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  1. Book ; Online ; Thesis: Identifying genome-wide transcription units from histone modifications using EPIGENE

    Sahu, Anshupa [Verfasser] / Chung, Ho-Ryun [Akademischer Betreuer]

    2021  

    Author's details Anshupa Sahu ; Betreuer: Ho-Ryun Chung
    Keywords Biowissenschaften, Biologie ; Life Science, Biology
    Subject code sg570
    Language English
    Publisher Philipps-Universität Marburg
    Publishing place Marburg
    Document type Book ; Online ; Thesis
    Database Digital theses on the web

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  2. Article ; Online: EPIGENE: genome-wide transcription unit annotation using a multivariate probabilistic model of histone modifications.

    Sahu, Anshupa / Li, Na / Dunkel, Ilona / Chung, Ho-Ryun

    Epigenetics & chromatin

    2020  Volume 13, Issue 1, Page(s) 20

    Abstract: Background: Understanding the transcriptome is critical for explaining the functional as well as regulatory roles of genomic regions. Current methods for the identification of transcription units (TUs) use RNA-seq that, however, require large quantities ...

    Abstract Background: Understanding the transcriptome is critical for explaining the functional as well as regulatory roles of genomic regions. Current methods for the identification of transcription units (TUs) use RNA-seq that, however, require large quantities of mRNA rendering the identification of inherently unstable TUs, e.g. miRNA precursors, difficult. This problem can be alleviated by chromatin-based approaches due to a correlation between histone modifications and transcription.
    Results: Here, we introduce EPIGENE, a novel chromatin segmentation method for the identification of active TUs using transcription-associated histone modifications. Unlike the existing chromatin segmentation approaches, EPIGENE uses a constrained, semi-supervised multivariate hidden Markov model (HMM) that models the observed combination of histone modifications using a product of independent Bernoulli random variables, to identify active TUs. Our results show that EPIGENE can identify genome-wide TUs in an unbiased manner. EPIGENE-predicted TUs show an enrichment of RNA Polymerase II at the transcription start site and in gene body indicating that they are indeed transcribed. Comprehensive validation using existing annotations revealed that 93% of EPIGENE TUs can be explained by existing gene annotations and 5% of EPIGENE TUs in HepG2 can be explained by microRNA annotations. EPIGENE outperformed the existing RNA-seq-based approaches in TU prediction precision across human cell lines. Finally, we identified 232 novel TUs in K562 and 43 novel cell-specific TUs all of which were supported by RNA Polymerase II ChIP-seq and Nascent RNA-seq data.
    Conclusion: We demonstrate the applicability of EPIGENE to identify genome-wide active TUs and to provide valuable information about unannotated TUs. EPIGENE is an open-source method and is freely available at: https://github.com/imbbLab/EPIGENE.
    MeSH term(s) Chromatin Immunoprecipitation Sequencing/methods ; Epigenomics/methods ; Hep G2 Cells ; Histone Code ; Humans ; K562 Cells ; Markov Chains ; Molecular Sequence Annotation/methods ; Software ; Transcription Initiation Site ; Transcriptome
    Language English
    Publishing date 2020-04-07
    Publishing country England
    Document type Journal Article ; Research Support, Non-U.S. Gov't
    ZDB-ID 2462129-8
    ISSN 1756-8935 ; 1756-8935
    ISSN (online) 1756-8935
    ISSN 1756-8935
    DOI 10.1186/s13072-020-00341-z
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  3. Article: Reconstruction of the origin of the first major SARS-CoV-2 outbreak in Germany

    Korencak, Marek / Sivalingam, Sugirthan / Sahu, Anshupa / Dressen, Dietmar / Schmidt, Axel / Brand, Fabian / Krawitz, Peter / Hart, Libor / Maria Eis-Hübinger, Anna / Buness, Andreas / Streeck, Hendrik

    Computational and Structural Biotechnology Journal. 2022, v. 20

    2022  

    Abstract: The first major COVID-19 outbreak in Germany occurred in Heinsberg in February 2020 with 388 officially reported cases. Unexpectedly, the first outbreak happened in a small town with little to no travelers. We used phylogenetic analyses to investigate ... ...

