Plotgardener: cultivating precise multi-panel figures in R.

MOTIVATION: The R programming language is one of the most widely used programming languages for transforming raw genomic datasets into meaningful biological conclusions through analysis and visualization, which has been largely facilitated by infrastructure and tools developed by the Bioconductor project. However, existing plotting packages rely on relative positioning and sizing of plots, which is often sufficient for exploratory analysis but is poorly suited for the creation of publication-quality multi-panel images inherent to scientific manuscript preparation. RESULTS: We present plotgardener, a coordinate-based genomic data visualization package that offers a new paradigm for multi-plot figure generation in R. Plotgardener allows precise, programmatic control over the placement, esthetics and arrangements of plots while maximizing user experience through fast and memory-efficient data access, support for a wide variety of data and file types, and tight integration with the Bioconductor environment. Plotgardener also allows precise placement and sizing of ggplot2 plots, making it an invaluable tool for R users and data scientists from virtually any discipline. AVAILABILITY AND IMPLEMENTATION: Package: https://bioconductor.org/packages/plotgardener, Code: https://github.com/PhanstielLab/plotgardener, Documentation: https://phanstiellab.github.io/plotgardener/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Bedtoolsr: An R package for genomic data analysis and manipulation.

The sequencing of the human genome and subsequent advances in DNA sequencing technology have created a need for computational tools to analyze and manipulate genomic data sets. The bedtools software suite and the R programming language have emerged as indispensable tools for this purpose but have lacked integration. Here we describe bedtoolsr, an R package that provides simple and intuitive access to all bedtools functions from within the R programming environment. We provide several usability enhancements, support compatibility with past and future versions of bedtools, and include unit tests to ensure stability. bedtoolsr provides a user-focused, harmonious integration of the bedtools software suite with the R programming language that should be of great use to the genomics community.

Global Post-Translational Modification Discovery.

A new global post-translational modification (PTM) discovery strategy, G-PTM-D, is described. A proteomics database containing UniProt-curated PTM information is supplemented with potential new modification types and sites discovered from a first-round search of mass spectrometry data with ultrawide precursor mass tolerance. A second-round search employing the supplemented database conducted with standard narrow mass tolerances yields deep coverage and a rich variety of peptide modifications with high confidence in complex unenriched samples. The G-PTM-D strategy represents a major advance to the previously reported G-PTM strategy and provides a powerful new capability to the proteomics research community.

Global Identification of Protein Post-translational Modifications in a Single-Pass Database Search.

Bottom-up proteomics database search algorithms used for peptide identification cannot comprehensively identify post-translational modifications (PTMs) in a single-pass because of high false discovery rates (FDRs). A new approach to database searching enables global PTM (G-PTM) identification by exclusively looking for curated PTMs, thereby avoiding the FDR penalty experienced during conventional variable modification searches. We identified over 2200 unique, high-confidence modified peptides comprising 26 different PTM types in a single-pass database search.

Amine-reactive neutron-encoded labels for highly plexed proteomic quantitation.

We describe a novel amine-reactive chemical label that exploits differential neutron-binding energy between (13)C and (15)N isotopes. These neutron-encoded (NeuCode) chemical labels enable up to 12-plex MS1-based protein quantification. Each structurally identical, but isotopically unique, tag is encoded with a 12.6-mDa mass difference-relative to its nearest neighbor-so that peptides bearing these NeuCode signatures do not increase spectral complexity and are detected only upon analysis with very high mass-resolving powers. We demonstrate that the method provides quantitative performance that is comparable to both metabolic labeling and isobaric tagging while combining the benefits of both strategies. Finally, we employ the tags to characterize the proteome of Saccharomyces cerevisiae during the diauxic shift, a metabolic transition from fermentation to aerobic respiration.

RNA-protein analysis using a conditional CRISPR nuclease.

RNA-binding proteins control the fate and function of the transcriptome in all cells. Here we present technology for isolating RNA-protein partners efficiently and accurately using an engineered clustered regularly interspaced short palindromic repeats (CRISPR) endoribonuclease. An inactive version of the Csy4 nuclease binds irreversibly to transcripts engineered with a 16-nt hairpin sequence at their 5' ends. Once immobilized by Csy4 on a solid support, contaminating proteins and other molecules can be removed by extensive washing. Upon addition of imidazole, Csy4 is activated to cleave the RNA, removing the hairpin tag and releasing the native transcript along with its specifically bound protein partners. This conditional Csy4 enzyme enables recovery of specific RNA-binding partners with minimal false-positive contamination. We use this method, coupled with quantitative MS, to identify cell type-specific human pre-microRNA-binding proteins. We also show that this technology is suitable for analyzing diverse size transcripts, and that it is suitable for adaptation to a high-throughput discovery format.

