dataesr/scientific-paragraphs-categorization huggingface dataset:
The processed data was mined using univariate and multivariate statistics to extract the significant differences or regulated features that drive sample clustering in group comparison. To translate these selected spectral feature changes to metabolites and pathways, Accurate-Mass and Retention-Time (AMRT) matching was performed against in-house and open-access metabolite databases (HMDB, MassBank and LipidMaps) and subsequently, pathway databases (KEGG, BioCyc). The total set of detected features in a respective analysis mode (after signal correction and noise filtering) was defined as the reference metabolome for pathway analysis. Finally, the validation of putative metabolite IDs, by MS/MS fragmentation pattern matching against METLIN, was pathway-guided, focusing on metabolites involved in significantly enriched pathways.
Product Lifecycle Management (PLM) was developed to provide collaborative, secure, and reliable tools for industrial manufacturing. It provides traceability, versioning, access rights and data integrity to complex data, originating from multiple, geographically distributed sources, in multiple formats. SWOMed is a biomedical PLM system developed during the interdisciplinary research project BIOMIST (ANR-13-CORD-0007) It provides a collaborative framework for biomedical data lifecycle management, with a focus on cohort imaging and human cognitive neuroscience studies (Allanic, M. et al. , Front. ICT 3, 35, 2017). In the context of the DRIVE-SPC research project, an extension of SWOMed is being developed in order to support and integrate complex preclinical imaging, biological and omics data, including proteomics.
Workflow4Metabolomics : new services and contributions for the metabolomics community [P43] Yann Guitton * 1 , Cécile Canlet 2 , Alexis Delabriere Metabolomics data analysis is a complex, multistep process, which is constantly evolving with the development of new analytical technologies, mathematical methods, and bioinformatics tools and databases. The Workflow4Metabolomics reference platform (workflow4metabolomics. org) provides an on-line, user-friendly and collaborative environment to build, run and share analysis workflows for the Metabolomics community. The Workflow4Metabolomics (W4M) Core Team aims to develop full pipelines using Galaxy framework for data analysis including preprocessing, normalization, quality control, statistical analysis and annotation steps to cover the main technologies: LC/MS, GC/MS, FIA/MS and NMR.
In addition to providing expert and reference workflows, the W4M infrastructure is fully open to community contributions. These contributions should take different forms: i) a complete integration, including standardized input/output formats or ii) a showcase for external developers as a functional version of a tool publicly available on the platform. The W4M Core Team provides also annual training courses with tutored session to help each participant to analyze their own dataset. Meanwhile, we foster the current version by developing new services. First of all, we continuously add new tools to expand data processing, analysis, and annotation possibilities. In addition to our Work-flow4Experimenters courses focusing on tutoring, we aim to develop online training sessions using new e-Learning technologies.
txt" post-processing file to highlight representative "leading" proteins among protein groups [P44] Oana Vigy * 1 , Martial Séveno † 2 , Serge Urbach † 1 , Mathilde Decourcelle † 2 , Philippe Marin 1 The MaxQuant software generates several output files that contain information about identified and quantified peptides and proteins. The "proteinGroups. txt" file is dedicated to identified proteins: each single row collapses into protein groups, all proteins that cannot be distinguished based on the identified peptides. Starting from our first use of MaxQuant in 2009, questions were about "how to obtain a list of identified proteins as less redundant as possible to perform further analysis", in other words, "which protein should be kept from its protein group?". To address this issue, we have developed an in-house bioinformatics tool that allows automatic selection of a representative protein ID in each protein group -based on the following criteria: -first, proteins with the most numerous identified peptides are isolated in a so called "match group" (proteins from the "Protein IDs" column with the maximum number of "peptides counts (all)").
-the "leading" protein is then defined as the best annotated protein. As we perform MaxQuant analysis mainly with UniProtKB databases, manually reviewed proteins are picked, i. e. SwissProt entries rather than TrEMBL ones. Particular cases (reverse, contaminants, isoforms, etc. ) are also handled. This tool automatically detects the leading protein in the majority of protein groups. Remaining cases wherein several proteins cannot be automatically distinguished are compiled and enriched with additional annotations to help the end-user in choosing the more representative protein. The "leading" tool, developed in Perl-CGI, has been improved over the past years to facilitate the post-treatment of "proteinGroups" files and to provide users a simple and friendly graphical interface (HTML5/CSS3) that allows data and log file exports.