    Abstract The first major COVID-19 outbreak in Germany occurred in Heinsberg in February 2020 with 388 officially reported cases. Unexpectedly, the first outbreak happened in a small town with little to no travelers. We used phylogenetic analyses to investigate the origin and spread of the virus in this outbreak. We sequenced 90 (23%) SARS-CoV-2 genomes from the 388 reported cases including the samples from the first documented cases. Phylogenetic analyses of these sequences revealed mainly two circulating strains with 74 samples assigned to lineage B.3 and 6 samples assigned to lineage B.1. Lineage B.3 was introduced first and probably caused the initial spread. Using phylogenetic analysis tools, we were able to identify closely related strains in France and hypothesized the possible introduction from France.
    Keywords COVID-19 infection ; Severe acute respiratory syndrome coronavirus 2 ; biotechnology ; genome ; phylogeny ; viruses ; France ; Germany
    Language English
    Size p. 2292-2296.
    Publishing place Elsevier B.V.
    Document type Article
    ZDB-ID 2694435-2
    ISSN 2001-0370
    ISSN 2001-0370
    DOI 10.1016/j.csbj.2022.05.011
    Database NAL-Catalogue (AGRICOLA)

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  4. Article ; Online: Machine Learning for Decision-Support in Acute Abdominal Pain - Proof of Concept and Central Considerations.

    Henn, Jonas / Hatterscheidt, Simon / Sahu, Anshupa / Buness, Andreas / Dohmen, Jonas / Arensmeyer, Jan / Feodorovici, Philipp / Sommer, Nils / Schmidt, Joachim / Kalff, Jörg C / Matthaei, Hanno

    Zentralblatt fur Chirurgie

    2023  Volume 148, Issue 4, Page(s) 376–383

    Abstract: Acute abdominal pain is a common presenting symptom in the emergency department and represents heterogeneous causes and diagnoses. There is often a decision to be made regarding emergency surgical care. Machine learning (ML) could be used here as a ... ...

    Title translation Maschinelles Lernen als Entscheidungshilfe bei akuten Bauchschmerzen – Proof-of-Concept und zentrale Überlegungen.
    Abstract Acute abdominal pain is a common presenting symptom in the emergency department and represents heterogeneous causes and diagnoses. There is often a decision to be made regarding emergency surgical care. Machine learning (ML) could be used here as a decision-support and relieve the time and personnel resource shortage.Patients with acute abdominal pain presenting to the Department of Surgery at Bonn University Hospital in 2020 and 2021 were retrospectively analyzed. Clinical parameters as well as laboratory values were used as predictors. After randomly splitting into a training and test data set (ratio 80 to 20), three ML algorithms were comparatively trained and validated. The entire procedure was repeated 20 times.A total of 1357 patients were identified and included in the analysis, with one in five (n = 276, 20.3%) requiring emergency abdominal surgery within 24 hours. Patients operated on were more likely to be male (p = 0.026), older (p = 0.006), had more gastrointestinal symptoms (nausea: p < 0.001, vomiting p < 0.001) as well as a more recent onset of pain (p < 0.001). Tenderness (p < 0.001) and guarding (p < 0.001) were more common in surgically treated patients and blood analyses showed increased inflammation levels (white blood cell count: p < 0.001, CRP: p < 0.001) and onset of organ dysfunction (creatinine: p < 0.014, quick p < 0.001). Of the three trained algorithms, the tree-based methods (h2o random forest and cforest) showed the best performance. The algorithms classified patients, i.e., predicted surgery, with a median AUC ROC of 0.81 and 0.79 and AUC PRC of 0.56 in test sets.A proof-of-concept was achieved with the development of an ML model for predicting timely surgical therapy for acute abdomen. The ML algorithm can be a valuable tool in decision-making. Especially in the context of heavily used medical resources, the algorithm can help to use these scarce resources more effectively. Technological progress, especially regarding artificial intelligence, increasingly enables evidence-based approaches in surgery but requires a strictly interdisciplinary approach. In the future, the use and handling of ML should be integrated into surgical training.
    MeSH term(s) Humans ; Abdomen, Acute ; Artificial Intelligence ; Retrospective Studies ; Machine Learning ; Algorithms
    Language English
    Publishing date 2023-08-10
    Publishing country Germany
    Document type Journal Article
    ZDB-ID 200935-3
    ISSN 1438-9592 ; 0044-409X
    ISSN (online) 1438-9592
    ISSN 0044-409X
    DOI 10.1055/a-2125-1559
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  5. Article: Reconstruction of the origin of the first major SARS-CoV-2 outbreak in Germany.

