Proposal for a standard representation of two-dimensional gel electrophoresis data

The global analysis of proteins is now feasible due to improvements in techniques such as two-dimensional gel electrophoresis (2-DE), mass spectrometry, yeast two-hybrid systems and the development of bioinformatics applications. The experiments form the basis of proteomics, and present significant challenges in data analysis, storage and querying. We argue that a standard format for proteome data is required to enable the storage, exchange and subsequent re-analysis of large datasets. We describe the criteria that must be met for the development of a standard for proteomics. We have developed a model to represent data from 2-DE experiments, including difference gel electrophoresis along with image analysis and statistical analysis across multiple gels. This part of proteomics analysis is not represented in current proposals for proteomics standards. We are working with the Proteomics Standards Initiative to develop a model encompassing biological sample origin, experimental protocols, a number of separation techniques and mass spectrometry. The standard format will facilitate the development of central repositories of data, enabling results to be verified or re-analysed, and the correlation of results produced by different research groups using a variety of laboratory techniques.     

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.     

Virtual Expert Mass Spectrometrist: iTRAQ tool for database‐dependent search,quantitation and result storage

A frequent goal of MS‐based proteomics experiments nowadays is to quantify changes in the abundance of proteins across several biological samples. The iTRAQ labeling method is a powerful technique; when combined with LC coupled to MS/MS it allows relative quantitation of up to eight different samples simultaneously. Despite the usefulness of iTRAQ current software solutions have limited functionality and require the combined use of several software programs for analysis of the data from different MS vendors. We developed an integrated tool, now available in the virtual expert mass spectrometrist (VEMS) program, for database‐dependent search of MS/MS spectra, quantitation and database storage for iTRAQ‐labeled samples. VEMS also provides useful alternative report types for large‐scale quantitative experiments. The implemented statistical algorithms build on quantitative algorithms previously used in proposed iTRAQ tools as described in detail herein. We propose a new algorithm, which provides more accurate peptide ratios for data that show an intensity‐dependent saturation. The accuracy of the proposed iTRAQ algorithm and the performance of VEMS are demonstrated by comparing results from VEMS, MASCOT and PEAKS Q obtained by analyzing data from a reference mixture of six proteins. Users can download VEMS and test data from “ http://www.portugene.com/software.html ”.     

Computational methods for the comparative quantification of proteins in label-free LCn-MS experiments

Liquid chromatography (LC) coupled to electrospray mass spectrometry (MS) is well established in high-throughput proteomics. The technology enables rapid identification of large numbers of proteins in a relatively short time. Comparative quantification of identified proteins from different samples is often regarded as the next step in proteomics experiments enabling the comparison of protein expression in different proteomes. Differential labeling of samples using stable isotope incorporation or conjugation is commonly used to compare protein levels between samples but these procedures are difficult to carry out in the laboratory and for large numbers of samples. Recently, comparative quantification of label-free LC(n)-MS proteomics data has emerged as an alternative approach. In this review, we discuss different computational approaches for extracting comparative quantitative information from label-free LC(n)-MS proteomics data. The procedure for computationally recovering the quantitative information is described. Furthermore, statistical tests used to evaluate the relevance of results will also be discussed.     

ICPLQuant – A software for non‐isobaric isotopic labeling proteomics

The main goal of many proteomics experiments is an accurate and rapid quantification and identification of regulated proteins in complex biological samples. The bottleneck in quantitative proteomics remains the availability of efficient software to evaluate and quantify the tremendous amount of mass spectral data acquired during a proteomics project. A new software suite, ICPLQuant, has been developed to accurately quantify isotope‐coded protein label (ICPL)‐labeled peptides on the MS level during LC‐MALDI and peptide mass fingerprint experiments. The tool is able to generate a list of differentially regulated peptide precursors for subsequent MS/MS experiments, minimizing time‐consuming acquisition and interpretation of MS/MS data. ICPLQuant is based on two independent units. Unit 1 performs ICPL multiplex detection and quantification and proposes peptides to be identified by MS/MS. Unit 2 combines MASCOT MS/MS protein identification with the quantitative data and produces a protein/peptide list with all the relevant information accessible for further data mining. The accuracy of quantification, selection of peptides for MS/MS‐identification and the automated output of a protein list of regulated proteins are demonstrated by the comparative analysis of four different mixtures of three proteins (Ovalbumin, Horseradish Peroxidase and Rabbit Albumin) spiked into the complex protein background of the DGPF Proteome Marker.     

