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See detailThe BIOMarkers in Atopic Dermatitis and Psoriasis (BIOMAP) Glossary: developing a lingua franca to facilitate data harmonisation and cross-cohort analyses
Broderick, Conor; Christian, Nils; Apfelbacher, Christian et al

in British Journal of Dermatology (2021)

Dear Editor, BIOMAP (BIOMarkers in Atopic dermatitis and Psoriasis) is a large European consortium aiming to advance personalised medicine for atopic dermatitis and psoriasis by identifying biomarkers ... [more ▼]

Dear Editor, BIOMAP (BIOMarkers in Atopic dermatitis and Psoriasis) is a large European consortium aiming to advance personalised medicine for atopic dermatitis and psoriasis by identifying biomarkers which predict therapeutic response and disease progression. BIOMAP brings together clinicians, researchers, patient organisations and pharmaceutical industry partners and encompasses data from over 60 individual studies, including randomised clinical trials, population-based cohorts and deeply-phenotyped disease registries. The curation and harmonisation of data and bio-samples from these established studies will facilitate cross-cohort clinical and molecular analyses, increasing the potential to identify small effect estimates and to better stratify disease subtypes. This letter serves to disseminate BIOMAP's pathway to data harmonisation and will inform future collaborative research endeavours. [less ▲]

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See detailCOBREXA.jl: constraint-based reconstruction and exascale analysis
Kratochvil, Miroslav UL; Heirendt, Laurent UL; Wilken, St Elmo et al

in Bioinformatics (2021)

COBREXA.jl is a Julia package for scalable, high-performance constraint-based reconstruction and analysis of very large-scale biological models. Its primary purpose is to facilitate the integration of ... [more ▼]

COBREXA.jl is a Julia package for scalable, high-performance constraint-based reconstruction and analysis of very large-scale biological models. Its primary purpose is to facilitate the integration of modern high performance computing environments with the processing and analysis of large-scale metabolic models of challenging complexity. We report the architecture of the package, and demonstrate how the design promotes analysis scalability on several use-cases with multi-organism community models.https://doi.org/10.17881/ZKCR-BT30.Supplementary data are available at Bioinformatics online. [less ▲]

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See detailPredictProtein - Predicting Protein Structure and Function for 29 Years
Bernhofer, Michael; Dallago, Christian; Karl, Tim et al

in Nucleic Acids Research (2021)

Since 1992 PredictProtein (https://predictprotein.org) is a one-stop online resource for protein sequence analysis with its main site hosted at the Luxembourg Centre for Systems Biomedicine (LCSB) and ... [more ▼]

Since 1992 PredictProtein (https://predictprotein.org) is a one-stop online resource for protein sequence analysis with its main site hosted at the Luxembourg Centre for Systems Biomedicine (LCSB) and queried monthly by over 3,000 users in 2020. PredictProtein was the first Internet server for protein predictions. It pioneered combining evolutionary information and machine learning. Given a protein sequence as input, the server outputs multiple sequence alignments, predictions of protein structure in 1D and 2D (secondary structure, solvent accessibility, transmembrane segments, disordered regions, protein flexibility, and disulfide bridges) and predictions of protein function (functional effects of sequence variation or point mutations, Gene Ontology (GO) terms, subcellular localization, and protein-, RNA-, and DNA binding). PredictProtein's infrastructure has moved to the LCSB increasing throughput; the use of MMseqs2 sequence search reduced runtime five-fold (apparently without lowering performance of prediction methods); user interface elements improved usability, and new prediction methods were added. PredictProtein recently included predictions from deep learning embeddings (GO and secondary structure) and a method for the prediction of proteins and residues binding DNA, RNA, or other proteins. PredictProtein.org aspires to provide reliable predictions to computational and experimental biologists alike. All scripts and methods are freely available for offline execution in high-throughput settings. [less ▲]

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See detailCardiovascular RNA markers and artificial intelligence may improve COVID-19 outcome: a position paper from the EU-CardioRNA COST Action CA17129
Badimon, Lina; Robinson, Emma L.; Jusic, Amela et al

in Cardiovascular Research (2021)

