NITPICK: peak identification for mass spectrometry data

Background  

The reliable extraction of features from mass spectra is a fundamental step in the automated analysis of proteomic mass spectrometry (MS) experiments.     

Calibration of mass spectrometric peptide mass fingerprint data without specific external or internal calibrants

Background  

Peptide Mass Fingerprinting (PMF) is a widely used mass spectrometry (MS) method of analysis of proteins and peptides. It relies on the comparison between experimentally determined and theoretical mass spectra. The PMF process requires calibration, usually performed with external or internal calibrants of known molecular masses.     

Improved machine learning method for analysis of gas phase chemistry of peptides

Background  

Accurate peptide identification is important to high-throughput proteomics analyses that use mass spectrometry. Search programs compare fragmentation spectra (MS/MS) of peptides from complex digests with theoretically derived spectra from a database of protein sequences. Improved discrimination is achieved with theoretical spectra that are based on simulating gas phase chemistry of the peptides, but the limited understanding of those processes affects the accuracy of predictions from theoretical spectra.     

HybGFS: a hybrid method for genome-fingerprint scanning

Background  

Protein identification based on mass spectrometry (MS) has previously been performed using peptide mass fingerprinting (PMF) or tandem MS (MS/MS) database searching. However, these methods cannot identify proteins that are not already listed in existing databases. Moreover, the alternative approach of de novo sequencing requires costly equipment and the interpretation of complex MS/MS spectra. Thus, there is a need for novel high-throughput protein-identification methods that are independent of existing predefined protein databases.     

Optimization of filtering criterion for SEQUEST database searching to improve proteome coverage in shotgun proteomics

Background  

In proteomic analysis, MS/MS spectra acquired by mass spectrometer are assigned to peptides by database searching algorithms such as SEQUEST. The assignations of peptides to MS/MS spectra by SEQUEST searching algorithm are defined by several scores including Xcorr, ΔCn, Sp, Rsp, matched ion count and so on. Filtering criterion using several above scores is used to isolate correct identifications from random assignments. However, the filtering criterion was not favorably optimized up to now.     

A compatible exon-exon junction database for the identification of exon skipping events using tandem mass spectrum data

Background  

Alternative splicing is an important gene regulation mechanism. It is estimated that about 74% of multi-exon human genes have alternative splicing. High throughput tandem (MS/MS) mass spectrometry provides valuable information for rapidly identifying potentially novel alternatively-spliced protein products from experimental datasets. However, the ability to identify alternative splicing events through tandem mass spectrometry depends on the database against which the spectra are searched.     

A machine learning approach to explore the spectra intensity pattern of peptides using tandem mass spectrometry data

Background  

A better understanding of the mechanisms involved in gas-phase fragmentation of peptides is essential for the development of more reliable algorithms for high-throughput protein identification using mass spectrometry (MS). Current methodologies depend predominantly on the use of derived m/z values of fragment ions, and, the knowledge provided by the intensity information present in MS/MS spectra has not been fully exploited. Indeed spectrum intensity information is very rarely utilized in the algorithms currently in use for high-throughput protein identification.     

Tandem mass spectrometry data quality assessment by self-convolution

Background  

Many algorithms have been developed for deciphering the tandem mass spectrometry (MS) data sets. They can be essentially clustered into two classes. The first performs searches on theoretical mass spectrum database, while the second based itself on de novo sequencing from raw mass spectrometry data. It was noted that the quality of mass spectra affects significantly the protein identification processes in both instances. This prompted the authors to explore ways to measure the quality of MS data sets before subjecting them to the protein identification algorithms, thus allowing for more meaningful searches and increased confidence level of proteins identified.     

In silico fragmentation for computer assisted identification of metabolite mass spectra

Background  

Mass spectrometry has become the analytical method of choice in metabolomics research. The identification of unknown compounds is the main bottleneck. In addition to the precursor mass, tandem MS spectra carry informative fragment peaks, but the coverage of spectral libraries of measured reference compounds are far from covering the complete chemical space. Compound libraries such as PubChem or KEGG describe a larger number of compounds, which can be used to compare their in silico fragmentation with spectra of unknown metabolites.     

multiplierz: an extensible API based desktop environment for proteomics data analysis

Background  

Efficient analysis of results from mass spectrometry-based proteomics experiments requires access to disparate data types, including native mass spectrometry files, output from algorithms that assign peptide sequence to MS/MS spectra, and annotation for proteins and pathways from various database sources. Moreover, proteomics technologies and experimental methods are not yet standardized; hence a high degree of flexibility is necessary for efficient support of high- and low-throughput data analytic tasks. Development of a desktop environment that is sufficiently robust for deployment in data analytic pipelines, and simultaneously supports customization for programmers and non-programmers alike, has proven to be a significant challenge.     

