11 results on '"List, Markus"'
Search Results
2. Computational strategies to combat COVID-19: useful tools to accelerate SARS-CoV-2 and coronavirus research.
- Author
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Hufsky F, Lamkiewicz K, Almeida A, Aouacheria A, Arighi C, Bateman A, Baumbach J, Beerenwinkel N, Brandt C, Cacciabue M, Chuguransky S, Drechsel O, Finn RD, Fritz A, Fuchs S, Hattab G, Hauschild AC, Heider D, Hoffmann M, Hölzer M, Hoops S, Kaderali L, Kalvari I, von Kleist M, Kmiecinski R, Kühnert D, Lasso G, Libin P, List M, Löchel HF, Martin MJ, Martin R, Matschinske J, McHardy AC, Mendes P, Mistry J, Navratil V, Nawrocki EP, O'Toole ÁN, Ontiveros-Palacios N, Petrov AI, Rangel-Pineros G, Redaschi N, Reimering S, Reinert K, Reyes A, Richardson L, Robertson DL, Sadegh S, Singer JB, Theys K, Upton C, Welzel M, Williams L, and Marz M
- Subjects
- Biomedical Research, COVID-19 epidemiology, COVID-19 virology, Genome, Viral, Humans, Pandemics, SARS-CoV-2 genetics, COVID-19 prevention & control, Computational Biology, SARS-CoV-2 isolation & purification
- Abstract
SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2) is a novel virus of the family Coronaviridae. The virus causes the infectious disease COVID-19. The biology of coronaviruses has been studied for many years. However, bioinformatics tools designed explicitly for SARS-CoV-2 have only recently been developed as a rapid reaction to the need for fast detection, understanding and treatment of COVID-19. To control the ongoing COVID-19 pandemic, it is of utmost importance to get insight into the evolution and pathogenesis of the virus. In this review, we cover bioinformatics workflows and tools for the routine detection of SARS-CoV-2 infection, the reliable analysis of sequencing data, the tracking of the COVID-19 pandemic and evaluation of containment measures, the study of coronavirus evolution, the discovery of potential drug targets and development of therapeutic strategies. For each tool, we briefly describe its use case and how it advances research specifically for SARS-CoV-2. All tools are free to use and available online, either through web applications or public code repositories. Contact:evbc@unj-jena.de., (© The Author(s) 2020. Published by Oxford University Press.)
- Published
- 2021
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- View/download PDF
3. In Silico Cell-Type Deconvolution Methods in Cancer Immunotherapy.
- Author
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Sturm G, Finotello F, and List M
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- Computer Simulation, Humans, Immune System cytology, Immune System immunology, Immune System metabolism, Models, Immunological, Neoplasms genetics, Neoplasms immunology, Transcriptome, Tumor Microenvironment, Computational Biology methods, Immunotherapy methods, Neoplasms therapy
- Abstract
Several computational methods have been proposed to infer the cellular composition from bulk RNA-seq data of a tumor biopsy sample. Elucidating interactions in the tumor microenvironment can yield unique insights into the status of the immune system. In immuno-oncology, this information can be crucial for deciding whether the immune system of a patient can be stimulated to target the tumor. Here, we shed a light on the working principles, capabilities, and limitations of the most commonly used methods for cell-type deconvolution in immuno-oncology and offer guidelines for method selection.
- Published
- 2020
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4. De Novo Pathway Enrichment with KeyPathwayMiner.
- Author
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Alcaraz N, Hartebrodt A, and List M
- Subjects
- Animals, Humans, Oligonucleotide Array Sequence Analysis methods, Protein Interaction Maps, Software, Computational Biology methods
- Abstract
Biomolecular networks such as protein-protein interaction networks provide a static picture of the interplay of genes and their products, and, consequently, they fail to capture dynamic changes taking place during the development of complex diseases. KeyPathwayMiner is a software platform designed to fill this gap by integrating previous knowledge captured in molecular interaction networks with OMICS datasets (DNA microarrays, RNA sequencing, genome-wide methylation studies, etc.) to extract connected subnetworks with a high number of deregulated genes. This protocol describes how to use KeyPathwayMiner for integrated analysis of multi-omics datasets in the network analysis tool Cytoscape and in a stand-alone web application available at https://keypathwayminer.compbio.sdu.dk .
- Published
- 2020
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5. Ten Simple Rules for Developing Usable Software in Computational Biology.