The coronavirus disease 2019 (COVID-19) pandemic has been as unprecedented as unexpected, affecting more than 105 million people worldwide as of 8 February 2020 and causing more than 2.3 million deaths ... [more ▼]

The coronavirus disease 2019 (COVID-19) pandemic has been as unprecedented as unexpected, affecting more than 105 million people worldwide as of 8 February 2020 and causing more than 2.3 million deaths according to the World Health Organization (WHO). Not only affecting the lungs but also provoking acute respiratory distress, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is able to infect multiple cell types including cardiac and vascular cells. Hence a significant proportion of infected patients develop cardiac events, such as arrhythmias and heart failure. Patients with cardiovascular comorbidities are at highest risk of cardiac death. To face the pandemic and limit its burden, health authorities have launched several fast-track calls for research projects aiming to develop rapid strategies to combat the disease, as well as longer-term projects to prepare for the future. Biomarkers have the possibility to aid in clinical decision-making and tailoring healthcare in order to improve patient quality of life. The biomarker potential of circulating RNAs has been recognized in several disease conditions, including cardiovascular disease. RNA biomarkers may be useful in the current COVID-19 situation. The discovery, validation, and marketing of novel biomarkers, including RNA biomarkers, require multi-centre studies by large and interdisciplinary collaborative networks, involving both the academia and the industry. Here, members of the EU-CardioRNA COST Action CA17129 summarize the current knowledge about the strain that COVID-19 places on the cardiovascular system and discuss how RNA biomarkers can aid to limit this burden. They present the benefits and challenges of the discovery of novel RNA biomarkers, the need for networking efforts, and the added value of artificial intelligence to achieve reliable advances. [less ▲]

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See detailCOVID19 Disease Map, a computational knowledge repository of virus-host interaction mechanisms.
Ostaszewski, Marek UL; Niarakis, Anna; Mazein, Alexander UL et al

in Molecular systems biology (2021), 17(10), 10387

We need to effectively combine the knowledge from surging literature with complex datasets to propose mechanistic models of SARS-CoV-2 infection, improving data interpretation and predicting key targets ... [more ▼]

We need to effectively combine the knowledge from surging literature with complex datasets to propose mechanistic models of SARS-CoV-2 infection, improving data interpretation and predicting key targets of intervention. Here, we describe a large-scale community effort to build an open access, interoperable and computable repository of COVID-19 molecular mechanisms. The COVID-19 Disease Map (C19DMap) is a graphical, interactive representation of disease-relevant molecular mechanisms linking many knowledge sources. Notably, it is a computational resource for graph-based analyses and disease modelling. To this end, we established a framework of tools, platforms and guidelines necessary for a multifaceted community of biocurators, domain experts, bioinformaticians and computational biologists. The diagrams of the C19DMap, curated from the literature, are integrated with relevant interaction and text mining databases. We demonstrate the application of network analysis and modelling approaches by concrete examples to highlight new testable hypotheses. This framework helps to find signatures of SARS-CoV-2 predisposition, treatment response or prioritisation of drug candidates. Such an approach may help deal with new waves of COVID-19 or similar pandemics in the long-term perspective. [less ▲]

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See detailRoad to effective data curation for translational research
Gu, Wei UL; Hasan, Samiul; Rocca-Serra, Philippe et al

in Drug Discovery Today (2020)

Translational research today is data-intensive and requires multi-stakeholder collaborations to generate and pool data together for integrated analysis. This leads to the challenge of harmonization of ... [more ▼]

Translational research today is data-intensive and requires multi-stakeholder collaborations to generate and pool data together for integrated analysis. This leads to the challenge of harmonization of data from different sources with different formats and standards, which is often overlooked during project planning and thus becomes a bottleneck of the research progress. We report on our experience and lessons learnt about data curation for translational research garnered over the course of the eTRIKS program (https://www.etriks.org), a unique, 5-year, cross-organizational, cross-cultural collaboration project funded by the Innovative Medicines Initiative of the EU. Here, we discuss the obstacles and suggest what steps are needed for effective data curation in translational research, especially for projects involving multiple organizations from academia and industry. [less ▲]

