Assembly of an interactive correlation network for the Arabidopsis genome using a novel heuristic clustering algorithm

Peer reviewedPublisher PD

Adaptation of the MapMan ontology to biotic stress responses: application in solanaceous species

<p>Abstract</p> <p>Background</p> <p>The results of transcriptome microarray analysis are usually presented as a list of differentially expressed genes. As these lists can be long, it is hard to interpret the desired experimental treatment effect on the physiology of analysed tissue, e.g. via selected metabolic or other pathways. For some organisms, gene ontologies and data visualization software have been implemented to overcome this problem, whereas for others, software adaptation is yet to be done.</p> <p>Results</p> <p>We present the classification of tentative potato contigs from the potato gene index (StGI) available from Dana-Farber Cancer Institute (DFCI) into the MapMan ontology to enable the application of the MapMan family of tools to potato microarrays. Special attention has been focused on mapping genes that could not be annotated based on similarity to Arabidopsis genes alone, thus possibly representing genes unique for potato. 97 such genes were classified into functional BINs (i.e. functional classes) after manual annotation. A new pathway, focusing on biotic stress responses, has been added and can be used for all other organisms for which mappings have been done. The BIN representation on the potato 10 k cDNA microarray, in comparison with all putative potato gene sequences, has been tested. The functionality of the prepared potato mapping was validated with experimental data on plant response to viral infection. In total 43,408 unigenes were mapped into 35 corresponding BINs.</p> <p>Conclusion</p> <p>The potato mappings can be used to visualize up-to-date, publicly available, expressed sequence tags (ESTs) and other sequences from GenBank, in combination with metabolic pathways. Further expert work on potato annotations will be needed with the ongoing EST and genome sequencing of potato. The current MapMan application for potato is directly applicable for analysis of data obtained on potato 10 k cDNA microarray by TIGR (The Institute for Genomic Research) but can also be used by researchers working on other potato gene sets. The potato mapping file and the stress mapping diagram are available from the MapMan website <abbrgrp><abbr bid="B1">1</abbr></abbrgrp>.</p

Quantitative RT-PCR Platform for Transcript Profiling of C-N Metabolism Related Genes in Durum Wheat: Study under a Future Climate Change Scenario

4 figuras. -- Póster presentado en el congreso: BIT's 6th Annual World Congress of Molecular & Cell Biology 2016. Theme: Unlocking the Secrets of Cells. Dalian (China), 25-28 de abril de 2016.Climate change is a major challenge to global food supply, thus is important understanding the mechanisms of crop responses to future environmental conditions. To achieve this goal, we developed a qRT-PCR platform for the expression analysis of more than a hundred C and N metabolism genes in durum wheat, based on available bread wheat genes and the identification of orthologs of known genes in other species. Additionally, we investigated the effect of elevated CO2 and temperature on primary metabolism of durum wheat grown in field chambers at two levels of N supply by combining transcript level analysis, using the qRT-PCR platform, with biochemical and physiological parameters in flag leaves at anthesis.This work was supported by the Spanish National R&D&i Plan of the Ministry of Economy and Competitiveness (grants AGL2006-13541-C02-02, AGL2009-11987 and BES-2010-031029 to R.V.).N

The potential of integrative phenomics to harness underutilized crops for improving stress resilience

The current agricultural and food system faces diverse and increasing challenges. These include feeding an ever-growing human population, expected to reach about 10 billion by 2050 combined with societal disruption, and the need to cope with the impact of climate change (FAO, 2022). Given that future environmental conditions will limit crop productivity (Zhao et al., 2017; Cooper et al., 2021) and the limited potential to continually increase the performance of staple crops by conventional breeding (Hickey et al., 2019), there is an urgent need to transform agricultural systems

SLocX: Predicting Subcellular Localization of Arabidopsis Proteins Leveraging Gene Expression Data

