Pragmatic Approach to In Situ Simulation to Identify Latent Safety Threats Before Moving to a Newly Built ICU.

OBJECTIVES: Transitions to new care environments may have unexpected consequences that threaten patient safety. We undertook a quality improvement project using in situ simulation to learn the new patient care environment and expose latent safety threats before transitioning patients to a newly built adult ICU. DESIGN: Descriptive review of a patient safety initiative. SETTING: A newly built 24-bed neurocritical care unit at a tertiary care academic medical center. SUBJECTS: Care providers working in neurocritical care unit. INTERVENTIONS: We implemented a pragmatic three-stage in situ simulation program to learn a new patient care environment, transitioning patients from an open bay unit to a newly built private room-based ICU. The project tested the safety and efficiency of new workflows created by new patient- and family-centric features of the unit. We used standardized patients and high-fidelity mannequins to simulate patient scenarios, with "test" patients created through all electronic databases. Relevant personnel from clinical and nonclinical services participated in simulations and/or observed scenarios. We held a debriefing after each stage and scenario to identify safety threats and other concerns. Additional feedback was obtained via a written survey sent to all participants. We prospectively surveyed for missed latent safety threats for 2 years following the simulation and fixed issues as they arose. MEASUREMENTS AND MAIN RESULTS: We identified and addressed 70 latent safety threats, including issues concerning physical environment, infection prevention, patient workflow, and informatics before the move into the new unit. We also developed an orientation manual that highlighted new physical and functional features of the ICU and best practices gleaned from the simulations. All participants agreed or strongly agreed that simulations were beneficial. Two-year follow-up revealed only two missed latent safety threats. CONCLUSIONS: In situ simulation effectively identifies latent safety threats surrounding the transition to new ICUs and should be considered before moving into new units.

A Galaxy of informatics resources for MS-based proteomics.

INTRODUCTION: Continuous advances in mass spectrometry (MS) technologies have enabled deeper and more reproducible proteome characterization and a better understanding of biological systems when integrated with other 'omics data. Bioinformatic resources meeting the analysis requirements of increasingly complex MS-based proteomic data and associated multi-omic data are critically needed. These requirements included availability of software that would span diverse types of analyses, scalability for large-scale, compute-intensive applications, and mechanisms to ease adoption of the software. AREAS COVERED: The Galaxy ecosystem meets these requirements by offering a multitude of open-source tools for MS-based proteomics analyses and applications, all in an adaptable, scalable, and accessible computing environment. A thriving global community maintains these software and associated training resources to empower researcher-driven analyses. EXPERT OPINION: The community-supported Galaxy ecosystem remains a crucial contributor to basic biological and clinical studies using MS-based proteomics. In addition to the current status of Galaxy-based resources, we describe ongoing developments for meeting emerging challenges in MS-based proteomic informatics. We hope this review will catalyze increased use of Galaxy by researchers employing MS-based proteomics and inspire software developers to join the community and implement new tools, workflows, and associated training content that will add further value to this already rich ecosystem.

Inappropriate Implantable Cardioverter-Defibrillator Therapy With the Use of an Underbody Electrosurgery Dispersive Electrode.

Perioperative management of implantable cardioverter-defibrillators is an important part of anesthetic care. Society recommendations and expert consensus statements exist to aid clinicians, and they have identified the umbilicus as an important landmark in decision-making. Implantable cardioverter-defibrillator antitachycardia therapy may not need to be deactivated for infraumbilical surgery because electromagnetic interference is unlikely to occur. The authors present two cases in which inappropriate antitachycardia therapy occurred intraoperatively with use of an underbody dispersive electrode, even though both surgeries were infraumbilical. The authors also present two cadaver models to demonstrate how monopolar electrosurgery below the umbilicus is sensed using both traditional and underbody dispersive electrosurgical return electrodes.

Metaproteomics Analysis of SARS-CoV-2-Infected Patient Samples Reveals Presence of Potential Coinfecting Microorganisms.

