[{"corr_author":"1","publication_status":"published","oa_version":"Published Version","article_number":"10153 ","volume":17,"acknowledged_ssus":[{"_id":"ScienComp"}],"dataavailabilitystatement":"This project uses the UK Biobank data under project number 35520.\r\nUK Biobank genotypic and phenotypic data is available through a\r\nformal request at (http://www.ukbiobank.ac.uk). All of the data generated\r\nin this study are provided in the Source Data file. Source data\r\nare provided with this paper. The CI-GWAS code is fully open source and available at https://github.\r\ncom/medical-genomics-group/ci-gwasand has been archived on\r\nZenodo52. The scripts used to execute the model are available at\r\nhttps://github.com/medical-genomics-group/ci-gwas. R version 4.2.1 is\r\navailable at https://www.r-project.org/. Plink version 1.9 is available at\r\nhttps://www.cog-genomics.org/plink/1.9/.","fulldoi":"https://doi.org/10.1038/s41467-026-76877-7","type":"journal_article","DOAJ_listed":"1","tmp":{"legal_code_url":"https://creativecommons.org/licenses/by-nc-nd/4.0/legalcode","short":"CC BY-NC-ND (4.0)","name":"Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0)","image":"/images/cc_by_nc_nd.png"},"publisher":"Springer Nature","scopus_import":"1","publication":"Nature Communications","researchdata_availability":"yes","abstract":[{"lang":"eng","text":"Mendelian randomization, the standard instrument variable causal-inference approach for genomics, can be severely biased by inadequate selection of appropriate instrument variables and horizontal pleiotropy. We introduce Causal Inference GWAS, a graphical approach that selects genetic instruments from summary statistics controlling for linkage and pleiotropy, accommodates rare variants and binary outcomes, distinguishes direct from indirect risk factors in high dimensions, and flags latent confounding. Applied to 9 risk factors, 4 metabolic disease outcomes, and 8.4M variants across 458,747 UK Biobank individuals, our method runs in 20 minutes, identifying only 696 genome-wide valid instruments. We replicate nearly all previously reported risk factor-outcome paths, but find that most are indistinguishable from unmeasured confounding. This suggests robust biobank causal inference will require longitudinal and family data to resolve temporal precedence from reverse causation. Our approach offers a principled first step toward screening genuine causal signal from phenotypic correlation in biobank data."}],"article_type":"original","quality_controlled":"1","article_processing_charge":"Yes","doi":"10.1038/s41467-026-76877-7","acknowledgement":"We thank Zoltan Kutalik andmembers of the Robinson group at ISTA for\r\ntheir comments, which improved this manuscript. We would like to\r\nacknowledge the participants and investigators of the UK Biobank study.\r\nHigh-performance computing was supported by the Scientific Service\r\nUnits (SSU) of IST Austria through resources provided by Scientific\r\nComputing (SciComp). This work was funded by a research collaboration agreement between\r\nBoehringer Ingelheim and the research group of MRR at the Institute of\r\nScience and Technology Austria. Additional funding was also provided\r\nby an SNSF Eccellenza Grant to MRR (PCEGP3-181181), and by core\r\nfunding from the Institute of Science and Technology Austria. MB was\r\nfunded by the National Science Center Poland grant SONATINA