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See detailSupporting findability of COVID-19 research with large-scale text mining of scientific publications
Welter, Danielle UL; Vega Moreno, Carlos Gonzalo UL; Biryukov, Maria UL et al

Poster (2020, November 27)

When the COVID-19 pandemic hit in early 2020, a lot of research efforts were quickly redirected towards studies on SARS-CoV2 and COVID-19 disease, from the sequencing and assembly of viral genomes to the ... [more ▼]

When the COVID-19 pandemic hit in early 2020, a lot of research efforts were quickly redirected towards studies on SARS-CoV2 and COVID-19 disease, from the sequencing and assembly of viral genomes to the elaboration of robust testing methodologies and the development of treatment and vaccination strategies. At the same time, a flurry of scientific publications around SARS-CoV-2 and COVID-19 began to appear, making it increasingly difficult for researchers to stay up-to-date with latest trends and developments in this rapidly evolving field. The BioKB platform is a pipeline which, by exploiting text mining and semantic technologies, helps researchers easily access semantic content of thousands of abstracts and full text articles. The content of the articles is analysed and concepts from a range of contexts, including proteins, species, chemicals, diseases and biological processes are tagged based on existing dictionaries of controlled terms. Co-occurring concepts are classified based on their asserted relationship and the resulting subject-relation-object triples are stored in a publicly accessible human- and machine-readable knowledge base. All concepts in the BioKB dictionaries are linked to stable, persistent identifiers, either a resource accession such as an Ensembl, Uniprot or PubChem ID for genes, proteins and chemicals, or an ontology term ID for diseases, phenotypes and other ontology terms. In order to improve COVID-19 related text mining, we extended the underlying dictionaries to include many additional viral species (via NCBI Taxonomy identifiers), phenotypes from the Human Phenotype Ontology (HPO), COVID-related concepts including clinical and laboratory tests from the COVID-19 ontology, as well as additional diseases (DO) and biological processes (GO). We also added all viral proteins found in UniProt and gene entries from EntrezGene to increase the sensitivity of the text mining pipeline to viral data. To date, BioKB has indexed over 270’000 sentences from 21’935 publications relating to coronavirus infections, with publications dating from 1963 to 2021, 3’863 of which were published this year. We are currently working to further refine the text mining pipeline by training it on the extraction of increasingly complex relations such as protein-phenotype relationships. We are also regularly adding new terms to our dictionaries for areas where coverage is currently low, such as clinical and laboratory tests and procedures and novel drug treatments. [less ▲]

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See detailGigaSOM.jl: High-performance clustering and visualization of huge cytometry datasets
Kratochvil, Miroslav UL; Hunewald, Oliver; Heirendt, Laurent UL et al

in GigaScience (2020), 9(11),

Background: The amount of data generated in large clinical and phenotyping studies that use single-cell cytometry is constantly growing. Recent technological advances allow the easy generation of data ... [more ▼]

Background: The amount of data generated in large clinical and phenotyping studies that use single-cell cytometry is constantly growing. Recent technological advances allow the easy generation of data with hundreds of millions of single-cell data points with >40 parameters, originating from thousands of individual samples. The analysis of that amount of high-dimensional data becomes demanding in both hardware and software of high-performance computational resources. Current software tools often do not scale to the datasets of such size; users are thus forced to downsample the data to bearable sizes, in turn losing accuracy and ability to detect many underlying complex phenomena. Results: We present GigaSOM.jl, a fast and scalable implementation of clustering and dimensionality reduction for flow and mass cytometry data. The implementation of GigaSOM.jl in the high-level and high-performance programming language Julia makes it accessible to the scientific community and allows for efficient handling and processing of datasets with billions of data points using distributed computing infrastructures. We describe the design of GigaSOM.jl, measure its performance and horizontal scaling capability, and showcase the functionality on a large dataset from a recent study. Conclusions: GigaSOM.jl facilitates the use of commonly available high-performance computing resources to process the largest available datasets within minutes, while producing results of the same quality as the current state-of-art software. Measurements indicate that the performance scales to much larger datasets. The example use on the data from a massive mouse phenotyping effort confirms the applicability of GigaSOM.jl to huge-scale studies. [less ▲]