In this Letter, we reanalyze published mass spectrometry data sets of clinical samples with a focus on determining the coinfection status of individuals infected with SARS-CoV-2 coronavirus. We demonstrate the use of ComPIL 2.0 software along with a metaproteomics workflow within the Galaxy platform to detect cohabitating potential pathogens in COVID-19 patients using mass spectrometry-based analysis. From a sample collected from gargling solutions, we detected Streptococcus pneumoniae (opportunistic and multidrug-resistant pathogen) and Lactobacillus rhamnosus (a probiotic component) along with SARS-Cov-2. We could also detect Pseudomonas sps. Bc-h from COVID-19 positive samples and Acinetobacter ursingii and Pseudomonas monteilii from COVID-19 negative samples collected from oro- and nasopharyngeal samples. We believe that the early detection and characterization of coinfections by using metaproteomics from COVID-19 patients will potentially impact the diagnosis and treatment of patients affected by SARS-CoV-2 infection.

Updates on metaQuantome Software for Quantitative Metaproteomics.

metaQuantome is a software suite that enables the quantitative analysis, statistical evaluation. and visualization of mass-spectrometry-based metaproteomics data. In the latest update of this software, we have provided several extensions, including a step-by-step training guide, the ability to perform statistical analysis on samples from multiple conditions, and a comparative analysis of metatranscriptomics data. The training module, accessed via the Galaxy Training Network, will help users to use the suite effectively both for functional as well as for taxonomic analysis. We extend the ability of metaQuantome to now perform multi-data-point quantitative and statistical analyses so that studies with measurements across multiple conditions, such as time-course studies, can be analyzed. With an eye on the multiomics analysis of microbial communities, we have also initiated the use of metaQuantome statistical and visualization tools on outputs from metatranscriptomics data, which complements the metagenomic and metaproteomic analyses already available. For this, we have developed a tool named MT2MQ ("metatranscriptomics to metaQuantome"), which takes in outputs from the ASaiM metatranscriptomics workflow and transforms them so that the data can be used as an input for comparative statistical analysis and visualization via metaQuantome. We believe that these improvements to metaQuantome will facilitate the use of the software for quantitative metaproteomics and metatranscriptomics and will enable multipoint data analysis. These improvements will take us a step toward integrative multiomic microbiome analysis so as to understand dynamic taxonomic and functional responses of these complex systems in a variety of biological contexts. The updated metaQuantome and MT2MQ are open-source software and are available via the Galaxy Toolshed and GitHub.

A rigorous evaluation of optimal peptide targets for MS-based clinical diagnostics of Coronavirus Disease 2019 (COVID-19).

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.

A Sectioning and Database Enrichment Approach for Improved Peptide Spectrum Matching in Large, Genome-Guided Protein Sequence Databases.

Multiomics approaches focused on mass spectrometry (MS)-based data, such as metaproteomics, utilize genomic and/or transcriptomic sequencing data to generate a comprehensive protein sequence database. These databases can be very large, containing millions of sequences, which reduces the sensitivity of matching tandem mass spectrometry (MS/MS) data to sequences to generate peptide spectrum matches (PSMs). Here, we describe and evaluate a sectioning method for generating an enriched database for those protein sequences that are most likely present in the sample. Our evaluation demonstrates how this method helps to increase the sensitivity of PSMs while maintaining acceptable false discovery rate statistics-offering a flexible alternative to traditional large database searching, as well as previously described two-step database searching methods for large sequence database applications. Furthermore, implementation in the Galaxy platform provides access to an automated and customizable workflow for carrying out the method. Additionally, the results of this study provide valuable insights into the advantages and limitations offered by available methods aimed at addressing challenges of genome-guided, large database applications in proteomics. Relevant raw data has been made available at https://zenodo.org/ using data set identifier "3754789" and https://arcticdata.io/catalog using data set identifier "A2VX06340".

Challenges in Peptide-Spectrum Matching: A Robust and Reproducible Statistical Framework for Removing Low-Accuracy, High-Scoring Hits.