UMO-\r\n2021/40/C/NZ2/00218.","related_material":{"record":[{"id":"18648","relation":"earlier_version","status":"public"}]},"file":[{"date_created":"2026-10-07T09:50:18Z","date_updated":"2026-10-07T09:50:18Z","content_type":"application/pdf","access_level":"open_access","file_name":"2026_NatureComm_Machnik.pdf","checksum":"05517674168a275c70f18e964ce33d8b","file_size":2345578,"creator":"dernst","file_id":"23070","success":1,"relation":"main_file"}],"_id":"23043","language":[{"iso":"eng"}],"day":"25","title":"Causal inference for multiple risk factors and diseases from genomics data","date_updated":"2026-10-16T11:01:16Z","ddc":["570"],"user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","date_published":"2026-08-25T00:00:00Z","das_tickbox":"1","OA_place":"publisher","file_date_updated":"2026-10-07T09:50:18Z","intvolume":"        17","supplementarymaterial":"no","publication_identifier":{"eissn":["2041-1723"]},"has_accepted_license":"1","oa":1,"author":[{"last_name":"Machnik","id":"3591A0AA-F248-11E8-B48F-1D18A9856A87","first_name":"Nick N","orcid":"0000-0001-6617-9742","full_name":"Machnik, Nick N"},{"last_name":"Mahmoudi","id":"b9f6d5ef-7774-11eb-a47f-df2c75c02ee7","first_name":"Seyed Mahdi","full_name":"Mahmoudi, Seyed Mahdi"},{"full_name":"Borczyk, Malgorzata","first_name":"Malgorzata","last_name":"Borczyk"},{"full_name":"Krätschmer, Ilse","orcid":"0000-0002-5636-9259","first_name":"Ilse","last_name":"Krätschmer","id":"30d4014e-7753-11eb-b44b-db6d61112e73"},{"first_name":"Markus J.","last_name":"Bauer","full_name":"Bauer, Markus J."},{"orcid":"0000-0001-8982-8813","full_name":"Robinson, Matthew Richard","first_name":"Matthew Richard","id":"E5D42276-F5DA-11E9-8E24-6303E6697425","last_name":"Robinson"}],"month":"08","citation":{"apa":"Machnik, N. N., Mahmoudi, S. M., Borczyk, M., Krätschmer, I., Bauer, M. J., &#38; Robinson, M. R. (2026). Causal inference for multiple risk factors and diseases from genomics data. <i>Nature Communications</i>. Springer Nature. <a href=\"https://doi.org/10.1038/s41467-026-76877-7\">https://doi.org/10.1038/s41467-026-76877-7</a>","short":"N.N. Machnik, S.M. Mahmoudi, M. Borczyk, I. Krätschmer, M.J. Bauer, M.R. Robinson, Nature Communications 17 (2026).","ama":"Machnik NN, Mahmoudi SM, Borczyk M, Krätschmer I, Bauer MJ, Robinson MR. Causal inference for multiple risk factors and diseases from genomics data. <i>Nature Communications</i>. 2026;17. doi:<a href=\"https://doi.org/10.1038/s41467-026-76877-7\">10.1038/s41467-026-76877-7</a>","mla":"Machnik, Nick N., et al. “Causal Inference for Multiple Risk Factors and Diseases from Genomics Data.” <i>Nature Communications</i>, vol. 17, 10153, Springer Nature, 2026, doi:<a href=\"https://doi.org/10.1038/s41467-026-76877-7\">10.1038/s41467-026-76877-7</a>.","ieee":"N. N. Machnik, S. M. Mahmoudi, M. Borczyk, I. Krätschmer, M. J. Bauer, and M. R. Robinson, “Causal inference for multiple risk factors and diseases from genomics data,” <i>Nature Communications</i>, vol. 17. Springer Nature, 2026.","ista":"Machnik NN, Mahmoudi SM, Borczyk M, Krätschmer I, Bauer MJ, Robinson MR. 2026. Causal inference for multiple risk factors and diseases from genomics data. Nature Communications. 17, 10153.","chicago":"Machnik, Nick N, Seyed Mahdi Mahmoudi, Malgorzata Borczyk, Ilse Krätschmer, Markus J. Bauer, and Matthew Richard Robinson. “Causal Inference for Multiple Risk Factors and Diseases from Genomics Data.” <i>Nature Communications</i>. Springer Nature, 2026. <a href=\"https://doi.org/10.1038/s41467-026-76877-7\">https://doi.org/10.1038/s41467-026-76877-7</a>."