Peak annotation and data analysis software tools for mass spectrometry imaging

La metabolòmica espacial és la disciplina que estudia les imatges de les distribucions de compostos químics de baix pes (metabòlits) a la superfície dels teixits biològics per revelar interaccions entre molècules. La imatge d'espectrometria de masses (MSI) és actualment la tècnica principal per obtenir informació d'imatges moleculars per a la metabolòmica espacial. MSI és una tecnologia d'imatges moleculars sense marcador que produeix espectres de masses que conserven les estructures espacials de les mostres de teixit. Això s'aconsegueix ionitzant petites porcions d'una mostra (un píxel) en un ràster definit a través de tota la seva superfície, cosa que dona com a resultat una col·lecció d'imatges de distribució de ions (registrades com a relacions massa-càrrega (m/z)) sobre la mostra. Aquesta tesi té com a objectius desenvolupar eines computacionals per a l'anotació de pics de MSI i el disseny de fluxos de treball per a l'anàlisi estadística i multivariant de dades MSI, inclosa la segmentació espacial. El treball realitzat en aquesta tesi es pot separar clarament en dues parts. En primer lloc, el desenvolupament d'una eina d'anotació de pics d'isòtops i adductes adequada per facilitar la identificació de compostos de rang de massa baix. Ara podem trobar fàcilment ions monoisotòpics als nostres conjunts de dades MSI gràcies al paquet de programari rMSIannotation. En segon lloc, el desenvolupament de eines de programari per a l’anàlisi de dades i la segmentació espacial basades en soft clustering per a dades MSI.La metabolómica espacial es la disciplina que estudia las imágenes de las distribuciones de compuestos químicos de bajo peso (metabolitos) en la superficie de los tejidos biológicos para revelar interacciones entre moléculas. Las imágenes de espectrometría de masas (MSI) es actualmente la principal técnica para obtener información de imágenes moleculares para la metabolómica espacial. MSI es una tecnología de imágenes moleculares sin marcador que produce espectros de masas que conservan las estructuras espaciales de las muestras de tejido. Esto se logra ionizando pequeñas porciones de una muestra (un píxel) en un ráster definido a través de toda su superficie, lo que da como resultado una colección de imágenes de distribución de iones (registradas como relaciones masa-carga (m/z)) sobre la muestra. Esta tesis tiene como objetivo desarrollar herramientas computacionales para la anotación de picos en MSI y en el diseño de flujos de trabajo para el análisis estadístico y multivariado de datos MSI, incluida la segmentación espacial. El trabajo realizado en esta tesis se puede separar claramente en dos partes. En primer lugar, el desarrollo de una herramienta de anotación de picos de isótopos y aductos adecuada para facilitar la identificación de compuestos de bajo rango de masa. Ahora podemos encontrar fácilmente iones monoisotópicos en nuestros conjuntos de datos MSI gracias al paquete de software rMSIannotation.Spatial metabolomics is the discipline that studies the images of the distributions of low weight chemical compounds (metabolites) on the surface of biological tissues to unveil interactions between molecules. Mass spectrometry imaging (MSI) is currently the principal technique to get molecular imaging information for spatial metabolomics. MSI is a labelfree molecular imaging technology that produces mass spectra preserving the spatial structures of tissue samples. This is achieved by ionizing small portions of a sample (a pixel) in a defined raster through all its surface, which results in a collection of ion distribution images (registered as mass-to-charge ratios (m/z)) over the sample. This thesis is aimed to develop computational tools for peak annotation in MSI and in the design of workflows for the statistical and multivariate analysis of MSI data, including spatial segmentation. The work carried out in this thesis can be clearly separated in two parts. Firstly, the development of an isotope and adduct peak annotation tool suited to facilitate the identification of the low mass range compounds. We can now easily find monoisotopic ions in our MSI datasets thanks to the rMSIannotation software package. Secondly, the development of software tools for data analysis and spatial segmentation based on soft clustering for MSI data. In this thesis, we have developed tools and methodologies to search for significant ions (rMSIKeyIon software package) and for the soft clustering of tissues (Fuzzy c-means algorithm)

Spartan Daily, October 2, 1981

Volume 77, Issue 22https://scholarworks.sjsu.edu/spartandaily/6798/thumbnail.jp

Context-sensitive Markov Models for Peptide Scoring and Identification from Tandem Mass Spectrometry

