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  1. Article ; Online: Author Correction: Butler enables rapid cloud-based analysis of thousands of human genomes.

    Yakneen, Sergei / Waszak, Sebastian M / Gertz, Michael / Korbel, Jan O

    Nature biotechnology

    2023  Volume 41, Issue 4, Page(s) 577

    Language English
    Publishing date 2023-03-22
    Publishing country United States
    Document type Published Erratum
    ZDB-ID 1311932-1
    ISSN 1546-1696 ; 1087-0156
    ISSN (online) 1546-1696
    ISSN 1087-0156
    DOI 10.1038/s41587-022-01554-1
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    In stock of ZB MED Cologne/Königswinter

    Zs.A 2167: Show issues Location:
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  2. Article ; Online: Effects of the COVID-19 pandemic on life scientists.

    Korbel, Jan O / Stegle, Oliver

    Genome biology

    2020  Volume 21, Issue 1, Page(s) 113

    MeSH term(s) Betacoronavirus ; COVID-19 ; Coronavirus Infections/epidemiology ; Humans ; Interdisciplinary Placement ; Laboratory Personnel/education ; Pandemics ; Pneumonia, Viral/epidemiology ; SARS-CoV-2
    Keywords covid19
    Language English
    Publishing date 2020-05-11
    Publishing country England
    Document type Editorial
    ZDB-ID 2040529-7
    ISSN 1474-760X ; 1465-6906
    ISSN (online) 1474-760X
    ISSN 1465-6906
    DOI 10.1186/s13059-020-02031-1
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  3. Article ; Online: Structural Variation in Cancer: Role, Prevalence, and Mechanisms.

    Cosenza, Marco Raffaele / Rodriguez-Martin, Bernardo / Korbel, Jan O

    Annual review of genomics and human genetics

    2022  Volume 23, Page(s) 123–152

    Abstract: Somatic rearrangements resulting in genomic structural variation drive malignant phenotypes by altering the expression or function of cancer genes. Pan-cancer studies have revealed that structural variants (SVs) are the predominant class of driver ... ...

    Abstract Somatic rearrangements resulting in genomic structural variation drive malignant phenotypes by altering the expression or function of cancer genes. Pan-cancer studies have revealed that structural variants (SVs) are the predominant class of driver mutation in most cancer types, but because they are difficult to discover, they remain understudied when compared with point mutations. This review provides an overview of the current knowledge of somatic SVs, discussing their primary roles, prevalence in different contexts, and mutational mechanisms. SVs arise throughout the life history of cancer, and 55% of driver mutations uncovered by the Pan-Cancer Analysis of Whole Genomes project represent SVs. Leveraging the convergence of cell biology and genomics, we propose a mechanistic classification of somatic SVs, from simple to highly complex DNA rearrangement classes. The actions of DNA repair and DNA replication processes together with mitotic errors result in a rich spectrum of SV formation processes, with cascading effects mediating extensive structural diversity after an initiating DNA lesion has formed. Thanks to new sequencing technologies, including the sequencing of single-cell genomes, open questions about the molecular triggers and the biomolecules involved in SV formation as well as their mutational rates can now be addressed.
    MeSH term(s) Genome, Human ; Genomic Structural Variation ; Genomics ; Humans ; Mutation ; Neoplasms/epidemiology ; Neoplasms/genetics ; Neoplasms/pathology ; Prevalence
    Language English
    Publishing date 2022-06-02
    Publishing country United States
    Document type Journal Article ; Review ; Research Support, Non-U.S. Gov't
    ZDB-ID 2037670-4
    ISSN 1545-293X ; 1527-8204
    ISSN (online) 1545-293X
    ISSN 1527-8204
    DOI 10.1146/annurev-genom-120121-101149
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  4. Article ; Online: Effects of the COVID-19 pandemic on life scientists

    Jan O. Korbel / Oliver Stegle

    Genome Biology, Vol 21, Iss 1, Pp 1-

    2020  Volume 5

    Keywords Biology (General) ; QH301-705.5 ; Genetics ; QH426-470 ; covid19
    Language English
    Publishing date 2020-05-01T00:00:00Z
    Publisher BMC
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  5. Article ; Online: Effects of the COVID-19 pandemic on life scientists

    Korbel, Jan O. / Stegle, Oliver

    Genome Biology

    2020  Volume 21, Issue 1

    Keywords covid19
    Language English
    Publisher Springer Science and Business Media LLC
    Publishing country us
    Document type Article ; Online
    ZDB-ID 2040529-7
    ISSN 1474-760X ; 1465-6906
    ISSN (online) 1474-760X
    ISSN 1465-6906
    DOI 10.1186/s13059-020-02031-1
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  7. Article ; Online: Effects of the COVID-19 pandemic on life scientists

    Korbel, Jan O. / Stegle, Oliver

    Genome biology, 21(1):113

    2020  

    Abstract: We will not know the long-term impact of the SARS-CoV-2 viral outbreak for some time yet, but many of us have already begun to feel the effects—not only on our daily lives but also on our work as life scientists. With partial or complete institutional ... ...

    Abstract We will not know the long-term impact of the SARS-CoV-2 viral outbreak for some time yet, but many of us have already begun to feel the effects—not only on our daily lives but also on our work as life scientists. With partial or complete institutional shutdowns in countries worldwide, the global COVID-19 health crisis has rapidly impacted the life science landscape, including our patterns of work. Some life scientists may today feel essentially “stuck,” unable to carry out experiments because of COVID-19-related working restrictions or because they need to look after children in connection with the closure of schools and kindergartens. This can be a frightening feeling, especially for young life scientists, who usually have short-term contracts and may worry about their future careers.
    Keywords COVID-19 ; covid19
    Language English
    Publishing country de
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  8. Article: Long-read sequencing and structural variant characterization in 1,019 samples from the 1000 Genomes Project.

