Network-assisted protein identification and data interpretation in shotgun proteomics.
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ABSTRACT:
SUBMITTER: Li J
PROVIDER: S-EPMC2736651 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:MotivationTandem mass spectrometry (MS/MS) offers fast and reliable characterization of complex protein mixtures, but suffers from low sensitivity in protein identification. In a typical shotgun proteomics experiment, it is assumed that all proteins are equally likely to be present. However, there is often other information available, e.g. the probability of a protein's presence is likely to correlate with its mRNA concentration.ResultsWe develop a Bayesian score that estimates the posterior probability of a protein's presence in the sample given its identification in an MS/MS experiment and its mRNA concentration measured under similar experimental conditions. Our method, MSpresso, substantially increases the number of proteins identified in an MS/MS experiment at the same error rate, e.g. in yeast, MSpresso increases the number of proteins identified by approximately 40%. We apply MSpresso to data from different MS/MS instruments, experimental conditions and organisms (Escherichia coli, human), and predict 19-63% more proteins across the different datasets. MSpresso demonstrates that incorporating prior knowledge of protein presence into shotgun proteomics experiments can substantially improve protein identification scores.Availability and implementationSoftware is available upon request from the authors. Mass spectrometry datasets and supplementary information are available from (http://www.marcottelab.org/MSpresso/).
| S-EPMC2682515 | biostudies-literature
Project description:Mass spectrometry (MS)-based shotgun proteomics is an effective technology for global proteome profiling. The ultimate goal is to assign tandem MS spectra to peptides and subsequently infer proteins and their abundance. In addition to database searching and protein assembly algorithms, computational approaches have been developed to integrate genomic, transcriptomic, and interactome information to improve peptide and protein identification. Earlier efforts focus primarily on making databases more comprehensive using publicly available genomic and transcriptomic data. More recently, with the increasing affordability of the Next Generation Sequencing (NGS) technologies, personalized protein databases derived from sample-specific genomic and transcriptomic data have emerged as an attractive strategy. In addition, incorporating interactome data not only improves protein identification but also puts identified proteins into their functional context and thus facilitates data interpretation. In this paper, we survey the major integrative bioinformatics approaches that have been developed during the past decade and discuss their merits and demerits.
| S-EPMC4059263 | biostudies-literature
Project description:BackgroundThe cornea is a specialized transparent connective tissue responsible for the majority of light refraction and image focus for the retina. There are three main layers of the cornea: the epithelium that is exposed and acts as a protective barrier for the eye, the center stroma consisting of parallel collagen fibrils that refract light, and the endothelium that is responsible for hydration of the cornea from the aqueous humor. Normal cornea is an immunologically privileged tissue devoid of blood vessels, but injury can produce a loss of these conditions causing invasion of other processes that degrade the homeostatic properties resulting in a decrease in the amount of light refracted onto the retina. Determining a measure and drift of phenotypic cornea state from normal to an injured or diseased state requires knowledge of the existing protein signature within the tissue. In the study of corneal proteins, proteomics procedures have typically involved the pulverization of the entire cornea prior to analysis. Separation of the epithelium and endothelium from the core stroma and performing separate shotgun proteomics using liquid chromatography/mass spectrometry results in identification of many more proteins than previously employed methods using complete pulverized cornea.ResultsRabbit corneas were purchased, the epithelium and endothelium regions were removed, proteins processed and separately analyzed using liquid chromatography/mass spectrometry. Proteins identified from separate layers were compared against results from complete corneal samples. Protein digests were separated using a six hour liquid chromatographic gradient and ion-trap mass spectrometry used for detection of eluted peptide fractions. The SEQUEST database search results were filtered to allow only proteins with match probabilities of equal or better than 10-3 and peptides with a probability of 10-2 or less with at least two unique peptides isolated within the run along with default Xcorr values. These parameters resulted in the identification of over 350 proteins, including over 225 new proteins not previously detected in the cornea by mass spectrometry. In addition, corneal layer separation resulted in identification of nearly every protein that was identified in the complete cornea assay. The epithelium and endothelium each revealed many unique proteomes specific to each layer. In the endothelium, the protein olfactomedin-like 3 was identified for the first time in the cornea by this analysis. Olfactomedin-3 is a neuronal expressed protein also known as optimedin that stimulates formation of cell adherent and cell-cell tight junctions and its expression modulates cytoskeleton organization and cell migration. However, the function of this protein in rabbit corneal endothelium is currently unknown.ConclusionThis manuscript presents a description of a more comprehensive proteomic profile for mammalian cornea compared to past methods. The use of simple dissection procedures of the tissue and the application of long chromatographic gradients, many more proteins can be identified.
