YPED:An Integrated Bioinformatics Suite and Database for Mass Spectrometry-based Proteomics Research

We report a significantly-enhanced bioinformatics suite and database for proteomics research called Yale Protein Expression Database(YPED) that is used by investigators at more than 300 institutions worldwide. YPED meets the data management, archival, and analysis needs of a high-throughput mass spectrometry-based proteomics research ranging from a singlelaboratory, group of laboratories within and beyond an institution, to the entire proteomics community. The current version is a significant improvement over the first version in that it contains new modules for liquid chromatography–tandem mass spectrometry(LC–MS/MS) database search results, label and label-free quantitative proteomic analysis, and several scoring outputs for phosphopeptide site localization. In addition, we have added both peptide and protein comparative analysis tools to enable pairwise analysis of distinct peptides/proteins in each sample and of overlapping peptides/proteins between all samples in multiple datasets. We have also implemented a targeted proteomics module for automated multiple reaction monitoring(MRM)/selective reaction monitoring(SRM) assay development. We have linked YPED's database search results and both label-based and label-free fold-change analysis to the Skyline Panorama repository for online spectra visualization. In addition, we have built enhanced functionality to curate peptide identifications into an MS/MS peptide spectral library for all of our protein database search identification results.     

Terminal proteomics: N- and C-terminal analyses for high-fidelity identification of proteins using MS

In proteomics, MS plays an essential role in identifying and quantifying proteins. To characterize mature target proteins from living cells, candidate proteins are often analyzed with PMF and MS/MS ion search methods in combination with computational search routines based on bioinformatics. In contrast to shotgun proteomics, which is widely used to identify proteins, proteomics based on the analysis of N- and C-terminal amino acid sequences (terminal proteomics) should render higher fidelity results because of the high information content of terminal sequence and potentially high throughput of the method not requiring very high sequence coverage to be achieved by extensive sequencing. In line with this expectation, we review recent advances in methods for N- and C-terminal amino acid sequencing of proteins. This review focuses mainly on the methods of N- and C-terminal analyses based on MALDI-TOF MS for its easy accessibility, with several complementary approaches using LC/MS/MS. We also describe problems associated with MS and possible remedies, including chemical and enzymatic procedures to enhance the fidelity of these methods.     

i2dash: Creation of Flexible,Interactive, and Web-based Dashboards for Visualization of Omics Data

Data visualization and interactive data exploration are important aspects of illustrating complex concepts and results from analyses of omics data. A suitable visualization has to be intuitive and accessible. Web-based dashboards have become popular tools for the arrangement, consolidation, and display of such visualizations. However, the combination of automated data processing pipelines handling omics data and dynamically generated, interactive dashboards is poorly solved. Here, we present i2dash, an R package intended to encapsulate functionality for the programmatic creation of customized dashboards. It supports interactive and responsive (linked) visualizations across a set of predefined graphical layouts. i2dash addresses the needs of data analysts/software developers for a tool that is compatible and attachable to any R-based analysis pipeline, thereby fostering the separation of data visualization on one hand and data analysis tasks on the other hand. In addition, the generic design of i2dash enables the development of modular extensions for specific needs. As a proof of principle, we provide an extension of i2dash optimized for single-cell RNA sequencing analysis, supporting the creation of dashboards for the visualization needs of such experiments. Equipped with these features, i2dash is suitable for extensive use in large-scale sequencing/bioinformatics facilities. Along this line, we provide i2dash as a containerized solution, enabling a straightforward large-scale deployment and sharing of dashboards using cloud services. i2dash is freely available via the R package archive CRAN (https://CRAN.R-project.org/package=i2dash).     

The utility of proteases in proteomics,from sequence profiling to structure and function analysis

In mass spectrometry (MS)-based bottom-up proteomics, protease digestion plays an essential role in profiling both proteome sequences and post-translational modifications (PTMs). Trypsin is the gold standard in digesting intact proteins into small-size peptides, which are more suitable for high-performance liquid chromatography (HPLC) separation and tandem MS (MS/MS) characterization. However, protein sequences lacking Lys and Arg cannot be cleaved by trypsin and may be missed in conventional proteomic analysis. Proteases with cleavage sites complementary to trypsin are widely applied in proteomic analysis to greatly improve the coverage of proteome sequences and PTM sites. In this review, we survey the common and newly emerging proteases used in proteomics analysis mainly in the last 5 years, focusing on their unique cleavage features and specific proteomics applications such as missing protein characterization, new PTM discovery, and de novo sequencing. In addition, we summarize the applications of proteases in structural proteomics and protein function analysis in recent years. Finally, we discuss the future development directions of new proteases and applications in proteomics.     