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See detailBioKC: a platform for quality controlled curation and annotation of systems biology models
Vega Moreno, Carlos Gonzalo UL; Groues, Valentin UL; Ostaszewski, Marek UL et al

Scientific Conference (2020, September 04)

Standardisation of biomedical knowledge into systems biology models is essential for the study of the biological function. However, biomedical knowledge curation is a laborious manual process aggravated ... [more ▼]

Standardisation of biomedical knowledge into systems biology models is essential for the study of the biological function. However, biomedical knowledge curation is a laborious manual process aggravated by the ever increasing growth of biomedical literature. High quality curation currently relies on pathway databases where outsider participation is minimal. The increasing demand of systems biology knowledge presents new challenges regarding curation, calling for new collaborative functionalities to improve quality control of the review process. These features are missing in the current systems biology environment, whose tools are not well suited for an open community-based model curation workflow. On one hand, diagram editors such as CellDesigner or Newt provide limited annotation features. On the other hand, most popular text annotations tools are not aimed for biomedical text annotation or model curation. Detaching the model curation and annotation tasks from diagram editing improves model iteration and centralizes the annotation of such models with supporting evidence. In this vain, we present BioKC, a web-based platform for systematic quality-controlled collaborative curation and annotation of biomedical knowledge following the standard data model from Systems Biology Markup Language (SBML). [less ▲]

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See detailGenetic Architecture of Parkinson's Disease in the Indian Population: Harnessing Genetic Diversity to Address Critical Gaps in Parkinson's Disease Research.
Rajan, Roopa; Divya, K. P.; Kandadai, Rukmini Mridula et al

in Frontiers in neurology (2020), 11

Over the past two decades, our understanding of Parkinson's disease (PD) has been gleaned from the discoveries made in familial and/or sporadic forms of PD in the Caucasian population. The transferability ... [more ▼]

Over the past two decades, our understanding of Parkinson's disease (PD) has been gleaned from the discoveries made in familial and/or sporadic forms of PD in the Caucasian population. The transferability and the clinical utility of genetic discoveries to other ethnically diverse populations are unknown. The Indian population has been under-represented in PD research. The Genetic Architecture of PD in India (GAP-India) project aims to develop one of the largest clinical/genomic bio-bank for PD in India. Specifically, GAP-India project aims to: (1) develop a pan-Indian deeply phenotyped clinical repository of Indian PD patients; (2) perform whole-genome sequencing in 500 PD samples to catalog Indian genetic variability and to develop an Indian PD map for the scientific community; (3) perform a genome-wide association study to identify novel loci for PD and (4) develop a user-friendly web-portal to disseminate results for the scientific community. Our "hub-spoke" model follows an integrative approach to develop a pan-Indian outreach to develop a comprehensive cohort for PD research in India. The alignment of standard operating procedures for recruiting patients and collecting biospecimens with international standards ensures harmonization of data/bio-specimen collection at the beginning and also ensures stringent quality control parameters for sample processing. Data sharing and protection policies follow the guidelines established by local and national authorities.We are currently in the recruitment phase targeting recruitment of 10,200 PD patients and 10,200 healthy volunteers by the end of 2020. GAP-India project after its completion will fill a critical gap that exists in PD research and will contribute a comprehensive genetic catalog of the Indian PD population to identify novel targets for PD. [less ▲]

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See detailPrevalence of SARS-CoV-2 infection in the Luxembourgish population: the CON-VINCE study.
Snoeck, Chantal J.; Vaillant, Michel; Abdelrahman, Tamir et al