A proteomics search algorithm specifically designed for high-resolution tandem mass spectra.

The acquisition of high-resolution tandem mass spectra (MS/MS) is becoming more prevalent in proteomics, but most researchers employ peptide identification algorithms that were designed prior to this development. Here, we demonstrate new software, Morpheus, designed specifically for high-mass accuracy data, based on a simple score that is little more than the number of matching products. For a diverse collection of data sets from a variety of organisms (E. coli, yeast, human) acquired on a variety of instruments (quadrupole-time-of-flight, ion trap-orbitrap, and quadrupole-orbitrap) in different laboratories, Morpheus gives more spectrum, peptide, and protein identifications at a 1% false discovery rate (FDR) than Mascot, Open Mass Spectrometry Search Algorithm (OMSSA), and Sequest. Additionally, Morpheus is 1.5 to 4.6 times faster, depending on the data set, than the next fastest algorithm, OMSSA. Morpheus was developed in C# .NET and is available free and open source under a permissive license.

Instant spectral assignment for advanced decision tree-driven mass spectrometry.

We have developed and implemented a sequence identification algorithm (inSeq) that processes tandem mass spectra in real-time using the mass spectrometer's (MS) onboard processors. The inSeq algorithm relies on accurate mass tandem MS data for swift spectral matching with high accuracy. The instant spectral processing technology takes ∼16 ms to execute and provides information to enable autonomous, real-time decision making by the MS system. Using inSeq and its advanced decision tree logic, we demonstrate (i) real-time prediction of peptide elution windows en masse (∼3 min width, 3,000 targets), (ii) significant improvement of quantitative precision and accuracy (~3x boost in detected protein differences), and (iii) boosted rates of posttranslation modification site localization (90% agreement in real-time vs. offline localization rate and an approximate 25% gain in localized sites). The decision tree logic enabled by inSeq promises to circumvent problems with the conventional data-dependent acquisition paradigm and provides a direct route to streamlined and expedient targeted protein analysis.

Characterizing peptide neutral losses induced by negative electron-transfer dissociation (NETD).

We implemented negative electron-transfer dissociation (NETD) on a hybrid ion trap/Orbitrap mass spectrometer to conduct ion/ion reactions using peptide anions and radical reagent cations. In addition to sequence-informative ladders of a•- and x-type fragment ions, NETD generated intense neutral loss peaks corresponding to the entire or partial side-chain cleavage from amino acids constituting a given peptide. Thus, a critical step towards the characterization of this recently introduced fragmentation technique is a systematic study of synthetic peptides to identify common neutral losses and preferential fragmentation pathways. Examining 46 synthetic peptides with high mass accuracy and high resolution analysis permitted facile determination of the chemical composition of each neutral loss. We identified 19 unique neutral losses from 14 amino acids and three modified amino acids, and assessed the specificity and sensitivity of each neutral loss using a database of 1542 confidently identified peptides generated from NETD shotgun experiments employing high-pH separations and negative electrospray ionization. As residue-specific neutral losses indicate the presence of certain amino acids, we determined that many neutral losses have potential diagnostic utility. We envision this catalogue of neutral losses being incorporated into database search algorithms to improve peptide identification specificity and to further advance characterization of the acidic proteome.

Gas-phase purification enables accurate, multiplexed proteome quantification with isobaric tagging.

We describe a mass spectrometry method, QuantMode, which improves accuracy of isobaric tag-based quantification by alleviating the pervasive problem of precursor interference, simultaneous isolation and fragmentation of impurities, through gas-phase purification. QuantMode analysis of a yeast sample 'contaminated' with interfering human peptides showed substantially improved quantitative accuracy compared to a standard scan, with a small loss of spectral identifications. This technique enables large-scale, multiplexed quantitative proteomics using isobaric tagging.

Proteomic and phosphoproteomic comparison of human ES and iPS cells.

Combining high-mass-accuracy mass spectrometry, isobaric tagging and software for multiplexed, large-scale protein quantification, we report deep proteomic coverage of four human embryonic stem cell and four induced pluripotent stem cell lines in biological triplicate. This 24-sample comparison resulted in a very large set of identified proteins and phosphorylation sites in pluripotent cells. The statistical analysis afforded by our approach revealed subtle but reproducible differences in protein expression and protein phosphorylation between embryonic stem cells and induced pluripotent cells. Merging these results with RNA-seq analysis data, we found functionally related differences across each tier of regulation. We also introduce the Stem Cell-Omics Repository (SCOR), a resource to collate and display quantitative information across multiple planes of measurement, including mRNA, protein and post-translational modifications.

A dynamic model of proteome changes reveals new roles for transcript alteration in yeast.