Spectra were baseline-subtracted, smoothed and peak picking was applied to the TIC spectra. The different tissue classes were determined so that every myofiber was correctly assigned: oxidative (OM) versus glycolytic metabolic (GM) type myofibers, accordingly to the immunofluorescence typology. A predictive model was then built in SCILS Lab software to discriminate the metabolic myofiber types using the MALDI spectral data, and used to classify spectral data acquired on tibialis muscle sections (intermediate metabolism). The performance of the model was evaluated through the percentage of correct assignment into each myofiber metabolic type class (internal validation of 96%). Myofiber type determination using MALDI-MSI tibialis spectra revealed that 11% of the myofibers belonged to the OM class and 89% of the myofibers belonged to the GM one.
Combining these imaging methodologies is highly beneficial only if datasets from different imaging platforms can be correlated. For this purpose, data have to be converted to a common format file, called "imzML", and displayed using a unique software with identical settings for visualization. The consistency of the strategy makes it a powerful and very promising tool to understand the molecular complexity of tissue section. Mots-Clés : Multimodal Imaging, MALDI, MS, FT, IR, Raman. Programme * Intervenant † Auteur correspondant: n. desbenoit@cbmn. u-bordeaux. fr Depuis 1950 nous sommes entrés dans l'anthropocène, époque marquée par l'activité de l'homme qui a un impact significatif sur l'écosystème. Un de ces effets est la présence de polluants émergents (drogues illicites ou composés pharmaceutiques) dans les eaux.
In order to understand the most complex biological interactions between cells, tissues, organs or individuals in a specific ecosystem, it is fundamental to first identify all the components, i. e. all the molecules that can be encountered. Over the last decade, identification of small biologically generated compounds, the metabolites, has emerged as the main issue in the field of metabolomics and despite all the great efforts of the community during the last years only a relatively small number of compounds have been referenced in accessible databases, and often with only hypothetical structures. Herein we propose to discuss the philosophical aspects of the important question "what is a known natural product?" in order to move forward to a new paradigm in metabolite identification: identifying the real from the possible.
With IMS, we did not observe significant conformational changes upon binding for these aptamers, but our lab has now evidence that duplex structures at low charge states collapse in the gas phase  (1). So we focused our attention on higher charge states and on more diverse structural models as Tetracycline and Cocaine aptamers, where both ligand and buffer are influencing their shape. The poster will explain in what respects our results are in line with or different from the published data. This work is funded by the EU H2020 (Marie Curie ITN "MetaRNA")  (1)   Even with cutting-edge mass spectrometers, the identification of observed signals in metabolomics is still challenging. If accurate mass measurement can lead to few possible elemental compositions, this number highly increases with mass.
Ce travail sera présenté et discuté. Metabolite structure elucidation is a major challenge in metabolomics. Tandem mass spectrometry (MS/MS) is a powerful method to obtain fragmentation fingerprints from charged molecular species from compounds, and libraries of MS/MS spectra from standard compounds or biological matrices of interest are generated by metabolomics laboratories and platforms. Characterizing the structural content of such spectral databases is of major interest since it would 1) provide new metrics for spectral matching (based on structural patterns), and 2) facilitate the interpretation of results. Recently, approaches such as GNPS  (Wang et al. 2014 ) and Mass2Motifs  (Van Der Hooft et al. 2016 ) have been developed to provide the user with a global overview of a (set of) acquisition(s).
These methods, however, are based on fragment ions only. In contrary, the concepts of fragmentation tree or graph (which are used by the top performing identification methods, as CSI:FingerID -Shen et al. 2014) are closer to the gas phase ion chemical process. We have therefore developed a graph mining approach to extract recurrent neutral loss patterns from a collection of low energy collision spectra: each spectrum is first converted into a graph where each edge is a neutral loss and each node is a fragment ion; the algorithm then identifies common edge patterns (including at least two neutral losses) between the graphs. We applied our software module to mine two distinct MS/MS datasets: 1) a library of 500 CID/HCD spectra from endogenous and exogenous compounds of interest for metabolomics studies of human biofluids; 2) a collection of spectra from the pathogenic fungus Penicillium verrucosum.