| S-EPMC2711935 | biostudies-literature
Project description:Shotgun proteomics coupled with database search software allows the identification of a large number of peptides in a single experiment. However, some existing search algorithms, such as SEQUEST, use score functions that are designed primarily to identify the best peptide for a given spectrum. Consequently, when comparing identifications across spectra, the SEQUEST score function Xcorr fails to discriminate accurately between correct and incorrect peptide identifications. Several machine learning methods have been proposed to address the resulting classification task of distinguishing between correct and incorrect peptide-spectrum matches (PSMs). A recent example is Percolator, which uses semisupervised learning and a decoy database search strategy to learn to distinguish between correct and incorrect PSMs identified by a database search algorithm. The current work describes three improvements to Percolator. (1) Percolator's heuristic optimization is replaced with a clear objective function, with intuitive reasons behind its choice. (2) Tractable nonlinear models are used instead of linear models, leading to improved accuracy over the original Percolator. (3) A method, Q-ranker, for directly optimizing the number of identified spectra at a specified q value is proposed, which achieves further gains.
| S-EPMC2710313 | biostudies-other
Project description:A new result report for Mascot search results is described. A greedy set cover algorithm is used to create a minimal set of proteins, which is then grouped into families on the basis of shared peptide matches. Protein families with multiple members are represented by dendrograms, generated by hierarchical clustering using the score of the nonshared peptide matches as a distance metric. The peptide matches to the proteins in a family can be compared side by side to assess the experimental evidence for each protein. If the evidence for a particular family member is considered inadequate, the dendrogram can be cut to reduce the number of distinct family members.
| S-EPMC3108832 | biostudies-literature
Project description:Alternative splicing is a pervasive process in eukaryotic organisms. More than 90% of human genes have alternatively spliced products, and aberrant splicing has been shown to be associated with many diseases. Current methods employed in the detection of splice variants include prediction by clustering of expressed sequence tags, exon microarray, and mRNA sequencing, all methods focusing on RNA-level information. There is a lack of tools for analyzing splice variants at the protein level. Here, we present SpliceVista, a tool for splice variant identification and visualization based on mass spectrometry proteomics data. SpliceVista retrieves gene structure and translated sequences from alternative splicing databases and maps MS-identified peptides to splice variants. The visualization module plots the exon composition of each splice variant and aligns identified peptides with transcript positions. If quantitative mass spectrometry data are used, SpliceVista plots the quantitative patterns for each peptide and provides users with the option to cluster peptides based on their quantitative patterns. SpliceVista can identify splice-variant-specific peptides, providing the possibility for variant-specific analysis. The tool was tested on two experimental datasets (PXD000065 and PXD000134). In A431 cells treated with gefitinib, 2983 splice-variant-specific peptides corresponding to 939 splice variants were identified. Through comparison of splice-variant-centric, protein-centric, and gene-centric quantification, several genes (e.g. EIF4H) were found to have differentially regulated splice variants after gefitinib treatment. The same discrepancy between protein-centric and splice-centric quantification was detected in the other dataset, in which induced pluripotent stem cells were compared with parental fibroblast and human embryotic stem cells. In addition, SpliceVista can be used to visualize novel splice variants inferred from peptide-level evidence. In summary, SpliceVista enables visualization, detection, and differential quantification of protein splice variants that are often missed in current proteomics pipelines.
| S-EPMC4047474 | biostudies-literature
Project description:A major scientific challenge at the present time for cancer research is the determination of the underlying biological basis for cancer development. It is further complicated by the heterogeneity of cancer's origin. Understanding the molecular basis of cancer requires studying the dynamic and spatial interactions among proteins in cells, signaling events among cancer cells, and interactions between the cancer cells and the tumor microenvironment. Recently, it has been proposed that large-scale protein expression analysis of cancer cell proteomes promises to be valuable for investigating mechanisms of cancer transformation. Advances in mass spectrometry technologies and bioinformatics tools provide a tremendous opportunity to qualitatively and quantitatively interrogate dynamic protein-protein interactions and differential regulation of cellular signaling pathways associated with tumor development. In this review, progress in shotgun proteomics technologies for examining the molecular basis of cancer development will be presented and discussed.
