VennMaster: area-proportional Euler diagrams for functional GO analysis of microarrays.

BACKGROUND: Microarray experiments generate vast amounts of data. The functional context of differentially expressed genes can be assessed by querying the Gene Ontology (GO) database via GoMiner. Directed acyclic graph representations, which are used to depict GO categories enriched with differentially expressed genes, are difficult to interpret and, depending on the particular analysis, may not be well suited for formulating new hypotheses. Additional graphical methods are therefore needed to augment the GO graphical representation. RESULTS: We present an alternative visualization approach, area-proportional Euler diagrams, showing set relationships with semi-quantitative size information in a single diagram to support biological hypothesis formulation. The cardinalities of sets and intersection sets are represented by area-proportional Euler diagrams and their corresponding graphical (circular or polygonal) intersection areas. Optimally proportional representations are obtained using swarm and evolutionary optimization algorithms. CONCLUSION: VennMaster's area-proportional Euler diagrams effectively structure and visualize the results of a GO analysis by indicating to what extent flagged genes are shared by different categories. In addition to reducing the complexity of the output, the visualizations facilitate generation of novel hypotheses from the analysis of seemingly unrelated categories that share differentially expressed genes.

AffyProbeMiner: a web resource for computing or retrieving accurately redefined Affymetrix probe sets.

MOTIVATION: Affymetrix microarrays are widely used to measure global expression of mRNA transcripts. That technology is based on the concept of a probe set. Individual probes within a probe set were originally designated by Affymetrix to hybridize with the same unique mRNA transcript. Because of increasing accuracy in knowledge of genomic sequences, however, a substantial number of the manufacturer's original probe groupings and mappings are now known to be inaccurate and must be corrected. Otherwise, analysis and interpretation of an Affymetrix microarray experiment will be in error. RESULTS: AffyProbeMiner is a computationally efficient platform-independent tool that uses all RefSeq mature RNA protein coding transcripts and validated complete coding sequences in GenBank to (1) regroup the individual probes into consistent probe sets and (2) remap the probe sets to the correct sets of mRNA transcripts. The individual probes are grouped into probe sets that are 'transcript-consistent' in that they hybridize to the same mRNA transcript (or transcripts) and, therefore, measure the same entity (or entities). About 65.6% of the probe sets on the HG-U133A chip were affected by the remapping. Pre-computed regrouped and remapped probe sets for many Affymetrix microarrays are made freely available at the AffyProbeMiner web site. Alternatively, we provide a web service that enables the user to perform the remapping for any type of short-oligo commercial or custom array that has an Affymetrix-format Chip Definition File (CDF). Important features that differentiate AffyProbeMiner from other approaches are flexibility in the handling of splice variants, computational efficiency, extensibility, customizability and user-friendliness of the interface. AVAILABILITY: The web interface and software (GPL open source license), are publicly-accessible at http://discover.nci.nih.gov/affyprobeminer.

Cancers as wounds that do not heal: differences and similarities between renal regeneration/repair and renal cell carcinoma.

Cancers have been described as wounds that do not heal, suggesting that the two share common features. By comparing microarray data from a model of renal regeneration and repair (RRR) with reported gene expression in renal cell carcinoma (RCC), we asked whether those two processes do, in fact, share molecular features and regulatory mechanisms. The majority (77%) of the genes expressed in RRR and RCC were concordantly regulated, whereas only 23% were discordant (i.e., changed in opposite directions). The orchestrated processes of regeneration, involving cell proliferation and immune response, were reflected in the concordant genes. The discordant gene signature revealed processes (e.g., morphogenesis and glycolysis) and pathways (e.g., hypoxia-inducible factor and insulin-like growth factor-I) that reflect the intrinsic pathologic nature of RCC. This is the first study that compares gene expression patterns in RCC and RRR. It does so, in particular, with relation to the hypothesis that RCC resembles the wound healing processes seen in RRR. However, careful attention to the genes that are regulated in the discordant direction provides new insights into the critical differences between renal carcinogenesis and wound healing. The observations reported here provide a conceptual framework for further efforts to understand the biology and to develop more effective diagnostic biomarkers and therapeutic strategies for renal tumors and renal ischemia.

A Galaxy Implementation of Next-Generation Clustered Heatmaps for Interactive Exploration of Molecular Profiling Data.

Clustered heatmaps are the most frequently used graphics for visualization of molecular profiling data in biology. However, they are generally rendered as static, or only modestly interactive, images. We have now used recent advances in web technologies to produce interactive "next-generation" clustered heatmaps (NG-CHM) that enable extreme zooming and navigation without loss of resolution. NG-CHMs also provide link-outs to additional information sources and include other features that facilitate deep exploration of the biology behind the image. Here, we describe an implementation of the NG-CHM system in the Galaxy bioinformatics platform. We illustrate the algorithm and available computational tool using RNA-seq data from The Cancer Genome Atlas program's Kidney Clear Cell Carcinoma project. Cancer Res; 77(21); e23-26. ©2017 AACR.

Agile methods in biomedical software development: a multi-site experience report.