mzResults: an interactive viewer for interrogation and distribution of proteomics results

The growing use of mass spectrometry in the context of biomedical research has been accompanied by an increased demand for distribution of results in a format that facilitates rapid and efficient validation of claims by reviewers and other interested parties. However, the continued evolution of mass spectrometry hardware, sample preparation methods, and peptide identification algorithms complicates standardization and creates hurdles related to compliance with journal submission requirements. Moreover, the recently announced Philadelphia Guidelines (1, 2) suggest that authors provide native mass spectrometry data files in support of their peer-reviewed research articles. These trends highlight the need for data viewers and other tools that work independently of manufacturers' proprietary data systems and seamlessly connect proteomics results with original data files to support user-driven data validation and review. Based upon our recently described API(1)-based framework for mass spectrometry data analysis (3, 4), we created an interactive viewer (mzResults) that is built on established database standards and enables efficient distribution and interrogation of results associated with proteomics experiments, while also providing a convenient mechanism for authors to comply with data submission standards as described in the Philadelphia Guidelines. In addition, the architecture of mzResults supports in-depth queries of the native mass spectrometry files through our multiplierz software environment. We use phosphoproteomics data to illustrate the features and capabilities of mzResults.     

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.     

From proteomics data representation to public data flow: a report on the HUPO-PSI workshop September 2011, Geneva, Switzerland

The plenary session of the Proteomics Standards Initiative (PSI) of the Human Proteome Organization at the Tenth annual HUPO World Congress updated the delegates on the ongoing activities of this group. The Molecular Interactions workgroup described the success of the PSICQUIC web service, which enables users to access multiple interaction resources with a single query. One user instance is the IMEx Consortium, which uses the service to enable users to access a non-redundant set of protein-protein interaction records. The mass spectrometry data formats, mzML for mass spectrometer output files and mzIdentML for the output of search engines, are now successfully established with increasing numbers of implementations. A format for the output of quantitative proteomics data, mzQuantML, and also TraML, for SRM/MRM transition lists, are both currently nearing completion. The corresponding MIAPE documents are being updated in line with advances in the field, as is the shared controlled vocabulary PSI-MS. In addition, the mzTab format was introduced, as a simpler way to report MS proteomics and metabolomics results. Finally, the ProteomeXchange Consortium, which will supply a single entry point for the submission of MS proteomics data to multiple data resources including PRIDE and PeptideAtlas, is currently being established.     

Computational and informatics strategies for identification of specific protein interaction partners in affinity purification mass spectrometry experiments

Analysis of protein interaction networks and protein complexes using affinity purification and mass spectrometry (AP/MS) is among most commonly used and successful applications of proteomics technologies. One of the foremost challenges of AP/MS data is a large number of false-positive protein interactions present in unfiltered data sets. Here we review computational and informatics strategies for detecting specific protein interaction partners in AP/MS experiments, with a focus on incomplete (as opposite to genome wide) interactome mapping studies. These strategies range from standard statistical approaches, to empirical scoring schemes optimized for a particular type of data, to advanced computational frameworks. The common denominator among these methods is the use of label-free quantitative information such as spectral counts or integrated peptide intensities that can be extracted from AP/MS data. We also discuss related issues such as combining multiple biological or technical replicates, and dealing with data generated using different tagging strategies. Computational approaches for benchmarking of scoring methods are discussed, and the need for generation of reference AP/MS data sets is highlighted. Finally, we discuss the possibility of more extended modeling of experimental AP/MS data, including integration with external information such as protein interaction predictions based on functional genomics data.     