Postexperiment monoisotopic mass filtering and refinement (PE-MMR) of tandem mass spectrometric data increases accuracy of peptide identification in LC/MS/MS

Methods for treating MS/MS data to achieve accurate peptide identification are currently the subject of much research activity. In this study we describe a new method for filtering MS/MS data and refining precursor masses that provides highly accurate analyses of massive sets of proteomics data. This method, coined "postexperiment monoisotopic mass filtering and refinement" (PE-MMR), consists of several data processing steps: 1) generation of lists of all monoisotopic masses observed in a whole LC/MS experiment, 2) clusterization of monoisotopic masses of a peptide into unique mass classes (UMCs) based on their masses and LC elution times, 3) matching the precursor masses of the MS/MS data to a representative mass of a UMC, and 4) filtration of the MS/MS data based on the presence of corresponding monoisotopic masses and refinement of the precursor ion masses by the UMC mass. PE-MMR increases the throughput of proteomics data analysis, by efficiently removing "garbage" MS/MS data prior to database searching, and improves the mass measurement accuracies (i.e. 0.05 +/- 1.49 ppm for yeast data (from 4.46 +/- 2.81 ppm) and 0.03 +/- 3.41 ppm for glycopeptide data (from 4.8 +/- 7.4 ppm)) for an increased number of identified peptides. In proteomics analyses of glycopeptide-enriched samples, PE-MMR processing greatly reduces the degree of false glycopeptide identification by correctly assigning the monoisotopic masses for the precursor ions prior to database searching. By applying this technique to analyses of proteome samples of varying complexities, we demonstrate herein that PE-MMR is an effective and accurate method for treating massive sets of proteomics data.     

Software for analysing ion mobility mass spectrometry data to improve peptide identification

The development of ion mobility (IM) MS instruments has the capability to provide an added dimension to peptide analysis pipelines in proteomics, but, as yet, there are few software tools available for analysing such data. IM can be used to provide additional separation of parent ions or product ions following fragmentation. In this work, we have created a set of software tools that are capable of converting three dimensional IM data generated from analysis of fragment ions into a variety of formats used in proteomics. We demonstrate that IM can be used to calculate the charge state of a fragment ion, demonstrating the potential to improve peptide identification by excluding non-informative ions from a database search. We also provide preliminary evidence of structural differences between b and y ions for certain peptide sequences but not others. All software tools and data sets are made available in the public domain at http://code.google.com/p/ion-mobility-ms-tools/.     

Proteomics data repositories: Providing a safe haven for your data and acting as a springboard for further research

Despite the fact that data deposition is not a generalised fact yet in the field of proteomics, several mass spectrometry (MS) based proteomics repositories are publicly available for the scientific community. The main existing resources are: the Global Proteome Machine Database (GPMDB), PeptideAtlas, the PRoteomics IDEntifications database (PRIDE), Tranche, and NCBI Peptidome. In this review the capabilities of each of these will be described, paying special attention to four key properties: data types stored, applicable data submission strategies, supported formats, and available data mining and visualization tools. Additionally, the data contents from model organisms will be enumerated for each resource. There are other valuable smaller and/or more specialized repositories but they will not be covered in this review. Finally, the concept behind the ProteomeXchange consortium, a collaborative effort among the main resources in the field, will be introduced.     

Bottom up proteomics data analysis strategies to explore protein modifications and genomic variants

The quest to understand biological systems requires further attention of the scientific community to the challenges faced in proteomics. In fact the complexity of the proteome reaches uncountable orders of magnitude. This means that significant technical and data‐analytic innovations will be needed for the full understanding of biology. Current state of art MS is probably our best choice for studying protein complexity and exploring new ways to use MS and MS derived data should be given higher priority. We present here a brief overview of visualization and statistical analysis strategies for quantitative peptide values on an individual protein basis. These analysis strategies can help pinpoint protein modifications, splice, and genomic variants of biological relevance. We demonstrate the application of these data analysis strategies using a bottom‐up proteomics dataset obtained in a drug profiling experiment. Furthermore, we have also observed that the presented methods are useful for studying peptide distributions from clinical samples from a large number of individuals. We expect that the presented data analysis strategy will be useful in the future to define functional protein variants in biological model systems and disease studies. Therefore robust software implementing these strategies is urgently needed.     