We show that meaningful chemical patterns can be extracted, which are useful to mine the content of the databases and interpret new product ion spectra of unknown compounds. The results will be presented and discussed during the workshop 3 (Monday 2 October). Mots-Clés : annotation, pattern mining, MS/MS * Intervenant † Auteur correspondant: alexis. delabriere@cea. fr The aim of the present investigation was to search for new antiviral diterpenoids by means of a bioassay-guided purification procedure from an EtOAc extract of E. pithyusa aerial parts. To gain more insight in the structural diversity of the diterpenoid content of E. pithyusa, extract and fractions were analyzed by LC-MS/MS-based molecular networking. In this work, six new premyrsinol esters and one new myrsinol ester were isolated from the aerial parts of Euphorbia pithyusa, together with two known dideoxyphorbol esters, following a bioactivity-guided purification procedure using a chikungunya virus (CHIKV) cell-based assay.
A ce jour, les bibliothèques GC-MS (NIST, Golm, Massbank) restent très utilisées pour l'identification des métabolites mais ne contiennent aucun spectre avec des masses précises bien que certains proviennent de GC-EI-ToF. Par conséquent, nous constituons une bibliothèque interne en haute résolution avec des standards purs et en matrices biologiques qui alimentera la base de données PeakForest de l'infrastructure française MetaboHUB. La mesure des masses précises ainsi que le développement de nouveaux outils d'automatisation du traitement de données devraient permettre de lever certains verrous rencontrés dans la recherche de biomarqueurs concernant l'identification des métabolites. (1) Gao X, Pujos-Guillot E, Sébédio JL. Development of a quantitative metabolomic approach to study clinical human fecal water metabolome based on trimethylsilylation derivatization and GC/MS analysis.
MS and MS/MS data were analyzed by Protein Pilot software v4 prior a validation of quantitative results by the R package IsobarPTM. Eighty-six proteins were found significantly dysregulated between the two conditions. Gene ontology and pathway analysis was performed and revealed that several signaling pathways, including Interferon type I and II, androgen receptor, TGF-beta and EGF/EGFR, were modified between the two conditions. Also network analysis showed that most of dysregulated proteins identified were upstream neighbors of apoptosis and then could be interesting therapeutic targets. Subcellular localization and changes of expression of three interesting proteins were further validated by western blot and confirmed the proteomic results. Finally, our study highlighted the impact of these three proteins on proliferation and apoptosis induction by conducting siRNA transfection in NSCLC cells.
Based on this HCP profiling a list of "usual suspects" was built and labelled synthetic surrogate peptides were obtained in order to quantify the corresponding HCPs along DSP. Analytical strategies used for both the identification and quantification of HCP will be described with a focus on the challenge of sensitivity for the detection of these low levels impurities. In discovery proteomics experiments, tandem mass spectrometry and Data-Dependent Acquisition (DDA) are classically used to identify peptides and proteins through database searching. This strategy suffers from different limitations like under-sampling and irreproducible precursor ion selection leading to a non-exhaustive analysis of large datasets. Data-Independent Acquisition (DIA) based on the fragmentation of all precursors contained in predetermined isolation windows has the potential to overcome these limitations.
In particular exhaustive MS/MS spectral library combined to effective targeted MS2 data extraction and scoring is expected to improve proteome coverage, enhance the quantification reproducibility and decrease missing value frequency. We present here an evaluation and comparison of DDA and DIA strategies in terms of detection sensitivity, proteome coverage, dynamic range, quantification reproducibility and false discovery proportion. We acquired data on complex protein lysates (like E. coli or yeast extracts) on a QExactive HF instrument from Thermo Scientific with a two-hours gradient using either a DDA or DIA acquisition mode. DIA data were processed with Spectronaut whereas DDA data were processed with Maxquant software. From the obtained results, we show that DIA is more sensitive than DDA.