},"date_created":"2026-10-04T22:01:53Z","OA_type":"gold","year":"2026","department":[{"_id":"MaRo"}],"status":"public"},{"corr_author":"1","publication_status":"published","oa_version":"Published Version","page":"138","alternative_title":["ISTA Thesis"],"fulldoi":"https://doi.org/10.15479/at:ista:18642","type":"dissertation","publisher":"Institute of Science and Technology Austria","project":[{"grant_number":"PCEGP3_181181","name":"Improving estimation and prediction of common complex disease risk","_id":"9B8D11D6-BA93-11EA-9121-9846C619BF3A"}],"researchdata_availability":"unclear","abstract":[{"text":"This thesis consists of two pieces of work in the broader field of computational biology,\r\nboth of which are methods for the analysis of large scale biological data, implemented in\r\nefficient software.\r\nChapter 2 introduces a statistical software for causal discovery and inference from observed\r\ngenetic marker and phenotypic trait data. We explore in simulation how well the method\r\ncan fine-map genetic effects, find the correct causal structure among tens of traits and\r\nmillions of genetic markers, and infer the causal effect size for the discovered causal\r\nrelations. We then apply the method to 8 million markers and 17 traits from the UK\r\nBiobank and show that many relationships found with other methods are likely due to\r\nthe effects of hidden confounders.\r\nChapter 3 describes how this method can be applied to longitudinal data. I show how one\r\ncan incorporate the background knowledge present in the known order of measurements to\r\nimprove the accuracy of the causal discovery process, and explore the method’s ability to\r\nidentify age specific genetic effects, and how the error rates of this recovery are influenced\r\nby missing data due to different censoring mechanisms.\r\nChapter 4 introduces a statistical software for the comparison of chromatin contact maps\r\nbased on the structural similarity index. We explore the robustness of the method to\r\nnoise and size differences of the compared maps, show how it can measure evolutionary\r\nconservation of topological features by providing a similarity ranking of syntenic regions,\r\nand finally how it can detect alterations in 3D genome structure due to genetic mutations\r\nin samples of medical relevance.\r\n","lang":"eng"}],"article_processing_charge":"No","supervisor":[{"orcid":"0000-0001-8982-8813","full_name":"Robinson, Matthew Richard","first_name":"Matthew Richard","last_name":"Robinson","id":"E5D42276-F5DA-11E9-8E24-6303E6697425"}],"doi":"10.15479/at:ista:18642","related_material":{"record":[{"id":"8707","status":"public","relation":"part_of_dissertation"},{"id":"18648","status":"public","relation":"part_of_dissertation"}]},"acknowledgement":"I would like to thank the Swiss National Science Foundation for funding parts of this work\r\nthrough the Eccellenza Grant \"Improving estimation and prediction of common complex\r\ndisease risk\" with grant number PCEGP3_181181.","file":[{"access_level":"open_access","content_type":"application/pdf","embargo":"2025-06-12","date_created":"2024-12-11T11:59:54Z","date_updated":"2025-06-12T22:30:02Z","relation":"main_file","file_id":"18649","checksum":"d45e4d170f9a70a1f69b44b99bd058e4","file_name":"NickMachnikThesisFinal_pdfa_conv.pdf","file_size":12845009,"creator":"nmachnik"},{"relation":"source_file","file_size":14189810,"checksum":"f88c9acc62002395ec4dcbdb5eea8b82","file_name":"thesis.zip","creator":"nmachnik","file_id":"18650","content_type":"application/zip","embargo_to":"open_access","access_level":"closed","date_updated":"2025-06-12T22:30:02Z","date_created":"2024-12-11T11:59:34Z"}],"language":[{"iso":"eng"}],"_id":"18642","day":"11","doi_confirm":"1","title":"Algorithms