, we demonstrated that the clustering results were highly associated with the clinical subtypes and clearly revealed the genomic diversity of breast cancer samples. CONCLUSION NETBAGs algorithm is able to classify samples by their genomic signatures into clinically significant phenotypes so that potential biomarkers can be identified. The approach may contribute to cancer research and clinical study of complex diseases.

Integrating Drug’s Mode of Action into Quantitative Structure–Activity Relationships for Improved Prediction of Drug-Induced Liver Injury

Drug-induced liver injury (DILI) is complex in mechanism. Different drugs could undergo different mechanisms but result in the same DILI type, while the same drug could lead to different DILI types via different mechanisms. Therefore, predicting a drugs potential for DILI should take its underlying mechanisms into consideration. To achieve that, we constructed a novel approach by incorporating the drugs Mode of Action (MOA) into Quantitative Structure-Activity Relationship (QSAR) modeling. This MOA-DILI approach was examined using a data set of 333 drugs. The drugs were first grouped according to their MOA profiles (positive or negative in each MOA) based on the Tox21 qHTS assays. QSAR models for individual MOA assays were developed and subsequently combined to obtain the MOA-DILI model. A hold-out testing strategy (222 drugs for training and 111 drugs as a test set) was employed, which yielded a predictive accuracy of 0.711. The MOA-DILI model was directly compared with the standard QSAR approach using the same hold-out strategy, and the QSAR model yielded an accuracy of 0.662. To minimize the random chance in splitting training/test sets, the hold-out testing process was repeated 1000 times, and the observed difference in prediction accuracy between MOA-DILI and QSARs was statistically significant (P value <0.0001). Out of 17 MOAs used, four assays (i.e., antioxidant response elements, PPAR-gamma, estrogen receptor, and thyroid receptor assays) contributed most to the improved prediction of the MOA-DILI model over QSARs. In conclusion, the MOA-DILI approach has the potential to significantly improve predictive outcomes and to reveal complex relationships between MOAs and DILI, all of which would be helpful in developing DILI predictive models in drug screening and for risk assessment of industrial chemicals.