Harvest: an open platform for developing web-based biomedical data discovery and reporting applications.

Biomedical researchers share a common challenge of making complex data understandable and accessible as they seek inherent relationships between attributes in disparate data types. Data discovery in this context is limited by a lack of query systems that efficiently show relationships between individual variables, but without the need to navigate underlying data models. We have addressed this need by developing Harvest, an open-source framework of modular components, and using it for the rapid development and deployment of custom data discovery software applications. Harvest incorporates visualizations of highly dimensional data in a web-based interface that promotes rapid exploration and export of any type of biomedical information, without exposing researchers to underlying data models. We evaluated Harvest with two cases: clinical data from pediatric cardiology and demonstration data from the OpenMRS project. Harvest's architecture and public open-source code offer a set of rapid application development tools to build data discovery applications for domain-specific biomedical data repositories. All resources, including the OpenMRS demonstration, can be found at http://harvest.research.chop.edu.

Harvest: a web-based biomedical data discovery and reporting application development platform.

Biomedical researchers share a common challenge of making complex data understandable and accessible. This need is increasingly acute as investigators seek opportunities for discovery amidst an exponential growth in the volume and complexity of laboratory and clinical data. To address this need, we developed Harvest, an open source framework that provides a set of modular components to aid the rapid development and deployment of custom data discovery software applications. Harvest incorporates visual representations of multidimensional data types in an intuitive, web-based interface that promotes a real-time, iterative approach to exploring complex clinical and experimental data. The Harvest architecture capitalizes on standards-based, open source technologies to address multiple functional needs critical to a research and development environment, including domain-specific data modeling, abstraction of complex data models, and a customizable web client.

De novo mutations in histone-modifying genes in congenital heart disease.

Congenital heart disease (CHD) is the most frequent birth defect, affecting 0.8% of live births. Many cases occur sporadically and impair reproductive fitness, suggesting a role for de novo mutations. Here we compare the incidence of de novo mutations in 362 severe CHD cases and 264 controls by analysing exome sequencing of parent-offspring trios. CHD cases show a significant excess of protein-altering de novo mutations in genes expressed in the developing heart, with an odds ratio of 7.5 for damaging (premature termination, frameshift, splice site) mutations. Similar odds ratios are seen across the main classes of severe CHD. We find a marked excess of de novo mutations in genes involved in the production, removal or reading of histone 3 lysine 4 (H3K4) methylation, or ubiquitination of H2BK120, which is required for H3K4 methylation. There are also two de novo mutations in SMAD2, which regulates H3K27 methylation in the embryonic left-right organizer. The combination of both activating (H3K4 methylation) and inactivating (H3K27 methylation) chromatin marks characterizes 'poised' promoters and enhancers, which regulate expression of key developmental genes. These findings implicate de novo point mutations in several hundreds of genes that collectively contribute to approximately 10% of severe CHD.

Molecular evolutionary analysis of cancer cell lines.

With genome-wide cancer studies producing large DNA sequence data sets, novel computational approaches toward better understanding the role of mutations in tumor survival and proliferation are greatly needed. Tumors are widely viewed to be influenced by Darwinian processes, yet molecular evolutionary analysis, invaluable in other DNA sequence studies, has seen little application in cancer biology. Here, we describe the phylogenetic analysis of 353 cancer cell lines based on multiple sequence alignments of 3,252 nucleotides and 1,170 amino acids built from the concatenation of variant codons and residues across 494 and 523 genes, respectively. Reconstructed phylogenetic trees cluster cell lines by shared DNA variant patterns rather than cancer tissue type, suggesting that tumors originating from diverse histologies have similar oncogenic pathways. A well-supported clade of 91 cancer cell lines representing multiple tumor types also had significantly different gene expression profiles from the remaining cell lines according to statistical analyses of mRNA microarray data. This suggests that phylogenetic clustering of tumor cell lines based on DNA variants might reflect functional similarities in cellular pathways. Positive selection analysis revealed specific DNA variants that might be potential driver mutations. Our study shows the potential role of molecular evolutionary analyses in tumor classification and the development of novel anticancer strategies.

Positive selection in penicillin-binding proteins 1a, 2b, and 2x from Streptococcus pneumoniae and its correlation with amoxicillin resistance development.

The efficacy of beta-lactam antibiotics in Streptococcus pneumoniae has been compromised because of the development of altered penicillin-binding proteins (PBPs), however, this has been less so for amoxicillin than for penicillin. Recently, there have been a number of important methods developed to detect molecular adaptation in protein coding genes. The purpose of this study is to employ modern molecular selection approaches to predict sites under positive selection pressure in PBPs, derived from a large international S. pneumoniae collection of amoxicillin resistant and susceptible isolates, and encompassing a comparative data set of 354 pbp1a, 335 pbp2b, and 389 pbp2x gene sequences. A correspondence discriminant analysis (CDA) of positively selected pbp sites and amoxicillin MIC (minimum inhibitory concentration) values is then used to detect sites under positive selection pressure that are important in discriminating different amoxicillin MICs. Molecular adaptation was evident throughout PBP2X, with numerous positively selected sites in both the transpeptidase (TP) and C-terminal domains, strongly correlated with discriminating amoxicillin MICs. In the case of PBP1A positive selection was present in the glycosyltransfer (GT), TP and C-terminal domains. Sites within the TP domain tended to be correlated with the discrimination of low from intermediate MICs, whereas sites within the C-terminal tail, with a discrimination of intermediate from fully resistant. Most of the positively selected sites within PBP2B were in the N-terminal domain and were not correlated with amoxicillin MICs, however, several sites taken from the literature for the TP domain were strongly associated with discriminating high from intermediate level amoxicillin resistance. Many of the positively selected sites could be directly associated with functional inferences based on the crystal structures of these proteins. Our results suggest that clinical emphasis on TP domain sequences of these proteins may result in missing information relevant to antibiotic resistance development.

Molecular evolution perspectives on intraspecific lateral DNA transfer of topoisomerase and gyrase loci in Streptococcus pneumoniae, with implications for fluoroquinolone resistance development and spread.

Fluoroquinolones are an important class of antibiotics for the treatment of infections arising from the gram-positive respiratory pathogen Streptococcus pneumoniae. Although there is evidence supporting interspecific lateral DNA transfer of fluoroquinolone target loci, no studies have specifically been designed to assess the role of intraspecific lateral transfer of these genes in the spread of fluoroquinolone resistance. This study involves a comparative evolutionary perspective, in which the evolutionary history of a diverse set of S. pneumoniae clinical isolates is reconstructed from an expanded multilocus sequence typing data set, with putative recombinants excluded. This control history is then assessed against networks of each of the four fluoroquinolone target loci from the same isolates. The results indicate that although the majority of fluoroquinolone target loci from this set of 60 isolates are consistent with a clonal dissemination hypothesis, 3 to 10% of the sequences are consistent with an intraspecific lateral transfer hypothesis. Also evident were examples of interspecific transfer, with two isolates possessing a parE-parC gene region arising from viridans group streptococci. The Spain 23F-1 clone is the most dominant fluoroquinolone-nonsusceptible clone in this set of isolates, and the analysis suggests that its members act as frequent donors of fluoroquinolone-nonsusceptible loci. Although the majority of fluoroquinolone target gene sequences in this set of isolates can be explained on the basis of clonal dissemination, a significant number are more parsimoniously explained by intraspecific lateral DNA transfer, and in situations of high S. pneumoniae population density, such events could be an important means of resistance spread.