Background The Human Protein Atlas (HPA) aims to map human proteins via multiple technologies including imaging, proteomics and transcriptomics. Access of the HPA data is mainly via web-based interface allowing views of individual proteins, which may not be optimal for data analysis of a gene set, or automatic retrieval of original images. Results HPAanalyze is an R package for retrieving and performing exploratory analysis of data from HPA. HPAanalyze provides functionality for importing data tables and xml files from HPA, exporting and visualizing data, as well as downloading all staining images of interest. The package is free, open source, and available via Bioconductor and GitHub. We provide examples of the use of HPAanalyze to investigate proteins altered in the deadly brain tumor glioblastoma. For example, we confirm Epidermal Growth Factor Receptor elevation and Phosphatase and Tensin Homolog loss and suggest the importance of the GTP Cyclohydrolase I/Tetrahydrobiopterin pathway. Additionally, we provide an interactive website for non-programmers to explore and visualize data without the use of R. Conclusions HPAanalyze integrates into the R workflow with the tidyverse framework, and it can be used in combination with Bioconductor packages for easy analysis of HPA data.

Kinomics is an emerging field of science that involves the study of global kinase activity. As kinases are essential players in virtually all cellular activities, kinomic testing can directly examine protein function, distinguishing kinomics from more remote, upstream components of the central dogma, such as genomics and transcriptomics. While there exist several different approaches for kinomic research, peptide microarrays are the most widely used and involve kinase activity assessment through measurement of phosphorylation of peptide substrates on the array. Unfortunately, bioinformatic tools for analyzing kinomic data are quite limited necessitating the development of accessible open access software in order to facilitate standardization and dissemination of kinomic data for scientific use. Here, we examine and present tools for data analysis for the popular PamChip® (PamGene International) kinomic peptide microarray. As a result, we propose (1) a procedural optimization of kinetic curve data capture, (2) new methods for background normalization, (3) guidelines for the detection of outliers during parameterization, and (4) a standardized data model to store array data at various analytical points. In order to utilize the new data model, we developed a series of tools to implement the new methods and to visualize the various data models. In the interest of accessibility, we developed this new toolbox as a series of JavaScript procedures that can be utilized as either server side resources (easily packaged as web services) or as client side scripts (web applications running in the browser). The aggregation of these tools within a Kinomics Toolbox provides an extensible web based analytic platform that researchers can engage directly and web programmers can extend. As a proof of concept, we developed three analytical tools, a technical reproducibility visualizer, an ANOVA based detector of differentially phosphorylated peptides, and a heatmap display with hierarchical clustering.

Based on published literature of clinical informaticians' reuse of EHR data, we characterize eight types of data content that, if included in the next generation of EHRs, would find immediate application in advanced informatics tools and techniques.

Many studies have suggested that inappropriate plasma usage is common. An important factor contributing to futile plasma administration in most patients is the nonlinear relationship between coagulation-factor levels and the volume of plasma transfused. In this review, a validated mathematical model and data from the literature will be used to illuminate 3 key properties of plasma transfusion. Those properties are as follows: the effect of plasma transfusion on international normalized ratio (INR) is transient; for the same volume of transfused plasma, a greater reduction in INR is observed at higher initial INRs; and the effect of plasma transfusion on INR correction (ie, the difference between initial and final INRs) diminishes as more plasma is transfused. Frequent misunderstanding of these properties may contribute to inappropriate plasma usage. Therefore, this review will assist physicians in navigating these common pitfalls. Stronger understanding of these principles may result in a reduction of inappropriate plasma transfusions, thus potentially enhancing patient safety and reducing healthcare costs.

Motivation Meiotic recombination facilitates the transmission of exchanged genetic material between homologous chromosomes and plays a crucial role in increasing the genetic variations in eukaryotic organisms. In humans, thousands of crossover events have been identified by genotyping related family members. However, most of these crossover regions span tens to hundreds of kb, which is not sufficient resolution to accurately identify the crossover breakpoints in a typical trio family. Results We have developed MRLR, a software using 10X linked reads to identify crossover events at a high resolution. By reconstructing the gamete genome, MRLR only requires a trio family dataset and can efficiently discover the crossover events. Using MRLR, we revealed a fine-scale pattern of crossover regions in six human families. From the two closest heterozygous alleles around the crossovers, we determined that MRLR achieved a median resolution 4.5 kb. This method can delineate a genome-wide landscape of crossover events at a precise scale, which is important for both functional and genomic features analysis of meiotic recombination. Availability and implementation MRLR is freely available at https://github.com/ChongLab/MRLR, implemented in Perl. Supplementary information Supplementary data are available at Bioinformatics online.