- Author
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List M, Ebert P, and Albrecht F
- Subjects
- Computational Biology, Guidelines as Topic, Software
- Abstract
Competing Interests: The authors have declared that no competing interests exist.
- Published
- 2017
- Full Text
- View/download PDF
6. KeyPathwayMinerWeb: online multi-omics network enrichment.
- Author
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List M, Alcaraz N, Dissing-Hansen M, Ditzel HJ, Mollenhauer J, and Baumbach J
- Subjects
- Case-Control Studies, Computational Biology statistics & numerical data, Datasets as Topic, Gene Expression Profiling, Gene Expression Regulation, Humans, Huntington Disease diagnosis, Internet, Protein Interaction Mapping, Computational Biology methods, Gene Regulatory Networks, Huntingtin Protein genetics, Huntington Disease genetics, User-Computer Interface
- Abstract
We present KeyPathwayMinerWeb, the first online platform for de novo pathway enrichment analysis directly in the browser. Given a biological interaction network (e.g. protein-protein interactions) and a series of molecular profiles derived from one or multiple OMICS studies (gene expression, for instance), KeyPathwayMiner extracts connected sub-networks containing a high number of active or differentially regulated genes (proteins, metabolites) in the molecular profiles. The web interface at (http://keypathwayminer.compbio.sdu.dk) implements all core functionalities of the KeyPathwayMiner tool set such as data integration, input of background knowledge, batch runs for parameter optimization and visualization of extracted pathways. In addition to an intuitive web interface, we also implemented a RESTful API that now enables other online developers to integrate network enrichment as a web service into their own platforms., (© The Author(s) 2016. Published by Oxford University Press on behalf of Nucleic Acids Research.)
- Published
- 2016
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7. EpiRegio: analysis and retrieval of regulatory elements linked to genes
- Author
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Baumgarten Nina, Hecker Dennis, Karunanithi Sivarajan, Schmidt Florian, List Markus, and Schulz Marcel H.
- Subjects
Epigenomics ,web server ,AcademicSubjects/SCI00010 ,Genomics ,Computational biology ,Biology ,Genome ,03 medical and health sciences ,0302 clinical medicine ,Genetics ,Humans ,Disease ,Regulatory Elements, Transcriptional ,Enhancer ,Gene ,030304 developmental biology ,Regulation of gene expression ,0303 health sciences ,enhancer target association ,Gene Expression Regulation ,Regulatory sequence ,Web Server Issue ,Chromatin Immunoprecipitation Sequencing ,enhancer ,gene regulation ,Candidate Disease Gene ,030217 neurology & neurosurgery ,Software ,Transcription Factors - Abstract
The data set contains all regulatory elements (REMs) and the additional information used to create the EpiRegio webserver (https://epiregio.de). The data set consists of 10 tables (CSV-files): GenomeAnnotation: contains information about genomeVersion, annotationVersion and databaseName (GenomeAnnotation_1.csv.gz) GeneAnnotation: Information of the genes (chr, start, end, geneID, geneSymbol, alternativeGeneID, isTF, strand and annotationVersion) (GeneAnnotation_1.csv.gz) GeneExpression of Blueprint and Roadmap: Per consortium one table containing information about geneID, sampleID, expressionLog2TPM and species (GeneExpression_Blueprint_1.csv.gz and GeneExpressionRoadmap_1.csv.gz) CellTypeInfo: Information of the used cell and tissue types (cellTypeID, cellTypeName and cellOntologyTerm) (CellTypeInfo.csv.gz) sampleInfo of Roadmap and Blueprint: Per consortium one table containing information about sampleID, originalSampleID, cellTypeID, origin and dataType (sampleInfo_Blueprint_1.csv.gz and sampleInfo_Roadmap_1.csv.gz) REMAnnotation: contains all predicted REMs using STITCHIT (chr, start, end, geneID, REMID, regressionCoefficient, pValue, normModelScore, meanDNase1Signal, sdDNase1Signal, consortium and version) (REMAnnotationModelScore_1.csv.gz) REMActivity: This table contains per REM the DNase-signal and the standardised DNase-signal per cell or tissue type (REMID, sampleID, dnase1Log2, standDnase1Log2 and version) (REMActivity_1.csv.gz) clusterREMs: contains all CREMs (REMID, CREMID, chr, start, end, REMsPerCREM and version) (clusterREMs_1.csv.gz) With these tables the underlying database of EpiRegio can easily be reconstructed. The source code for the current version of the EpiRegio webserver version is available at 10.5281/zenodo.3751189. EpiRegio uses the STITCHIT algorithm, which is currently under revision. The preprint is available at http://dx.doi.org/10.1101/585125., This work has been supported by the DZHK (German Centre for Cardiovascular Research, 81Z0200101) and the Cardio-Pulmonary Institute (CPI) [EXC 2026], and the DFG SFB/TRR 267 Noncoding RNAs in the cardiovascular system.
