2,879 research outputs found
Lavallée D., Julien M., dir. 2012 – Prehistoria de la costa extremo-sur del Perú. Los pescadores arcaicos de la Quebrada de los Burros (10 000 – 7 000 a.P.)
No full textLanglais Mathieu. Lavallée D., Julien M., dir. 2012 – Prehistoria de la costa extremo-sur del Perú. Los pescadores arcaicos de la Quebrada de los Burros (10 000 – 7 000 a.P.). In: Bulletin de la Société préhistorique française, tome 111, n°1, 2014. pp. 152-153
Protein-protein interaction confidence assessment and network clustering computational analysis
No full textProtein-protein interactions represent a crucial source of information for the understanding of the biological mechanisms of the cell. In order to be useful, high quality protein-protein interactions must be computationally extracted from the noisy datasets produced by high-throughput experiments such as affinity purification. Even when filtered protein-protein interaction datasets are obtained, the task of analyzing the network formed by these numerous interactions remains tremendous. Protein-protein interaction networks are large, intricate, and require computational approaches to provide meaningful biological insights. The overall objective of this thesis is to explore algorithms assessing the quality of protein-protein interactions and facilitating the analysis of their networks. This work is divided into four results: 1) a novel Bayesian approach to model contaminants originating from affinity purifications, 2) a new method to identify and evaluate the quality of protein-protein interactions independently in different cell compartments, 3) an algorithm computing the statistical significance of clusterings of proteins sharing the same functional annotation in protein-protein interaction networks, and 4) a computational tool performing sequence motif discovery in 5' untranslated regions as well as evaluating the clustering of such motifs in protein-protein interaction networks.Les interactions protéine-protéine représentent une source d'information essentielle à la compréhension des divers méchanismes biologiques de la cellule. Cependant, les expériences à haut débit qui identifient ces interactions, comme la purification par affinité, produisent un très grand nombre de faux-positifs. Des méthodes computationelles sont donc requises afin d'extraire de ces ensembles de données les interactions protéine-protéine de grande qualité. Toutefois, même lorsque filtrés, ces ensembles de données forment des réseaux très complexes à analyser. Ces réseaux d'interactions protéine-protéine sont d'une taille importante, d'une grande complexité et requièrent des approches computationelles sophistiquées afin d'en retirer des informations possédant une réelle portée biologique. L'objectif de cette thèse est d'explorer des algorithmes évaluant la qualité d'interactions protéine-protéine et de faciliter l'analyse des réseaux qu'elles composent. Ce travail de recherche est divisé en quatre principaux résultats: 1) une nouvelle approche bayésienne permettant la modélisation des contaminants provenant de la purification par affinité, 2) une nouvelle méthode servant à la découverte et l'évaluation de la qualité d'interactions protéine-protéine à l'intérieur de différents compartiments de la cellule, 3) un algorithme détectant les regroupements statistiquement significatifs de protéines partageant une même annotation fonctionnelle dans un réseau d'interactions protéine-protéine et 4) un outil computationel qui a pour but la découverte de motifs de séquences dans les régions 5' non traduites tout en évaluant le regroupement de ces motifs dans les réseaux d'interactions protéine-protéine
Optimizing Protein Characterization using Machine Learning-Guided Mass Spectrometry
No full textMass spectrometry-based proteomics excels at high-throughput identification of proteins expressed in complex biological samples. However, the technology struggles to identify low abundance proteins due to large amounts of redundant data acquired for high abundance proteins with little collected for low abundance proteins. To improve the identification sensitivity of these proteins, I designed a machine learning classifier that assesses protein identification confidence on-the-fly, during mass spectrometry analysis. Proteins deemed confidently identified are excluded from further analysis, saving mass spectrometry resources for lower abundance proteins. Simulating data from a HEK293 cell lysate mass spectrometry analysis, our algorithm uses 16.2% - 66.2% fewer mass spectrometry resources with a 2.6% - 39.5% drop in protein identifications. When applied to live mass spectrometry experiments, these saved resources will likely improve the overall protein identification sensitivity of the experiment, particularly for lower abundance proteins, and will therefore provide a better understanding of the cell’s biology
Improving Protein Identification In Mass Spectrometry Imaging Using Machine Learning and Spatial Spectral Information