    Korencak, Marek / Sivalingam, Sugirthan / Sahu, Anshupa / Dressen, Dietmar / Schmidt, Axel / Brand, Fabian / Krawitz, Peter / Hart, Libor / Maria Eis-Hübinger, Anna / Buness, Andreas / Streeck, Hendrik

    Computational and structural biotechnology journal

    2022  Volume 20, Page(s) 2292–2296

    Abstract: The first major COVID-19 outbreak in Germany occurred in Heinsberg in February 2020 with 388 officially reported cases. Unexpectedly, the first outbreak happened in a small town with little to no travelers. We used phylogenetic analyses to investigate ... ...

    Abstract The first major COVID-19 outbreak in Germany occurred in Heinsberg in February 2020 with 388 officially reported cases. Unexpectedly, the first outbreak happened in a small town with little to no travelers. We used phylogenetic analyses to investigate the origin and spread of the virus in this outbreak. We sequenced 90 (23%) SARS-CoV-2 genomes from the 388 reported cases including the samples from the first documented cases. Phylogenetic analyses of these sequences revealed mainly two circulating strains with 74 samples assigned to lineage B.3 and 6 samples assigned to lineage B.1. Lineage B.3 was introduced first and probably caused the initial spread. Using phylogenetic analysis tools, we were able to identify closely related strains in France and hypothesized the possible introduction from France.
    Language English
    Publishing date 2022-05-10
    Publishing country Netherlands
    Document type Journal Article
    ZDB-ID 2694435-2
    ISSN 2001-0370
    ISSN 2001-0370
    DOI 10.1016/j.csbj.2022.05.011
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  6. Article ; Online: Induction of Interferon-Stimulated Genes Correlates with Reduced Growth of Influenza A Virus in Lungs after RIG-I Agonist Treatment of Ferrets.

    Schwab, Lara S U / Londrigan, Sarah L / Brooks, Andrew G / Hurt, Aeron C / Sahu, Anshupa / Deng, Yi-Mo / Moselen, Jean / Coch, Christoph / Zillinger, Thomas / Hartmann, Gunther / Reading, Patrick C

    Journal of virology

    2022  Volume 96, Issue 16, Page(s) e0055922

    Abstract: Intracellular RIG-I receptors represent key innate sensors of RNA virus infection, and RIG-I activation results in the induction of hundreds of host effector genes, including interferon-stimulated genes (ISGs). Synthetic RNA agonists targeting RIG-I have ...

    Abstract Intracellular RIG-I receptors represent key innate sensors of RNA virus infection, and RIG-I activation results in the induction of hundreds of host effector genes, including interferon-stimulated genes (ISGs). Synthetic RNA agonists targeting RIG-I have shown promise as antivirals against a broad spectrum of viruses, including influenza A virus (IAV), in both
    MeSH term(s) Animals ; Antiviral Agents/pharmacology ; Ferrets/metabolism ; Humans ; Immunity, Innate ; Influenza A virus/genetics ; Influenza, Human ; Interferons/metabolism ; Leukocytes, Mononuclear/metabolism ; Lung ; Mice ; Virus Replication/genetics
    Chemical Substances Antiviral Agents ; Interferons (9008-11-1)
    Language English
    Publishing date 2022-08-02
    Publishing country United States
    Document type Journal Article ; Research Support, Non-U.S. Gov't
    ZDB-ID 80174-4
    ISSN 1098-5514 ; 0022-538X
    ISSN (online) 1098-5514
    ISSN 0022-538X
    DOI 10.1128/jvi.00559-22
    Database MEDical Literature Analysis and Retrieval System OnLINE