Despite the growing volume of experimentally validated knowledge about the subcellular localization of plant proteins, a well performing in silico prediction tool is still a necessity. Existing tools, which employ information derived from protein sequence alone, offer limited accuracy and/or rely on full sequence availability. We explored whether gene expression profiling data can be harnessed to enhance prediction performance. To achieve this, we trained several support vector machines to predict the subcellular localization of Arabidopsis thaliana proteins using sequence derived information, expression behavior, or a combination of these data and compared their predictive performance through a cross-validation test. We show that gene expression carries information about the subcellular localization not available in sequence information, yielding dramatic benefits for plastid localization prediction, and some notable improvements for other compartments such as the mitochondrion, the Golgi, and the plasma membrane. Based on these results, we constructed a novel subcellular localization prediction engine, SLocX, combining gene expression profiling data with protein sequence-based information. We then validated the results of this engine using an independent test set of annotated proteins and a transient expression of GFP fusion proteins. Here, we present the prediction framework and a website of predicted localizations for Arabidopsis. The relatively good accuracy of our prediction engine, even in cases where only partial protein sequence is available (e.g., in sequences lacking the N-terminal region), offers a promising opportunity for similar application to non-sequenced or poorly annotated plant species. Although the prediction scope of our method is currently limited by the availability of expression information on the ATH1 array, we believe that the advances in measuring gene expression technology will make our method applicable for all Arabidopsis proteins

Ontologies for increasing the FAIRness of plant research data

The importance of improving the FAIRness (findability, accessibility, interoperability, reusability) of research data is undeniable, especially in the face of large, complex datasets currently being produced by omics technologies. Facilitating the integration of a dataset with other types of data increases the likelihood of reuse, and the potential of answering novel research questions. Ontologies are a useful tool for semantically tagging datasets as adding relevant metadata increases the understanding of how data was produced and increases its interoperability. Ontologies provide concepts for a particular domain as well as the relationships between concepts. By tagging data with ontology terms, data becomes both human and machine interpretable, allowing for increased reuse and interoperability. However, the task of identifying ontologies relevant to a particular research domain or technology is challenging, especially within the diverse realm of fundamental plant research. In this review, we outline the ontologies most relevant to the fundamental plant sciences and how they can be used to annotate data related to plant-specific experiments within metadata frameworks, such as Investigation-Study-Assay (ISA). We also outline repositories and platforms most useful for identifying applicable ontologies or finding ontology terms.Comment: 34 pages, 4 figures, 1 table, 1 supplementary tabl

DataPLANT – Ein NFDI-Konsortium der Pflanzen-Grundlagenforschung

In der modernen hypothesen-basierten Forschung sind Forschende zwingend auf Dienste und Infrastrukturen für Forschungsdatenmanagement (FDM) angewiesen, welche die Erfassung, die Verarbeitung, den Austausch und die Archivierung von Forschungsdatensätzen erleichtern. Dabei schafft ein modernes FDM erst die Verknüpfung von interdisziplinärer Expertise, sowie Vergleich und Integration verschiedener Analyseergebnisse mit dem darauf beruhenden immensen zusätzlichen Erkenntnisgewinn. Das Ziel des Projektes DataPLANT[1] besteht darin, diesen Mehrwert für die Pflanzen-Grundlagenforschung zu schaffen. Auf diesem Fachgebiet werden die (molekularen) Prinzipien des pflanzlichen Lebens untersucht, welche beispielsweise das Pflanzenwachstum, den Ernteertrag oder die Biomasseproduktion bestimmen. Die hierzu eingesetzten Methoden von Transkriptomik, Proteomik und Metabolomik bis hin zu bildgebenden Verfahren erzeugen hochdimensionale, polymorphe Daten, die verarbeitet, fusioniert und interpretiert werden müssen. Eine erfolgreiche Nutzung von Daten unterschiedlicher Modalitäten – aus vielen Quellen und Experimenten, vorverarbeitet oder analysiert mit einer Vielzahl von Algorithmen – erfordert eine Kontextualisierung der Daten. Hierzu zählt die Annotation mit detaillierten Metadaten ebenso wie ein eindeutiges Referenzieren der jeweiligen Daten mit ihren Abhängigkeiten. Die FAIR Data[2] and Linked Open Data[3]-Prinzipien bieten entscheidende Richtlinien für den verantwortungsvollen Umgang mit Forschungsdaten.   [1] Homepage und Community-Portal von DataPLANT, https://www.nfdi4plants.de, aufgerufen am 31.01.2021. [2] Vgl. Wilkinson, Mark D. et al. „The FAIR Guiding Principles for scientific data management and stewardship“. Scientific Data 3, Nr. 160018 (2016): 1-9. https://doi.org/10.1038/sdata.2016.18 [3] Vgl. Bizer, Christian et al. „Linked Data on the web (LDOW2008).” Proceedings of the 17th international conference on World Wide Web, (2008): 1265-1266. https://doi.org/10.1145/1367497.1367760 rschungsdaten