Computational methods for peptide identification via tandem mass spectrometry (MS/MS) lie at the heart of proteomic characterization of biological samples. Due to the complex nature of peptide fragmentation process inside mass spectrometers, most extant methods underutilize the intensity information available in the tandem mass spectrum. Further, high noise content and variability in MS/MS datasets present significant data analysis challenges. These factors contribute to loss of identifications, necessitating development of more complex approaches. This dissertation develops and evaluates a novel probabilistic framework called Context-Sensitive Peptide Identification (CSPI) for improving peptide scoring and identification from MS/MS data. Employing Input-Output Hidden Markov Models (IO-HMM), CSPI addresses the above computational challenges by modeling the effect of peptide physicochemical features ("context") on their observed (normalized) MS/MS spectrum intensities. Flexibility and scalability of the CSPI framework enables incorporation of many different kinds of features from the domain into the modeling task. Design choices also include the underlying parameter representation and allow learning complex probability distributions and dependencies embedded in the data. Empirical evaluation on multiple datasets of varying sizes and complexity demonstrates that CSPI's intensity-based scores significantly improve peptide identification performance, identifying up to ~25% more peptides at 1% False Discovery Rate (FDR) as compared with popular state-of-the-art approaches. It is further shown that a weighted score combination procedure that includes CSPI scores along with other commonly used scores leads to greater discrimination between true and false identifications, achieving ~4-8% more correct identifications at 1% FDR compared with the case without CSPI features. Superior performance of the CSPI framework has the potential to impact downstream proteomic investigations (like protein identification, quantification and differential expression) that utilize results from peptide-level analyses. Being computationally intensive, the design and implementation of CSPI supports efficient handling of large MS/MS datasets, achieved through database indexing and parallelization of the computational workflow using multiprocessing architecture

A Multimodal Approach to Sarcasm Detection on Social Media

In recent times, a major share of human communication takes place online. The main reason being the ease of communication on social networking sites (SNSs). Due to the variety and large number of users, SNSs have drawn the attention of the computer science (CS) community, particularly the affective computing (also known as emotional AI), information retrieval, natural language processing, and data mining groups. Researchers are trying to make computers understand the nuances of human communication including sentiment and sarcasm. Emotion or sentiment detection requires more insights about the communication than it does for factual information retrieval. Sarcasm detection is particularly more difficult than categorizing sentiment. Because, in sarcasm, the intended meaning of the expression by the user is opposite to the literal meaning. Because of its complex nature, it is often difficult even for human to detect sarcasm without proper context. However, people on social media succeed in detecting sarcasm despite interacting with strangers across the world. That motivates us to investigate the human process of detecting sarcasm on social media where abundant context information is often unavailable and the group of users communicating with each other are rarely well-acquainted. We have conducted a qualitative study to examine the patterns of users conveying sarcasm on social media. Whereas most sarcasm detection systems deal in word-by-word basis to accomplish their goal, we focused on the holistic sentiment conveyed by the post. We argue that utilization of word-level information will limit the systems performance to the domain of the dataset used to train the system and might not perform well for non-English language. As an endeavor to make our system less dependent on text data, we proposed a multimodal approach for sarcasm detection. We showed the applicability of images and reaction emoticons as other sources of hints about the sentiment of the post. Our research showed the superior results from a multimodal approach when compared to a unimodal approach. Multimodal sarcasm detection systems, as the one presented in this research, with the inclusion of more modes or sources of data might lead to a better sarcasm detection model

Metabolomics Data Processing and Data Analysis—Current Best Practices

Metabolomics data analysis strategies are central to transforming raw metabolomics data files into meaningful biochemical interpretations that answer biological questions or generate novel hypotheses. This book contains a variety of papers from a Special Issue around the theme “Best Practices in Metabolomics Data Analysis”. Reviews and strategies for the whole metabolomics pipeline are included, whereas key areas such as metabolite annotation and identification, compound and spectral databases and repositories, and statistical analysis are highlighted in various papers. Altogether, this book contains valuable information for researchers just starting in their metabolomics career as well as those that are more experienced and look for additional knowledge and best practice to complement key parts of their metabolomics workflows