Workflows for large-scale (MS)-based shotgun proteomics can potentially lead to costly errors in the form of incorrect peptide-spectrum matches (PSMs). To improve the robustness of these workflows, we have investigated the use of the precursor mass discrepancy (PMD) to detect and filter potentially false PSMs that have, nonetheless, a high confidence score. We identified and addressed three cases of unexpected bias in PMD results: time of acquisition within a liquid chromatography-mass spectrometry (LC-MS) run, decoy PSMs, and length of the peptide. We created a postanalysis Bayesian confidence measure based on score and PMD, called PMD-false discovery rate (FDR). We tested PMD-FDR on four data sets across three types of MS-based proteomics projects: standard (single organism; reference database), proteogenomics (single organism; customized genomic-based database plus reference), and metaproteomics (microorganism community; customized conglomerate database). On a ground-truth data set and other representative data, PMD-FDR was able to detect 60-80% of likely incorrect PSMs (false-hits) while losing only 5% of correct PSMs (true-hits). PMD-FDR can also be used to evaluate data quality for results generated within different experimental PSM-generating workflows, assisting in method development. Going forward, PMD-FDR should provide detection of high scoring but likely false-hits, aiding applications that rely heavily on accurate PSMs, such as proteogenomics and metaproteomics.

Fecal Coliform Bacterial Detection to Assess Enema Adherence in HIV Prevention Clinical Studies.

Evaluating the efficacy of any HIV prevention strategy is dependent on ensuring and objectively monitoring adherence to the intervention. Medicated rectal enemas are a potential method for providing topical, episodic HIV prophylaxis during receptive anal intercourse. Assessing adherence to recommended enema dosing regimens is essential in evaluating the utility of this strategy. We utilized fecal coliform bacteria on used enema tips as a marker for enema use. Enema tip coliforms were tested by repurposing a microtiter plate-based water quality test designed to detect fecal contamination of water. Coliform detection occurred with 100% sensitivity and specificity when tips were assayed on day of use. The assay performed well post-7 day sample storage at room temperature, yielding a sensitivity of 80% and specificity of 93%. All (n = 64) samples collected in a subset of the DREAM-01 rectal microbicide enema clinical trial tested positive, even when tips were evaluated > 7 days post-reported use. The coliform-based enema tip assay allows monitoring of adherence in interventions involving rectal enemas in a sensitive, specific and inexpensive manner. The test performs well in clinical trial settings.

Bridging the Chromosome-centric and Biology/Disease-driven Human Proteome Projects: Accessible and Automated Tools for Interpreting the Biological and Pathological Impact of Protein Sequence Variants Detected via Proteogenomics.

The Chromosome-centric Human Proteome Project (C-HPP) seeks to comprehensively characterize all protein products coded by the genome, including those expressed sequence variants confirmed via proteogenomics methods. The closely related Biology/Disease-driven Human Proteome Project (B/D-HPP) seeks to understand the biological and pathological associations of expressed protein products, especially those carrying sequence variants that may be drivers of disease. To achieve these objectives, informatics tools are required that interpret potential functional or disease implications of variant protein sequence detected via proteogenomics. Toward this end, we have developed an automated workflow within the Galaxy for Proteomics (Galaxy-P) platform, which leverages the Cancer-Related Analysis of Variants Toolkit (CRAVAT) and makes it interoperable with proteogenomic results. Protein sequence variants confirmed by proteogenomics are assessed for potential structure-function effects as well as associations with cancer using CRAVAT's rich suite of functionalities, including visualization of results directly within the Galaxy user interface. We demonstrate the effectiveness of this workflow on proteogenomic results generated from an MCF7 breast cancer cell line. Our free and open software should enable improved interpretation of the functional and pathological effects of protein sequence variants detected via proteogenomics, acting as a bridge between the C-HPP and B/D-HPP.

Cobalamin riboswitches exhibit a broad range of ability to discriminate between methylcobalamin and adenosylcobalamin.

