sc-SynO use case 2: proliferative cardiomyocyte identification in the validation datasets

Validation of the sc-SynO model for the second use case of proliferative cardiomyocytes annotation. a) UMAP representation of the manually clustered single-nuclei dataset of Linscheid et al. (2019) Precicted cells of sc-SynO are highlighted in blue (based on top 20 selected features in the training model), red (based on top 100 selected features in the training model) cells not chosen are grey. b) UMAP representation of the manually clustered dataset of Vidal et al. (2020). PPrecicted cells of ...

Creators: Markus Wolfien, Saptarshi Bej

Submitter: Markus Wolfien

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sc-SynO use case 1: cardiac glial cell identification in the validation datasets

Validation of the sc-SynO model for the first use case of cardiac glial cell annotation. UMAP representation of the manually clustered Bl6 dataset of Wolfien et al. (2020) Precicted cells of sc-SynO are highlighted in blue, cells not chosen are grey. UMAP representation of the manually clustered dataset of Vidal (2019). Precicted cells of sc-SynO are highlighted in blue, cells not chosen are grey. Average expression of the respective top five cardiac glial cell marker genes for both validation ...

Creators: Markus Wolfien, Saptarshi Bej

Submitter: Markus Wolfien

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R script to generate the preprocessing input for scSynO

Here is the detailed R script to generate the input needed by scSynO for synthetic cell generation and classification model training.

The code that can be embedded into any other Seurat data processing workflow is:

cell_expression_target_cluster <- as.matrix(GetAssayData(seuratobject, slot = "data")[, WhichCells(seuratobject, ident = "target_cluster_number")]) cell_expression_all_other_clusters <- as.matrix(GetAssayData(seuratobject, slot = "data")[, WhichCells(seuratobject, ident = ...

Creator: Markus Wolfien

Submitter: Markus Wolfien

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R script for scSynO usage of the Allen Brain Atlas data

Single nuclei transcriptomics data as .csv files from the Allen Brain atlas data set of mus musculus (https://celltypes.brain-map.org/) have been utilized as an input for scSynO. The underlying analysis is part of the manuscript entitled "Automated annotation of rare-cell types from single-cell RNA-sequencing data through synthetic oversampling". Data anaylsis and visalizations were mainly generated with the Seurat R package (https://satijalab.org/seurat/archive/v3.2/spatial_vignette.html)

Creator: Markus Wolfien

Submitter: Markus Wolfien

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Dynamic model of glycolysis, pyruvate metabolism and NoxE in M. pneumonaie with innitial parameter values and annotation

Dynamic model of glycolysis, pyruvate metabolism and NoxE. The model is parameterized by selecting the best out of 100 parameter set using Copasi's Genetic algorithm with 1000 itterations and 500 simmulatanious models.

Creator: Niels Zondervan

Submitter: Niels Zondervan

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Harmonizing semantic annotations for computational models in biology

Abstract (Expand)

Life science researchers use computational models to articulate and test hypotheses about the behavior of biological systems. Semantic annotation is a critical component for enhancing the interoperability … and reusability of such models as well as for the integration of the data needed for model parameterization and validation. Encoded as machine-readable links to knowledge resource terms, semantic annotations describe the computational or biological meaning of what models and data represent. These annotations help researchers find and repurpose models, accelerate model composition and enable knowledge integration across model repositories and experimental data stores. However, realizing the potential benefits of semantic annotation requires the development of model annotation standards that adhere to a community-based annotation protocol. Without such standards, tool developers must account for a variety of annotation formats and approaches, a situation that can become prohibitively cumbersome and which can defeat the purpose of linking model elements to controlled knowledge resource terms. Currently, no consensus protocol for semantic annotation exists among the larger biological modeling community. Here, we report on the landscape of current annotation practices among the COmputational Modeling in BIology NEtwork community and provide a set of recommendations for building a consensus approach to semantic annotation.