Identifying components of protein complexes in C. elegans using co-immunoprecipitation and mass spectrometry

Mass spectrometry-based proteomics is rapidly becoming an essential tool for biologists. One of the most common applications is identifying the components of protein complexes isolated by co-immunoprecipitation. In this review, we discuss the co-immunoprecipitation, mass spectrometry and data analysis techniques that have been used successfully to define protein complexes in C. elegans research. In this discussion, two strategies emerged. One approach is to use stringent biochemical purification methods and attempt to identify a small number of complex components with a high degree of certainty based on MS data. A second approach is to use less stringent purification and identification parameters, and ultimately test a longer list of potential binding partners in biological validation assays. This should provide a useful guide for biologists planning proteomic experiments.     

Strategies for shotgun identification of integral membrane proteins by tandem mass spectrometry

Integral membrane proteins (IMPs) are difficult to identify, mainly for two reasons: the hydrophobicity of IMPs and their low abundance. Sample preparation is a key component in the large-scale identification of IMPs. In this review, we survey strategies for shotgun identification of IMPs by MS/MS. We will discuss enrichment, solubilization, separation, and digestion of IMPs, and data analysis for membrane proteomics.     

8-plex quantitation of changes in cerebrospinal fluid protein expression in subjects undergoing intravenous immunoglobulin treatment for Alzheimer's disease

An 8-plex version of an isobaric reagent for the quantitation of proteins using shotgun methods is presented. The 8-plex version of the reagent relies on amine-labeling chemistry of peptides similar to 4-plex reagents. MS/MS reporter ions at 113, 114, 115, 116, 117, 118, 119, and 121 m/z are used to quantify protein expression. This technology which was first applied to a test mixture consisting of eight proteins and resulted in accurate quantitation, has the potential to increase throughput of analysis for quantitative shotgun proteomics experiments when compared to 2- and 4-plex methods. The technology was subsequently applied to a longitudinal study of cerebrospinal fluid (CSF) proteins from subjects undergoing intravenous Ig treatment for Alzheimer's disease. Results from this study identify a number of protein expression changes that occur in CSF after 3 and 6 months of treatment compared to a baseline and compared to a drug washout period. A visualization tool was developed for this dataset and is presented. The tool can aid in the identification of key peptides and measurements. One conclusion aided by the visualization tool is that there are differences in considering peptide-based observations versus protein-based observations from quantitative shotgun proteomics studies.     

Glycoproteomics based on tandem mass spectrometry of glycopeptides

Next to the identification of proteins and the determination of their expression levels, the analysis of post-translational modifications (PTM) is becoming an increasingly important aspect in proteomics. Here, we review mass spectrometric (MS) techniques for the study of protein glycosylation at the glycopeptide level. Enrichment and separation techniques for glycoproteins and glycopeptides from complex (glyco-)protein mixtures and digests are summarized. Various tandem MS (MS/MS) techniques for the analysis of glycopeptides are described and compared with respect to the information they provide on peptide sequence, glycan attachment site and glycan structure. Approaches using electrospray ionization and matrix-assisted laser desorption/ionization (MALDI) of glycopeptides are presented and the following fragmentation techniques in glycopeptide analysis are compared: collision-induced fragmentation on different types of instruments, metastable fragmentation after MALDI ionization, infrared multi-photon dissociation, electron-capture dissociation and electron-transfer dissociation. This review discusses the potential and limitations of tandem mass spectrometry of glycopeptides as a tool in structural glycoproteomics.     

Fishing for biomarkers: analyzing mass spectrometry data with the new ClinProTools software

Recently, applications of mass spectrometry in the field of clinical proteomics have gained tremendous visibility in the scientific and clinical community. One major objective is the search for potential biomarkers in complex body fluids like serum, plasma, urine, saliva, or cerebral spinal fluid. For this purpose, efficient visualization of large data sets derived from patient cohorts is crucial to provide clinical experts an interactive impression of the data quality. Additionally, it is necessary to apply statistical analysis and pattern matching algorithms to attain validated signal patterns that may allow for later applications in sample classification. We introduce the new ClinProTools bioinformatics software, which performs all major steps of profiling, screening, and monitoring applications in clinical proteomics. ClinProTools is the data interpretation software of the mass spectrometry-based ClinProt solutions for biomarker analysis. ClinProTools performs data pretreatment, visualization, statistics, pattern determination, pattern evaluation, and classification of spectra. This article will focus on ClinProTool's powerful and intuitive visualization options for clinical proteomics applications.     

