The Artificial Intelligence Workbench: a retrospective review

Last decade, biomedical and bioinformatics researchers have been demanding advanced and user-friendly applications for real use in practice. In this context, the Artificial Intelligence Workbench, an open-source Java desktop application framework for scientific software development, emerged with the goal of provid-ing support to both fundamental and applied research in the domain of transla-tional biomedicine and bioinformatics. AIBench automatically provides function-alities that are common to scientific applications, such as user parameter defini-tion, logging facilities, multi-threading execution, experiment repeatability, work-flow management, and fast user interface development, among others. Moreover, AIBench promotes a reusable component based architecture, which also allows assembling new applications by the reuse of libraries from existing projects or third-party software. Ten years have passed since the first release of AIBench, so it is time to look back and check if it has fulfilled the purposes for which it was conceived to and how it evolved over time

Analysis of protein complexes through model‐based biclustering of label‐free quantitative AP‐MS data

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/102630/1/msb201041.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/102630/2/msb201041-sup-0001.pd

The use of omic technologies applied to traditional Chinese medicine research

Version of RecordPublishe

Methods for visual mining of genomic and proteomic data atlases

<p>Abstract</p> <p>Background</p> <p>As the volume, complexity and diversity of the information that scientists work with on a daily basis continues to rise, so too does the requirement for new analytic software. The analytic software must solve the dichotomy that exists between the need to allow for a high level of scientific reasoning, and the requirement to have an intuitive and easy to use tool which does not require specialist, and often arduous, training to use. Information visualization provides a solution to this problem, as it allows for direct manipulation and interaction with diverse and complex data. The challenge addressing bioinformatics researches is how to apply this knowledge to data sets that are continually growing in a field that is rapidly changing.</p> <p>Results</p> <p>This paper discusses an approach to the development of visual mining tools capable of supporting the mining of massive data collections used in systems biology research, and also discusses lessons that have been learned providing tools for both local researchers and the wider community. Example tools were developed which are designed to enable the exploration and analyses of both proteomics and genomics based atlases. These atlases represent large repositories of raw and processed experiment data generated to support the identification of biomarkers through mass spectrometry (the PeptideAtlas) and the genomic characterization of cancer (The Cancer Genome Atlas). Specifically the tools are designed to allow for: the visual mining of thousands of mass spectrometry experiments, to assist in designing informed targeted protein assays; and the interactive analysis of hundreds of genomes, to explore the variations across different cancer genomes and cancer types.</p> <p>Conclusions</p> <p>The mining of massive repositories of biological data requires the development of new tools and techniques. Visual exploration of the large-scale atlas data sets allows researchers to mine data to find new meaning and make sense at scales from single samples to entire populations. Providing linked task specific views that allow a user to start from points of interest (from diseases to single genes) enables targeted exploration of thousands of spectra and genomes. As the composition of the atlases changes, and our understanding of the biology increase, new tasks will continually arise. It is therefore important to provide the means to make the data available in a suitable manner in as short a time as possible. We have done this through the use of common visualization workflows, into which we rapidly deploy visual tools. These visualizations follow common metaphors where possible to assist users in understanding the displayed data. Rapid development of tools and task specific views allows researchers to mine large-scale data almost as quickly as it is produced. Ultimately these visual tools enable new inferences, new analyses and further refinement of the large scale data being provided in atlases such as PeptideAtlas and The Cancer Genome Atlas.</p

The Influence of Spectral Quality on Primary and Secondary Metabolism of Hydroponically Grown Basil

This dissertation explores the influence of spectral quality from supplemental lighting and seasonal changes on primary and secondary metabolism in hydroponically grown greenhouse basil. It aims to enhance understanding of plant/light interactions and provide practical insights for light emitting diode (LED) manufacturers and commercial growers. The research is premised on the hypothesis that altering spectral quality can significantly impact primary and secondary metabolism, potentially improving flavor and increasing phytonutrients with health benefits. This project involved four phases, each building on the results of the previous ones. In Phase 1, different basil varieties were evaluated to determine aroma volatile profiles and concentrations of key secondary metabolites. In Phase 2, discrete narrow-band blue/red (B/R) wavelengths were used to investigate their impact on aroma volatile concentrations and secondary metabolic resource partitioning in basil, revealing the influence of both seasonal and supplemental lighting effects on plant metabolism. Phase 3 explored the impacts of full spectrum white LEDs and high pressure sodium (HPS) on yield and nutrient accumulation, comparing these to the optimal narrowband B/R identified in Phase 2. The final phase connected all phases, comparing the best narrowband and full spectrum treatments to a traditional HPS treatment and natural light control. These treatments were tested across various parameters, with photosynthesis and primary metabolic data recorded, yields and biometric data taken, aroma compound concentrations, and other secondary metabolic data collected. A sensory panel was conducted, and mRNA sequencing performed to determine differences in metabolic pathway expression based on lighting treatment. Analytical data from the different light treatments, sensory panel, and mRNA data were evaluated to determine which lighting regime had the most positive impact on plant physiology and biochemistry. Variation in spectral quality across seasons influences primary and secondary metabolism, in addition to the spectral qualities of different types of supplemental lighting treatments. This holistic, interdisciplinary approach revealed a light treatment that balances yield, nutrient content, and flavor preference, providing a superior product highly preferred by consumers. The research presented in this document significantly expands our understanding of the complex interplay between light conditions and plant physiology, with implications for improving crop yield and quality in controlled environment agriculture

Tissue and population-level diversity in plant secondary metabolism: a systematic exploration using MS/MS structural analysis

Plants are amazing synthetic chemists that create diversified secondary metabolites which play myriad ecological roles for their survival and reproductive fitness in nature. The structural complexity of secondary metabolism has severely hampered its functional analysis. The potential of MS-based metabolomics and of the large-scale acquisition of tandem MS (MS/MS) spectra is limited by the absence of straightforward classification and visualization pipelines so that secondary metabolite and the underlying pathway interpretations can be easily made. From a mechanistic standpoint, secondary metabolism diversity attributes to the occurrence of multiplicity of genes in plant genomes. Yet the majorities of metabolic gene functions remain however unknown. In this thesis, I developed a workflow to systematically explore the diversity of secondary metabolism in Nicotiana attenuata – a metabolically rich ecological model plant. I first characterize the metabolic space of this model plant using the large-scale acquisition of MS/MS spectral information in a data-independent manner and the computational re-assembly of non-redundant MS/MS spectra. The resulting MS signatures were then aligned and visualized to rapidly formulate structural hypotheses. Using natural variation, I examined the correlations among jasmonate signaling and large-scale defense metabolism. The resulting correlation maps uncovered new metabolic layers in a plant’s jasmonate-mediated defensive arsenal. In the tissue-level exploration of secondary metabolite diversity, Transciptomic and metabolomic information and their variance as analyzed by information theory were used for the predictions of tissue-specific function of genes responsible for the metabolic signatures

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)