E-print/Working paper (2020)

BACKGROUND: After the World Health Organization declared the outbreak of coronavirus disease to be a public health emergency of international concern on January 30, 2020, the first SARS-CoV-2 infection ... [more ▼]

BACKGROUND: After the World Health Organization declared the outbreak of coronavirus disease to be a public health emergency of international concern on January 30, 2020, the first SARS-CoV-2 infection was detected in Luxembourg on February 29, 2020. Representative population-based data, including asymptomatic individuals for assessing the viral spread and immune response were, however, lacking worldwide. METHODS: Using a panel-based method, we implemented a representative sample of the Luxembourgish population based on age, gender and residency for testing for SARS-CoV-2 infection and antibody status in order to define prevalence irrespective of clinical symptoms. Participants were contacted via email to fill an online questionnaire before biosampling at local laboratories. All participants provided information related to clinical symptoms, epidemiology, socioeconomic and psychological assessments and underwent biosampling, rRT-PCR testing and serology for SARS-CoV-2. RESULTS: We included a total of 1862 individuals in our representative sample of the general Luxembourgish population. Of these, 5 individuals had a current positive result for infection with SARS-CoV-2 based on rRT-PCR. Four of these individuals were oligosymptomatic and one was asymptomatic. Overall we found a positive IgG antibody status in 35 individuals (1.97%), of which 11 reported to be tested positive by rRT-PCR for SARS-CoV-2 previously and showed in addition their IgG positive status also a positive status for IgA. Our data indicate a prevalence of 0.3% for active SARS-CoV-2 infection and an infection rate of 2.15% in the Luxembourgish population between 18 and 79 years of age. CONCLUSIONS: Luxembourgish residents show a low rate of acute infections after 7 weeks of confinement and present with an antibody profile indicative of a more recent immune response to SARS-CoV-2. All infected individuals were oligo- or asymptomatic. Bi-weekly follow-up visits over the next 2 months will inform about the viral spread by a- and oligosymptomatic carriers and the individual changes in the immune profile.Competing Interest StatementThe authors have declared no competing interest.Clinical TrialNCT04379297Funding StatementThe CON-VINCE Study is funded by the Research Fund Luxembourg (FNR; CON-VINCE) and the André Losch Foundation (Luxembourg).Author DeclarationsAll relevant ethical guidelines have been followed; any necessary IRB and/or ethics committee approvals have been obtained and details of the IRB/oversight body are included in the manuscript.YesAll necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived.YesI understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance).Yes I have followed all appropriate research reporting guidelines and uploaded the relevant EQUATOR Network research reporting checklist(s) and other pertinent material as supplementary files, if applicable.YesDue to ethical concerns, supporting data cannot be made openly available. [less ▲]

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See detailBioKC: a collaborative platform for systems biology model curation and annotation
Vega Moreno, Carlos Gonzalo UL; Groues, Valentin UL; Ostaszewski, Marek UL et al

in bioRxiv (2020)

Curation of biomedical knowledge into standardised and inter-operable systems biology models is essential for studying complex biological processes. However, systems-level curation is a laborious manual ... [more ▼]

Curation of biomedical knowledge into standardised and inter-operable systems biology models is essential for studying complex biological processes. However, systems-level curation is a laborious manual process, especially when facing ever increasing growth of domain literature. Currently, these systems-level curation efforts concentrate around dedicated pathway databases, with a limited input from the research community. The demand for systems biology knowledge increases with new findings demonstrating elaborate relationships between multiple molecules, pathways and cells. This new challenge calls for novel collaborative tools and platforms allowing to improve the quality and the output of the curation process. In particular, in the current systems biology environment, curation tools lack reviewing features and are not well suited for an open, community-based curation workflows. An important concern is the complexity of the curation process and the limitations of the tools supporting it. Currently, systems-level curation combines model-building with diagram layout design. However, diagram editing tools offer limited annotation features. On the other hand, text-oriented tools have insufficient capabilities representing and annotating relationships between biological entities. Separating model curation and annotation from diagram editing enables iterative and distributed building of annotated models. Here, we present BioKC (Biological Knowledge Curation), a web-based collaborative platform for the curation and annotation of biomedical knowledge following the standard data model from Systems Biology Markup Language (SBML).Competing Interest StatementThe authors have declared no competing interest. [less ▲]

