1,721,069 research outputs found
Comparing Peptide Spectra Matches Across Search Engines
No full textMass spectrometry is extremely efficient for sequencing small peptides generated by, for example, a trypsin digestion of a complex mixture. Current instruments have the capacity to generate 50-100 K MSMS spectra from a single run. Of these ~30-50% is typically assigned to peptide matches on a 1% FDR threshold. The remaining spectra need more research to explain. We address here whether the 30-50% matched spectra provide consensus matches when using different database-dependent search pipelines. Although the majority of the spectra peptide assignments concur across search engines, our conclusion is that database-dependent search engines still require improvements.</p
Data Imputation in Merged Isobaric Labeling-Based Relative Quantification Datasets
No full textThe data-dependent acquisition in mass spectrometry-based proteomics combined with quantitative analysis using isobaric labeling (iTRAQ and TMT) inevitably introduces missing values in proteomic experiments where a number of LC-runs are combined, especially in the growing field of shotgun clinical proteomics, where the protein profiles from the proteomics analysis of several hundred patient samples are compared and correlated to clinical traits such as a specific disease or disease treatment in order to link specific outcomes to one or more proteins. In the context of clinical research it is evident that missing values in such datasets reduce the power of the downstream statistical analysis therefore may hampers the linking of the expression of disease traits to the expression of specific proteins that may be useful for prognostic, diagnostic, or predictive purposes. In our study, we tested three data imputation approaches initially developed for microarray data for the imputation of missing values in datasets that are generated by several runs of shotgun proteomic experiments and where the data were relative protein abundances based on isobaric tags (iTRAQ and TMT). Our conclusion is that imputation methods based on k Nearest Neighbors successfully impute missing values in datasets with up to 50% missing values.</p
Protein identification by peptide mass fingerprinting
No full text Peptide mass fingerprinting is an effective way of identifying, e.g., gel-separated proteins, by matching experimentally obtained peptide mass data against large databases. However, several factors are known to influence the quality of the resulting matches, such as proteins contaminating the sample in question, modifications altering the mass of the peptides, ionization efficiency of the individual peptides, and the degree of missed cleavage sites. Here, these factors are discussed and methods for elimination of contaminants from the dataset and prediction of various modifications are introduced. Useful tips on how to specify various search parameters and how to manually evaluate the search results are also given.</p
High-throughput siRNA screening applied to the ubiquitin-proteasome system
No full textThe ubiquitin-proteasome system is the major pathway for intracellular protein degradation in eukaryotic cells. Due to the large number of genes dedicated to the ubiquitin-proteasome system, mapping degradation pathways for short lived proteins is a daunting task, in particular in mammalian cells that are not genetically tractable as, for instance, a yeast model system. Here, we describe a method relying on high-throughput cellular imaging of cells transfected with a targeted siRNA library to screen for components involved in degradation of a protein of interest. This method is a rapid and cost-effective tool which is also highly applicable for other studies on gene function.</p
Targeted proteomics as a tool for quantifying urine-based biomarkers
No full textMass spectrometry based proteomics approaches are routinely used to discover candidate biomarkers. These studies often use small number of samples to discover candidate proteins that are later validated on a large cohort of samples. Targeted proteomics has emerged as a powerful method for quantification of multiple proteins in complex biological matrix. Parallel reaction monitoring (PRM) and selected reaction monitoring (SRM) are two main methods of choice for quantifying and validating proteins across hundreds to thousands of samples. Over the years, many software tools have become available that enable the users to carry out the analysis. In this chapter, we describe selection of proteotypic peptides, sample preparation, generating a response curve, data acquisition and analysis of PRM data using Skyline software for targeted proteomics to quantify candidate markers in urine.</p
Calibration of matrix-assisted laser desorption/ionization time-of-flight peptide mass fingerprinting spectra
No full textThis chapter describes a number of aspects important for calibration of matrix-assisted laser desorption/ionization time-of-flight spectra prior to peptide mass fingerprinting searches. Both multipoint internal calibration and mass defect-based calibration is illustrated. The chapter describes how potential internal calibrants, like tryptic autodigest peptides and keratin-related peptides, can be identified and used for high-precision calibration. Furthermore, the construction of project/user-specific lists of potential calibrants is illustrated
Mass spectrometry-based proteomics analysis of whole cell and secreted extracellular vesicles of four diffuse large B-cell lymphoma cell lines
Full text linkDISCUSSION: Here we implemented an experimental and computational workflow to study the protein
expression profiles of small extracellular vesicles from in vitro cultured DLBCL cell lines. Our main
question was whether the proteomes of small extracellular vesicles could be used to segregate
the prognostic cell of origin subtypes of DLBCL. We selected 4 DLBCL cell line models, the DB and
HT cells representing germinal center b cell like (GCB) and RIVA and OCI-ly3 cells as activated b
cell like (ABC) DLBCLs. We cultured cells considering suppliers instructions to make sure our
results are comparable to others using the same models. We tried to truthfully report our
experimental conditions and sEVs purification protocol to prevent possible misinterpretation of data. To our knowledge this is the first high accuracy, global, quantitative proteomics study to
comprehensively compare the proteomes of sEVs derived from DLBCL cells. We considered the
expression of other biomolecules in sEVs. However, proteins are main effectors of encoding
genes and among the most frequently therapeutically targeted biomolecules. We decided to
take advantage of recent developments in mass spectrometry instruments147 and data analysis
strategies127 to investigate DLBCL sEVs proteomes
Sequence search and comparative genomic analysis of SUMO-activating enzymes using CoGe
No full textThe growing number of genome sequences completed during the last few years has made necessary the development of bioinformatics tools for the easy access and retrieval of sequence data, as well as for downstream comparative genomic analyses. Some of these are implemented as online platforms that integrate genomic data produced by different genome sequencing initiatives with data mining tools as well as various comparative genomic and evolutionary analysis possibilities.Here, we use the online comparative genomics platform CoGe ( http://www.genomevolution.org/coge/ ) (Lyons and Freeling. Plant J 53:661-673, 2008; Tang and Lyons. Front Plant Sci 3:172, 2012) (1) to retrieve the entire complement of orthologous and paralogous genes belonging to the SUMO-Activating Enzymes 1 (SAE1) gene family from a set of species representative of the Brassicaceae plant eudicot family with genomes fully sequenced, and (2) to investigate the history, timing, and molecular mechanisms of the gene duplications driving the evolutionary expansion and functional diversification of the SAE1 family in Brassicaceae
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