dataesr/scientific-paragraphs-categorization huggingface dataset:
Yet, the consortium received over 1000 datasets from more than 25 labs covering various species, organs, conditions, acquired with various sample preparation protocols, and various IMS technologies. All annotations are made public through the web interface, allowing searching in particular for specific datasets or compounds, and enable the clustering of datasets with similar molecular content that often matches either the type of tissue or the condition. This metabolite annotation will be illustrated in different examples. Mining all these big datasets enabled our consortium to create the first of its kind spatial metabolite knowledgebase. Graphene success results from its versatile physicochemical and biological properties making it an attractive nanomaterial. Indeed, those nanoparticles such as carbon-nanotubes, nanodots or graphene oxide (GO) sheets can be used in multiple areas including therapeutics with bioengineering or drug delivery.
The quantitative proteomics approach was carried out by using the FASP (Filter Aided Sample Preparation) method combined with a dual protease digestion (Lys-C/ trypsin) before labelling the resulting peptides with iTRAQ 8-plex reagents. Then, the labelled peptides were fractionated in two dimensions (OFFGEL/RP nanoLC) prior MALDI-TOF/TOF mass spectrometry analysis. MS and MS/MS data were analysed by Protein Pilot software and quantitation was validated by the R package IsobarPTM. This proteomic approach led to the identification and quantitation of 834 proteins and evidenced the capacity of KDACis to reverse the tumor metabolic phenotype, an effect enhanced under hypoxia conditions, by changing metabolic enzyme expression profiles, especially in glycolysis and Krebs cycle. These enzyme expression changes were validated by Western Blot analysis.
Dix MII ont pu être purifiés, caractérisés voire identifiés par RMN. Leurs effets stimulateurs ou inhibiteurs sur la croissance des bactéries et leur capacité à fixer l'azote ont été évalués pour certains d'entre eux. Higher plants, as autotrophic organisms, are effective sources of molecules. They hold great promise for metabolic engineering, but the behavior of plant metabolism at the network level is still incompletely described. Although structural models (stoichiometry matrices) and pathway databases are extremely useful, they cannot describe the complexity of the metabolic context and new tools are required to visually represent integrated biocurated knowledge for use by both humans and computers. Here, we describe ChloroKB, a web-application (http://chlorokb. fr/) for visual exploration and analysis of the Arabidopsis thaliana metabolic network in the chloroplast and related cellular pathways.
The performances of the method were investigated on known mixtures (such as synthetic urine) and on plasma datasets from duck and human (plasma NIST SRM1950) using a library of 175 pure metabolites. Results show noteworthy performances, outperforming current existing methods (namely MetaboHunter, BATMAN, Bayesil or Chenomx) for the identification and the quantification of metabolites. Furthermore, ASICS could easily be used routinely according to its reasonable computational time (no more than 3 minutes for the whole analysis of a complex spectrum). In conclusion, ASICS is a completely automated procedure for metabolites identification and quantification in 1H NMR spectra of biological mixtures. It will enable empowering NMR-based metabolomics by quickly and accurately helping experts to obtain metabolic profiles.
ASICS is already available as a R function or within the more user-friendly Galaxy/W4M interface developed by the MetaboHUB infrastructure and IFB (French Institute of Bioinformatics). The emergence of dereplication strategies over the last decades has brought new perspectives to speed up natural product screening programs. Here is presented a 13C NMR-based dereplication method dedicated to the chemical profiling of natural metabolite mixtures, without need for any fractionation or purification steps  [1]. It consists of a database searching approach that allows metabolite identification by comparing experimental 13C NMR data of the studied extract to a set of predicted 13C NMR data of known metabolites stored in a database. The comparison is performed by the mean of a developed algorithm that evaluates the quality of the matching between experimental and predicted data.