Authors: Maxwell Lewis Neal, Matthias König, David Nickerson, Göksel Mısırlı, Reza Kalbasi, Andreas Dräger, Koray Atalag, Vijayalakshmi Chelliah, Michael T Cooling, Daniel L Cook, Sharon Crook, Miguel de Alba, Samuel H Friedman, Alan Garny, John H Gennari, Padraig Gleeson, Martin Golebiewski, Michael Hucka, Nick Juty, Chris Myers, Brett G Olivier, Herbert M Sauro, Martin Scharm, Jacky L Snoep, Vasundra Touré, Anil Wipat, Olaf Wolkenhauer, Dagmar Waltemath

Date Published: 1st Mar 2019

Publication Type: Journal

The German Corona Consensus Dataset (GECCO): A standardized dataset for COVID-19 research

Abstract (Expand)

Background: The current COVID-19 pandemic has led to a surge of research activity. While this research provides important insights, the multitude of studies results in an increasing segmentation of … information. To ensure comparability across projects and institutions, standard datasets are needed. Here, we introduce the "German Corona Consensus Dataset" (GECCO), a uniform dataset that uses international terminologies and health IT standards to improve interoperability of COVID-19 data. Methods: Based on previous work (e.g., the ISARIC-WHO COVID-19 case report form) and in coordination with experts from university hospitals, professional associations and research initiatives, data elements relevant for COVID-19 research were collected, prioritized and consolidated into a compact core dataset. The dataset was mapped to international terminologies, and the Fast Healthcare Interoperability Resources (FHIR) standard was used to define interoperable, machine-readable data formats. Results: A core dataset consisting of 81 data elements with 281 response options was defined, including information about, for example, demography, anamnesis, symptoms, therapy, medications or laboratory values of COVID-19 patients. Data elements and response options were mapped to SNOMED CT, LOINC, UCUM, ICD-10-GM and ATC, and FHIR profiles for interoperable data exchange were defined. Conclusion: GECCO provides a compact, interoperable dataset that can help to make COVID-19 research data more comparable across studies and institutions. The dataset will be further refined in the future by adding domain-specific extension modules for more specialized use cases.

Authors: Julian Sass, Alexander Bartschke, Moritz Lehne, Andrea Essenwanger, Eugenia Rinaldi, Stefanie Rudolph, Kai Uwe Heitmann, Joerg Janne Vehreschild, Christof von Kalle, Sylvia Thun

Date Published: 29th Jul 2020

Publication Type: Journal

Cultivar-specific transcriptome and pan-transcriptome reconstruction of tetraploid potato

Abstract (Expand)

Background: Although the reference genome of Solanum tuberosum group Phureja double-monoploid (DM) clone is available, knowledge on the genetic diversity of the highly heterozygous tetraploid group … Tuberosum, representing most cultivated varieties, remains largely unexplored. This lack of knowledge hinders further progress in potato research and its subsequent applications in breeding. Results: For the DM genome assembly, two only partially-overlapping gene models exist differing in a unique set of genes and intron/exon structure predictions. First step was to merge and manually curate the merged gene model, creating a union of genes in Phureja scaffold. We next compiled available RNA-Seq datasets (cca. 1.5 billion reads) for three tetraploid potato genotypes (cultivar Désirée, cultivar Rywal, and breeding clone PW363) with diverse breeding pedigrees. Short-read transcriptomes were assembled using CLC, Trinity, Velvet, and rnaSPAdes de novo assemblers using different settings to test for optimal outcome. In addition, for cultivar Rywal, PacBio Iso-Seq full-length transcriptome sequencing was also performed. Revised EvidentialGene redundancy-reducing pipeline was employed to produce accurate and complete cultivar-specific transcriptomes from assemblers output, as well as to attain the pan-transcriptome. Due to being the most diverse dataset in terms of tissues (stem, seedlings and roots) and experimental conditions, cv. Désirée was the most complete transcriptome (95.8% BUSCO completeness). For cv. Rywal and breeding clone PW363 data were available for leaf samples only and the resulting transcriptomes were less complete than cv. Désirée (89.8% and 89.3% BUSCO completeness, respectively). Cross comparison of these cultivar-specific transcriptomes and merged DM gene model suggests that the core potato transcriptome is comprised of 16,339 genes. The pan-transcriptome contains a total of 95,779 transcripts, of which 54,614 transcripts are not present in the Phureja genome. These represent the variants of the novel genes found in the potato pan-genome. Conclusions: Our analysis shows that the available gene model of double-monoploid potato from group Phureja is, to some degree, not complete. The generated transcriptomes and pan-transcriptome represent a valuable resource for potato gene variability exploration, high-throughput -omics analyses, and future breeding programmes.