No full textMass spectrometry imaging (MSI) is a high-throughput technique that in addition to performing protein identification, can capture the spatial localization of proteins within biological tissue. Nevertheless, sample pre-processing and MSI instrumentation limit protein identification capability in MSI compared to more standard tandem mass spectrometry-based proteomics methods. Despite these limitations, the current protein identification approaches used in MSI were originally designed for standard mass spectrometry-based proteomics and do not take advantage of the spatial information acquired in MSI. Herein, I explore the benefit of using the spatial spectral information for protein identification using two objectives. For the first objective, I developed a novel supervised learning spatially-aware protein identification algorithm (SAPID) for mass spectrometry imaging and benchmarked it against ProteinProphet and Percolator, which are state-of-the-art tools for protein identification confidence assessment. I showed that SAPID identifies on average 20% more proteins at <1% false discovery rate compared to the other two algorithms.Furthermore, more proteins are identified when spatial features are used to identify proteins compared to when they are not suggesting their additional benefit. For the second objective, I used SAPID to rescue false positive and false negative protein identifications made by ProteinProphet. By examining a combination of data sampling and learning algorithms, I was able to achieve a good classification performance compared to the baseline given the extremeimbalance in the dataset. Finally, by improving proteome characterization in MSI, our approach will help providing a better understanding of the processes taking place in biological tissues
Studying the Temporal Dynamics of the Gut Microbiota Using Metabolic Stable Isotope Labeling and Metaproteomics
No full textThe gut microbiome and its metabolic processes are dynamic systems. Surprisingly, our understanding of gut microbiome dynamics is limited. Here we report a metaproteomic workflow that involves protein stable isotope probing (protein-SIP) and identification/quantification of partially labeled peptides. We also developed a package, which we call MetaProfiler, that corrects for false identifications and performs phylogenetic and time series analysis for the study of microbiome dynamics. From the stool sample of five mice that were fed with 15-N hydrolysate from Ralstonia eutropha, we identified 15,297 non-redundant unlabeled peptides of which 10,839 of their heavy counterparts were quantified. These peptides revealed incorporation profiles over time that were different between and within taxa, as well as between and within clusters of orthologous groups (COGs). Our study helps unravel the complex dynamics of protein synthesis and bacterial dynamics in the mouse gut microbiome
Implementation of a Bioanalytical Metaproteomics Assay and Design of Bioinformatics Algorithms to Investigate Microbiome-Modulating Effects of Resistant Starches
No full textThe human gut microbiome exists as a community of microorganisms in symbiosis with its host. Prebiotics are functional compounds that modulate this microbial community, promoting the growth and activity of bacteria that are beneficial to human health. Resistant starches (RS), a subclass of prebiotics, are compounds linked to a number of host-beneficial effects when included in human diets. Understanding how RS shapes gut flora composition and function is crucial to understanding these effects; however, these effects are clouded by the complexity of the microbiome’s interactions. Comprehensively characterizing microbiome shifts as the result of prebiotics is an intriguing bioanalytical problem. In the thesis project, I hypothesize that: RS changes microbiome biochemical pathway expression community-wide and at different taxonomic levels; that RS forms will affect microbiome bacterial taxonomic distribution; and that a linear programming optimization approach can parsimoniously distribute ambiguous peptide abundances amongst their constituent species, leading to different interpretations of functional and structural characteristics in microbiome metaproteomics data. To address these hypotheses, the thesis project utilizes a combined metaproteomics and bioinformatics approach. The Figeys lab-developed RapidAIM bioanalytical assay is deployed to generate label-free mass spectrometry metaproteomics data, testing for these effects experimentally. Further, Cerberus, a bioinformatics platform for microbiome metaproteomics analyses, was developed to integrate workflows from different software sources into a unified pipeline. Cerberus also implements a novel linear optimization approach addressing the shared-peptide problem. Through experimental data analyses using Cerberus, microbiomes encountering RS showed concerted taxonomic shifts, general and specific functional modulations linked to these taxonomic changes, and a significantly variable pathway expression profile for host-beneficial microbiome processes. The peptide-species linear optimization procedure demonstrates how naïve approaches to the shared-peptide problem greatly skew downstream taxonomic and functional analyses in metaproteomics experiments, marking an important consideration for microbiome studies seeking to resolve taxon-specific alterations