The algorithm calculates a score value for each metabolite stored in the database and returns a list of metabolites that are the most likely to be present in the metabolite mixture. As an example, this method was applied on a crude alkaloid extract obtained from the leaves of Peumus boldus, resulting in the identification of eight major alkaloids including isocorydine, rogersine, boldine, reticuline, coclaurine, laurotetanine, N-methylcoclaurine, and norisocorydine, as well as three monoterpenes including p-cymene, eucalyptol, and α-terpinene. [  L'analyse de mélanges complexes s'effectue classiquement par le couplage " en ligne " d'une technique chromatographique avec une technique d'identification spectrale. Cette procédure est privilégiée lors d'analyse "de routine", cependant, dès lors que l'étape d'identification se complexifie, la procédure nécessite un fractionnement de l'échantillon qui peut se poursuivre jusqu'à la purification d'un constituant afin d'aboutir à son élucidation structurale  (1).
We recently showed that winter bear serum triggers changes in cultivated human myotubes that mimics a hibernation-like state. The identification of the active components in the bear serum would pave the way for innovative solutions to fight human muscle atrophy, one of the main deleterious characteristics of human ageing and physical inactivity. eta-hydroxybutyrate (betaOH) could be a good candidate as its concentration is markedly increased in winter bear serum and betaOH-supplemented diets have positive effects on human muscle mass preservation. Hence, human myotubes were incubated with either FCS or bear serum in the presence of various betaOH concentrations (N=4/condition). After extraction, proteins were separated on SDS-PAGE gels (4 bands) and tryspin-digested. Tryptic peptides were analyzed using a quantitative label-free-based method on a nanoUPLC-system (nanoAcquity, Waters) coupled to a Q-Exactive Plus (Thermo) before data analysis using MaxQuant (Swissprot Homo sapiens database).
The second point is the development of a new instrument called SpiderMass, for real-time analysis of biomolecules by laser ablation at 2940 nm coupled to MS for the characterization of biological tissue ex vivo and in vivo. First, the development of this instrument was made and optimized on the different parts that compose it. Molecular profiles generated in real-time show signals corresponding to metabolites and lipids. The low-invasive and virtually painless nature of the laser irradiation was obtained through in vivo studies on voluntary individual phalanxes. Finally, some of the developments made on the SpiderMass was dedicated for clinical application, which is ovarian cancer, to develop databases of molecular profiles corresponding to different grades of the disease. Les tumeurs sont des régions très hétérogènes à la fois au niveau moléculaire et au niveau de la diversité cellulaire composant le microenvironnement tumoral.
Ces résultats permettent d'envisager la mise en place d'une stratégie d'analyse lipidomique de ces composés, et l'enrichissement des bases de données existantes par l'annotation proposée. (1) Spickett, C. M. ,  A. Reis, and A. R. Pitt, Free Radic Biol Med, 2011. 51(12) : p. 2133-49. Mots-Clés : Phospholipides oxydés, Spectrométrie de masse, Identification, Fragmentation Programme * Intervenant † Auteur correspondant: spiro. khoury@inra. fr.
An integrated innovative top-down proteomics workflow for the rapid discrimination of enterobacterial pathogens [O76] Pathogen identification is crucial to confirm bacterial infections and guide antimicrobial therapy. Although MALDI-TOF mass spectrometry (MS) has proved a powerful tool for microbial identification, clinical laboratories need more reliable methods since some bacteria remain difficult to identify (absence of specific profile, lack of appropriate references in the database, no differentiation of sub-species). Top-down proteomics (TDP) is an emerging technology based on the analysis of intact proteins using very high-resolution MS. The major advantage of TDP, in comparison to the more usual bottom-up MS approach, is its ability to address protein variations, characterize proteoforms arising from alternative splicing, allelic variation, or post-translational modification, and negate the problem of protein inference.