Riboswitches are a widely distributed class of regulatory RNAs in bacteria that modulate gene expression via small-molecule-induced conformational changes. Generally, these RNA elements are grouped into classes based upon conserved primary and secondary structure and their cognate effector molecule. Although this approach has been very successful in identifying new riboswitch families and defining their distributions, small sequence differences between structurally related RNAs can alter their ligand selectivity and regulatory behavior. Herein, we use a structure-based mutagenic approach to demonstrate that cobalamin riboswitches have a broad spectrum of preference for the two biological forms of cobalamin in vitro using isothermal titration calorimetry. This selectivity is primarily mediated by the interaction between a peripheral element of the RNA that forms a T-loop module and a subset of nucleotides in the cobalamin-binding pocket. Cell-based fluorescence reporter assays in Escherichia coli revealed that mutations that switch effector preference in vitro lead to differential regulatory responses in a biological context. These data demonstrate that a more comprehensive analysis of representative sequences of both previously and newly discovered classes of riboswitches might reveal subgroups of RNAs that respond to different effectors. Furthermore, this study demonstrates a second distinct means by which tertiary structural interactions in cobalamin riboswitches dictate ligand selectivity.

B12 cofactors directly stabilize an mRNA regulatory switch.

Structures of riboswitch receptor domains bound to their effector have shown how messenger RNAs recognize diverse small molecules, but mechanistic details linking the structures to the regulation of gene expression remain elusive. To address this, here we solve crystal structures of two different classes of cobalamin (vitamin B(12))-binding riboswitches that include the structural switch of the downstream regulatory domain. These classes share a common cobalamin-binding core, but use distinct peripheral extensions to recognize different B(12) derivatives. In each case, recognition is accomplished through shape complementarity between the RNA and cobalamin, with relatively few hydrogen bonding interactions that typically govern RNA-small molecule recognition. We show that a composite cobalamin-RNA scaffold stabilizes an unusual long-range intramolecular kissing-loop interaction that controls mRNA expression. This is the first, to our knowledge, riboswitch crystal structure detailing how the receptor and regulatory domains communicate in a ligand-dependent fashion to regulate mRNA expression.

Complex interactive effects of water mold, herbicide, and the fungus Batrachochytrium dendrobatidis on Pacific treefrog Hyliola regilla hosts.

Infectious diseases pose a serious threat to global biodiversity. However, their ecological impacts are not independent of environmental conditions. For example, the pathogenic fungus Batrachochytrium dendrobatidis (Bd), which has contributed to population declines and extinctions in many amphibian species, interacts with several environmental factors to influence its hosts, but potential interactions with other pathogens and environmental contaminants are understudied. We examined the combined effects of Bd, a water mold (Achlya sp.), and the herbicide Roundup® Regular (hereafter, Roundup®) on larval Pacific treefrog Hyliola regilla hosts. We employed a 2 wk, fully factorial laboratory experiment with 3 ecologically realistic levels (0, 1, and 2 mg l-1 of active ingredient) of field-formulated Roundup®, 2 Achlya treatments (present and absent), and 2 Bd treatments (present and absent). Our results were consistent with sublethal interactive effects involving all 3 experimental factors. When Roundup® was absent, the proportion of Bd-exposed larvae infected with Bd was elevated in the presence of Achlya, consistent with Achlya acting as a synergistic cofactor that facilitated the establishment of Bd infection. However, this Achlya effect became nonsignificant at 1 mg l-1 of the active ingredient of Roundup® and disappeared at the highest Roundup® concentration. In addition, Roundup® decreased Bd loads among Bd-exposed larvae. Our study suggests complex interactive effects of a water mold and a contaminant on Bd infection in amphibian hosts. Achlya and Roundup® were both correlated with altered patterns of Bd infection, but in different ways, and Roundup® appeared to remove the influence of Achlya on Bd.

Proteogenomic Analysis of a Hibernating Mammal Indicates Contribution of Skeletal Muscle Physiology to the Hibernation Phenotype.