| S-EPMC2352161 | biostudies-other
Project description:Label-free quantification of shotgun LC-MS/MS data is the prevailing approach in quantitative proteomics but remains computationally nontrivial. The central data analysis step is the detection of peptide-specific signal patterns, called features. Peptide quantification is facilitated by associating signal intensities in features with peptide sequences derived from MS2 spectra; however, missing values due to imperfect feature detection are a common problem. A feature detection approach that directly targets identified peptides (minimizing missing values) but also offers robustness against false-positive features (by assigning meaningful confidence scores) would thus be highly desirable. We developed a new feature detection algorithm within the OpenMS software framework, leveraging ideas and algorithms from the OpenSWATH toolset for DIA/SRM data analysis. Our software, FeatureFinderIdentification ("FFId"), implements a targeted approach to feature detection based on information from identified peptides. This information is encoded in an MS1 assay library, based on which ion chromatogram extraction and detection of feature candidates are carried out. Significantly, when analyzing data from experiments comprising multiple samples, our approach distinguishes between "internal" and "external" (inferred) peptide identifications (IDs) for each sample. On the basis of internal IDs, two sets of positive (true) and negative (decoy) feature candidates are defined. A support vector machine (SVM) classifier is then trained to discriminate between the sets and is subsequently applied to the "uncertain" feature candidates from external IDs, facilitating selection and confidence scoring of the best feature candidate for each peptide. This approach also enables our algorithm to estimate the false discovery rate (FDR) of the feature selection step. We validated FFId based on a public benchmark data set, comprising a yeast cell lysate spiked with protein standards that provide a known ground-truth. The algorithm reached almost complete (>99%) quantification coverage for the full set of peptides identified at 1% FDR (PSM level). Compared with other software solutions for label-free quantification, this is an outstanding result, which was achieved at competitive quantification accuracy and reproducibility across replicates. The FDR for the feature selection was estimated at a low 1.5% on average per sample (3% for features inferred from external peptide IDs). The FFId software is open-source and freely available as part of OpenMS ( www.openms.org ).
| S-EPMC5547443 | biostudies-literature
Project description:Protein turnover is a well-controlled process in which polypeptides are constantly being degraded and subsequently replaced with newly synthesized copies. Extraction of composite spectral envelopes from complex LC/MS shotgun proteomics data can be a challenging task, due to the inherent complexity of biological samples. With partial metabolic labeling experiments this complexity increases as a result of the emergence of additional isotopic peaks. Automated spectral extraction and subsequent protein turnover calculations enable the analysis of gigabytes of data within minutes, a prerequisite for systems biology high throughput studies. Here we present a fully automated method for protein turnover calculations from shotgun proteomics data. The approach enables the analysis of complex shotgun LC/MS 15N partial metabolic labeling experiments. Spectral envelopes of 1419 peptides can be extracted within an hour. The method quantifies turnover by calculating the Relative Isotope Abundance (RIA), which is defined as the ratio between the intensity sum of all heavy (15N) to the intensity sum of all light (14N) and heavy peaks. To facilitate this process, we have developed a computer program based on our method, which is freely available to download at http://promex.pph.univie.ac.at/protover.
| S-EPMC3988089 | biostudies-literature
Project description:This data article associated with the manuscript "A high glucose levels is associated with decreased aspirin-mediated acetylation of platelet cyclooxygenase (COX)-1 at serine 529: a pilot study" (Finamore et al., 2018) refers to the shotgun proteomics approach carried out on platelet protein extracts from diabetic patients and healthy controls. Platelet proteins were in vitro incubated with 500 µM aspirin for 30 min at 37 °C to enhance the acetylation process. After protein digestion with trypsin, DDA data were acquired on a Thermo QExactive plus using 3 technical replicate injections per sample. Here, we were able to elucidate the preferential sites of aspirin-induced acetylation on a significant fraction of the platelet proteome and to quantify the impact of diabetes on the effect of aspirin on several platelet proteins. Data are available via ProteomeXchange with identifier PXD011582.
| S-EPMC6288982 | biostudies-literature