A Machine Learning Approach to Decipher Protein-Protein Interactions in Human Plasma to Facilitate the Characterization of Metabolic Pathways
No full textImmunoprecipitation coupled to mass spectrometry (IP-MS) methods are often used to identify protein-protein interactions (PPIs) in biological samples. While these approaches are prone to false-positive identifications through contamination and antibody non-specific binding,
their results can be filtered by combining the use of negative controls and computational modelling. However, such filtering does not effectively detect false-positive interactions when IP-MS is performed on human plasma samples, given a higher propensity for non-specific
interactions. Therein, proteins cannot be overexpressed or inhibited, and existing modelling algorithms are not adapted for execution without such controls. Hence, we introduce MAGPIE, a novel machine learning-based approach for identifying PPIs in human plasma using IP-MS, which leverages negative controls that include antibodies targeting proteins not known to be present in human plasma. Unsupervised learning algorithms are first applied to label-free MS quantification data to identify a set of high-quality negative controls that can be used for false-
positive interaction modelling. MAGPIE then uses a logistic regression classifier to assess the reliability of PPIs detected in IP-MS experiments using antibodies targeting known plasma proteins. When applied to five IP-MS experiments, our algorithm identified 68 PPIs with an FDR
of 20%. MAGPIE significantly outperformed a state-of-the-art PPI discovery tool, detecting three times more interactions at half the FDR. PPIs identified by MAGPIE are further supported by known or predicted interactions in the STRING PPI repository. Finally, our approach provides an unprecedented ability to detect human plasma PPIs, enabling a better understanding of biological processes in plasma
JOM_R3_Online_Supplements – Supplemental material for Understanding Work Teams From a Network Perspective: A Review and Future Research Directions
No full textSupplemental material, JOM_R3_Online_Supplements for Understanding Work Teams From a Network Perspective: A Review and Future Research Directions by Semin Park, Travis J. Grosser, Adam A. Roebuck and John E. Mathieu in Journal of Management</p
Intelligent Differential Ion Mobility Spectrometry (iDMS): A Machine Learning Algorithm that Simplifies Optimization of Lipidomic Differential Ion Mobility Spectrometry Parameters
No full textGlycosphingolipids such as α- and β-glucosylceramides (GlcCers) and α- and β- galactosylceramides (GalCers) are stereoisomers differentially synthesized by gut bacteria and their mammalian hosts in response to environmental insult. Thus, lipidomic assessment of α- and β-GlcCers and α- and β-GalCers is crucial for inferring biological functions and biomarker discovery. However, simultaneous quantification of these stereoisomeric lipids is difficult due to their virtually identical structures. Differential mobility mass spectrometry (DMS), as an orthogonal separation to high performance liquid chromatography used in electrospray ionization, tandem mass spectrometry (LC-ESI-MS/MS), can be used to separate stereoisomeric lipids. Generating LC-ESI-DMS-MS/MS methods for lipidomic analyses is exceedingly difficult demanding intensive manual optimization of DMS parameters that depend on the availability of synthetic lipid standards. Where synthetic standards do not exist, method development is not possible. To address this challenge, I developed a supervised in silico machine learning approach to accelerate method development for ion mobility-based quantification of lipid stereoisomers. I hypothesized that supervised neural network models could be used to learn the relationships between lipid structural characteristics and optimal DMS machine parameter values thereby reducing the total number of empirical experiments required to develop a DMS method and enabling users to “predict” DMS parameters for analytes that lack synthetic standards. Specifically, this thesis describes a supervised learning approach that learns the relationship between two DMS machine parameter values (separation voltage and compensation voltage) and two lipid structural features (N-Acyl chain length and degree of unsaturation). I describe here, iDMS, an algorithm that was trained on 17 lipid species, and can further simulate results of DMS manual method development and suggest optimal parameter values for 47 lipid species. This approach promises to greatly accelerate the development of assays for the detection of lipid stereoisomers in biological samples
- …
