SummaryTo identify the genes and gene functions that underlie key aspects of legume biology, researchers have selected the cool season legume Medicago truncatula as a model system for legume research. The mission of the M. truncatula Consortium is to promote unrestricted sharing of data and information that are provided by Medicago research groups worldwide. Through integration of a variety of data and tools, the medicago.org site intends to facilitate progress in the fields of structural, comparative, and functional genomics. To this goal, and as a consortium partner, the Center for Computational Genomics and Bioinformatics (CCGB) at the University of Minnesota has developed MtDB2.0, the M. truncatula database version 2.0. The MtDB2.0 database is the first step toward the global integration of M. truncatula genomic, genetic, and biological information. MtDB2.0 is a relational database that integrates M. truncatula transcriptome data and provides a wide range of user-defined data mining options. The database is interrogated through a series of interfaces, with 58 options grouped into two filters. Sequence identifiers from all public M. truncatula sites [e.g., IDs from GenBank, CCGB, The Institute for Genomic Research (TIGR), National Center for Genome Resources (NCGR), and I'Institut National de la Recherche Agronomique (INRA)] are fully cross-referenced to facilitate comparisons between different sites, and hypertext links to the appropriate database records are provided for all queries' results. MtDB's goal is to provide researchers with the means to quickly and independently identify sequences that match specific research interests based on user-defined criteria. MtDB2.0 offers unrestricted access to advanced and powerful querying tools unmatched by any other public databases. Structurad Query Language (SQL)-encoded queries with a Java-based Web user interface, incorporate different filtering that allow sophisticated data mining of the expressed sequence tag sequencing project results, including the CCGB M. truncatula Unigene set generated with the Phrap assembler. The underlying database and query software have been designed for ease of updates and portability to other model organisms. Public access to the database is at http://www.medicago.org/MtDB.
'Omics methods have empowered scientists to tackle the complexity of microbial communities on a scale not attainable before. Individually, omics analyses can provide great insight; while combined as "meta-omics", they enhance the understanding of which organisms occupy specific metabolic niches, how they interact, and how they utilize environmental nutrients. Here we present three integrative meta-omics workflows, developed in Galaxy, for enhanced analysis and integration of metagenomics, metatranscriptomics, and metaproteomics, combined with our newly developed web-application, ViMO (Visualizer for Meta-Omics) to analyse metabolisms in complex microbial communities.
Clinical metaproteomics has the potential to offer insights into the host-microbiome interactions underlying diseases. However, the field faces challenges in characterizing microbial proteins found in clinical samples, usually present at low abundance relative to the host proteins. As a solution, we have developed an integrated workflow coupling mass spectrometry-based analysis with customized bioinformatic identification, quantification, and prioritization of microbial proteins, enabling targeted assay development to investigate host-microbe dynamics in disease. The bioinformatics tools are implemented in the Galaxy ecosystem, offering the development and dissemination of complex bioinformatic workflows. The modular workflow integrates MetaNovo (to generate a reduced protein database), SearchGUI/PeptideShaker and MaxQuant [to generate peptide-spectral matches (PSMs) and quantification], PepQuery2 (to verify the quality of PSMs), Unipept (for taxonomic and functional annotation), and MSstatsTMT (for statistical analysis). We have utilized this workflow in diverse clinical samples, from the characterization of nasopharyngeal swab samples to bronchoalveolar lavage fluid. Here, we demonstrate its effectiveness via analysis of residual fluid from cervical swabs. The complete workflow, including training data and documentation, is available via the Galaxy Training Network, empowering non-expert researchers to utilize these powerful tools in their clinical studies.
We describe a use of formal methods to specify and check a Web Services protocol. The Web Services Atomic Transaction protocol was specified in TLA+ and checked with the TLC model checker. A modest effort revealed oversights that caused unanticipated behaviors of the protocol; these were corrected by clarifications and changes to the protocol.