for causal learning and comparative analysis for genomic data","user_id":"317138e5-6ab7-11ef-aa6d-ffef3953e345","date_updated":"2026-10-16T11:01:16Z","ddc":["576"],"date_published":"2024-12-11T00:00:00Z","OA_place":"publisher","file_date_updated":"2025-06-12T22:30:02Z","supplementarymaterial":"unclear","publication_identifier":{"issn":["2663-337X"]},"has_accepted_license":"1","degree_awarded":"PhD","oa":1,"author":[{"orcid":"0000-0001-6617-9742","full_name":"Machnik, Nick N","last_name":"Machnik","id":"3591A0AA-F248-11E8-B48F-1D18A9856A87","first_name":"Nick N"}],"month":"12","date_created":"2024-12-10T13:49:15Z","citation":{"mla":"Machnik, Nick N. <i>Algorithms for Causal Learning and Comparative Analysis for Genomic Data</i>. Institute of Science and Technology Austria, 2024, doi:<a href=\"https://doi.org/10.15479/at:ista:18642\">10.15479/at:ista:18642</a>.","ama":"Machnik NN. Algorithms for causal learning and comparative analysis for genomic data. 2024. doi:<a href=\"https://doi.org/10.15479/at:ista:18642\">10.15479/at:ista:18642</a>","short":"N.N. Machnik, Algorithms for Causal Learning and Comparative Analysis for Genomic Data, Institute of Science and Technology Austria, 2024.","apa":"Machnik, N. N. (2024). <i>Algorithms for causal learning and comparative analysis for genomic data</i>. Institute of Science and Technology Austria. <a href=\"https://doi.org/10.15479/at:ista:18642\">https://doi.org/10.15479/at:ista:18642</a>","chicago":"Machnik, Nick N. “Algorithms for Causal Learning and Comparative Analysis for Genomic Data.” Institute of Science and Technology Austria, 2024. <a href=\"https://doi.org/10.15479/at:ista:18642\">https://doi.org/10.15479/at:ista:18642</a>.","ista":"Machnik NN. 2024. Algorithms for causal learning and comparative analysis for genomic data. Institute of Science and Technology Austria.","ieee":"N. N. Machnik, “Algorithms for causal learning and comparative analysis for genomic data,” Institute of Science and Technology Austria, 2024."},"year":"2024","department":[{"_id":"GradSch"},{"_id":"MaRo"}],"status":"public"},{"year":"2024","OA_type":"free access","citation":{"apa":"Machnik, N. N., Mahmoudi, S. M., Borczyk, M., Krätschmer, I., Bauer, M. J., &#38; Robinson, M. R. (2024). Causal inference for multiple risk factors and diseases from genomics data. <i>bioRxiv</i>. <a href=\"https://doi.org/10.1101/2023.12.06.570392\">https://doi.org/10.1101/2023.12.06.570392</a>","short":"N.N. Machnik, S.M. Mahmoudi, M. Borczyk, I. Krätschmer, M.J. Bauer, M.R. Robinson, BioRxiv (2024).","ama":"Machnik NN, Mahmoudi SM, Borczyk M, Krätschmer I, Bauer MJ, Robinson MR. Causal inference for multiple risk factors and diseases from genomics data. <i>bioRxiv</i>. 2024. doi:<a href=\"https://doi.org/10.1101/2023.12.06.570392\">10.1101/2023.12.06.570392</a>","mla":"Machnik, Nick N., et al. “Causal Inference for Multiple Risk Factors and Diseases from Genomics Data.” <i>BioRxiv</i>, 2024, doi:<a href=\"https://doi.org/10.1101/2023.12.06.570392\">10.1101/2023.12.06.570392</a>.","ieee":"N. N. Machnik, S. M. Mahmoudi, M. Borczyk, I. Krätschmer, M. J. Bauer, and M. R. Robinson, “Causal inference for multiple risk factors and diseases from genomics data,” <i>bioRxiv</i>. 