On the analysis of glycomics mass spectrometry data via the regularized area under the ROC curve

Background  

Novel molecular and statistical methods are in rising demand for disease diagnosis and prognosis with the help of recent advanced biotechnology. High-resolution mass spectrometry (MS) is one of those biotechnologies that are highly promising to improve health outcome. Previous literatures have identified some proteomics biomarkers that can distinguish healthy patients from cancer patients using MS data. In this paper, an MS study is demonstrated which uses glycomics to identify ovarian cancer. Glycomics is the study of glycans and glycoproteins. The glycans on the proteins may deviate between a cancer cell and a normal cell and may be visible in the blood. High-resolution MS has been applied to measure relative abundances of potential glycan biomarkers in human serum. Multiple potential glycan biomarkers are measured in MS spectra. With the objection of maximizing the empirical area under the ROC curve (AUC), an analysis method was considered which combines potential glycan biomarkers for the diagnosis of cancer.     

2DB: a Proteomics database for storage,analysis, presentation,and retrieval of information from mass spectrometric experiments

Background  

The amount of information stemming from proteomics experiments involving (multi dimensional) separation techniques, mass spectrometric analysis, and computational analysis is ever-increasing. Data from such an experimental workflow needs to be captured, related and analyzed. Biological experiments within this scope produce heterogenic data ranging from pictures of one or two-dimensional protein maps and spectra recorded by tandem mass spectrometry to text-based identifications made by algorithms which analyze these spectra. Additionally, peptide and corresponding protein information needs to be displayed.     

A mass accuracy sensitive probability based scoring algorithm for database searching of tandem mass spectrometry data

Background  

Liquid chromatography coupled with tandem mass spectrometry (LC-MS/MS) has become one of the most used tools in mass spectrometry based proteomics. Various algorithms have since been developed to automate the process for modern high-throughput LC-MS/MS experiments.     

A novel approach to denoising ion trap tandem mass spectra

Background  

Mass spectrometers can produce a large number of tandem mass spectra. They are unfortunately noise-contaminated. Noises can affect the quality of tandem mass spectra and thus increase the false positives and false negatives in the peptide identification. Therefore, it is appealing to develop an approach to denoising tandem mass spectra.     

Detection of co-eluted peptides using database search methods

Background  

Current experimental techniques, especially those applying liquid chromatography mass spectrometry, have made high-throughput proteomic studies possible. The increase in throughput however also raises concerns on the accuracy of identification or quantification. Most experimental procedures select in a given MS scan only a few relatively most intense parent ions, each to be fragmented (MS²) separately, and most other minor co-eluted peptides that have similar chromatographic retention times are ignored and their information lost.     

A method for improving SELDI-TOF mass spectrometry data quality

Background  

Surface-enhanced laser desorption/ionization time-of-flight mass spectrometry (SELDI-TOF MS) is a powerful tool for rapidly generating high-throughput protein profiles from a large number of samples. However, the events that occur between the first and last sample run are likely to introduce technical variation in the results.     

Peptide identification based on fuzzy classification and clustering

Background

The sequence database searching has been the dominant method for peptide identification, in which a large number of peptide spectra generated from LC/MS/MS experiments are searched using a search engine against theoretical fragmentation spectra derived from a protein sequences database or a spectral library. Selecting trustworthy peptide spectrum matches (PSMs) remains a challenge.

Results

A novel scoring method named FC-Ranker is developed to assign a nonnegative weight to each target PSM based on the possibility of its being correct. Particularly, the scores of PSMs are updated by using a fuzzy SVM classification model and a fuzzy silhouette index iteratively. Trustworthy PSMs will be assigned high scores when the algorithm stops.

Conclusions

Our experimental studies show that FC-Ranker outperforms other post-database search algorithms over a variety of datasets, and it can be extended to solve a general classification problem with uncertain labels.

     

MIDAS-G: a computational platform for investigating fragmentation rules of tandem mass spectrometry in metabolomics

Introduction

Tandem mass spectrometry (MS/MS) has been widely used for identifying metabolites in many areas. However, computationally identifying metabolites from MS/MS data is challenging due to the unknown of fragmentation rules, which determine the precedence of chemical bond dissociation. Although this problem has been tackled by different ways, the lack of computational tools to flexibly represent adjacent structures of chemical bonds is still a long-term bottleneck for studying fragmentation rules.

Objectives

This study aimed to develop computational methods for investigating fragmentation rules by analyzing annotated MS/MS data.

Methods

We implemented a computational platform, MIDAS-G, for investigating fragmentation rules. MIDAS-G processes a metabolite as a simple graph and uses graph grammars to recognize specific chemical bonds and their adjacent structures. We can apply MIDAS-G to investigate fragmentation rules by adjusting bond weights in the scoring model of the metabolite identification tool and comparing metabolite identification performances.

Results

We used MIDAS-G to investigate four bond types on real annotated MS/MS data in experiments. The experimental results matched data collected from wet labs and literature. The effectiveness of MIDAS-G was confirmed.

Conclusion

We developed a computational platform for investigating fragmentation rules of tandem mass spectrometry. This platform is freely available for download.

     

Proteomic analysis of phosphoproteins sensitive to a phosphatidylinositol 3-kinase inhibitor, ZSTK474, by using SELDI-TOF MS

Background  

Phosphoproteins play important roles in a vast series of biological processes. Recent proteomic technologies offer the comprehensive analyses of phosphoproteins. Recently, we demonstrated that surface-enhanced laser desorption/ionization time of flight mass (SELDI-TOF MS) would detect phosphoproteins quantitatively, which was a new application of SELDI-TOF MS.