Luis Mendoza

A guided tour of the Trans‐Proteomic Pipeline

The Trans‐Proteomic Pipeline (TPP) is a suite of software tools for the analysis of MS/MS data sets. The tools encompass most of the steps in a proteomic data analysis workflow in a single, integrated software system. Specifically, the TPP supports all steps from spectrometer output file conversion to protein‐level statistical validation, including quantification by stable isotope ratios. We describe here the full workflow of the TPP and the tools therein, along with an example on a sample data set, demonstrating that the setup and use of the tools are straightforward and well supported and do not require specialized informatic resources or knowledge.

iProphet: Multi-level Integrative Analysis of Shotgun Proteomic Data Improves Peptide and Protein Identification Rates and Error Estimates

The combination of tandem mass spectrometry and sequence database searching is the method of choice for the identification of peptides and the mapping of proteomes. Over the last several years, the volume of data generated in proteomic studies has increased dramatically, which challenges the computational approaches previously developed for these data. Furthermore, a multitude of search engines have been developed that identify different, overlapping subsets of the sample peptides from a particular set of tandem mass spectrometry spectra. We present iProphet, the new addition to the widely used open-source suite of proteomic data analysis tools Trans-Proteomics Pipeline. Applied in tandem with PeptideProphet, it provides more accurate representation of the multilevel nature of shotgun proteomic data. iProphet combines the evidence from multiple identifications of the same peptide sequences across different spectra, experiments, precursor ion charge states, and modified states. It also allows accurate and effective integration of the results from multiple database search engines applied to the same data. The use of iProphet in the Trans-Proteomics Pipeline increases the number of correctly identified peptides at a constant false discovery rate as compared with both PeptideProphet and another state-of-the-art tool Percolator. As the main outcome, iProphet permits the calculation of accurate posterior probabilities and false discovery rate estimates at the level of sequence identical peptide identifications, which in turn leads to more accurate probability estimates at the protein level. Fully integrated with the Trans-Proteomics Pipeline, it supports all commonly used MS instruments, search engines, and computer platforms. The performance of iProphet is demonstrated on two publicly available data sets: data from a human whole cell lysate proteome profiling experiment representative of typical proteomic data sets, and from a set of Streptococcus pyogenes experiments more representative of organism-specific composite data sets.

PASSEL: The PeptideAtlas SRMexperiment library

Public repositories for proteomics data have accelerated proteomics research by enabling more efficient cross‐analyses of datasets, supporting the creation of protein and peptide compendia of experimental results, supporting the development and testing of new software tools, and facilitating the manuscript review process. The repositories available to date have been designed to accommodate either shotgun experiments or generic proteomic data files. Here, we describe a new kind of proteomic data repository for the collection and representation of data from selected reaction monitoring (SRM) measurements. The PeptideAtlas SRM Experiment Library (PASSEL) allows researchers to easily submit proteomic data sets generated by SRM. The raw data are automatically processed in a uniform manner and the results are stored in a database, where they may be downloaded or browsed via a web interface that includes a chromatogram viewer. PASSELenables cross‐analysis of SRMdata, supports optimization of SRMdata collection, and facilitates the review process of SRMdata. Further, PASSELwill help in the assessment of proteotypic peptide performance in a wide array of samples containing the same peptide, as well as across multiple experimental protocols.

TraML—A Standard Format for Exchange of Selected Reaction Monitoring Transition Lists

Targeted proteomics via selected reaction monitoring is a powerful mass spectrometric technique affording higher dynamic range, increased specificity and lower limits of detection than other shotgun mass spectrometry methods when applied to proteome analyses. However, it involves selective measurement of predetermined analytes, which requires more preparation in the form of selecting appropriate signatures for the proteins and peptides that are to be targeted. There is a growing number of software programs and resources for selecting optimal transitions and the instrument settings used for the detection and quantification of the targeted peptides, but the exchange of this information is hindered by a lack of a standard format. We have developed a new standardized format, called TraML, for encoding transition lists and associated metadata. In addition to introducing the TraML format, we demonstrate several implementations across the community, and provide semantic validators, extensive documentation, and multiple example instances to demonstrate correctly written documents. Widespread use of TraML will facilitate the exchange of transitions, reduce time spent handling incompatible list formats, increase the reusability of previously optimized transitions, and thus accelerate the widespread adoption of targeted proteomics via selected reaction monitoring.