Authors: Marko Petek, Maja Zagorščak, Živa Ramšak, Sheri Sanders, Elizabeth Tseng, Mohamed Zouine, Anna Coll, Kristina Gruden

Date Published: No date defined

Publication Type: Not specified

Annotation and merging of SBML models with semanticSBML

Abstract (Expand)

SUMMARY: Systems Biology Markup Language (SBML) is the leading exchange format for mathematical models in Systems Biology. Semantic annotations link model elements with external knowledge via unique … database identifiers and ontology terms, enabling software to check and process models by their biochemical meaning. Such information is essential for model merging, one of the key steps towards the construction of large kinetic models. SemanticSBML is a tool that helps users to check and edit MIRIAM annotations and SBO terms in SBML models. Using a large collection of biochemical names and database identifiers, it supports modellers in finding the right annotations and in merging existing models. Initially, an element matching is derived from the MIRIAM annotations and conflicting element attributes are categorized and highlighted. Conflicts can then be resolved automatically or manually, allowing the user to control the merging process in detail. AVAILABILITY: SemanticSBML comes as a free software written in Python and released under the GPL 3. A Debian package, a source package for other Linux distributions, a Windows installer and an online version of semanticSBML with limited functionality are available at http://www.semanticsbml.org. A preinstalled version can be found on the Linux live DVD SB.OS, available at http://www.sbos.eu. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Authors: , Jannis Uhlendorf, Timo Lubitz, Marvin Schulz, , Wolfram Liebermeister

Date Published: 17th Nov 2009

Publication Type: Not specified

Stealthy annotation of experimental biology by spreadsheets

Abstract (Expand)

The increase in volume and complexity of biological data has led to increased requirements to reuse that data. Consistent and accurate metadata is essential for this task, creating new challenges in … semantic data annotation and in the constriction of terminologies and ontologies used for annotation. The BioSharing community are developing standards and terminologies for annotation, which have been adopted across bioinformatics, but the real challenge is to make these standards accessible to laboratory scientists. Widespread adoption requires the provision of tools to assist scientists whilst reducing the complexities of working with semantics. This paper describes unobtrusive ‘stealthy’ methods for collecting standards compliant, semantically annotated data and for contributing to ontologies used for those annotations. Spreadsheets are ubiquitous in laboratory data management. Our spreadsheet-based RightField tool enables scientists to structure information and select ontology terms for annotation within spreadsheets, producing high quality, consistent data without changing common working practices. Furthermore, our Populous spreadsheet tool proves effective for gathering domain knowledge in the form of Web Ontology Language (OWL) ontologies. Such a corpus of structured and semantically enriched knowledge can be extracted in Resource Description Framework (RDF), providing further means for searching across the content and contributing to Open Linked Data (http://linkeddata.org/)

Authors: , , Matthew Horridge, Simon Jupp, , , , , Robert Stevens,

Date Published: 1st Feb 2013

Publication Type: Journal

RightField: embedding ontology annotation in spreadsheets.

Abstract (Expand)

MOTIVATION: In the Life Sciences, guidelines, checklists and ontologies describing what metadata is required for the interpretation and reuse of experimental data are emerging. Data producers, however, … may have little experience in the use of such standards and require tools to support this form of data annotation. RESULTS: RightField is an open source application that provides a mechanism for embedding ontology annotation support for Life Science data in Excel spreadsheets. Individual cells, columns or rows can be restricted to particular ranges of allowed classes or instances from chosen ontologies. The RightField-enabled spreadsheet presents selected ontology terms to the users as a simple drop-down list, enabling scientists to consistently annotate their data. The result is 'semantic annotation by stealth', with an annotation process that is less error-prone, more efficient, and more consistent with community standards. AVAILABILITY AND IMPLEMENTATION: RightField is open source under a BSD license and freely available from http://www.rightfield.org.uk

Authors: , , Matthew Horridge, , , , ,

Date Published: 15th Jul 2011

Publication Type: Journal

My data are your data

Abstract (Expand)

Encouraging more broad and inclusive data sharing in today's world will involve concerted community efforts to overcome technical barriers and human foibles. Vivien Marx investigates. (includes …s comments from Carole Goble, and mentions SysMO, SEEK and RightField).

Author: Vivien Marx

Date Published: 7th Jun 2012

Publication Type: Not specified

Graph based storage and retrieval of computational models

Presented by Ron Henkel at SySMO PALs meeting 29-30 November 2012 in Heidelberg

Creator: Ron Henkel

Submitter: Olga Krebs

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