- Published
- 2020
8. Classification of Breast Cancer Subtypes by combining Gene Expression and {DNA} Methylation Data
- Author
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List, Markus, Hauschild, Anne-Christin, Tan, Qihua, Kruse, Torben A, Mollenhauer, Jan, Baumbach, Jan, and Batra, Richa
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Gene Expression Profiling ,Computational Biology ,Gene Expression ,Reproducibility of Results ,Data processing, computer science, computer systems ,Breast Neoplasms ,General Medicine ,DNA Methylation ,Prognosis ,Epigenesis, Genetic ,Gene Expression Regulation, Neoplastic ,Artificial Intelligence ,Humans ,Female ,TP248.13-248.65 ,Algorithms ,Software ,Biotechnology ,Oligonucleotide Array Sequence Analysis - Abstract
Selecting the most promising treatment strategy for breast cancer crucially depends on determining the correct subtype. In recent years, gene expression profiling has been investigated as an alternative to histochemical methods. Since databases like TCGA provide easy and unrestricted access to gene expression data for hundreds of patients, the challenge is to extract a minimal optimal set of genes with good prognostic properties from a large bulk of genes making a moderate contribution to classification. Several studies have successfully applied machine learning algorithms to solve this so-called gene selection problem. However, more diverse data from other OMICS technologies are available, including methylation. We hypothesize that combining methylation and gene expression data could already lead to a largely improved classification model, since the resulting model will reflect differences not only on the transcriptomic, but also on an epigenetic level. We compared so-called random forest derived classification models based on gene expression and methylation data alone, to a model based on the combined features and to a model based on the gold standard PAM50. We obtained bootstrap errors of 10-20% and classification error of 1-50%, depending on breast cancer subtype and model. The gene expression model was clearly superior to the methylation model, which was also reflected in the combined model, which mainly selected features from gene expression data. However, the methylation model was able to identify unique features not considered as relevant by the gene expression model, which might provide deeper insights into breast cancer subtype differentiation on an epigenetic level. Selecting the most promising treatment strategy for breast cancer crucially depends on determining the correct subtype. In recent years, gene expression profiling has been investigated as an alternative to histochemical methods. Since databases like TCGA provide easy and unrestricted access to gene expression data for hundreds of patients, the challenge is to extract a minimal optimal set of genes with good prognostic properties from a large bulk of genes making a moderate contribution to classification. Several studies have successfully applied machine learning algorithms to solve this so-called gene selection problem. However, more diverse data from other OMICS technologies are available, including methylation. We hypothesize that combining methylation and gene expression data could already lead to a largely improved classification model, since the resulting model will reflect differences not only on the transcriptomic, but also on an epigenetic level. We compared so-called random forest derived classification models based on gene expression and methylation data alone, to a model based on the combined features and to a model based on the gold standard PAM50. We obtained bootstrap errors of 10-20% and classification error of 1-50%, depending on breast cancer subtype and model. The gene expression model was clearly superior to the methylation model, which was also reflected in the combined model, which mainly selected features from gene expression data. However, the methylation model was able to identify unique features not considered as relevant by the gene expression model, which might provide deeper insights into breast cancer subtype differentiation on an epigenetic level.