Trans-Proteomic Pipeline, a standardized data processing pipeline for large-scale reproducible proteomics informatics

Democratization of genomics technologies has enabled the rapid determination of genotypes. More recently the democratization of comprehensive proteomics technologies is enabling the determination of the cellular phenotype and the molecular events that define its dynamic state. Core proteomic technologies include MS to define protein sequence, protein:protein interactions, and protein PTMs. Key enabling technologies for proteomics are bioinformatic pipelines to identify, quantitate, and summarize these events. The Trans‐Proteomics Pipeline (TPP) is a robust open‐source standardized data processing pipeline for large‐scale reproducible quantitative MS proteomics. It supports all major operating systems and instrument vendors via open data formats. Here, we provide a review of the overall proteomics workflow supported by the TPP, its major tools, and how it can be used in its various modes from desktop to cloud computing. We describe new features for the TPP, including data visualization functionality. We conclude by describing some common perils that affect the analysis of MS/MS datasets, as well as some major upcoming features.

MaRiMba: a software application for spectral library-based MRM transition list assembly.

Multiple reaction monitoring mass spectrometry (MRM-MS) is a targeted analysis method that has been increasingly viewed as an avenue to explore proteomes with unprecedented sensitivity and throughput. We have developed a software tool, called MaRiMba, to automate the creation of explicitly defined MRM transition lists required to program triple quadrupole mass spectrometers in such analyses. MaRiMba creates MRM transition lists from downloaded or custom-built spectral libraries, restricts output to specified proteins or peptides, and filters based on precursor peptide and product ion properties. MaRiMba can also create MRM lists containing corresponding transitions for isotopically heavy peptides, for which the precursor and product ions are adjusted according to user specifications. This open-source application is operated through a graphical user interface incorporated into the Trans-Proteomic Pipeline, and it outputs the final MRM list to a text file for upload to MS instruments. To illustrate the use of MaRiMba, we used the tool to design and execute an MRM-MS experiment in which we targeted the proteins of a well-defined and previously published standard mixture.

State of the Human Proteome in 2014/2015 As Viewed through PeptideAtlas: Enhancing Accuracy and Coverage through the AtlasProphet

The Human PeptideAtlas is a compendium of the highest quality peptide identifications from over 1000 shotgun mass spectrometry proteomics experiments collected from many different laboratories, all reanalyzed through a uniform processing pipeline. The latest 2015-03 build contains substantially more input data than past releases, is mapped to a recent version of our merged reference proteome, and uses improved informatics processing and the development of the AtlasProphet to provide the highest quality results. Within the set of ∼20,000 neXtProt primary entries, 14,070 (70%) are confidently detected in the latest build, 5% are ambiguous, 9% are redundant, leaving the total percentage of proteins for which there are no mapping detections at just 16% (3166), all derived from over 133 million peptide-spectrum matches identifying more than 1 million distinct peptides using AtlasProphet to characterize and classify the protein matches. Improved handling for detection and presentation of single amino-acid variants (SAAVs) reveals the detection of 5326 uniquely mapping SAAVs across 2794 proteins. With such a large amount of data, the control of false positives is a challenge. We present the methodology and results for maintaining rigorous quality along with a discussion of the implications of the remaining sources of errors in the build.

