LIVIVO - The Search Portal for Life Sciences

zur deutschen Oberfläche wechseln
Advanced search

Search results

Result 1 - 10 of total 82

Search options

  1. Article ; Online: DeepSom: a CNN-based approach to somatic variant calling in WGS samples without a matched normal.

    Vilov, Sergey / Heinig, Matthias

    Bioinformatics (Oxford, England)

    2023  Volume 39, Issue 1

    Abstract: Motivation: Somatic mutations are usually called by analyzing the DNA sequence of a tumor sample in conjunction with a matched normal. However, a matched normal is not always available, for instance, in retrospective analysis or diagnostic settings. For ...

    Abstract Motivation: Somatic mutations are usually called by analyzing the DNA sequence of a tumor sample in conjunction with a matched normal. However, a matched normal is not always available, for instance, in retrospective analysis or diagnostic settings. For such cases, tumor-only somatic variant calling tools need to be designed. Previously proposed approaches demonstrate inferior performance on whole-genome sequencing (WGS) samples.
    Results: We present the convolutional neural network-based approach called DeepSom for detecting somatic single nucleotide polymorphism and short insertion and deletion variants in tumor WGS samples without a matched normal. We validate DeepSom by reporting its performance on five different cancer datasets. We also demonstrate that on WGS samples DeepSom outperforms previously proposed methods for tumor-only somatic variant calling.
    Availability and implementation: DeepSom is available as a GitHub repository at https://github.com/heiniglab/DeepSom.
    Supplementary information: Supplementary data are available at Bioinformatics online.
    MeSH term(s) Humans ; Software ; Retrospective Studies ; High-Throughput Nucleotide Sequencing/methods ; Whole Genome Sequencing ; Neoplasms/genetics
    Language English
    Publishing date 2023-01-25
    Publishing country England
    Document type Journal Article ; Research Support, Non-U.S. Gov't
    ZDB-ID 1422668-6
    ISSN 1367-4811 ; 1367-4803
    ISSN (online) 1367-4811
    ISSN 1367-4803
    DOI 10.1093/bioinformatics/btac828
    Database MEDical Literature Analysis and Retrieval System OnLINE

    More links

    Kategorien

    In stock of ZB MED Cologne/Königswinter

    Zs.A 2374: Show issues Location:
    Je nach Verfügbarkeit (siehe Angabe bei Bestand)
    bis Jg. 1994: Bestellungen von Artikeln über das Online-Bestellformular
    Jg. 1995 - 2021: Lesesall (2.OG)
    ab Jg. 2022: Lesesaal (EG)

    Order via subito

    This service is chargeable due to the Delivery terms set by subito. Orders including an article and supplementary material will be classified as separate orders. In these cases, fees will be demanded for each order.

  2. Article: Using Gene Expression to Annotate Cardiovascular GWAS Loci.

    Heinig, Matthias

    Frontiers in cardiovascular medicine

    2018  Volume 5, Page(s) 59

    Abstract: Genetic variants at hundreds of loci associated with cardiovascular phenotypes have been identified by genome wide association studies. Most of these variants are located in intronic or intergenic regions rendering the functional and mechanistic follow ... ...

    Abstract Genetic variants at hundreds of loci associated with cardiovascular phenotypes have been identified by genome wide association studies. Most of these variants are located in intronic or intergenic regions rendering the functional and mechanistic follow up difficult. These non-protein-coding regions harbor regulatory sequences. Thus the study of genetic variants associated with transcription-so called expression quantitative trait loci-has emerged as a promising approach to identify regulatory sequence variants. The genes and pathways they control constitute candidate causal drivers at cardiovascular risk loci. This review provides an overview of the expression quantitative trait loci resources available for cardiovascular genetics research and the most commonly used approaches for candidate gene identification.
    Language English
    Publishing date 2018-06-05
    Publishing country Switzerland
    Document type Journal Article ; Review
    ZDB-ID 2781496-8
    ISSN 2297-055X
    ISSN 2297-055X
    DOI 10.3389/fcvm.2018.00059
    Database MEDical Literature Analysis and Retrieval System OnLINE

    More links

    Kategorien

    Order via subito

    This service is chargeable due to the Delivery terms set by subito. Orders including an article and supplementary material will be classified as separate orders. In these cases, fees will be demanded for each order.