BACKGROUND: Agile is an iterative approach to software development that relies on strong collaboration and automation to keep pace with dynamic environments. We have successfully used agile development approaches to create and maintain biomedical software, including software for bioinformatics. This paper reports on a qualitative study of our experiences using these methods. RESULTS: We have found that agile methods are well suited to the exploratory and iterative nature of scientific inquiry. They provide a robust framework for reproducing scientific results and for developing clinical support systems. The agile development approach also provides a model for collaboration between software engineers and researchers. We present our experience using agile methodologies in projects at six different biomedical software development organizations. The organizations include academic, commercial and government development teams, and included both bioinformatics and clinical support applications. We found that agile practices were a match for the needs of our biomedical projects and contributed to the success of our organizations. CONCLUSION: We found that the agile development approach was a good fit for our organizations, and that these practices should be applicable and valuable to other biomedical software development efforts. Although we found differences in how agile methods were used, we were also able to identify a set of core practices that were common to all of the groups, and that could be a focus for others seeking to adopt these methods.

High-Throughput GoMiner, an 'industrial-strength' integrative gene ontology tool for interpretation of multiple-microarray experiments, with application to studies of Common Variable Immune Deficiency (CVID).

BACKGROUND: We previously developed GoMiner, an application that organizes lists of 'interesting' genes (for example, under-and overexpressed genes from a microarray experiment) for biological interpretation in the context of the Gene Ontology. The original version of GoMiner was oriented toward visualization and interpretation of the results from a single microarray (or other high-throughput experimental platform), using a graphical user interface. Although that version can be used to examine the results from a number of microarrays one at a time, that is a rather tedious task, and original GoMiner includes no apparatus for obtaining a global picture of results from an experiment that consists of multiple microarrays. We wanted to provide a computational resource that automates the analysis of multiple microarrays and then integrates the results across all of them in useful exportable output files and visualizations. RESULTS: We now introduce a new tool, High-Throughput GoMiner, that has those capabilities and a number of others: It (i) efficiently performs the computationally-intensive task of automated batch processing of an arbitrary number of microarrays, (ii) produces a human-or computer-readable report that rank-orders the multiple microarray results according to the number of significant GO categories, (iii) integrates the multiple microarray results by providing organized, global clustered image map visualizations of the relationships of significant GO categories, (iv) provides a fast form of 'false discovery rate' multiple comparisons calculation, and (v) provides annotations and visualizations for relating transcription factor binding sites to genes and GO categories. CONCLUSION: High-Throughput GoMiner achieves the desired goal of providing a computational resource that automates the analysis of multiple microarrays and integrates results across all of the microarrays. For illustration, we show an application of this new tool to the interpretation of altered gene expression patterns in Common Variable Immune Deficiency (CVID). High-Throughput GoMiner will be useful in a wide range of applications, including the study of time-courses, evaluation of multiple drug treatments, comparison of multiple gene knock-outs or knock-downs, and screening of large numbers of chemical derivatives generated from a promising lead compound.

Practical applications of usability theory to electronic data collection for clinical trials.

Pharmaceutical and device companies are more frequently considering and using electronic data collection (EDC) to collect patient-reported outcomes such as satisfaction and quality of life for clinical trials. The transition from paper-and-pencil data collection to EDC is not without risks. The unique context of clinical trials presents challenges that, if not addressed, can lead to expensive mistakes. The advantages inherent to EDC can easily be cancelled out without careful attention to the characteristics of the clinical setting. This paper provides an overview of EDC issues specific to clinical trials and health care settings. In particular, it evaluates usability issues associated with methods of EDC and suggests strategies to minimize potential problems. Lessons learned from usability testing in the unique setting of the clinical trial can be applied to other projects to decrease costs, enhance the quality of the data, and minimize time to analysis.

Mistaken identifiers: gene name errors can be introduced inadvertently when using Excel in bioinformatics.

BACKGROUND: When processing microarray data sets, we recently noticed that some gene names were being changed inadvertently to non-gene names. RESULTS: A little detective work traced the problem to default date format conversions and floating-point format conversions in the very useful Excel program package. The date conversions affect at least 30 gene names; the floating-point conversions affect at least 2,000 if Riken identifiers are included. These conversions are irreversible; the original gene names cannot be recovered. CONCLUSIONS: Users of Excel for analyses involving gene names should be aware of this problem, which can cause genes, including medically important ones, to be lost from view and which has contaminated even carefully curated public databases. We provide work-arounds and scripts for circumventing the problem.

GoMiner: a resource for biological interpretation of genomic and proteomic data.

We have developed GoMiner, a program package that organizes lists of 'interesting' genes (for example, under- and overexpressed genes from a microarray experiment) for biological interpretation in the context of the Gene Ontology. GoMiner provides quantitative and statistical output files and two useful visualizations. The first is a tree-like structure analogous to that in the AmiGO browser and the second is a compact, dynamically interactive 'directed acyclic graph'. Genes displayed in GoMiner are linked to major public bioinformatics resources.

Development of gene ontology tool for biological interpretation of genomic and proteomic data.

We have designed and developed a Gene Ontology based navigation tool, GoMiner, which organizes lists of interesting genes from a microarray or a protein array experiment for biological interpretation. It provides quantitative and statistical output files and useful visualization (e.g., a tree-like structure) to map the list of genes to its biological functional categories. It also provides links to other resources such as pubmed, locuslink, and biological molecular interaction map and signaling pathway packages.