Precursor-ion mass re-estimation improves peptide identification on hybrid instruments

Mass spectrometry-based proteomics experiments have become an important tool for studying biological systems. Identifying the proteins in complex mixtures by assigning peptide fragmentation spectra to peptide sequences is an important step in the proteomics process. The 1-2 ppm mass-accuracy of hybrid instruments, like the LTQ-FT, has been cited as a key factor in their ability to identify a larger number of peptides with greater confidence than competing instruments. However, in replicate experiments of an 18-protein mixture, we note parent masses deviate 171 ppm, on average, for ion-trap data directed identifications and 8 ppm, on average, for preview Fourier transform (FT) data directed identifications. These deviations are neither caused by poor calibration nor by excessive ion-loading and are most likely due to errors in parent mass estimation. To improve these deviations, we introduce msPrefix, a program to re-estimate a peptide's parent mass from an associated high-accuracy full-scan survey spectrum. In 18-protein mixture experiments, msPrefix parent mass estimates deviate only 1 ppm, on average, from the identified peptides. In a cell lysate experiment searched with a tolerance of 50 ppm, 2295 peptides were confidently identified using native data and 4560 using msPrefixed data. Likewise, in a plasma experiment searched with a tolerance of 50 ppm, 326 peptides were identified using native data and 1216 using msPrefixed data. msPrefix is also able to determine which MS/MS spectra were possibly derived from multiple precursor ions. In complex mixture experiments, we demonstrate that more than 50% of triggered MS/MS may have had multiple precursor ions and note that spectra with multiple candidate ions are less likely to result in an identification using TANDEM. These results demonstrate integration of msPrefix into traditional shotgun proteomics workflows significantly improves identification results.     

KYSS: Mass spectrometry data quality assessment for protein analysis and large-scale proteomics

We introduce the computer tool “Know Your Samples” (KYSS) for assessment and visualisation of large scale proteomics datasets, obtained by mass spectrometry (MS) experiments. KYSS facilitates the evaluation of sample preparation protocols, LC peptide separation, and MS and MS/MS performance by monitoring the number of missed cleavages, precursor ion charge states, number of protein identifications and peptide mass error in experiments. KYSS generates several different protein profiles based on protein abundances, and allows for comparative analysis of multiple experiments. KYSS was adapted for blood plasma proteomics and provides concentrations of identified plasma proteins. We demonstrate the utility of the KYSS tool for MS based proteome analysis of blood plasma and for assessment of hydrogel particles for depletion of abundant proteins in plasma. The KYSS software is open source and is freely available at http://kyssproject.github.io/.     

The mzTab Data Exchange Format: Communicating Mass-spectrometry-based Proteomics and Metabolomics Experimental Results to a Wider Audience