Here, we developed a unique integrated approach, combining large-scale LC-MS/MS TDP with an innovative software tool, able to discriminate bacterial pathogens. As a proof of concept, we focused on enterobacterial species that are difficult to unambiguously identify with MALDI-TOF. Briefly, 12 different bacterial strains of E. coli, Shigella and Salmonella species were cultured. Following a lysis step, intact proteins were extracted and analyzed by LC-MS/MS on an Orbitrap Fusion Lumos mass spectrometer using EThcD fragmentation. All data were processed with Prosight PD 2. 1 and Diagno-TOP, a new software tool developed in-house, which clusters spectra according to their similarity, without any database search or protein identification. In total, our dataset comprised 36 LC-MS/MS runs containing 200,744 MS/MS spectra corresponding to 93,372 spectra clusters.
As suggested by their names, the NRPs structure cannot be read directly from the genome hence the protein databases contain very little structural information about the NRPs. NRPs are, by nature, metabolites containing building blocks, called monomers, including proteogenic and non-proteogenic amino acids and derivatives, aliphatic chains, chromophores, fatty acids. Nowadays, more than 500 monomers. have been founds in NRPs composition in the NORINE database that currently gathers more than 1200 NRPs structures  (Caboche et al. , 2007; Flissi et al. 2016). The NRP synthesis occurs a long large enzymatic complexes called non ribosomal peptide synthetases (NRPS) and lead to linear, cyclic, semi-cyclic or branched structures of masses between 500 and 2,000 Da. NRPs have various activities ranging from anti-bacterial, antifungal, anti-inflammatory, surfactants, siderophore and so one.
According to their mass range, NRPs do not fully belong to metabolomics or proteomics fields and the analytical methods developed in its respective fields are not fully transposable for the "NRPomics" (defined as the systematic study of NRPs). Here, we will illustrate that the combination of NORINE database information, the measurement accuracy using FT-ICR mass spectrometer and the Kendrick mass defect  (Kendrick, 1963)   L'étude du métabolisme central et énergétique requiert des séparations chromatographiques qui ne sont possibles qu'en chromatographie ionique, en particulier la séparation des hexoses phosphates. Couplé à un spectrométre de masse haute résolution Orbitrap, il est possible d'accéder à la fois à la quantification absolue des metabolites et à l'analyse des isotopologues obtenus aprés marquage, deux éléments essentiels dans les analyses métaboliques des flux.
In recent years, integrated approaches coupling mass spectrometry instruments, in particular those running soft ionization methods, with transcriptomics databases and high throughput pharmacological screening assays have emerged to accelerate the discovery of venom derivative drugs. Moreover, the advances in mass spectrometry and bioinformatic softwares now allow the identification of more and more sequences with greater accuracy and reliability in very short time, which is highly beneficial to the high-throughput discovery of new sequences of interest. Here we report the first whole venom analysis of the common European adder (Vipera aspis), including some proteomic study by LC-MS/MS analysis (Q-exactive and SYNAPT-G2) and fully validated transcriptomic sequences using PEAKS software. The many sequence variants of various toxin classes as well as the putative novel toxins identified provide new opportunities for the discovery of bioactives of interest.
ProteoRE is built upon the Galaxy framework, a well-maintained software platform, providing simple interfaces to tools and online access to computational resources in a collaborative and transparent way. Methods: Starting from proteome software output files (e. g. MaxQuant, Proline), various components have been designed driven by expertise and needs from our collaborators. These modules embedded into Galaxy components have been implemented either by reusing tools (from the Galaxy Tool Shed) or by wrapping Bioconductor packages and external code, and further beta-tested. Results: We have set up two use cases scenarios derived from our own research projects: the first case consists in interpreting a large proteins identification list while the second entails selection of biomarkers candidates based on biochemical criteria.
Current components include customary data manipulation (e. g. filtering, sorting, and ranking), annotation (e. g. information retrieval from public resources), downstream analysis (e. g. cross-comparison and enrichment analysis) and graphical representation. A first ProteoRE instance integrating these components and tools, is now deployed for ergonomics assessment before public release in early 2018. Conclusions: While Galaxy-based tools offer services for primary proteomics data analyses (e. g. MS data conversion, protein database tools, search algorithms), tools focusing on downstream analysis are still lacking. The ProteoRE platform proposes to fill this gap with the hope of promoting proteomics data in the Life Science community. Mots-Clés : Computational workflow, Proteomics data, Bioinformatics, Annotation, Galaxy.