2024.","chicago":"Machnik, Nick N, Seyed Mahdi Mahmoudi, Malgorzata Borczyk, Ilse Krätschmer, Markus J. Bauer, and Matthew Richard Robinson. “Causal Inference for Multiple Risk Factors and Diseases from Genomics Data.” <i>BioRxiv</i>, 2024. <a href=\"https://doi.org/10.1101/2023.12.06.570392\">https://doi.org/10.1101/2023.12.06.570392</a>.","ista":"Machnik NN, Mahmoudi SM, Borczyk M, Krätschmer I, Bauer MJ, Robinson MR. 2024. Causal inference for multiple risk factors and diseases from genomics data. bioRxiv, <a href=\"https://doi.org/10.1101/2023.12.06.570392\">10.1101/2023.12.06.570392</a>."},"date_created":"2024-12-11T10:42:59Z","department":[{"_id":"MaRo"}],"status":"public","oa":1,"author":[{"orcid":"0000-0001-6617-9742","full_name":"Machnik, Nick N","id":"3591A0AA-F248-11E8-B48F-1D18A9856A87","last_name":"Machnik","first_name":"Nick N"},{"last_name":"Mahmoudi","id":"b9f6d5ef-7774-11eb-a47f-df2c75c02ee7","first_name":"Seyed Mahdi","full_name":"Mahmoudi, Seyed Mahdi"},{"last_name":"Borczyk","first_name":"Malgorzata","full_name":"Borczyk, Malgorzata"},{"first_name":"Ilse","last_name":"Krätschmer","id":"30d4014e-7753-11eb-b44b-db6d61112e73","orcid":"0000-0002-5636-9259","full_name":"Krätschmer, Ilse"},{"full_name":"Bauer, Markus J.","last_name":"Bauer","first_name":"Markus J."},{"full_name":"Robinson, Matthew Richard","orcid":"0000-0001-8982-8813","id":"E5D42276-F5DA-11E9-8E24-6303E6697425","last_name":"Robinson","first_name":"Matthew Richard"}],"month":"08","user_id":"317138e5-6ab7-11ef-aa6d-ffef3953e345","date_published":"2024-08-10T00:00:00Z","date_updated":"2026-10-25T23:31:29Z","OA_place":"repository","das_tickbox":"0","supplementarymaterial":"yes","language":[{"iso":"eng"}],"_id":"18648","day":"10","title":"Causal inference for multiple risk factors and diseases from genomics data","doi":"10.1101/2023.12.06.570392","acknowledgement":"We thank Zoltan Kutalik and members of the Robinson group \r\nat ISTA for their comments, which improved this manuscript. This work was funded \r\nby a research collaboration agreement between Boehringer Ingelheim and the research \r\ngroup of MRR at the Institute of Science and Technology Austria. Additional funding \r\nwas also provided by an SNSF Eccellenza Grant to MRR (PCEGP3-181181), and by \r\ncore funding from the Institute of Science and Technology Austria. We would like \r\nto acknowledge the participants and investigators of the UK Biobank study. High- \r\nperformance computing was supported by the Scientific Service Units (SSU) of IST \r\nAustria through resources provided by Scientific Computing (SciComp). ","related_material":{"record":[{"id":"23043","relation":"later_version","status":"public"},{"id":"18642","relation":"dissertation_contains","status":"public"}]},"publication":"bioRxiv","project":[{"_id":"9B8D11D6-BA93-11EA-9121-9846C619BF3A","name":"Improving estimation and prediction of common complex disease risk","grant_number":"PCEGP3_181181"},{"_id":"bd936e6f-d553-11ed-ba76-a82299f63e8c","grant_number":"590359","name":"Advanced statistical modelling to facilitate more accurate characterisation of disease phenotypes, improved genetic mapping, and effective therapeutic hypothesis generation"}],"researchdata_availability":"not applicable","abstract":[{"lang":"eng","text":"Statistical causal learning in genomics relies on the instrumental variable method of\r\nMendelian Randomization (MR). Currently, an overwhelming number of MR studies\r\npurport to show causal relationships among a wide range of risk factors and outcomes.