The transcriptome and proteome change dynamically as cells respond to environmental stress; however, prior proteomic studies reported poor correlation between mRNA and protein, rendering their relationships unclear. To address this, we combined high mass accuracy mass spectrometry with isobaric tagging to quantify dynamic changes in ~2500 Saccharomyces cerevisiae proteins, in biological triplicate and with paired mRNA samples, as cells acclimated to high osmolarity. Surprisingly, while transcript induction correlated extremely well with protein increase, transcript reduction produced little to no change in the corresponding proteins. We constructed a mathematical model of dynamic protein changes and propose that the lack of protein reduction is explained by cell-division arrest, while transcript reduction supports redistribution of translational machinery. Furthermore, the transient 'burst' of mRNA induction after stress serves to accelerate change in the corresponding protein levels. We identified several classes of post-transcriptional regulation, but show that most of the variance in protein changes is explained by mRNA. Our results present a picture of the coordinated physiological responses at the levels of mRNA, protein, protein-synthetic capacity, and cellular growth.

Characterization and diagnostic value of amino acid side chain neutral losses following electron-transfer dissociation.

Using a large set of high mass accuracy and resolution ETD tandem mass spectra, we characterized ETD-induced neutral losses. From these data we deduced the chemical formula for 20 of these losses. Many of them have been previously observed in electron-capture dissociation (ECD) spectra, such as losses of the side chains of arginine, aspartic acid, glutamic acid, glutamine, asparagine, leucine, histidine, and carbamidomethylated cysteine residues. With this information, we examined the diagnostic value of these amino acid-specific losses. Among 1285 peptide-spectrum matches, 92.5% have agreement between neutral loss-derived peptide amino acid composition and the peptide sequences. Moreover, we show that peptides can be uniquely identified by using only the accurate precursor mass and amino acid composition based on neutral losses; the median number of sequence candidates from an accurate mass query is reduced from 21 to 8 by adding side chain loss information. Besides increasing confidence in peptide identification, our findings suggest the potential use of these diagnostic losses in ETD spectra to improve false discovery rate estimation and to enhance the performance of scoring functions in database search algorithms.

COMPASS: a suite of pre- and post-search proteomics software tools for OMSSA.

Here we present the Coon OMSSA Proteomic Analysis Software Suite (COMPASS): a free and open-source software pipeline for high-throughput analysis of proteomics data, designed around the Open Mass Spectrometry Search Algorithm. We detail a synergistic set of tools for protein database generation, spectral reduction, peptide false discovery rate analysis, peptide quantitation via isobaric labeling, protein parsimony and protein false discovery rate analysis, and protein quantitation. We strive for maximum ease of use, utilizing graphical user interfaces and working with data files in the original instrument vendor format. Results are stored in plain text comma-separated value files, which are easy to view and manipulate with a text editor or spreadsheet program. We illustrate the operation and efficacy of COMPASS through the use of two LC-MS/MS data sets. The first is a data set of a highly annotated mixture of standard proteins and manually validated contaminants that exhibits the identification workflow. The second is a data set of yeast peptides, labeled with isobaric stable isotope tags and mixed in known ratios, to demonstrate the quantitative workflow. For these two data sets, COMPASS performs equivalently or better than the current de facto standard, the Trans-Proteomic Pipeline.

Sub-part-per-million precursor and product mass accuracy for high-throughput proteomics on an electron transfer dissociation-enabled orbitrap mass spectrometer.

We demonstrate a new approach for internal mass calibration on an electron transfer dissociation-enabled linear ion trap-orbitrap hybrid mass spectrometer. Fluoranthene cations, a byproduct of the reaction used for generation of electron transfer dissociation reagent anions, are co-injected with the analyte cations in all orbitrap mass analysis events. The fluoranthene cations serve as a robust internal calibrant with minimal impact on scan time (<20 ms) or spectral quality. Following external mass calibration, 60 replicate LC-MS/MS runs of a complex peptide mixture were collected over the course of approximately 136 h (almost 6 days). Using only standard external mass calibration, the mass accuracy for a typical analysis was -3.31 +/- 0.93 ppm (sigma) for precursors and -2.32 +/- 0.89 ppm for products. After application of internal recalibration, mass accuracy improved to +0.77 +/- 0.71 ppm for precursors and +0.17 +/- 0.67 ppm for products. When all 60 replicate runs were analyzed together without internal mass recalibration, the mass accuracy was -1.23 +/- 1.54 ppm for precursors and -0.18 +/- 1.42 ppm for products, nearly a 2-fold drop in precision relative to an individual run. After internal mass recalibration, this improved to +0.80 +/- 0.70 ppm for precursors and +0.16 +/- 0.67 ppm for products, roughly equivalent to that obtained in a single run, demonstrating a near complete elimination of mass calibration drift.