This demonstrates the relevance of our strategy. The identification of the other candidate biomarkers is ongoing (online database, literature and LC-MS/MS on LTQ-OrbiTrap). This work will contribute to provide new biomarkers of food intake that are needed to improve the quality of dietary assessment in epidemiological studies. Monoclonal antibodies (mAbs) have emerged as important therapeutics for the treatment of lifethreatening diseases like cancer and autoimmune diseases. The knowledge that the top-selling mAbs are, or will become, available to the market in the near future as "generics" has driven an explosion of interest in biosimilars. The successes of mAbs have furthermore triggered the development of various next generation formats. In oncology, antibody-drug conjugates (ADCs) are particularly promising, since they synergistically combine a specific mAb linked to a biologically active cytotoxic drug via a stable linker.
An animal experiment was carried out on 24 sows during pregnancy and lactation, half of which were fed with a diet enriched in fatty acids, and the other half fed normal non-supplemented diet. Serum was collected from piglets at four weeks after birth during pre-weaning. Serum samples (n=12 per group) were deproteinized by ice-cold methanol and analysed in a randomized order by LC-MS positive ionisation mode using an Acquity UPLC I Class System coupled to Xevo G2-S QTof. Chromatographic separation was performed employing an Acquity HSS T3 analytical column (1. 8 µm 2. 1×100 mm). Generated LC-ESI(+)-HRMS data was processed with Progenesis software, and data analysed employing multivariate analysis including Principal Component Analysis (PCA) and Orthogonal Partial Least Square -Discriminant Analysis (OPLS-DA) that were applied to build descriptive and predictive models.
Computed scores allowed to spot the best method(s) for the analysis of every detected compound. Furthermore, the coverage and complementarity of the methods was further evaluated. As a result, the combination of all LC methods and ionization modes led to an overall coverage of 80% of the proposed library. It was found that when only two or three out of the four analytical methods were carefully chosen, the obtained coverage decreased by only 10%. Finally, the most informative set of LC methods and ionization modes was applied to the investigation of biological matrices, and the annotation of metabolites was carried out by using the constructed database. Sample preparation is a key step for proteomics, both in discovery and in targeted modes. It could deeply modify the range of information provided by mass spectrometry, even when using the latest technologies.
Database searching with 13C(6) (K) or 13C(6) (R) labeling as fixed modification uniquely identifies proteins coming from microdissected elements. Finally, in order to confirm the enrichment of identified proteins in comparison with the total cellular proteome and thus the significant presence of these proteins in the compartment of interest, we performed a relative label free quantification between the microdissected fraction and a total extract of labeled cells. This original association of several advanced technologies is geared to the needs of lower the level of contaminations and increase the level of sensitivity for subcellular proteomics. As a proof of concept we applied this strategy on invadosomes, a F-actin based structures involved in extracellular matrix degradation and cell invasion.
In addition, most pipelines are proprietary, inducing to a black box aspect which makes the test, examination or modification of the process leading to results an impossible task. The subject of this tutorial is protein identification by database searching using tandem Mass Spectrometry (MS/MS) data. Here we present how the famous open source programming language and environment for statistical computing, R, can be a user-friendly tool for protein identification. We use in this tutorial rTANDEM, a Bioconductor package that encapsulates in R the popular algorithm X!Tandem excluding the independent installation of the search engine and eliminating the black box phenomenon. The package makes possible the performance of MS2 spectra identification in the R environment and takes advantage of R's data processing efficiencies to investigate results.
MS-Angel: a solution for real-time proteomics workflows [P35] Julie Poisat * † 1 , David Bouyssié 1 , Alexandre Burel 2 , Christophe Bruley 3 , Sarah Cianferani 2 , Jérôme Garin 3 , Odile Burlet-Schiltz 1 Search engines are major tools to identify proteins through the mass spectrometry-based proteomic analysis of peptide mixtures. While they were often used in stand-alone applications, today it becomes common to see complex data processing workflows involving multiple search engines. Indeed, software like OpenMS, Proteome Discover and SearchGUI allow the submission of peaklist files to several search engines simultaneously. However, these tools are lacking a useful Mascot Daemon feature, which is able to monitor the creation of acquisition files to trigger automatically their processing in background.