\r\nHere, we show that selecting instrument variables from genome-wide association study\r\nestimates leads to high false discovery rates for many MR approaches, which can be\r\ngreatly reduced by employing a graphical inference approach which: (i) explicitly tests\r\ninstrumental variable assumptions; (ii) distinguishes direct from indirect factors in very\r\nhigh-dimensional data; (iii) discriminates pleiotropic from trait-specific markers, controlling for LD genome-wide; (iv) accommodates rare variants and binary outcomes in a\r\nprincipled way; and (v) identifies potential unobserved latent confounding. For 17 traits\r\nand 8.4M variants recorded for 458,747 individuals in the UK Biobank, we show that\r\nstandard MR analysis gives an abundance of findings that disappear under stringent\r\nassumption checks, with many relationships reflecting potential unmeasured confounding. This implies that mixtures of temporal precedence and potential for reverse-causality\r\nprohibit understanding the underlying nature of phenotypic and genetic correlations in\r\nbiobank data. We propose that well-curated longitudinal records are likely needed and\r\nthat our approach provides a first-step toward robust principled screening for potential\r\ncausal links.\r\n"}],"article_processing_charge":"No","type":"preprint","license":"https://creativecommons.org/licenses/by-nc/4.0/","fulldoi":"https://doi.org/10.1101/2023.12.06.570392","tmp":{"legal_code_url":"https://creativecommons.org/licenses/by-nc/4.0/legalcode","short":"CC BY-NC (4.0)","name":"Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0)","image":"/images/cc_by_nc.png"},"main_file_link":[{"url":"https://doi.org/10.1101/2023.12.06.570392","open_access":"1"}],"corr_author":"1","oa_version":"Preprint","publication_status":"published","acknowledged_ssus":[{"_id":"ScienComp"}]},{"status":"public","citation":{"mla":"Ing-Simmons, Elizabeth, et al. “Reply to: Revisiting the Use of Structural Similarity Index in Hi-C.” <i>Nature Genetics</i>, vol. 55, no. 12, Springer Nature, 2023, pp. 2053–55, doi:<a href=\"https://doi.org/10.1038/s41588-023-01595-5\">10.1038/s41588-023-01595-5</a>.","short":"E. Ing-Simmons, N.N. Machnik, J.M. Vaquerizas, Nature Genetics 55 (2023) 2053–2055.","apa":"Ing-Simmons, E., Machnik, N. N., &#38; Vaquerizas, J. M. (2023). Reply to: Revisiting the use of structural similarity index in Hi-C. <i>Nature Genetics</i>. Springer Nature. <a href=\"https://doi.org/10.1038/s41588-023-01595-5\">https://doi.org/10.1038/s41588-023-01595-5</a>","ama":"Ing-Simmons E, Machnik NN, Vaquerizas JM. Reply to: Revisiting the use of structural similarity index in Hi-C. <i>Nature Genetics</i>. 2023;55(12):2053-2055. doi:<a href=\"https://doi.org/10.1038/s41588-023-01595-5\">10.1038/s41588-023-01595-5</a>","ieee":"E. Ing-Simmons, N. N. Machnik, and J. M. Vaquerizas, “Reply to: Revisiting the use of structural similarity index in Hi-C,” <i>Nature Genetics</i>, vol. 55, no. 12. Springer Nature, pp. 2053–2055, 2023.","ista":"Ing-Simmons E, Machnik NN, Vaquerizas JM. 2023. Reply to: Revisiting the use of structural similarity index in Hi-C. Nature Genetics. 