Image analysis tools and emerging algorithms for expression proteomics

Since their origins in academic endeavours in the 1970s, computational analysis tools have matured into a number of established commercial packages that underpin research in expression proteomics. In this paper we describe the image analysis pipeline for the established 2-DE technique of protein separation, and by first covering signal analysis for MS, we also explain the current image analysis workflow for the emerging high-throughput 'shotgun' proteomics platform of LC coupled to MS (LC/MS). The bioinformatics challenges for both methods are illustrated and compared, whereas existing commercial and academic packages and their workflows are described from both a user's and a technical perspective. Attention is given to the importance of sound statistical treatment of the resultant quantifications in the search for differential expression. Despite wide availability of proteomics software, a number of challenges have yet to be overcome regarding algorithm accuracy, objectivity and automation, generally due to deterministic spot-centric approaches that discard information early in the pipeline, propagating errors. We review recent advances in signal and image analysis algorithms in 2-DE, MS, LC/MS and Imaging MS. Particular attention is given to wavelet techniques, automated image-based alignment and differential analysis in 2-DE, Bayesian peak mixture models, and functional mixed modelling in MS, and group-wise consensus alignment methods for LC/MS.     

Arabidopsis thaliana proteomics: from proteome to genome

Proteomics has become an important approach for investigating cellular processes and network functions. Significant improvements have been made during the last few years in technologies for high-throughput proteomics, both at the level of data analysis software and mass spectrometry hardware. As proteomics technologies advance and become more widely accessible, efforts of cataloguing and quantifying full proteomes are underway to complement other genomics approaches, such as RNA and metabolite profiling. Of particular interest is the application of proteome data to improve genome annotation and to include information on post-translational protein modifications with the annotation of the corresponding gene. This type of analysis requires a paradigm shift because amino acid sequences must be assigned to peptides without relying on existing protein databases. In this review, advances and current limitations of full proteome analysis are briefly highlighted using the model plant Arabidopsis thaliana as an example. Strategies to identify peptides are also discussed on the basis of MS/MS data in a protein database-independent approach.     

GProX, a user-friendly platform for bioinformatics analysis and visualization of quantitative proteomics data

Recent technological advances have made it possible to identify and quantify thousands of proteins in a single proteomics experiment. As a result of these developments, the analysis of data has become the bottleneck of proteomics experiment. To provide the proteomics community with a user-friendly platform for comprehensive analysis, inspection and visualization of quantitative proteomics data we developed the Graphical Proteomics Data Explorer (GProX)(1). The program requires no special bioinformatics training, as all functions of GProX are accessible within its graphical user-friendly interface which will be intuitive to most users. Basic features facilitate the uncomplicated management and organization of large data sets and complex experimental setups as well as the inspection and graphical plotting of quantitative data. These are complemented by readily available high-level analysis options such as database querying, clustering based on abundance ratios, feature enrichment tests for e.g. GO terms and pathway analysis tools. A number of plotting options for visualization of quantitative proteomics data is available and most analysis functions in GProX create customizable high quality graphical displays in both vector and bitmap formats. The generic import requirements allow data originating from essentially all mass spectrometry platforms, quantitation strategies and software to be analyzed in the program. GProX represents a powerful approach to proteomics data analysis providing proteomics experimenters with a toolbox for bioinformatics analysis of quantitative proteomics data. The program is released as open-source and can be freely downloaded from the project webpage at http://gprox.sourceforge.net.     

Algorithms and tools for analysis and management of mass spectrometry data

Mass spectrometry (MS) is a technique that is used for biological studies. It consists in associating a spectrum to a biological sample. A spectrum consists of couples of values (intensity, m/z), where intensity measures the abundance of biomolecules (as proteins) with a mass-to-charge ratio (m/z) present in the originating sample. In proteomics experiments, MS spectra are used to identify pattern expressions in clinical samples that may be responsible of diseases. Recently, to improve the identification of peptides/proteins related to patterns, MS/MS process is used, consisting in performing cascade of mass spectrometric analysis on selected peaks. Latter technique has been demonstrated to improve the identification and quantification of proteins/peptide in samples. Nevertheless, MS analysis deals with a huge amount of data, often affected by noises, thus requiring automatic data management systems. Tools have been developed and most of the time furnished with the instruments allowing: (i) spectra analysis and visualization, (ii) pattern recognition, (iii) protein databases querying, (iv) peptides/proteins quantification and identification. Currently most of the tools supporting such phases need to be optimized to improve the protein (and their functionalities) identification processes. In this article we survey on applications supporting spectrometrists and biologists in obtaining information from biological samples, analyzing available software for different phases. We consider different mass spectrometry techniques, and thus different requirements. We focus on tools for (i) data preprocessing, allowing to prepare results obtained from spectrometers to be analyzed; (ii) spectra analysis, representation and mining, aimed to identify common and/or hidden patterns in spectra sets or in classifying data; (iii) databases querying to identify peptides; and (iv) improving and boosting the identification and quantification of selected peaks. We trace some open problems and report on requirements that represent new challenges for bioinformatics.