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    In stock of ZB MED Cologne/Königswinter

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  7. Article: Machine Learning for Decision-Support in Acute Abdominal Pain – Proof of Concept and Central Considerations

    Henn, Jonas / Hatterscheidt, Simon / Sahu, Anshupa / Buness, Andreas / Dohmen, Jonas / Arensmeyer, Jan / Feodorovici, Philipp / Sommer, Nils / Schmidt, Joachim / Kalff, Jörg C. / Matthaei, Hanno

    Zentralblatt für Chirurgie - Zeitschrift für Allgemeine, Viszeral-, Thorax- und Gefäßchirurgie

    2023  Volume 148, Issue 04, Page(s) 376–383

    Abstract: Acute abdominal pain is a common presenting symptom in the emergency department and represents heterogeneous causes and diagnoses. There is often a decision to be made regarding emergency surgical care. Machine learning (ML) could be used ... ...

    Abstract Acute abdominal pain is a common presenting symptom in the emergency department and represents heterogeneous causes and diagnoses. There is often a decision to be made regarding emergency surgical care. Machine learning (ML) could be used here as a decision-support and relieve the time and personnel resource shortage. Patients with acute abdominal pain presenting to the Department of Surgery at Bonn University Hospital in 2020 and 2021 were retrospectively analyzed. Clinical parameters as well as laboratory values were used as predictors. After randomly splitting into a training and test data set (ratio 80 to 20), three ML algorithms were comparatively trained and validated. The entire procedure was repeated 20 times. A total of 1357 patients were identified and included in the analysis, with one in five (n = 276, 20.3%) requiring emergency abdominal surgery within 24 hours. Patients operated on were more likely to be male (p = 0.026), older (p = 0.006), had more gastrointestinal symptoms (nausea: p < 0.001, vomiting p < 0.001) as well as a more recent onset of pain (p < 0.001). Tenderness (p < 0.001) and guarding (p < 0.001) were more common in surgically treated patients and blood analyses showed increased inflammation levels (white blood cell count: p < 0.001, CRP: p < 0.001) and onset of organ dysfunction (creatinine: p < 0.014, quick p < 0.001). Of the three trained algorithms, the tree-based methods (h2o random forest and cforest) showed the best performance. The algorithms classified patients, i.e., predicted surgery, with a median AUC ROC of 0.81 and 0.79 and AUC PRC of 0.56 in test sets. A proof-of-concept was achieved with the development of an ML model for predicting timely surgical therapy for acute abdomen. The ML algorithm can be a valuable tool in decision-making. Especially in the context of heavily used medical resources, the algorithm can help to use these scarce resources more effectively. Technological progress, especially regarding artificial intelligence, increasingly enables evidence-based approaches in surgery but requires a strictly interdisciplinary approach. In the future, the use and handling of ML should be integrated into surgical training.
    Keywords maschinelles Lernen ; künstliche Intelligenz ; klinische Entscheidungsfindung ; Entscheidungshilfe ; akutes Abdomen ; machine learning ; artificial intelligence ; acute abdominal pain ; clinical decision making ; decision support
    Language English
    Publishing date 2023-08-01
    Publisher Georg Thieme Verlag KG
    Publishing place Stuttgart ; New York
    Document type Article
    ZDB-ID 200935-3
    ISSN 1438-9592 ; 0044-409X
    ISSN (online) 1438-9592
    ISSN 0044-409X
    DOI 10.1055/a-2125-1559
    Database Thieme publisher's database

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