EuPathDB: the eukaryotic pathogen genomics database resource

The Eukaryotic Pathogen Genomics Database Resource (EuPathDB, http://eupathdb.org) is a collection of databases covering 170+ eukaryotic pathogens (protists & fungi), along with relevant free-living and non-pathogenic species, and select pathogen hosts. To facilitate the discovery of meaningful biological relationships, the databases couple preconfigured searches with visualization and analysis tools for comprehensive data mining via intuitive graphical interfaces and APIs. All data are analyzed with the same workflows, including creation of gene orthology profiles, so data are easily compared across data sets, data types and organisms. EuPathDB is updated with numerous new analysis tools, features, data sets and data types. New tools include GO, metabolic pathway and word enrichment analyses plus an online workspace for analysis of personal, non-public, large-scale data. Expanded data content is mostly genomic and functional genomic data while new data types include protein microarray, metabolic pathways, compounds, quantitative proteomics, copy number variation, and polysomal transcriptomics. New features include consistent categorization of searches, data sets and genome browser tracks; redesigned gene pages; effective integration of alternative transcripts; and a EuPathDB Galaxy instance for private analyses of a user's data. Forthcoming upgrades include user workspaces for private integration of data with existing EuPathDB data and improved integration and presentation of host–pathogen interactions

EuPathDB: the eukaryotic pathogen database

ABSTRACT EuPathDB (http://eupathdb.org) resources include 11 databases supporting eukaryotic pathogen genomic and functional genomic data, isolate data and phylogenomics. EuPathDB resources are built using the same infrastructure and provide a sophisticated search strategy system enabling complex interrogations of underlying data. Recent advances in EuPathDB resources include the design and implementation of a new data loading workflow, a new database supporting Piroplasmida (i.e. Babesia and Theileria), the addition of large amounts of new data and data types and the incorporation of new analysis tools. New data include genome sequences and annotation, strand-specific RNA-seq data, splice junction predictions (based on RNAseq), phosphoproteomic data, high-throughput phenotyping data, single nucleotide polymorphism data based on high-throughput sequencing (HTS) and expression quantitative trait loci data. New analysis tools enable users to search for DNA motifs and define genes based on their genomic colocation, view results from searches graphically (i.e. genes mapped to chromosomes or isolates displayed on a map) and analyze data from columns in result tables (word cloud and histogram summaries of column content). The manuscript herein describes updates to EuPathDB since the previous report published in NAR in 2010

Measuring Compositions in Organic Depth Profiling: Results from a VAMAS Interlaboratory Study

We report the results of a VAMAS (Versailles Project on Advanced Materials and Standards) interlaboratory study on the measurement of composition in organic depth profiling. Layered samples with known binary compositions of Irganox 1010 and either Irganox 1098 or Fmoc-pentafluoro-l-phenylalanine in each layer were manufactured in a single batch and distributed to more than 20 participating laboratories. The samples were analyzed using argon cluster ion sputtering and either X-ray photoelectron spectroscopy (XPS) or time-of-flight secondary ion mass spectrometry (ToF-SIMS) to generate depth profiles. Participants were asked to estimate the volume fractions in two of the layers and were provided with the compositions of all other layers. Participants using XPS provided volume fractions within 0.03 of the nominal values. Participants using ToF-SIMS either made no attempt, or used various methods that gave results ranging in error from 0.02 to over 0.10 in volume fraction, the latter representing a 50% relative error for a nominal volume fraction of 0.2. Error was predominantly caused by inadequacy in the ability to compensate for primary ion intensity variations and the matrix effect in SIMS. Matrix effects in these materials appear to be more pronounced as the number of atoms in both the primary analytical ion and the secondary ion increase. Using the participants’ data we show that organic SIMS matrix effects can be measured and are remarkably consistent between instruments. We provide recommendations for identifying and compensating for matrix effects. Finally, we demonstrate, using a simple normalization method, that virtually all ToF-SIMS participants could have obtained estimates of volume fraction that were at least as accurate and consistent as XPS