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See detailDAISY: A Data Information System for accountability under the General Data Protection Regulation
Becker, Regina UL; Alper, Pinar UL; Groues, Valentin UL et al

in GigaScience (2019), 8(12),

The new European legislation on data protection, namely, the General Data Protection Regulation (GDPR), has introduced comprehensive requirements for the documentation about the processing of personal ... [more ▼]

The new European legislation on data protection, namely, the General Data Protection Regulation (GDPR), has introduced comprehensive requirements for the documentation about the processing of personal data as well as informing the data subjects of its use. GDPR’s accountability principle requires institutions, projects, and data hubs to document their data processings and demonstrate compliance with the GDPR. In response to this requirement, we see the emergence of commercial data-mapping tools, and institutions creating GDPR data register with such tools. One shortcoming of this approach is the genericity of tools, and their process-based model not capturing the project-based, collaborative nature of data processing in biomedical research.We have developed a software tool to allow research institutions to comply with the GDPR accountability requirement and map the sometimes very complex data flows in biomedical research. By analysing the transparency and record-keeping obligations of each GDPR principle, we observe that our tool effectively meets the accountability requirement.The GDPR is bringing data protection to center stage in research data management, necessitating dedicated tools, personnel, and processes. Our tool, DAISY, is tailored specifically for biomedical research and can help institutions in tackling the documentation challenge brought about by the GDPR. DAISY is made available as a free and open source tool on Github. DAISY is actively being used at the Luxembourg Centre for Systems Biomedicine and the ELIXIR-Luxembourg data hub. [less ▲]

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See detailData and knowledge management in translational research: implementation of the eTRIKS platform for the IMI OncoTrack consortium
Gu, Wei UL; Yildirimman, Reha; Van der Stuyft, Emmanuel et al

in BMC Bioinformatics (2019), 20(1), 164

For large international research consortia, such as those funded by the European Union’s Horizon 2020 programme or the Innovative Medicines Initiative, good data coordination practices and tools are ... [more ▼]

For large international research consortia, such as those funded by the European Union’s Horizon 2020 programme or the Innovative Medicines Initiative, good data coordination practices and tools are essential for the successful collection, organization and analysis of the resulting data. Research consortia are attempting ever more ambitious science to better understand disease, by leveraging technologies such as whole genome sequencing, proteomics, patient-derived biological models and computer-based systems biology simulations. [less ▲]

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See detailThe Luxembourg Parkinson’s Study: A Comprehensive Approach for Stratification and Early Diagnosis
Hipp Epouse D'amico, Géraldine UL; Vaillant, Michel; Diederich, Nico J. et al

in Frontiers in Aging Neuroscience (2018), 10

While genetic advances have successfully defined part of the complexity in Parkinson’s disease (PD), the clinical characterization of phenotypes remains challenging. Therapeutic trials and cohort studies ... [more ▼]

While genetic advances have successfully defined part of the complexity in Parkinson’s disease (PD), the clinical characterization of phenotypes remains challenging. Therapeutic trials and cohort studies typically include patients with earlier disease stages and exclude comorbidities, thus ignoring a substantial part of the real-world PD population. To account for these limitations, we implemented the Luxembourg PD study as a comprehensive clinical, molecular and device-based approach including patients with typical PD and atypical parkinsonism, irrespective of their disease stage, age, comorbidities, or linguistic background. To provide a large, longitudinally followed, and deeply phenotyped set of patients and controls for clinical and fundamental research on PD, we implemented an open-source digital platform that can be harmonized with international PD cohort studies. Our interests also reflect Luxembourg-specific areas of PD research, including vision, gait, and cognition. This effort is flanked by comprehensive biosampling efforts assuring high quality and sustained availability of body liquids and tissue biopsies. We provide evidence for the feasibility of such a cohort program with deep phenotyping and high quality biosampling on parkinsonism in an environment with structural specificities and alert the international research community to our willingness to collaborate with other centers. The combination of advanced clinical phenotyping approaches including device-based assessment will create a comprehensive assessment of the disease and its variants, its interaction with comorbidities and its progression. We envision the Luxembourg Parkinson’s study as an important research platform for defining early diagnosis and progression markers that translate into stratified treatment approaches. [less ▲]

