35,318 research outputs found
Integrative Analysis Frameworks for Improved Peptide and Protein Identifications from Tandem Mass Spectrometry Data.
Tandem mass spectrometry (MS/MS) followed by database search is the method of choice for high throughput protein identification in modern proteomic studies. Database searching methods employ spectral matching algorithms and statistical models to identify and quantify proteins in a sample. The major focus of these statistical methods is to assign probability scores to the identifications to distinguish between high confidence, reliable identifications that may be accepted (typically corresponding to a false discovery rate, FDR, of 1% or 5%) and lower confidence, spurious identifications that are rejected. These identification probabilities are determined, in general, considering only evidence from the MS/MS data. However, considering the wealth of external (orthogonal) data available for most biological systems, integrating such orthogonal information into proteomics analysis pipelines can be a promising approach to improve the sensitivity of these analysis pipelines and rescue true positive identifications that were rejected for want of sufficient evidence supporting their presence.
In this dissertation, approaches based on naive bayes rescoring, search space restriction, and a hybrid approach that combines both are described for integrating orthogonal information in proteomic analysis pipelines. These methods have been applied for integrating transcript abundance data from RNA-seq and identification frequency data from the Global Proteome Machine database, GPMDB (one of the largest repositories of proteomic experiment results), into analysis pipelines, improving the number of peptide and protein identifications from MS/MS data. Further, estimation of false discovery rates in very large proteomic datasets was also investigated. In very large datasets, usually resulting from integrating data from multiple experiments, some assumptions used in typical target-decoy based FDR estimation in smaller datasets no longer hold true, resulting in artificially inflated error rates. Alternative approaches that would allow accurate FDR estimation in these large scale datasets have been described and benchmarked.PHDBioinformaticsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/116717/1/avinashs_1.pd
Bayesian nonparametric models for peak identification in MALDI-TOF mass spectroscopy
We present a novel nonparametric Bayesian approach based on L\'{e}vy Adaptive
Regression Kernels (LARK) to model spectral data arising from MALDI-TOF (Matrix
Assisted Laser Desorption Ionization Time-of-Flight) mass spectrometry. This
model-based approach provides identification and quantification of proteins
through model parameters that are directly interpretable as the number of
proteins, mass and abundance of proteins and peak resolution, while having the
ability to adapt to unknown smoothness as in wavelet based methods. Informative
prior distributions on resolution are key to distinguishing true peaks from
background noise and resolving broad peaks into individual peaks for multiple
protein species. Posterior distributions are obtained using a reversible jump
Markov chain Monte Carlo algorithm and provide inference about the number of
peaks (proteins), their masses and abundance. We show through simulation
studies that the procedure has desirable true-positive and false-discovery
rates. Finally, we illustrate the method on five example spectra: a blank
spectrum, a spectrum with only the matrix of a low-molecular-weight substance
used to embed target proteins, a spectrum with known proteins, and a single
spectrum and average of ten spectra from an individual lung cancer patient.Comment: Published in at http://dx.doi.org/10.1214/10-AOAS450 the Annals of
Applied Statistics (http://www.imstat.org/aoas/) by the Institute of
Mathematical Statistics (http://www.imstat.org
Software Tools and Approaches for Compound Identification of LC-MS/MS Data in Metabolomics.
The annotation of small molecules remains a major challenge in untargeted mass spectrometry-based metabolomics. We here critically discuss structured elucidation approaches and software that are designed to help during the annotation of unknown compounds. Only by elucidating unknown metabolites first is it possible to biologically interpret complex systems, to map compounds to pathways and to create reliable predictive metabolic models for translational and clinical research. These strategies include the construction and quality of tandem mass spectral databases such as the coalition of MassBank repositories and investigations of MS/MS matching confidence. We present in silico fragmentation tools such as MS-FINDER, CFM-ID, MetFrag, ChemDistiller and CSI:FingerID that can annotate compounds from existing structure databases and that have been used in the CASMI (critical assessment of small molecule identification) contests. Furthermore, the use of retention time models from liquid chromatography and the utility of collision cross-section modelling from ion mobility experiments are covered. Workflows and published examples of successfully annotated unknown compounds are included
Identifying metabolites by integrating metabolome databases with mass spectrometry cheminformatics.
Novel metabolites distinct from canonical pathways can be identified through the integration of three cheminformatics tools: BinVestigate, which queries the BinBase gas chromatography-mass spectrometry (GC-MS) metabolome database to match unknowns with biological metadata across over 110,000 samples; MS-DIAL 2.0, a software tool for chromatographic deconvolution of high-resolution GC-MS or liquid chromatography-mass spectrometry (LC-MS); and MS-FINDER 2.0, a structure-elucidation program that uses a combination of 14 metabolome databases in addition to an enzyme promiscuity library. We showcase our workflow by annotating N-methyl-uridine monophosphate (UMP), lysomonogalactosyl-monopalmitin, N-methylalanine, and two propofol derivatives
Extensive mass spectrometry-based analysis of the fission yeast proteome: the Schizosaccharomyces pombe PeptideAtlas
We report a high quality and system-wide proteome catalogue covering 71% (3,542 proteins) of the predicted genes of fission yeast, Schizosaccharomyces pombe, presenting the largest protein dataset to date for this important model organism. We obtained this high proteome and peptide (11.4 peptides/protein) coverage by a combination of extensive sample fractionation, high resolution Orbitrap mass spectrometry, and combined database searching using the iProphet software as part of the Trans-Proteomics Pipeline. All raw and processed data are made accessible in the S. pombe PeptideAtlas. The identified proteins showed no biases in functional properties and allowed global estimation of protein abundances. The high coverage of the PeptideAtlas allowed correlation with transcriptomic data in a system-wide manner indicating that post-transcriptional processes control the levels of at least half of all identified proteins. Interestingly, the correlation was not equally tight for all functional categories ranging from r(s) >0.80 for proteins involved in translation to r(s) <0.45 for signal transduction proteins. Moreover, many proteins involved in DNA damage repair could not be detected in the PeptideAtlas despite their high mRNA levels, strengthening the translation-on-demand hypothesis for members of this protein class. In summary, the extensive and publicly available S. pombe PeptideAtlas together with the generated proteotypic peptide spectral library will be a useful resource for future targeted, in-depth, and quantitative proteomic studies on this microorganism
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