Value of using multiple proteases for large-scale mass spectrometry-based proteomics.

Large-scale protein sequencing methods rely on enzymatic digestion of complex protein mixtures to generate a collection of peptides for mass spectrometric analysis. Here we examine the use of multiple proteases (trypsin, LysC, ArgC, AspN, and GluC) to improve both protein identification and characterization in the model organism Saccharomyces cerevisiae. Using a data-dependent, decision tree-based algorithm to tailor MS(2) fragmentation method to peptide precursor, we identified 92 095 unique peptides (609 665 total) mapping to 3908 proteins at a 1% false discovery rate (FDR). These results were a significant improvement upon data from a single protease digest (trypsin) - 27 822 unique peptides corresponding to 3313 proteins. The additional 595 protein identifications were mainly from those at low abundances (i.e., < 1000 copies/cell); sequence coverage for these proteins was likewise improved nearly 3-fold. We demonstrate that large portions of the proteome are simply inaccessible following digestion with a single protease and that multiple proteases, rather than technical replicates, provide a direct route to increase both protein identifications and proteome sequence coverage.

Phosphoproteomics for the masses.

Protein phosphorylation serves as a primary mechanism of signal transduction in the cells of biological organisms. Technical advancements over the last several years in mass spectrometry now allow for the large-scale identification and quantitation of in vivo phosphorylation at unprecedented levels. These developments have occurred in the areas of sample preparation, instrumentation, quantitative methodology, and informatics so that today, 10 000-20 000 phosphorylation sites can be identified and quantified within a few weeks. With the rapid development and widespread availability of such data, its translation into biological insight and knowledge is a current obstacle. Here we present an overview of how this technology came to be and is currently applied, as well as future challenges for the field.

Large-scale phosphoprotein analysis in Medicago truncatula roots provides insight into in vivo kinase activity in legumes.

Nitrogen fixation in legumes requires the development of root organs called nodules and their infection by symbiotic rhizobia. Over the last decade, Medicago truncatula has emerged as a major model plant for the analysis of plant-microbe symbioses and for addressing questions pertaining to legume biology. While the initiation of symbiosis and the development of nitrogen-fixing root nodules depend on the activation of a protein phosphorylation-mediated signal transduction cascade in response to symbiotic signals produced by the rhizobia, few sites of in vivo phosphorylation have previously been identified in M. truncatula. We have characterized sites of phosphorylation on proteins from M. truncatula roots, from both whole cell lysates and membrane-enriched fractions, using immobilized metal affinity chromatography and tandem mass spectrometry. Here, we report 3,457 unique phosphopeptides spanning 3,404 nonredundant sites of in vivo phosphorylation on 829 proteins in M. truncatula Jemalong A17 roots, identified using the complementary tandem mass spectrometry fragmentation methods electron transfer dissociation and collision-activated dissociation. With this being, to our knowledge, the first large-scale plant phosphoproteomic study to utilize electron transfer dissociation, analysis of the identified phosphorylation sites revealed phosphorylation motifs not previously observed in plants. Furthermore, several of the phosphorylation motifs, including LxKxxs and RxxSxxxs, have yet to be reported as kinase specificities for in vivo substrates in any species, to our knowledge. Multiple sites of phosphorylation were identified on several key proteins involved in initiating rhizobial symbiosis, including SICKLE, NUCLEOPORIN133, and INTERACTING PROTEIN OF DMI3. Finally, we used these data to create an open-access online database for M. truncatula phosphoproteomic data.

Analysis of tandem mass spectra by FTMS for improved large-scale proteomics with superior protein quantification.

Using a newly developed dual-cell quadrupole linear ion trap-orbitrap hybrid mass spectrometer (dcQLT-orbitrap), we demonstrate the utility of collecting high-resolution tandem mass spectral data for large-scale shotgun proteomics. Multiple nanoLC-MS/MS experiments on both an older generation quadrupole linear ion trap-orbitrap hybrid (QLT-orbitrap) and the dcQLT-orbitrap, using both resonant-excitation CAD and beam-type CAD (HCD), were performed. Resulting from various technological advances (e.g., a stacked ring ion guide AP inlet, a dual cell QLT), the dcQLT-orbitrap exhibited increased duty cycle (approximately 1.5-2 times) and sensitivity for both CAD (ion trap detection) and HCD (orbitrap detection) methods. As compared to the older system, the dcQLT-orbitrap produced significantly more unique peptide identifications for both methods (approximately 30% improvement for CAD and approximately 115% improvement for HCD). The sizable improvement of the HCD method on the dcQLT-orbitrap system outperforms the current standard method of CAD with ion trap detection for large-scale analysis. Finally, we demonstrate that the increased HCD performance translates to a direct and substantial improvement in protein quantitation precision using isobaric tags.