Abstract Background The Coronavirus Disease 2019 (COVID-19) global pandemic has had a profound, lasting impact on the world's population. A key aspect to providing care for those with COVID-19 and checking its further spread is early and accurate diagnosis of infection, which has been generally done via methods for amplifying and detecting viral RNA molecules. Detection and quantitation of peptides using targeted mass spectrometry-based strategies has been proposed as an alternative diagnostic tool due to direct detection of molecular indicators from non-invasively collected samples as well as the potential for high-throughput analysis in a clinical setting; many studies have revealed the presence of viral peptides within easily accessed patient samples. However, evidence suggests that some viral peptides could serve as better indicators of COVID-19 infection status than others, due to potential misidentification of peptides derived from human host proteins, poor spectral quality, high limits of detection etc. Methods In this study we have compiled a list of 636 peptides identified from Sudden Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) samples, including from in vitro and clinical sources. These datasets were rigorously analyzed using automated, Galaxy-based workflows containing tools such as PepQuery, BLAST-P, and the Multi-omic Visualization Platform as well as the open-source tools MetaTryp and Proteomics Data Viewer (PDV). Results Using PepQuery for confirming peptide spectrum matches, we were able to narrow down the 639-peptide possibilities to 87 peptides that were most robustly detected and specific to the SARS-CoV-2 virus. The specificity of these sequences to coronavirus taxa was confirmed using Unipept and BLAST-P. Through stringent p-value cutoff combined with manual verification of peptide spectrum match quality, 4 peptides derived from the nucleocapsid phosphoprotein and membrane protein were found to be most robustly detected across all cell culture and clinical samples, including those collected non-invasively. Conclusion We propose that these peptides would be of the most value for clinical proteomics applications seeking to detect COVID-19 from patient samples. We also contend that samples harvested from the upper respiratory tract and oral cavity have the highest potential for diagnosis of SARS-CoV-2 infection from easily collected patient samples using mass spectrometry-based proteomics assays.
To analyze the ordering patterns of noninvasive cardiologic procedures, 192 procedures were prospectively studied at a tertiary care military teaching hospital. Ninety patients underwent echocardiography, 87 underwent exercise thallium scan, and 15 underwent radionuclide ventriculography. The requesting physician completed a questionnaire when the procedure was scheduled and another one after receiving the results of the procedure. Two internists and two cardiologists independently reviewed the questionnaires and the patients' charts after each procedure was completed. Only 72% of the time were all the reviewers in agreement with the indications for procedures. At least three of the four reviewers agreed that the procedure was indicated in 85% of the cases. In only 5% of the cases did three of four reviewers consider the procedure not indicated. At least three of the four reviewers agreed that the procedure resulted in a change in management in 57% of cases, whereas three of four reviewers considered management was not altered in 25%. Thus, in the setting described, most cardiologic noninvasive procedures were ordered for appropriate reasons.
Summary The learning of process and reactive schizophrenics was compared on two lists of words on which the social connotations of the words on the lists were varied. One list was composed of an equal number of socially positive, socially negative, and nonsocial verbs, and the other list was composed of an equal number of socially positive and socially negative adjectives. The results indicated that reactive schizophrenics showed better learning on positive than negative words and showed better learning than do process schizophrenics on positive words. The two groups did not differ on negative words.
Current practice in mass spectrometry (MS)-based proteomics is to identify peptides by comparison of experimental mass spectra with theoretical mass spectra derived from a reference protein database; however, this strategy necessarily fails to detect peptide and protein sequences that are absent from the database. We and others have recently shown that customized proteomic databases derived from RNA-Seq data can be employed for MS-searching to both improve MS analysis and identify novel peptides. While this general strategy constitutes a significant advance for the discovery of novel protein variations, it has not been readily transferable to other laboratories due to the need for many specialized software tools. To address this problem, we have implemented readily accessible, modifiable, and extensible workflows within Galaxy-P, short for Galaxy for Proteomics, a web-based bioinformatic extension of the Galaxy framework for the analysis of multi-omics (e.g. genomics, transcriptomics, proteomics) data.We present three bioinformatic workflows that allow the user to upload raw RNA sequencing reads and convert the data into high-quality customized proteomic databases suitable for MS searching. We show the utility of these workflows on human and mouse samples, identifying 544 peptides containing single amino acid polymorphisms (SAPs) and 187 peptides corresponding to unannotated splice junction peptides, correlating protein and transcript expression levels, and providing the option to incorporate transcript abundance measures within the MS database search process (reduced databases, incorporation of transcript abundance for protein identification score calculations, etc.).Using RNA-Seq data to enhance MS analysis is a promising strategy to discover novel peptides specific to a sample and, more generally, to improve proteomics results. The main bottleneck for widespread adoption of this strategy has been the lack of easily used and modifiable computational tools. We provide a solution to this problem by introducing a set of workflows within the Galaxy-P framework that converts raw RNA-Seq data into customized proteomic databases.