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See detailFractalis: A scalable open-source service for platform-independent interactive visual analysis of biomedical data
Herzinger, Sascha UL; Groues, Valentin UL; Gu, Wei UL et al

in GigaScience (2018)

Background: Translational research platforms share the aim to promote a deeper understanding of stored data by providing visualization and analysis tools for data exploration and hypothesis generation ... [more ▼]

Background: Translational research platforms share the aim to promote a deeper understanding of stored data by providing visualization and analysis tools for data exploration and hypothesis generation. However, such tools are usually platform-bound and are not easily reusable by other systems. Furthermore, they rarely address access restriction issues when direct data transfer is not permitted. In this article we present an analytical service that works in tandem with a visualization library to address these problems. Findings: Using a combination of existing technologies and a platform-specific data abstraction layer we developed a service that is capable of providing existing web-based data warehouses and repositories with platform-independent visual analytical capabilities. The design of this service also allows for federated data analysis by eliminating the need to move the data directly to the researcher. Instead, all operations are based on statistics and interactive charts without direct access to the dataset. Conclusion: The software presented in this article has a potential to help translational researchers achieve a better understanding of a given dataset and quickly generate new hypothesis. Furthermore, it provides a framework that can be used to share and reuse explorative analysis tools within the community. [less ▲]

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See detailPresenting and Sharing Clinical Data using the eTRIKS Standards Master Tree for tranSMART
Barbosa-Silva, Adriano; Bratfalean, Dorina; Gu, Wei UL et al

in Bioinformatics (2018)

Motivation Standardization and semantic alignment have been considered one of the major challenges for data integration in clinical research. The inclusion of the CDISC SDTM clinical data standard into ... [more ▼]

Motivation Standardization and semantic alignment have been considered one of the major challenges for data integration in clinical research. The inclusion of the CDISC SDTM clinical data standard into the tranSMART i2b2 via a guiding master ontology tree positively impacts and supports the efficacy of data sharing, visualization and exploration across datasets. Results We present here a schema for the organization of SDTM variables into the tranSMART i2b2 tree along with a script and test dataset to exemplify the mapping strategy. The eTRIKS master tree concept is demonstrated by making use of fictitious data generated for four patients, including 16 SDTM clinical domains. We describe how the usage of correct visit names and data labels can help to integrate multiple readouts per patient and avoid ETL crashes when running a tranSMART loading routine. Availability The eTRIKS Master Tree package and test datasets are publicly available at https://doi.org/10.5281/zenodo.1009098 and a functional demo installation at https://public.etriks.org/transmart/datasetExplorer/ under eTRIKS - Master Tree branch, where the discussed examples can be visualized. [less ▲]

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See detailConfronting the catalytic dark matter encoded by sequenced genomes
Ellens, Kenneth W.; Christian, Nils; Satagopam, Venkata UL et al

in Nucleic Acids Research (2017), 45(20), 11495-11514

The post-genomic era has provided researchers with a deluge of protein sequences. However, a significant fraction of the proteins encoded by sequenced genomes remains without an identified function. Here ... [more ▼]