The HUPO Proteomics Standards Initiative has developed several standardized data formats to facilitate data sharing in mass spectrometry (MS)-based proteomics. These allow researchers to report their complete results in a unified way. However, at present, there is no format to describe the final qualitative and quantitative results for proteomics and metabolomics experiments in a simple tabular format. Many downstream analysis use cases are only concerned with the final results of an experiment and require an easily accessible format, compatible with tools such as Microsoft Excel or R.We developed the mzTab file format for MS-based proteomics and metabolomics results to meet this need. mzTab is intended as a lightweight supplement to the existing standard XML-based file formats (mzML, mzIdentML, mzQuantML), providing a comprehensive summary, similar in concept to the supplemental material of a scientific publication. mzTab files can contain protein, peptide, and small molecule identifications together with experimental metadata and basic quantitative information. The format is not intended to store the complete experimental evidence but provides mechanisms to report results at different levels of detail. These range from a simple summary of the final results to a representation of the results including the experimental design. This format is ideally suited to make MS-based proteomics and metabolomics results available to a wider biological community outside the field of MS. Several software tools for proteomics and metabolomics have already adapted the format as an output format. The comprehensive mzTab specification document and extensive additional documentation can be found online.Mass spectrometry (MS)¹ has become a major analysis tool in the life sciences (1). It is currently used in different modes for several “omics” approaches, proteomics and metabolomics being the most prominent. In both disciplines, one major burden in the exchange, communication, and large-scale (re-) analysis of MS-based data is the significant number of software pipelines and, consequently, heterogeneous file formats used to process, analyze, and store these experimental results, including both identification and quantification data. Publication guidelines from scientific journals and funding agencies'' requirements for public data availability have led to an increasing amount of MS-based proteomics and metabolomics data being submitted to public repositories, such as those of the ProteomeXchange consortium (2) or, in the case of metabolomics, the resources from the nascent COSMOS (Coordination of Standards in Metabolomics) initiative (3).In the past few years, the Human Proteome Organization Proteomics Standards Initiative (PSI) has developed several vendor-neutral standard data formats to overcome the representation heterogeneity. The Human Proteome Organization PSI promotes the usage of three XML file formats to fully report the data coming from MS-based proteomics experiments (including related metadata): mzML (4) to store the “primary” MS data (the spectra and chromatograms), mzIdentML (5) to report peptide identifications and inferred protein identifications, and mzQuantML (6) to store quantitative information associated with these results.Even though the existence of the PSI standard data formats represents a huge step forward, these formats cannot address all use cases related to proteomics and metabolomics data exchange and sharing equally well. During the development of mzML, mzIdentML, and mzQuantML, the main focus lay on providing an exact and comprehensive representation of the gathered results. All three formats can be used within analysis pipelines and as interchange formats between independent analysis tools. It is thus vital that these formats be capable of storing the full data and analysis that led to the results. Therefore, all three formats result in relatively complex schemas, a clear necessity for adequate representation of the complexity found in MS-based data.An inevitable drawback of this approach is that data consumers can find it difficult to quickly retrieve the required information. Several application programming interfaces (APIs) have been developed to simplify software development based on these formats (7–9), but profound proteomics and bioinformatics knowledge still is required in order to use them efficiently and take full advantage of the comprehensive information contained.The new file format presented here, mzTab, aims to describe the qualitative and quantitative results for MS-based proteomics and metabolomics experiments in a consistent, simpler tabular format, abstracting from the mass spectrometry details. The format contains identifications, basic quantitative information, and related metadata. With mzTab''s flexible design, it is possible to report results at different levels ranging from a simple summary or subset of the complete information (e.g. the final results) to fairly comprehensive representation of the results including the experimental design. Many downstream analysis use cases are only concerned with the final results of an experiment in an easily accessible format that is compatible with tools such as Microsoft Excel® or R (10) and can easily be adapted by existing bioinformatics tools. Therefore, mzTab is ideally suited to make MS proteomics and metabolomics results available to the wider biological community, beyond the field of MS.mzTab follows a similar philosophy as the other tab-delimited format recently developed by the PSI to represent molecular interaction data, MITAB (11). MITAB is a simpler tab-delimited format, whereas PSI-MI XML (12), the more detailed XML-based format, holds the complete evidence. The microarray community makes wide use of the format MAGE-TAB (13), another example of such a solution that can cover the main use cases and, for the sake of simplicity, is often preferred to the XML standard format MAGE-ML (14). Additionally, in MS-based proteomics, several software packages, such as Mascot (15), OMSSA (16), MaxQuant (17), OpenMS/TOPP (18, 19), and SpectraST (20), also support the export of their results in a tab-delimited format next to a more complete and complex default format. These simple formats do not contain the complete information but are nevertheless sufficient for the most frequent use cases.mzTab has been designed with the same purpose in mind. It can be used alone or in conjunction with mzML (or other related MS data formats such as mzXML (21) or text-based peak list formats such as MGF), mzIdentML, and/or mzQuantML. Several highly successful concepts taken from the development process of mzIdentML and mzQuantML were adapted to the text-based nature of mzTab.In addition, there is a trend to perform more integrated experimental workflows involving both proteomics and metabolomics data. Thus, we developed a standard format that can represent both types of information in a single file.     