    Schloissnig, Siegfried / Pani, Samarendra / Rodriguez-Martin, Bernardo / Ebler, Jana / Hain, Carsten / Tsapalou, Vasiliki / Söylev, Arda / Hüther, Patrick / Ashraf, Hufsah / Prodanov, Timofey / Asparuhova, Mila / Hunt, Sarah / Rausch, Tobias / Marschall, Tobias / Korbel, Jan O

    bioRxiv : the preprint server for biology

    2024  

    Abstract: Structural variants (SVs) contribute significantly to human genetic diversity and ... ...

    Abstract Structural variants (SVs) contribute significantly to human genetic diversity and disease
    Language English
    Publishing date 2024-04-20
    Publishing country United States
    Document type Preprint
    DOI 10.1101/2024.04.18.590093
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  9. Article ; Online: Genomic data sharing in Europe is stumbling-Could a code of conduct prevent its fall?

    Molnár-Gábor, Fruzsina / Korbel, Jan O

    EMBO molecular medicine

    2020  Volume 12, Issue 3, Page(s) e11421

    Abstract: Genomic data sharing is becoming more important as scientists join forces across borders in biomedical research for the benefit of patients and society. The EU's General Data Protection Regulation (GDPR) helps simplify sharing of such data at the ... ...

    Abstract Genomic data sharing is becoming more important as scientists join forces across borders in biomedical research for the benefit of patients and society. The EU's General Data Protection Regulation (GDPR) helps simplify sharing of such data at the European and international level. However, initial optimism has dried up as EU member states go their own ways in implementing the GDPR into national laws, and as legal cases challenging data sharing reach courts. Codes of conduct could facilitate data sharing in Europe and better connect it to global health research. This commentary explains the potential of codes of conduct for addressees and drafters. Codes are no panacea though; other measures may be necessary to ensure that Europe remains collaborative and competitive in biomedical research. Nevertheless, codes of conduct would bring immediate benefits and, in the long term, could foster a true European ecosystem for joint biomedical research and easier international data sharing.
    MeSH term(s) Biomedical Research ; Europe ; Genomics ; Humans ; Information Dissemination
    Language English
    Publishing date 2020-02-18
    Publishing country England
    Document type Journal Article ; Research Support, Non-U.S. Gov't
    ZDB-ID 2467145-9
    ISSN 1757-4684 ; 1757-4676
    ISSN (online) 1757-4684
    ISSN 1757-4676
    DOI 10.15252/emmm.201911421
    Database MEDical Literature Analysis and Retrieval System OnLINE

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    Zs.A 6784: Show issues Location:
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  10. Article ; Online: Mutation-Attention (MuAt): deep representation learning of somatic mutations for tumour typing and subtyping.

    Sanjaya, Prima / Maljanen, Katri / Katainen, Riku / Waszak, Sebastian M / Aaltonen, Lauri A / Stegle, Oliver / Korbel, Jan O / Pitkänen, Esa

    Genome medicine

    2023  Volume 15, Issue 1, Page(s) 47

    Abstract: Background: Cancer genome sequencing enables accurate classification of tumours and tumour subtypes. However, prediction performance is still limited using exome-only sequencing and for tumour types with low somatic mutation burden such as many ... ...

    Abstract Background: Cancer genome sequencing enables accurate classification of tumours and tumour subtypes. However, prediction performance is still limited using exome-only sequencing and for tumour types with low somatic mutation burden such as many paediatric tumours. Moreover, the ability to leverage deep representation learning in discovery of tumour entities remains unknown.
    Methods: We introduce here Mutation-Attention (MuAt), a deep neural network to learn representations of simple and complex somatic alterations for prediction of tumour types and subtypes. In contrast to many previous methods, MuAt utilizes the attention mechanism on individual mutations instead of aggregated mutation counts.
    Results: We trained MuAt models on 2587 whole cancer genomes (24 tumour types) from the Pan-Cancer Analysis of Whole Genomes (PCAWG) and 7352 cancer exomes (20 types) from the Cancer Genome Atlas (TCGA). MuAt achieved prediction accuracy of 89% for whole genomes and 64% for whole exomes, and a top-5 accuracy of 97% and 90%, respectively. MuAt models were found to be well-calibrated and perform well in three independent whole cancer genome cohorts with 10,361 tumours in total. We show MuAt to be able to learn clinically and biologically relevant tumour entities including acral melanoma, SHH-activated medulloblastoma, SPOP-associated prostate cancer, microsatellite instability, POLE proofreading deficiency, and MUTYH-associated pancreatic endocrine tumours without these tumour subtypes and subgroups being provided as training labels. Finally, scrunity of MuAt attention matrices revealed both ubiquitous and tumour-type specific patterns of simple and complex somatic mutations.
    Conclusions: Integrated representations of somatic alterations learnt by MuAt were able to accurately identify histological tumour types and identify tumour entities, with potential to impact precision cancer medicine.
    MeSH term(s) Neoplasms/genetics ; Neoplasms/pathology ; Humans ; Deep Learning ; Benchmarking ; Mutation
    Language English
    Publishing date 2023-07-07
    Publishing country England
    Document type Journal Article
    ZDB-ID 2484394-5
    ISSN 1756-994X ; 1756-994X
    ISSN (online) 1756-994X
    ISSN 1756-994X
    DOI 10.1186/s13073-023-01204-4
    Database MEDical Literature Analysis and Retrieval System OnLINE

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