  3. Article ; Online: Predictive model of transcriptional elongation control identifies trans regulatory factors from chromatin signatures.

    Akcan, Toray S / Vilov, Sergey / Heinig, Matthias

    Nucleic acids research

    2023  Volume 51, Issue 4, Page(s) 1608–1624

    Abstract: Promoter-proximal Polymerase II (Pol II) pausing is a key rate-limiting step for gene expression. DNA and RNA-binding trans-acting factors regulating the extent of pausing have been identified. However, we lack a quantitative model of how interactions of ...

    Abstract Promoter-proximal Polymerase II (Pol II) pausing is a key rate-limiting step for gene expression. DNA and RNA-binding trans-acting factors regulating the extent of pausing have been identified. However, we lack a quantitative model of how interactions of these factors determine pausing, therefore the relative importance of implicated factors is unknown. Moreover, previously unknown regulators might exist. Here we address this gap with a machine learning model that accurately predicts the extent of promoter-proximal Pol II pausing from large-scale genome and transcriptome binding maps and gene annotation and sequence composition features. We demonstrate high accuracy and generalizability of the model by validation on an independent cell line which reveals the model's cell line agnostic character. Model interpretation in light of prior knowledge about molecular functions of regulatory factors confirms the interconnection of pausing with other RNA processing steps. Harnessing underlying feature contributions, we assess the relative importance of each factor, quantify their predictive effects and systematically identify previously unknown regulators of pausing. We additionally identify 16 previously unknown 7SK ncRNA interacting RNA-binding proteins predictive of pausing. Our work provides a framework to further our understanding of the regulation of the critical early steps in transcriptional elongation.
    MeSH term(s) Cell Line ; Chromatin ; Gene Expression Regulation ; RNA Polymerase II/metabolism ; Transcription, Genetic ; Transcriptional Elongation Factors/metabolism ; Transcriptome ; Transcription Elongation, Genetic
    Chemical Substances Chromatin ; RNA Polymerase II (EC 2.7.7.-) ; Transcriptional Elongation Factors
    Language English
    Publishing date 2023-02-02
    Publishing country England
    Document type Journal Article ; Research Support, Non-U.S. Gov't
    ZDB-ID 186809-3
    ISSN 1362-4962 ; 1362-4954 ; 0301-5610 ; 0305-1048
    ISSN (online) 1362-4962 ; 1362-4954
    ISSN 0301-5610 ; 0305-1048
    DOI 10.1093/nar/gkac1272
    Database MEDical Literature Analysis and Retrieval System OnLINE

    More links

    Kategorien

    In stock of ZB MED Cologne/Königswinter

    Zs.A 1067: Show issues Location:
    Je nach Verfügbarkeit (siehe Angabe bei Bestand)
    bis Jg. 1994: Bestellungen von Artikeln über das Online-Bestellformular
    Jg. 1995 - 2021: Lesesall (1.OG)
    ab Jg. 2022: Lesesaal (EG)

    Order via subito

    This service is chargeable due to the Delivery terms set by subito. Orders including an article and supplementary material will be classified as separate orders. In these cases, fees will be demanded for each order.