Prediction of missed cleavage sites in tryptic peptides aids protein identification in proteomics

Protein identification via peptide mass fingerprinting (PMF) remains a key component of high-throughput proteomics experiments in post-genomic science. Candidate protein identifications are made using bioinformatic tools from peptide peak lists obtained via mass spectrometry (MS). These algorithms rely on several search parameters, including the number of potential uncut peptide bonds matching the primary specificity of the hydrolytic enzyme used in the experiment. Typically, up to one of these "missed cleavages" are considered by the bioinformatics search tools, usually after digestion of the in silico proteome by trypsin. Using two distinct, nonredundant datasets of peptides identified via PMF and tandem MS, a simple predictive method based on information theory is presented which is able to identify experimentally defined missed cleavages with up to 90% accuracy from amino acid sequence alone. Using this simple protocol, we are able to "mask" candidate protein databases so that confident missed cleavage sites need not be considered for in silico digestion. We show that that this leads to an improvement in database searching, with two different search engines, using the PMF dataset as a test set. In addition, the improved approach is also demonstrated on an independent PMF data set of known proteins that also has corresponding high-quality tandem MS data, validating the protein identifications. This approach has wider applicability for proteomics database searching, and the program for predicting missed cleavages and masking Fasta-formatted protein sequence databases has been made available via http:// ispider.smith.man.ac uk/MissedCleave.     

A phase synchronization clustering algorithm for identifying interesting groups of genes from cell cycle expression data

Background

Quantitative proteomics holds great promise for identifying proteins that are differentially abundant between populations representing different physiological or disease states. A range of computational tools is now available for both isotopically labeled and label-free liquid chromatography mass spectrometry (LC-MS) based quantitative proteomics. However, they are generally not comparable to each other in terms of functionality, user interfaces, information input/output, and do not readily facilitate appropriate statistical data analysis. These limitations, along with the array of choices, present a daunting prospect for biologists, and other researchers not trained in bioinformatics, who wish to use LC-MS-based quantitative proteomics.

Results

We have developed Corra, a computational framework and tools for discovery-based LC-MS proteomics. Corra extends and adapts existing algorithms used for LC-MS-based proteomics, and statistical algorithms, originally developed for microarray data analyses, appropriate for LC-MS data analysis. Corra also adapts software engineering technologies (e.g. Google Web Toolkit, distributed processing) so that computationally intense data processing and statistical analyses can run on a remote server, while the user controls and manages the process from their own computer via a simple web interface. Corra also allows the user to output significantly differentially abundant LC-MS-detected peptide features in a form compatible with subsequent sequence identification via tandem mass spectrometry (MS/MS). We present two case studies to illustrate the application of Corra to commonly performed LC-MS-based biological workflows: a pilot biomarker discovery study of glycoproteins isolated from human plasma samples relevant to type 2 diabetes, and a study in yeast to identify in vivo targets of the protein kinase Ark1 via phosphopeptide profiling.

Conclusion

The Corra computational framework leverages computational innovation to enable biologists or other researchers to process, analyze and visualize LC-MS data with what would otherwise be a complex and not user-friendly suite of tools. Corra enables appropriate statistical analyses, with controlled false-discovery rates, ultimately to inform subsequent targeted identification of differentially abundant peptides by MS/MS. For the user not trained in bioinformatics, Corra represents a complete, customizable, free and open source computational platform enabling LC-MS-based proteomic workflows, and as such, addresses an unmet need in the LC-MS proteomics field.     

De novo peptide sequencing via tandem mass spectrometry.

