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1.
Mol Cell Proteomics ; 19(10): 1602-1618, 2020 10.
Article in English | MEDLINE | ID: mdl-32636234

ABSTRACT

A key point in achieving accurate intact glycopeptide identification is the definition of the glycan composition file that is used to match experimental with theoretical masses by a glycoproteomics search engine. At present, these files are mainly built from searching the literature and/or querying data sources focused on posttranslational modifications. Most glycoproteomics search engines include a default composition file that is readily used when processing MS data. We introduce here a glycan composition visualizing and comparative tool associated with the GlyConnect database and called GlyConnect Compozitor. It offers a web interface through which the database can be queried to bring out contextual information relative to a set of glycan compositions. The tool takes advantage of compositions being related to one another through shared monosaccharide counts and outputs interactive graphs summarizing information searched in the database. These results provide a guide for selecting or deselecting compositions in a file in order to reflect the context of a study as closely as possible. They also confirm the consistency of a set of compositions based on the content of the GlyConnect database. As part of the tool collection of the Glycomics@ExPASy initiative, Compozitor is hosted at https://glyconnect.expasy.org/compozitor/ where it can be run as a web application. It is also directly accessible from the GlyConnect database.


Subject(s)
Glycomics , Polysaccharides/metabolism , Animals , CHO Cells , Cricetulus , Databases, Factual , Humans , Immunoglobulin G/metabolism , Integrins/metabolism , Mucins/metabolism , Polysaccharides/chemistry
2.
Int J Cancer ; 146(5): 1299-1306, 2020 03 01.
Article in English | MEDLINE | ID: mdl-31444973

ABSTRACT

Despite an increased awareness of the problematic of cell line cross-contamination and misidentification, it remains nowadays a major source of erroneous experimental results in biomedical research. To prevent it, researchers are expected to frequently test the authenticity of the cell lines they are working on. STR profiling was selected as the international reference method to perform cell line authentication. While the experimental protocols and manipulations for generating a STR profile are well described, the available tools and workflows to analyze such data are lacking. The Cellosaurus knowledge resource aimed to improve the situation by compiling all the publicly available STR profiles from the literature and other databases. As a result, it grew to become the largest database in terms of human STR profiles, with 6,474 distinct cell lines having an associated STR profile (release July 31, 2019). Here we present CLASTR, the Cellosaurus STR similarity search tool enabling users to compare one or more STR profiles with those available in the Cellosaurus cell line knowledge resource. It aims to help researchers in the process of cell line authentication by providing numerous functionalities. The tool is publicly accessible on the SIB ExPASy server (https://web.expasy.org/cellosaurus-str-search) and its source code is available on GitHub under the GPL-3.0 license.


Subject(s)
Cell Line Authentication/methods , Data Mining/methods , Microsatellite Repeats/genetics , Animals , Biomarkers/analysis , Cell Line , DNA Fingerprinting , Databases, Factual , Dogs , Humans , Mice , Software
4.
J Proteome Res ; 17(12): 4160-4170, 2018 12 07.
Article in English | MEDLINE | ID: mdl-30175587

ABSTRACT

The practice of data sharing in the proteomics field took off and quickly spread in recent years as a result of collective effort. Nowadays, most journal editors mandate the submission of the original raw mass spectra to one of the databases of the ProteomeXchange consortium. With the exception of large institutional initiatives such as PeptideAtlas or the GPMDB, few new studies are however based on the reanalysis of mass spectrometry data. A wealth of information is thus left unexploited in public databases and repositories. Here, we present the large-scale reanalysis of 41 publicly available data sets corresponding to experiments carried out on the HeLa cancer cell line using a custom workflow. In addition to the search of new post-translational modification sites and "missing proteins", our main goal is to identify single amino acid variants and evaluate their impact on protein expression and stability through the spectral counting quantification approach. The X!Tandem software was selected to perform the search of a total of 56 363 701 tandem mass spectra against a customized variant protein database, compiled by the application of the in-house MzVar tool on HeLa-specific somatic and genomic variants retrieved from the COSMIC cell line project. After filtering the resulting identifications with a 1% FDR threshold computed at the protein level, 49 466 unique peptides were identified in 7266 protein entries, allowing the validation of 5576 protein entries in accordance with the HPP guidelines version 2.1. A new "missing protein" was observed (FRAT2, NX_O75474, chromosome 10), and 189 new phosphorylation and 392 new protein N-terminal acetylation sites could be identified. Twenty-four variant peptides were also identified, corresponding to 21 variants in 21 proteins. For three of the nine heterozygous cases where both the variant peptide and its wild-type counterpart were detected, the application of a two-tailed sign test showed a significant difference in the abundance of the two peptide versions.


Subject(s)
Databases, Protein , Genetic Variation , Protein Processing, Post-Translational , Proteome/analysis , Acetylation , Amino Acid Sequence , Cell Line, Tumor , HeLa Cells , Humans , Phosphorylation , Proteomics/methods , Software
5.
J Proteome Res ; 15(11): 3998-4019, 2016 11 04.
Article in English | MEDLINE | ID: mdl-27444420

ABSTRACT

The Chromosome-Centric Human Proteome Project (C-HPP) aims to identify "missing" proteins in the neXtProt knowledgebase. We present an in-depth proteomics analysis of the human sperm proteome to identify testis-enriched missing proteins. Using protein extraction procedures and LC-MS/MS analysis, we detected 235 proteins (PE2-PE4) for which no previous evidence of protein expression was annotated. Through LC-MS/MS and LC-PRM analysis, data mining, and immunohistochemistry, we confirmed the expression of 206 missing proteins (PE2-PE4) in line with current HPP guidelines (version 2.0). Parallel reaction monitoring acquisition and sythetic heavy labeled peptides targeted 36 ≪one-hit wonder≫ candidates selected based on prior peptide spectrum match assessment. 24 were validated with additional predicted and specifically targeted peptides. Evidence was found for 16 more missing proteins using immunohistochemistry on human testis sections. The expression pattern for some of these proteins was specific to the testis, and they could possibly be valuable markers with fertility assessment applications. Strong evidence was also found of four "uncertain" proteins (PE5); their status should be re-examined. We show how using a range of sample preparation techniques combined with MS-based analysis, expert knowledge, and complementary antibody-based techniques can produce data of interest to the community. All MS/MS data are available via ProteomeXchange under identifier PXD003947. In addition to contributing to the C-HPP, we hope these data will stimulate continued exploration of the sperm proteome.


Subject(s)
Proteome/analysis , Spermatozoa/chemistry , Chromatography, Liquid , Data Mining , Databases, Protein , Humans , Immunohistochemistry , Male , Proteomics/methods , Tandem Mass Spectrometry , Testis/chemistry
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