  4. Book ; Online ; Thesis: Computational methods for design and analysis of population-based multiomics studies

    Schmid, Katharina Theresia Verfasser] / [Heinig, Matthias [Akademischer Betreuer] / Gagneur, Julien Gutachter] / Love, Michael [Gutachter] / [Heinig, Matthias [Gutachter]

    2023  

    Author's details Katharina Theresia Schmid ; Gutachter: Julien Gagneur, Michael Love, Matthias Heinig ; Betreuer: Matthias Heinig
    Keywords Biowissenschaften, Biologie ; Life Science, Biology
    Subject code sg570
    Language English
    Publisher Universitätsbibliothek der TU München
    Publishing place München
    Document type Book ; Online ; Thesis
    Database Digital theses on the web

    Full text online

    More links

    Kategorien

    Inter-library loan at ZB MED

    Your chosen title can be delivered directly to ZB MED Cologne location if you are registered as a user at ZB MED Cologne.

  5. Article: Machine learning reveals STAT motifs as predictors for GR-mediated gene repression.

    Höllbacher, Barbara / Strickland, Benjamin / Greulich, Franziska / Uhlenhaut, N Henriette / Heinig, Matthias

    Computational and structural biotechnology journal

    2023  Volume 21, Page(s) 1697–1710

    Abstract: Glucocorticoids are potent immunosuppressive drugs, but long-term treatment leads to severe side-effects. While there is a commonly accepted model for GR-mediated gene activation, the mechanism behind repression remains elusive. Understanding the ... ...

    Abstract Glucocorticoids are potent immunosuppressive drugs, but long-term treatment leads to severe side-effects. While there is a commonly accepted model for GR-mediated gene activation, the mechanism behind repression remains elusive. Understanding the molecular action of the glucocorticoid receptor (GR) mediated gene repression is the first step towards developing novel therapies. We devised an approach that combines multiple epigenetic assays with 3D chromatin data to find sequence patterns predicting gene expression change. We systematically tested> 100 models to evaluate the best way to integrate the data types and found that GR-bound regions hold most of the information needed to predict the polarity of Dex-induced transcriptional changes. We confirmed NF-κB motif family members as predictors for gene repression and identified STAT motifs as additional negative predictors.
    Language English
    Publishing date 2023-02-11
    Publishing country Netherlands
    Document type Journal Article
    ZDB-ID 2694435-2
    ISSN 2001-0370
    ISSN 2001-0370
    DOI 10.1016/j.csbj.2023.02.015
    Database MEDical Literature Analysis and Retrieval System OnLINE

    More links

    Kategorien

    Order via subito

    This service is chargeable due to the Delivery terms set by subito. Orders including an article and supplementary material will be classified as separate orders. In these cases, fees will be demanded for each order.

  6. Article ; Online: Speos: an ensemble graph representation learning framework to predict core gene candidates for complex diseases.

    Ratajczak, Florin / Joblin, Mitchell / Hildebrandt, Marcel / Ringsquandl, Martin / Falter-Braun, Pascal / Heinig, Matthias

    Nature communications

    2023  Volume 14, Issue 1, Page(s) 7206

    Abstract: Understanding phenotype-to-genotype relationships is a grand challenge of 21st century biology with translational implications. The recently proposed "omnigenic" model postulates that effects of genetic variation on traits are mediated by core-genes and - ...