Gene expression profiling in susceptible interaction of grapevine with its fungal pathogen Eutypa lata: Extending MapMan ontology for grapevine

<p>Abstract</p> <p>Background</p> <p>Whole genome transcriptomics analysis is a very powerful approach because it gives an overview of the activity of genes in certain cells or tissue types. However, biological interpretation of such results can be rather tedious. MapMan is a software tool that displays large datasets (e.g. gene expression data) onto diagrams of metabolic pathways or other processes and thus enables easier interpretation of results. The grapevine (<it>Vitis vinifera</it>) genome sequence has recently become available bringing a new dimension into associated research. Two microarray platforms were designed based on the TIGR Gene Index database and used in several physiological studies.</p> <p>Results</p> <p>To enable easy and effective visualization of those and further experiments, annotation of <it>Vitis vinifera </it>Gene Index (VvGI version 5) to MapMan ontology was set up. Due to specificities of grape physiology, we have created new pictorial representations focusing on three selected pathways: carotenoid pathway, terpenoid pathway and phenylpropanoid pathway, the products of these pathways being important for wine aroma, flavour and colour, as well as plant defence against pathogens. This new tool was validated on Affymetrix microarrays data obtained during berry ripening and it allowed the discovery of new aspects in process regulation. We here also present results on transcriptional profiling of grape plantlets after exposal to the fungal pathogen <it>Eutypa lata </it>using Operon microarrays including visualization of results with MapMan. The data show that the genes induced in infected plants, encode pathogenesis related proteins and enzymes of the flavonoid metabolism, which are well known as being responsive to fungal infection.</p> <p>Conclusion</p> <p>The extension of MapMan ontology to grapevine together with the newly constructed pictorial representations for carotenoid, terpenoid and phenylpropanoid metabolism provide an alternative approach to the analysis of grapevine gene expression experiments performed with Affymetrix or Operon microarrays. MapMan was first validated on an already published dataset and later used to obtain an overview of transcriptional changes in a susceptible grapevine – <it>Eutypa lata </it>interaction at the time of symptoms development, where we showed that the responsive genes belong to families known to be involved in the plant defence towards fungal infection (PR-proteins, enzymes of the phenylpropanoid pathway).</p

GMDCSB.DB: the Golm Metabolome Database

Summary: Metabolomics, in particular gas chromatography-mass spectrometry (GC-MS) based metabolite profiling of biological extracts, is rapidly becoming one of the cornerstones of functional genomics and systems biology. Metabolite profiling has profound applications in discovering the mode of action of drugs or herbicides, and in unravelling the effect of altered gene expression on metabolism and organism performance in biotechnological applications. As such the technology needs to be available to many laboratories. For this, an open exchange of information is required, like that already achieved for transcript and protein data. One of the key-steps in metabolite profiling is the unambiguous identification of metabolites in highly complex metabolite preparations from biological samples. Collections of mass spectra, which comprise frequently observed metabolites of either known or unknown exact chemical structure, represent the most effective means to pool the identification efforts currently performed in many laboratories around the world. Here we present GMD, The Golm Metabolome Database, an open access metabolome database, which should enable these processes. GMD provides public access to custom mass spectral libraries, metabolite profiling experiments as well as additional information and tools, e.g. with regard to methods, spectral information or compounds. The main goal will be the representation of an exchange platform for experimental research activities and bioinformatics to develop and improve metabolomics by multidisciplinary cooperation. Availability: http://csbdb.mpimp-golm.mpg.de/gmd.html Contact: [email protected] Supplementary information: http://csbdb.mpimp-golm.mpg.d