The post-genomic era has provided researchers with a deluge of protein sequences. However, a significant fraction of the proteins encoded by sequenced genomes remains without an identified function. Here, we aim at determining how many enzymes of uncertain or unknown function are still present in the Saccharomyces cerevisiae and human proteomes. Using information available in the Swiss-Prot, BRENDA and KEGG databases in combination with a Hidden Markov Model-based method, we estimate that >600 yeast and 2000 human proteins (>30% of their proteins of unknown function) are enzymes whose precise function(s) remain(s) to be determined. This illustrates the impressive scale of the ‘unknown enzyme problem’. We extensively review classical biochemical as well as more recent systematic experimental and computational approaches that can be used to support enzyme function discovery research. Finally, we discuss the possible roles of the elusive catalysts in light of recent developments in the fields of enzymology and metabolism as well as the significance of the unknown enzyme problem in the context of metabolic modeling, metabolic engineering and rare disease research. [less ▲]

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See detailSmartR: An open-source platform for interactive visual analytics for translational research data.
Herzinger, Sascha UL; Gu, Wei UL; Satagopam, Venkata UL et al

in Bioinformatics (2017)

In translational research, efficient knowledge exchange between the different fields of expertise is crucial. An open platform that is capable of storing a multitude of data types such as clinical, pre ... [more ▼]

In translational research, efficient knowledge exchange between the different fields of expertise is crucial. An open platform that is capable of storing a multitude of data types such as clinical, pre-clinical, or OMICS data combined with strong visual analytical capabilities will significantly accelerate the scientific progress by making data more accessible and hypothesis generation easier. The open data warehouse tranSMART is capable of storing a variety of data types and has a growing user community including both academic institutions and pharmaceutical companies. tranSMART, however, currently lacks interactive and dynamic visual analytics and does not permit any post-processing interaction or exploration. For this reason, we developed SmartR , a plugin for tranSMART, that equips the platform not only with several dynamic visual analytical workflows, but also provides its own framework for the addition of new custom workflows. Modern web technologies such as D3.js or AngularJS were used to build a set of standard visualizations that were heavily improved with dynamic elements. Contact: reinhard.schneider@uni.lu. Supplementary information: Supplementary data are available at Bioinformatics online. Availability: : The source code is licensed under the Apache 2.0 License and is freely available on GitHub: https://github.com/transmart/SmartR. [less ▲]

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See detailMINERVA—a platform for visualization and curation of molecular interaction networks
Gawron, Piotr; Ostaszewski, Marek UL; Satagopam, Venkata UL et al

in NPJ Systems Biology and Applications (2016)

Our growing knowledge about various molecular mechanisms is becoming increasingly more structured and accessible. Different repositories of molecular interactions and available literature enable ... [more ▼]

Our growing knowledge about various molecular mechanisms is becoming increasingly more structured and accessible. Different repositories of molecular interactions and available literature enable construction of focused and high-quality molecular interaction networks. Novel tools for curation and exploration of such networks are needed, in order to foster the development of a systems biology environment. In particular, solutions for visualization, annotation and data cross-linking will facilitate usage of network-encoded knowledge in biomedical research. To this end we developed the MINERVA (Molecular Interaction NEtwoRks VisuAlization) platform, a standalone webservice supporting curation, annotation and visualization of molecular interaction networks in Systems Biology Graphical Notation (SBGN)-compliant format. MINERVA provides automated content annotation and verification for improved quality control. The end users can explore and interact with hosted networks, and provide direct feedback to content curators. MINERVA enables mapping drug targets or overlaying experimental data on the visualized networks. Extensive export functions enable downloading areas of the visualized networks as SBGN-compliant models for efficient reuse of hosted networks. The software is available under Affero GPL 3.0 as a Virtual Machine snapshot, Debian package and Docker instance at http://r3lab.uni.lu/web/minerva-website/. We believe that MINERVA is an important contribution to systems biology community, as its architecture enables set-up of locally or globally accessible SBGN-oriented repositories of molecular interaction networks. Its functionalities allow overlay of multiple information layers, facilitating exploration of content and interpretation of data. Moreover, annotation and verification workflows of MINERVA improve the efficiency of curation of networks, allowing life-science researchers to better engage in development and use of biomedical knowledge repositories. [less ▲]

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