Peptide sequencing via tandem mass spectrometry (MS/MS) is one of the most powerful tools in proteomics for identifying proteins. Because complete genome sequences are accumulating rapidly, the recent trend in interpretation of MS/MS spectra has been database search. However, de novo MS/MS spectral interpretation remains an open problem typically involving manual interpretation by expert mass spectrometrists. We have developed a new algorithm, SHERENGA, for de novo interpretation that automatically learns fragment ion types and intensity thresholds from a collection of test spectra generated from any type of mass spectrometer. The test data are used to construct optimal path scoring in the graph representations of MS/MS spectra. A ranked list of high scoring paths corresponds to potential peptide sequences. SHERENGA is most useful for interpreting sequences of peptides resulting from unknown proteins and for validating the results of database search algorithms in fully automated, high-throughput peptide sequencing.     

Statistics for proteomics: a review of tools for analyzing experimental data

Most proteomics experiments make use of 'high throughput' technologies such as 2-DE, MS or protein arrays to measure simultaneously the expression levels of thousands of proteins. Such experiments yield large, high-dimensional data sets which usually reflect not only the biological but also technical and experimental factors. Statistical tools are essential for evaluating these data and preventing false conclusions. Here, an overview is given of some typical statistical tools for proteomics experiments. In particular, we present methods for data preprocessing (e.g. calibration, missing values estimation and outlier detection), comparison of protein expression in different groups (e.g. detection of differentially expressed proteins or classification of new observations) as well as the detection of dependencies between proteins (e.g. protein clusters or networks). We also discuss questions of sample size planning for some of these methods.     

Publishing large proteome datasets: scientific policy meets emerging technologies

Currently, there are various approaches to proteomic analyses based on either 2D gel or HPLC separation platforms, generating data of different formats, structures and types. Identification of these separated proteins or peptide fragments is typically achieved by mass spectrometry (MS) measurements that use either accurate mass measurements or fragmentation (MS–MS) information. Integrating the information generated from these different platforms is essential if proteomics is to succeed. A further challenge lies in generating standards that can accept the hundreds-of-thousands of mass spectra produced per analysis based on threshold or probability measurements. Finally, peer review and electronic publication processes will be crucial to the dissemination and use of proteomic information. Merging the policy requirements of data-intensive research with information technology will enable scientists to gain real value from global proteomics information.     

On the proper use of mass accuracy in proteomics

Mass measurement is the main outcome of mass spectrometry-based proteomics yet the potential of recent advances in accurate mass measurements remains largely unexploited. There is not even a clear definition of mass accuracy in the proteomics literature, and we identify at least three uses of this term: anecdotal mass accuracy, statistical mass accuracy, and the maximum mass deviation (MMD) allowed in a database search. We suggest using the second of these terms as the generic one. To make the best use of the mass precision offered by modern instruments we propose a series of simple steps involving recalibration of the data on "internal standards" contained in every proteomics data set. Each data set should be accompanied by a plot of mass errors from which the appropriate MMD can be chosen. More advanced uses of high mass accuracy include an MMD that depends on the signal abundance of each peptide. Adapting search engines to high mass accuracy in the MS/MS data is also a high priority. Proper use of high mass accuracy data can make MS-based proteomics one of the most "digital" and accurate post-genomics disciplines.     

jmzReader: A Java parser library to process and visualize multiple text and XML-based mass spectrometry data formats

We here present the jmzReader library: a collection of Java application programming interfaces (APIs) to parse the most commonly used peak list and XML-based mass spectrometry (MS) data formats: DTA, MS2, MGF, PKL, mzXML, mzData, and mzML (based on the already existing API jmzML). The library is optimized to be used in conjunction with mzIdentML, the recently released standard data format for reporting protein and peptide identifications, developed by the HUPO proteomics standards initiative (PSI). mzIdentML files do not contain spectra data but contain references to different kinds of external MS data files. As a key functionality, all parsers implement a common interface that supports the various methods used by mzIdentML to reference external spectra. Thus, when developing software for mzIdentML, programmers no longer have to support multiple MS data file formats but only this one interface. The library (which includes a viewer) is open source and, together with detailed documentation, can be downloaded from http://code.google.com/p/jmzreader/.