- Published
- 2014
- Full Text
- View/download PDF
9. JAMI: fast computation of conditional mutual information for ceRNA network analysis.
- Author
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Hornakova, Andrea, List, Markus, Vreeken, Jilles, and Schulz, Marcel H
- Subjects
- *
COMPUTATIONAL biology , *MICRORNA , *GENE expression , *GENOMES , *DISEASES - Abstract
Motivation Genome-wide measurements of paired miRNA and gene expression data have enabled the prediction of competing endogenous RNAs (ceRNAs). It has been shown that the sponge effect mediated by protein-coding as well as non-coding ceRNAs can play an important regulatory role in the cell in health and disease. Therefore, many computational methods for the computational identification of ceRNAs have been suggested. In particular, methods based on Conditional Mutual Information (CMI) have shown promising results. However, the currently available implementation is slow and cannot be used to perform computations on a large scale. Results Here, we present JAMI, a Java tool that uses a non-parametric estimator for CMI values from gene and miRNA expression data. We show that JAMI speeds up the computation of ceRNA networks by a factor of ∼70 compared to currently available implementations. Further, JAMI supports multi-threading to make use of common multi-core architectures for further performance gain. Requirements Java 8. Availability and implementation JAMI is available as open-source software from https://github.com/SchulzLab/JAMI. Supplementary information Supplementary data are available at Bioinformatics online. [ABSTRACT FROM AUTHOR]
- Published
- 2018
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10. On the performance of de novo pathway enrichment.
- Author
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Batra, Richa, Alcaraz, Nicolas, Gitzhofer, Kevin, Pauling, Josch, Ditzel, Henrik J., Hellmuth, Marc, Baumbach, Jan, and List, Markus
- Subjects
BIOLOGICAL networks ,BIOINFORMATICS ,COMPUTATIONAL biology ,MOLECULAR interactions ,MOLECULAR biology - Abstract
De novo pathway enrichment is a powerful approach to discover previously uncharacterized molecular mechanisms in addition to already known pathways. To achieve this, condition-specific functional modules are extracted from large interaction networks. Here, we give an overview of the state of the art and present the first framework for assessing the performance of existing methods. We identified 19 tools and selected seven representative candidates for a comparative analysis with more than 12,000 runs, spanning different biological networks, molecular profiles, and parameters. Our results show that none of the methods consistently outperforms the others. To mitigate this issue for biomedical researchers, we provide guidelines to choose the appropriate tool for a given dataset. Moreover, our framework is the first attempt for a quantitative evaluation of de novo methods, which will allow the bioinformatics community to objectively compare future tools against the state of the art. Computational biology: Evaluation of network-based pathway enrichment tools De novo pathway enrichment methods are essential to understand disease complexity. They can uncover disease-specific functional modules by integrating molecular interaction networks with expression profiles. However, how should researchers choose one method out of several? In this article, a group of scientists from Denmark and Germany presents the first attempt to quantitatively evaluate existing methods. This framework will help the biomedical community to find the appropriate tool(s) for their data. They created synthetic gold standards and simulated expression profiles to perform a systematic assessment of various tools. They observed that the choice of interaction network, parameter settings, preprocessing of expression data and statistical properties of the expression profiles influence the results to a large extent. The results reveal strengths and limitations of the individual methods and suggest using two or more tools to obtain comprehensive disease-modules. [ABSTRACT FROM AUTHOR]
- Published
- 2017
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11. Microarray R-based analysis of complex lysate experiments with MIRACLE.
- Author
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List, Markus, Block, Ines, Pedersen, Marlene Lemvig, Christiansen, Helle, Schmidt, Steffen, Thomassen, Mads, Tan, Qihua, Baumbach, Jan, and Mollenhauer, Jan
- Subjects
- *
HYDROPHILIC interaction liquid chromatography , *PROTEIN microarrays , *PROTEIN research , *DATA analysis , *CHEMICAL sample preparation , *COMPUTATIONAL biology - Abstract
Motivation: Reverse-phase protein arrays (RPPAs) allow sensitive quantification of relative protein abundance in thousands of samples in parallel. Typical challenges involved in this technology are antibody selection, sample preparation and optimization of staining conditions. The issue of combining effective sample management and data analysis, however, has been widely neglected.Results: This motivated us to develop MIRACLE, a comprehensive and user-friendly web application bridging the gap between spotting and array analysis by conveniently keeping track of sample information. Data processing includes correction of staining bias, estimation of protein concentration from response curves, normalization for total protein amount per sample and statistical evaluation. Established analysis methods have been integrated with MIRACLE, offering experimental scientists an end-to-end solution for sample management and for carrying out data analysis. In addition, experienced users have the possibility to export data to R for more complex analyses. MIRACLE thus has the potential to further spread utilization of RPPAs as an emerging technology for high-throughput protein analysis.Availability: Project URL: http://www.nanocan.org/miracle/Contact: mlist@health.sdu.dkSupplementary information: Supplementary data are available at Bioinformatics online. [ABSTRACT FROM AUTHOR]
- Published
- 2014
- Full Text
- View/download PDF
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