Indeed, we were able to quantify respectively 3000 and 500 additional peptides and proteins with DIA from a 10 ng E. coli digest. For more complex samples, using an exhaustive library, DIA allowed to quantify 25% more proteins than DDA. To test false discovery proportion at the identification level using DIA, we processed data using a library from another specie. Interestingly, it did not generate more false identifications than DDA data analysis through database searching. The dynamic range and the quality of quantification results will be illustrated with yeast samples spiked with known amount of protein standards. All together, these results support DIA as an exquisite strategy for the identification and quantification of proteins in complex biological systems.
The study also opens some interesting perspectives, notably the use of tandem mass tags for the study of redox PTMs in tissues and sera. (1) Shakir S, Vinh J, Chiappetta G. Quantitative analysis of the cysteine redoxome by iodoacetyl tandem mass tags. Data independent analysis (DIA) has become increasingly popular in the field of quantitative proteomics. Here we describe and evaluate a DIA mode of operation called SONAR, developed and implemented on a hybrid quadrupole/oa-time-of-flight (Q-TOF) mass spectrometer. Briefly, data are collected using a continuously scanning quadrupole, isolating specific m/z regions prior to oa-TOF analysis. MS scans alternate between precursor (low energy) and CID product ions (high energy). This method enhances duty-cycle and produces highly specific and unbiased multi-dimensional data sets, which can be searched using a traditional discovery proteomics workflow with a SwissProt database or using a targeted spectral library approach.
To evaluate the quantitative performance of SONAR, the metaproteome quantification standards were used, which consist of tryptic digests from three different proteomes (Human, yeast and E. coli) mixed at different levels. Data were processed using ProteinLynx Global Server 3. 0. 3 (Waters) with a reviewed SwissProt database. Additionally, we interrogated the data with spectral library-based analysis workflows using OpenSWATH (ETH Zurich) and Spectronaut (Biognosys). The LFQbench software (Mainz University) was used to determine both precision and accuracy of the SONAR label-free quantification results at peptide and protein level. Overall, data acquired with SONAR showed high technical reproducibility and excellent quantification accuracy with over 4 orders of magnitude dynamic range. Cette étude protéomique a pour but de suivre les histones nucléaires selon 3 aspects : l'évolution de leur quantité totale par cellule, celle de l'expression de leurs différentes isoformes et variants ainsi que les MPTs.
Conclusion: We developed a 2D-precursor selection algorithm for TIMS-PASEF that determines and schedules precursors in complex samples within 100-200 ms, well suited for an LC timescale. Mots-Clés : PASEF, ion mobility, shotgun proteomics, identifications Programme * Intervenant † Auteur correspondant: pierre-olivier. schmit@bruker. com microDIA acquisition combined to a novel software tool enables direct identification of peptides in pseudo-SRM MS/MS spectra from sequence databases [P111] Pierre-Olivier Schmit Background: Typical data independent acquisition (DIA) approaches use wide isolation windows that produce noisy MS/MS data and require sample specific spectral libraries to be generated for confident peptide identification. Large DIA isolation windows have the drawback of complicating peptide modification analyses -especially when modified and unmodified versions of the peptides are co-isolated.
To address these limitations, we developed the microDIA workflow that uses sequentially overlapping precursor isolation windows combined with an automated MS/MS spectrum deconvolution It produces highly specific MS/MS spectra with ± 1 m/z mass-isolation windows across the precursor range spanning most detectable tryptic peptides in a single shot analysis. The workflow is implemented in the Protalizer TM software platform, that enables direct peptide and protein identification without requiring a spectral library. Methods: We acquired data using 6 m/z wide DIA windows, each overlapping 2 m/z and covering a 430-910m/z mass range with a 37Hz rate using an Impact II UHR-Q-TOF coupled to a nano Elute UH-PLC (Bruker Daltonics). Hela tryptic digest or mixtures of Hela, Yeast and E. Coli lysates triptic digests -mixedaccording to Navarro et al (  2016 ) have been separated with a 2h gradient.