55(12), 2053–2055.","chicago":"Ing-Simmons, Elizabeth, Nick N Machnik, and Juan M. Vaquerizas. “Reply to: Revisiting the Use of Structural Similarity Index in Hi-C.” <i>Nature Genetics</i>. Springer Nature, 2023. <a href=\"https://doi.org/10.1038/s41588-023-01595-5\">https://doi.org/10.1038/s41588-023-01595-5</a>."},"date_created":"2023-12-17T23:00:53Z","year":"2023","department":[{"_id":"MaRo"}],"external_id":{"pmid":["38052961"],"isi":["001169777400004"]},"month":"12","author":[{"first_name":"Elizabeth","last_name":"Ing-Simmons","full_name":"Ing-Simmons, Elizabeth"},{"last_name":"Machnik","id":"3591A0AA-F248-11E8-B48F-1D18A9856A87","first_name":"Nick N","full_name":"Machnik, Nick N","orcid":"0000-0001-6617-9742"},{"last_name":"Vaquerizas","first_name":"Juan M.","full_name":"Vaquerizas, Juan M."}],"intvolume":"        55","publication_identifier":{"eissn":["1546-1718"],"issn":["1061-4036"]},"date_published":"2023-12-01T00:00:00Z","date_updated":"2025-09-09T14:00:46Z","user_id":"317138e5-6ab7-11ef-aa6d-ffef3953e345","title":"Reply to: Revisiting the use of structural similarity index in Hi-C","_id":"14689","language":[{"iso":"eng"}],"day":"01","pmid":1,"issue":"12","isi":1,"doi":"10.1038/s41588-023-01595-5","quality_controlled":"1","article_processing_charge":"No","publication":"Nature Genetics","article_type":"letter_note","publisher":"Springer Nature","scopus_import":"1","fulldoi":"https://doi.org/10.1038/s41588-023-01595-5","type":"journal_article","page":"2053-2055","volume":55,"publication_status":"published","oa_version":"None"},{"month":"01","article_processing_charge":"No","author":[{"orcid":"0000-0003-4074-2570","full_name":"Maslov, Mikhail","first_name":"Mikhail","last_name":"Maslov","id":"2E65BB0E-F248-11E8-B48F-1D18A9856A87"},{"full_name":"Kondrashov, Fyodor","orcid":"0000-0001-8243-4694","first_name":"Fyodor","id":"44FDEF62-F248-11E8-B48F-1D18A9856A87","last_name":"Kondrashov"},{"id":"45DF286A-F248-11E8-B48F-1D18A9856A87","last_name":"Artner","first_name":"Christina","full_name":"Artner, Christina"},{"full_name":"Hennessey-Wesen, Mike","first_name":"Mike","last_name":"Hennessey-Wesen","id":"3F338C72-F248-11E8-B48F-1D18A9856A87"},{"orcid":"0000-0001-6041-254X","full_name":"Kavcic, Bor","last_name":"Kavcic","id":"350F91D2-F248-11E8-B48F-1D18A9856A87","first_name":"Bor"},{"first_name":"Nick N","last_name":"Machnik","id":"3591A0AA-F248-11E8-B48F-1D18A9856A87","full_name":"Machnik, Nick N","orcid":"0000-0001-6617-9742"},{"first_name":"Roshan K","last_name":"Satapathy","id":"46046B7A-F248-11E8-B48F-1D18A9856A87","full_name":"Satapathy, Roshan K"},{"last_name":"Tomanek","id":"3981F020-F248-11E8-B48F-1D18A9856A87","first_name":"Isabella","full_name":"Tomanek, Isabella","orcid":"0000-0001-6197-363X"}],"abstract":[{"lang":"eng","text":"The main idea behind the Core Project is to teach first year students at IST scientific communication skills and let them practice by presenting their research within an interdisciplinary environment. Over the course of the first semester, students participated in seminars, where they shared their results with the colleagues from other fields and took part in discussions on relevant subjects. The main focus during this sessions was on delivering the information in a simplified and comprehensible way, going into the very basics of a subject if necessary. At the end, the students were asked to present their research in the written form to exercise their writing skills. The reports were gathered in this document. All of them were reviewed by the  teaching assistants and write-ups illustrating unique stylistic features and, in general, an outstanding level of writing skills, were honorably mentioned in the section \"Selected Reports\"."