To satisfy this need we decided to create MS-Angel, an open-source software managing the automated detection of newly created files and their processing. MS-Angel workflows include a lot more operations than the proteins identification. It is possible to combine in a single batch the conversion of files (e. g. into peaklists), the submission of spectra to search engine(s), the execution of custom scripts. MS-Angel is designed to work with Mascot, and SearchGUI is being integrated to support many open source search engines. Moreover, MS-Angel has been developed to be coupled to Proline, an open-source software suite dedicated to the processing of mass spectrometry data, especially target/decoy validation and label-free quantitation. Using MS-Angel, users can schedule the import of the MS/MS search result files into Proline as soon as they are created; then validate and quantify the data altogether.
MS-Angel is thus a new free, fully automated solution for the management of raw files and the submission of searches in databases. Combined to Proline, it offers a fairly comprehensive and easy-to-use pipeline for the analysis of LC-MS/MS data. The first public version is available at http://proline. profiproteomics. fr/download. The next version, embedding SearchGUI, should be released with Proline 2. 0 (end of 2017). A standalone version coming without Proline -providing limited features -is planned. Time-of-flight secondary ion mass spectrometry (ToF-SIMS) is a powerful surface analysis technique that allows the characterisation of both organic and inorganic molecules and their spatial distribution. Its high spatial resolution and sensitivity make ToF-SIMS especially suited for the study of art or archaeological artefacts  (1).
We will show through a spiked standard sample  [1]  that Proline implements an efficient cross assignment procedure, which makes it possible to reach out a low level of missing values rate without adversely affecting the coefficient of variation of measured ions. The accuracy of the final protein abundance measurement is investigated by using the same dataset but focusing on the spiked UPS1 proteins, whose theoretical relative abundances in the different samples are known. We will demonstrate that the observed ratios are close to the expected ones, combined with a limited dispersion around the median values. [1]  Ramus et al. (2016). Benchmarking quantitative label-free LC-MS data processing workflows using a complex spiked proteomic standard dataset. Journal of Proteomics 132, 51-62. The mzDB format is an efficient file format for large mass spectrometry (MS) datasets relying on the SQLite lightweight database management system.
Fragmentation events matching to the extracted m/z can also be overlaid. The software can also perform elution peaks detection by using the algorithm implemented in the Proline software or peak grouping into MS features through the Proline deisotoping algorithm. Since mzDB format support DIA MS data, mzScope also provides MS2 chromatogram extraction within a selected isolation window that can be performed on user supplied m/z values or from ion fragment masses imported from a spectral library. Finally, the software supplies various metrics computed from the raw MS data that can be used as identification-free quality control of the coupling of chromatography with MS instrumentation. Those features combined with the efficiency of the mzDB format make mzScope a suitable tool for MS data visualization and exploration or for raw MS signal comparison.
Unfortunately, these tools suffer from minor inconsistencies (e. g. NatC substrates prediction for TermiNator3) to major drawbacks (e. g. transit-peptide cleavage-site accuracy for the TargetP suite). Our large-scale N-terminomics analyses provide highly relevant data integrated in a dedicated database (eNergiomeDB). Subcellular localization, protein N-terminus status and mature N-terminal start position are checked and validated for all collected data. This essential step benefit of data redundancy and annotations available in various databases such as PPDB, SUBA, At_Chloro, UniprotKB, etc. The eN-ergiomeDB contains almost 250,000 mature experimental N-termini related to more than 6,000 distinct non-redundant proteins (2,538 from A. thaliana, 3507 from H. Sapiens). More than 175,000 validated peptides (associated to 1,925 proteins) were used to improve the existing N-terminus prediction tools and to develop new ones.