Trans-Proteomic Pipeline supports and improves analysis of electron transfer dissociation data sets

Electron transfer dissociation (ETD) is an alternative fragmentation technique to CID that has recently become commercially available. ETD has several advantages over CID. It is less prone to fragmenting amino acid side chains, especially those that are modified, thus yielding fragment ion spectra with more uniform peak intensities. Further, precursor ions of longer peptides and higher charge states can be fragmented and identified. However, analysis of ETD spectra has a few important differences that require the optimization of the software packages used for the analysis of CID data or the development of specialized tools. We have adapted the Trans‐Proteomic Pipeline to process ETD data. Specifically, we have added support for fragment ion spectra from high‐charge precursors, compatibility with charge‐state estimation algorithms, provisions for the use of the Lys‐C protease, capabilities for ETD spectrum library building, and updates to the data formats to differentiate CID and ETD spectra. We show the results of processing data sets from several different types of ETD instruments and demonstrate that application of the ETD‐enhanced Trans‐Proteomic Pipeline can increase the number of spectrum identifications at a fixed false discovery rate by as much as 100% over native output from a single sequence search engine.

The microvesicle component of HIV-1 inocula modulates dendritic cell infection and maturation and enhances adhesion to and activation of T lymphocytes.

HIV-1 is taken up by immature monocyte derived dendritic cells (iMDDCs) into tetraspanin rich caves from which the virus can either be transferred to T lymphocytes or enter into endosomes resulting in degradation. HIV-1 binding and fusion with the DC membrane results in low level de novo infection that can also be transferred to T lymphocytes at a later stage. We have previously reported that HIV-1 can induce partial maturation of iMDDCs at both stages of trafficking. Here we show that CD45+ microvesicles (MV) which contaminate purified HIV-1 inocula due to similar size and density, affect DC maturation, de novo HIV-1 infection and transfer to T lymphocytes. Comparing iMDDCs infected with CD45-depleted HIV-1BaL or matched non-depleted preparations, the presence of CD45+ MVs was shown to enhance DC maturation and ICAM-1 (CD54) expression, which is involved in DC∶T lymphocyte interactions, while restricting HIV-1 infection of MDDCs. Furthermore, in the DC culture HIV-1 infected (p24+) MDDCs were more mature than bystander cells. Depletion of MVs from the HIV-1 inoculum markedly inhibited DC∶T lymphocyte clustering and the induction of alloproliferation as well as limiting HIV-1 transfer from DCs to T lymphocytes. The effects of MV depletion on these functions were reversed by the re-addition of purified MVs from activated but not non-activated SUPT1.CCR5-CL.30 or primary T cells. Analysis of the protein complement of these MVs and of these HIV-1 inocula before and after MV depletion showed that Heat Shock Proteins (HSPs) and nef were the likely DC maturation candidates. Recombinant HSP90α and β and nef all induced DC maturation and ICAM-1 expression, greater when combined. These results suggest that MVs contaminating HIV-1 released from infected T lymphocytes may be biologically important, especially in enhancing T cell activation, during uptake by DCs in vitro and in vivo, particularly as MVs have been detected in the circulation of HIV-1 infected subjects.

Processing Shotgun Proteomics Data on the Amazon Cloud with the Trans-Proteomic Pipeline

Cloud computing, where scalable, on-demand compute cycles and storage are available as a service, has the potential to accelerate mass spectrometry-based proteomics research by providing simple, expandable, and affordable large-scale computing to all laboratories regardless of location or information technology expertise. We present new cloud computing functionality for the Trans-Proteomic Pipeline, a free and open-source suite of tools for the processing and analysis of tandem mass spectrometry datasets. Enabled with Amazon Web Services cloud computing, the Trans-Proteomic Pipeline now accesses large scale computing resources, limited only by the available Amazon Web Services infrastructure, for all users. The Trans-Proteomic Pipeline runs in an environment fully hosted on Amazon Web Services, where all software and data reside on cloud resources to tackle large search studies. In addition, it can also be run on a local computer with computationally intensive tasks launched onto the Amazon Elastic Compute Cloud service to greatly decrease analysis times. We describe the new Trans-Proteomic Pipeline cloud service components, compare the relative performance and costs of various Elastic Compute Cloud service instance types, and present on-line tutorials that enable users to learn how to deploy cloud computing technology rapidly with the Trans-Proteomic Pipeline. We provide tools for estimating the necessary computing resources and costs given the scale of a job and demonstrate the use of cloud enabled Trans-Proteomic Pipeline by performing over 1100 tandem mass spectrometry files through four proteomic search engines in 9 h and at a very low cost.