L'utilisation des logiciels PEAKS et Proteinscape et des bases de données Swissprot et NCBI ont permis d'identifier certaines espèces, en prenant en considération les dégradations pouvant subvenir au cours des siècles. L'absence de certaines espèces des bases de données (lama, guanaco, vigogne), ou leur proximité (mouton et chèvre) nous ont amené à rechercher des marqueurs sur fibres modernes marqueurs et à séquencer après PCR certaines kératines et KAPs d'intérêt. Notre étude a ainsi permis de dégager une nouvelle séquence isomorphe permettant la distinction mouton-chèvre. Ces résultats ouvrent de nouvelles perspectives concernant l'analyse des tissus anciens. Mots-Clés : protéomique, kératine, fibres animales, archéologie, textiles * Intervenant.
Une méthode "intelligente" a été mise en place par la création d'une base de données des résultats des analyses effectuées sur la plateforme. Un outil de contrôle en temps réel d'un spectromètre de masse, supporté par une API, a été développé dans le but d'exploiter cette base de données afin de réaliser l'identification en temps réel de protéines analysées, par comparaison des évènements MS et MS/MS. Cette identification permet de mettre à jour à la volée la liste d'exclusion du spectromètre de masse, de façon à exclure les peptides prédits et concentrer le temps machine sur des molécules moins intenses et moins connues. Mots-Clés : Protéomique, spectrométrie de masse en tandem, optimisation temps-machine, C#, SQLite, Perl, programmation orientée objet Programme * Intervenant † Auteur correspondant: david.
Pour chaque phase, une méthode de séparation spécifique par UHPLC a été optimisée. Plusieurs paramètres de détection en masse haute résolution (LTQ Orbitrap XL) ont été évalués tel que la source d'ionisation (ESI ou APCI) et l'énergie de fragmentation. Les pics chromatographiques ont été annotés par interrogation des bases de données de fragmentation in silico tels que LipidBlast et LipidMaps. Cette méthode nous a permis d'identifier une dizaine de classes de lipides différents. Ainsi parmi les 300 pics détectés, le 2,3-dihydroxypropyl-dodecanoate et le 1-O-tetradecanoylglycerol caractéristiques de l'espèce ont été identifiés. Enfin, des réseaux moléculaires ont été construits pour avoir une vue globale de la composition qualitative de chaque extrait. L'approche développée dans cette étude permet d'obtenir une empreinte globale du lipidome d'un extrait lipidique de Serenoa repens et peut être appliquée dans le cadre d'un contrôle qualité de la matière première ou d'identification d'actifs.
The multivariate data analysis using a combination of hierarchical clustering with principal component score analysis and correlation network analysis highlighted the' metabolomics' distance and closeness between the samples according to both genotypes and geographical regions. A major environmental impact on crop seeds metabolome was observed with different trends, depending on the varieties. In fact, some varieties were very sensitive to environment whereas others are less responsive. The comparison of the most impacting environment on seeds metabolome has shown a dramatic effect between the balance in the molecules of interest such as polyphenols and glucosinolates. Lastly, the identified secondary metabolites were mapped into the KEGG pathway database to reveal the most sensitive gene targets that could be sensitive to the environmental influence.
Granules from potato are the largest among common starch sources, with a diameter reaching up to 100 µm. Theoretical calculations show that the amount of the various proteins in a single 100 µm starch granule from potato lies between 178 fmol and 75 amol inside a starch granule which is within the detection limit of nanoLC-nanoESI-MS/MS shotgun mass spectrometry. In our experimental workflow, starch granules are surface stripped with SDS 2%, prior to glucan denaturation by heat. Then, the proteins are reduced, alkylated and digested by trypsin using the eFASP method. Finally, peptides were analyzed on a Q-Exactive Orbitrap mass spectrometer and raw data are faced to several potato databanks. Protein relative quantification is realized using emPAI score. Analyses have been realized on 300 mg of granules (around 10 6 granules) fractionated according to their size (<11 µm; 11-31 µm; 31-50 µm and 50-100 µm).