Mammalian hibernation is a strategy employed by many species to survive fluctuations in resource availability and environmental conditions. Hibernating mammals endure conditions of dramatically depressed heart rate, body temperature, and oxygen consumption yet do not show the typical pathological response. Because of the high abundance and metabolic cost of skeletal muscle, not only must it adjust to the constraints of hibernation, but also it is positioned to play a more active role in the initiation and maintenance of the hibernation phenotype. In this study, MS/MS proteomic data from thirteen-lined ground squirrel skeletal muscles were searched against a custom database of transcriptomic and genomic protein predictions built using the platform Galaxy-P. This proteogenomic approach allows for a thorough investigation of skeletal muscle protein abundance throughout their circannual cycle. Of the 1563 proteins identified by these methods, 232 were differentially expressed. These data support previously reported physiological transitions, while also offering new insight into specific mechanisms of how their muscles might be reducing nitrogenous waste, preserving mass and function, and signaling to other tissues. Additionally, the combination of proteomic and transcriptomic data provides unique opportunities for estimating post-transcriptional regulation in skeletal muscle throughout the year and improving genomic annotation for this nonmodel organism.

Metaproteomic analysis using the Galaxy framework.

Metaproteomics characterizes proteins expressed by microorganism communities (microbiome) present in environmental samples or a host organism (e.g. human), revealing insights into the molecular functions conferred by these communities. Compared to conventional proteomics, metaproteomics presents unique data analysis challenges, including the use of large protein databases derived from hundreds or thousands of organisms, as well as numerous processing steps to ensure high data quality. These challenges limit the use of metaproteomics for many researchers. In response, we have developed an accessible and flexible metaproteomics workflow within the Galaxy bioinformatics framework. Via analysis of human oral tissue exudate samples, we have established a modular Galaxy-based workflow that automates a reduction method for searching large sequence databases, enabling comprehensive identification of host proteins (human) as well as "meta-proteins" from the nonhost organisms. Downstream, automated processing steps enable basic local alignment search tool analysis and evaluation/visualization of peptide sequence match quality, maximizing confidence in results. Outputted results are compatible with tools for taxonomic and functional characterization (e.g. Unipept, MEGAN5). Galaxy also allows for the sharing of complete workflows with others, promoting reproducibility and also providing a template for further modification and enhancement. Our results provide a blueprint for establishing Galaxy as a solution for metaproteomic data analysis. All MS data have been deposited in the ProteomeXchange with identifier PXD001655 (http://proteomecentral.proteomexchange.org/dataset/PXD001655).

Characterizing Cardiac Molecular Mechanisms of Mammalian Hibernation via Quantitative Proteogenomics.

This study uses advanced proteogenomic approaches in a nonmodel organism to elucidate cardioprotective mechanisms used during mammalian hibernation. Mammalian hibernation is characterized by drastic reductions in body temperature, heart rate, metabolism, and oxygen consumption. These changes pose significant challenges to the physiology of hibernators, especially for the heart, which maintains function throughout the extreme conditions, resembling ischemia and reperfusion. To identify novel cardioadaptive strategies, we merged large-scale RNA-seq data with large-scale iTRAQ-based proteomic data in heart tissue from 13-lined ground squirrels (Ictidomys tridecemlineatus) throughout the circannual cycle. Protein identification and data analysis were run through Galaxy-P, a new multiomic data analysis platform enabling effective integration of RNA-seq and MS/MS proteomic data. Galaxy-P uses flexible, modular workflows that combine customized sequence database searching and iTRAQ quantification to identify novel ground squirrel-specific protein sequences and provide insight into molecular mechanisms of hibernation. This study allowed for the quantification of 2007 identified cardiac proteins, including over 350 peptide sequences derived from previously uncharacterized protein products. Identification of these peptides allows for improved genomic annotation of this nonmodel organism, as well as identification of potential splice variants, mutations, and genome reorganizations that provides insights into novel cardioprotective mechanisms used during hibernation.

Wrangling Galaxy's reference data.