    Abstract Understanding phenotype-to-genotype relationships is a grand challenge of 21st century biology with translational implications. The recently proposed "omnigenic" model postulates that effects of genetic variation on traits are mediated by core-genes and -proteins whose activities mechanistically influence the phenotype, whereas peripheral genes encode a regulatory network that indirectly affects phenotypes via core gene products. Here, we develop a positive-unlabeled graph representation-learning ensemble-approach based on a nested cross-validation to predict core-like genes for diverse diseases using Mendelian disorder genes for training. Employing mouse knockout phenotypes for external validations, we demonstrate that core-like genes display several key properties of core genes: Mouse knockouts of genes corresponding to our most confident predictions give rise to relevant mouse phenotypes at rates on par with the Mendelian disorder genes, and all candidates exhibit core gene properties like transcriptional deregulation in disease and loss-of-function intolerance. Moreover, as predicted for core genes, our candidates are enriched for drug targets and druggable proteins. In contrast to Mendelian disorder genes the new core-like genes are enriched for druggable yet untargeted gene products, which are therefore attractive targets for drug development. Interpretation of the underlying deep learning model suggests plausible explanations for our core gene predictions in form of molecular mechanisms and physical interactions. Our results demonstrate the potential of graph representation learning for the interpretation of biological complexity and pave the way for studying core gene properties and future drug development.
    MeSH term(s) Animals ; Mice ; Craniocerebral Trauma ; Drug Delivery Systems ; Drug Development ; Phenotype ; RNA
    Chemical Substances RNA (63231-63-0)
    Language English
    Publishing date 2023-11-08
    Publishing country England
    Document type Journal Article ; Research Support, Non-U.S. Gov't
    ZDB-ID 2553671-0
    ISSN 2041-1723 ; 2041-1723
    ISSN (online) 2041-1723
    ISSN 2041-1723
    DOI 10.1038/s41467-023-42975-z
    Database MEDical Literature Analysis and Retrieval System OnLINE

    More links

    Kategorien

    Order via subito

    This service is chargeable due to the Delivery terms set by subito. Orders including an article and supplementary material will be classified as separate orders. In these cases, fees will be demanded for each order.

  7. Book ; Online ; Thesis: Multi-omics integration for Atrial Fibrillation

    Assum, Ines Marion Verfasser] / [Heinig, Matthias [Akademischer Betreuer] / Gagneur, Julien [Gutachter] / Heinig, Matthias [Gutachter]

    2022  

    Author's details Ines Marion Assum ; Gutachter: Julien Gagneur, Matthias Heinig ; Betreuer: Matthias Heinig
    Keywords Biowissenschaften, Biologie ; Life Science, Biology
    Subject code sg570
    Language English
    Publisher Universitätsbibliothek der TU München
    Publishing place München
    Document type Book ; Online ; Thesis
    Database Digital theses on the web

    Full text online

    More links

    Kategorien

    Inter-library loan at ZB MED

    Your chosen title can be delivered directly to ZB MED Cologne location if you are registered as a user at ZB MED Cologne.

  8. Article ; Online: Risk Prediction of Cardiovascular Events by Exploration of Molecular Data with Explainable Artificial Intelligence.

    Westerlund, Annie M / Hawe, Johann S / Heinig, Matthias / Schunkert, Heribert

    International journal of molecular sciences

    2021  Volume 22, Issue 19

    Abstract: Cardiovascular diseases (CVD) annually take almost 18 million lives worldwide. Most lethal events occur months or years after the initial presentation. Indeed, many patients experience repeated complications or require multiple interventions (recurrent ... ...

    Abstract Cardiovascular diseases (CVD) annually take almost 18 million lives worldwide. Most lethal events occur months or years after the initial presentation. Indeed, many patients experience repeated complications or require multiple interventions (recurrent events). Apart from affecting the individual, this leads to high medical costs for society. Personalized treatment strategies aiming at prediction and prevention of recurrent events rely on early diagnosis and precise prognosis. Complementing the traditional environmental and clinical risk factors, multi-omics data provide a holistic view of the patient and disease progression, enabling studies to probe novel angles in risk stratification. Specifically, predictive molecular markers allow insights into regulatory networks, pathways, and mechanisms underlying disease. Moreover, artificial intelligence (AI) represents a powerful, yet adaptive, framework able to recognize complex patterns in large-scale clinical and molecular data with the potential to improve risk prediction. Here, we review the most recent advances in risk prediction of recurrent cardiovascular events, and discuss the value of molecular data and biomarkers for understanding patient risk in a systems biology context. Finally, we introduce explainable AI which may improve clinical decision systems by making predictions transparent to the medical practitioner.
    MeSH term(s) Artificial Intelligence ; Biomarkers/metabolism ; Cardiovascular Diseases/metabolism ; Cardiovascular Diseases/pathology ; Humans ; Prognosis ; Risk Factors
    Chemical Substances Biomarkers
    Language English
    Publishing date 2021-09-24
    Publishing country Switzerland
    Document type Journal Article ; Review
    ZDB-ID 2019364-6
    ISSN 1422-0067 ; 1422-0067 ; 1661-6596
    ISSN (online) 1422-0067
    ISSN 1422-0067 ; 1661-6596
    DOI 10.3390/ijms221910291
    Database MEDical Literature Analysis and Retrieval System OnLINE