UNLABELLED: The Galaxy platform has developed into a fully featured collaborative workbench, with goals of inherently capturing provenance to enable reproducible data analysis, and of making it straightforward to run one's own server. However, many Galaxy platform tools rely on the presence of reference data, such as alignment indexes, to function efficiently. Until now, the building of this cache of data for Galaxy has been an error-prone manual process lacking reproducibility and provenance. The Galaxy Data Manager framework is an enhancement that changes the management of Galaxy's built-in data cache from a manual procedure to an automated graphical user interface (GUI) driven process, which contains the same openness, reproducibility and provenance that is afforded to Galaxy's analysis tools. Data Manager tools allow the Galaxy administrator to download, create and install additional datasets for any type of reference data in real time. AVAILABILITY AND IMPLEMENTATION: The Galaxy Data Manager framework is implemented in Python and has been integrated as part of the core Galaxy platform. Individual Data Manager tools can be defined locally or installed from a ToolShed, allowing the Galaxy community to define additional Data Manager tools as needed, with full versioning and dependency support.

The Xeromphalina campanella/kauffmanii complex: species delineation and biogeographical patterns of speciation.

European, North American and northeastern Asian collections of Xeromphalina section Xeromphalina were studied by sequencing the nuc rDNA ITS1-5.8SITS2 and 28S 5' regions and partial RNA polymerase II second largest subunit gene (RPB2). Previously designated Xeromphalina campanella I is designated X. campanella s. str. and a neotype for this species from the topotype region is established. This species is shown to be a discrete, cold-tolerant organism that is distributed across North America and Eurasia and does not exhibit significant geographical partitioning. A second closely related phylogenetic species previously designated X. campanella II, proposed as X. enigmatica, cannot be distinguished from X. campanella morphologically but is reproductively isolated and is sympatric with X. campanella across much of Eurasia and North America. Unlike X. campanella it shows geographical partitioning and some of the geographical populations likely have become reproductively isolated. Phylogenetic and geographical evidence suggests that X. enigmatica may have given rise to the eastern North American endemic, Xeromphalina kauffmanii, which also is reproductively isolated and is characterized by a hardwood substrate and a difference in basidiospore shape. Two putatively interbreeding haplotypes are evident for both eastern North American X. kauffmanii and eastern North American X. enigmatica and might be contributions from different glacial refugia. Cryptic taxa related to X. enigmatica are identified but not named due to small sample sizes including Asian taxa 1-5 and an apparent endemic from Idaho and British Columbia. Several species-delineation procedures were attempted and compared with this complex molecular dataset. Rosenberg's PAB statistic and PID (liberal) were the most liberal, assigning species status to haplotypes or interbreeding clades within species. PID (strict) and PRD (randomly distinct) were more stringent. Ability to intercross was the most stringent criterion for species delineation and did not correlate well with PAB, PID and PRD delineations.

Flexible and accessible workflows for improved proteogenomic analysis using the Galaxy framework.

Proteogenomics combines large-scale genomic and transcriptomic data with mass-spectrometry-based proteomic data to discover novel protein sequence variants and improve genome annotation. In contrast with conventional proteomic applications, proteogenomic analysis requires a number of additional data processing steps. Ideally, these required steps would be integrated and automated via a single software platform offering accessibility for wet-bench researchers as well as flexibility for user-specific customization and integration of new software tools as they emerge. Toward this end, we have extended the Galaxy bioinformatics framework to facilitate proteogenomic analysis. Using analysis of whole human saliva as an example, we demonstrate Galaxy's flexibility through the creation of a modular workflow incorporating both established and customized software tools that improve depth and quality of proteogenomic results. Our customized Galaxy-based software includes automated, batch-mode BLASTP searching and a Peptide Sequence Match Evaluator tool, both useful for evaluating the veracity of putative novel peptide identifications. Our complex workflow (approximately 140 steps) can be easily shared using built-in Galaxy functions, enabling their use and customization by others. Our results provide a blueprint for the establishment of the Galaxy framework as an ideal solution for the emerging field of proteogenomics.

Using Galaxy-P to leverage RNA-Seq for the discovery of novel protein variations.

BACKGROUND: 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. RESULTS: 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.). CONCLUSIONS: 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.