    More links

    Kategorien

    Order via subito

    This service is chargeable due to the Delivery terms set by subito. Orders including an article and supplementary material will be classified as separate orders. In these cases, fees will be demanded for each order.

  9. Book ; Online ; Thesis: Deciphering regulatory molecular mechanisms using graphical models

    Hawe, Johann Sebastian Verfasser] / [Heinig, Matthias [Akademischer Betreuer] / Gagneur, Julien [Gutachter] / Heinig, Matthias [Gutachter]

    2021  

    Author's details Johann Sebastian Hawe ; Gutachter: Julien Gagneur, Matthias Heinig ; Betreuer: Matthias Heinig
    Keywords Biowissenschaften, Biologie ; Life Science, Biology
    Subject code sg570
    Language English
    Publisher Universitätsbibliothek der TU München
    Publishing place München
    Document type Book ; Online ; Thesis
    Database Digital theses on the web

    Full text online

    More links

    Kategorien

    Inter-library loan at ZB MED

    Your chosen title can be delivered directly to ZB MED Cologne location if you are registered as a user at ZB MED Cologne.

  10. Article: Inferring Interaction Networks From Multi-Omics Data.

    Hawe, Johann S / Theis, Fabian J / Heinig, Matthias

    Frontiers in genetics

    2019  Volume 10, Page(s) 535

    Abstract: A major goal in systems biology is a comprehensive description of the entirety of all complex interactions between different types of biomolecules-also referred to as the interactome-and how these interactions give rise to higher, cellular and organism ... ...

    Abstract A major goal in systems biology is a comprehensive description of the entirety of all complex interactions between different types of biomolecules-also referred to as the interactome-and how these interactions give rise to higher, cellular and organism level functions or diseases. Numerous efforts have been undertaken to define such interactomes experimentally, for example yeast-two-hybrid based protein-protein interaction networks or ChIP-seq based protein-DNA interactions for individual proteins. To complement these direct measurements, genome-scale quantitative multi-omics data (transcriptomics, proteomics, metabolomics, etc.) enable researchers to predict novel functional interactions between molecular species. Moreover, these data allow to distinguish relevant functional from non-functional interactions in specific biological contexts. However, integration of multi-omics data is not straight forward due to their heterogeneity. Numerous methods for the inference of interaction networks from homogeneous functional data exist, but with the advent of large-scale paired multi-omics data a new class of methods for inferring comprehensive networks across different molecular species began to emerge. Here we review state-of-the-art techniques for inferring the topology of interaction networks from functional multi-omics data, encompassing graphical models with multiple node types and quantitative-trait-loci (QTL) based approaches. In addition, we will discuss Bayesian aspects of network inference, which allow for leveraging already established biological information such as known protein-protein or protein-DNA interactions, to guide the inference process.
    Language English
    Publishing date 2019-06-12
    Publishing country Switzerland
    Document type Journal Article ; Review
    ZDB-ID 2606823-0
    ISSN 1664-8021
    ISSN 1664-8021
    DOI 10.3389/fgene.2019.00535
    Database MEDical Literature Analysis and Retrieval System OnLINE

    More links

    Kategorien

    Order via subito

    This service is chargeable due to the Delivery terms set by subito. Orders including an article and supplementary material will be classified as separate orders. In these cases, fees will be demanded for each order.

To top