}],"has_accepted_license":"1","file":[{"relation":"main_file","file_size":169620437,"creator":"dernst","file_name":"Core_Project_Proceedings_mod.pdf","file_id":"8152","content_type":"application/pdf","access_level":"local","date_updated":"2020-07-22T14:45:07Z","date_created":"2020-07-22T14:45:07Z"}],"status":"public","year":"2020","date_created":"2020-07-22T14:48:14Z","citation":{"ama":"Maslov M, Kondrashov F, Artner C, et al. <i>Core Project Proceedings</i>. IST Austria; 2020.","short":"M. Maslov, F. Kondrashov, C. Artner, M. Hennessey-Wesen, B. Kavcic, N.N. Machnik, R.K. Satapathy, I. Tomanek, Core Project Proceedings, IST Austria, 2020.","apa":"Maslov, M., Kondrashov, F., Artner, C., Hennessey-Wesen, M., Kavcic, B., Machnik, N. N., … Tomanek, I. (2020). <i>Core Project Proceedings</i>. IST Austria.","mla":"Maslov, Mikhail, et al. <i>Core Project Proceedings</i>. IST Austria, 2020.","chicago":"Maslov, Mikhail, Fyodor Kondrashov, Christina Artner, Mike Hennessey-Wesen, Bor Kavcic, Nick N Machnik, Roshan K Satapathy, and Isabella Tomanek. <i>Core Project Proceedings</i>. IST Austria, 2020.","ista":"Maslov M, Kondrashov F, Artner C, Hennessey-Wesen M, Kavcic B, Machnik NN, Satapathy RK, Tomanek I. 2020. Core Project Proceedings, IST Austria, 425p.","ieee":"M. Maslov <i>et al.</i>, <i>Core Project Proceedings</i>. IST Austria, 2020."},"title":"Core Project Proceedings","page":"425","oa_version":"None","publication_status":"published","day":"28","_id":"8151","language":[{"iso":"eng"}],"file_date_updated":"2020-07-22T14:45:07Z","publisher":"IST Austria","type":"report","extern":"1","date_updated":"2024-09-16T06:03:22Z","date_published":"2020-01-28T00:00:00Z","ddc":["510","530","570"],"user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87"},{"language":[{"iso":"eng"}],"_id":"8707","day":"19","isi":1,"pmid":1,"title":"CHESS enables quantitative comparison of chromatin contact data and automatic feature extraction","date_updated":"2026-10-25T23:31:29Z","date_published":"2020-10-19T00:00:00Z","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","OA_place":"repository","intvolume":"        52","publication_identifier":{"eissn":["1546-1718"],"issn":["1061-4036"]},"oa":1,"author":[{"full_name":" Galan, Silvia","first_name":"Silvia","last_name":" Galan"},{"full_name":"Machnik, Nick N","orcid":"0000-0001-6617-9742","id":"3591A0AA-F248-11E8-B48F-1D18A9856A87","last_name":"Machnik","first_name":"Nick N"},{"last_name":"Kruse","first_name":"Kai","full_name":"Kruse, Kai"},{"full_name":"Díaz, Noelia","first_name":"Noelia","last_name":"Díaz"},{"last_name":"Marti-Renom","first_name":"Marc A","full_name":"Marti-Renom, Marc A"},{"first_name":"Juan M","last_name":"Vaquerizas","full_name":"Vaquerizas, Juan M"}],"external_id":{"pmid":["33077914"],"isi":["000579693500004"]},"month":"10","date_created":"2020-10-25T23:01:20Z","citation":{"mla":"Galan, Silvia, et al. “CHESS Enables Quantitative Comparison of Chromatin Contact Data and Automatic Feature Extraction.” <i>Nature Genetics</i>, vol. 52, Springer Nature, 2020, pp. 1247–55, doi:<a href=\"https://doi.org/10.1038/s41588-020-00712-y\">10.1038/s41588-020-00712-y</a>.","ama":"Galan S, Machnik NN, Kruse K, Díaz N, Marti-Renom MA, Vaquerizas JM. CHESS enables quantitative comparison of chromatin contact data and automatic feature extraction. <i>Nature Genetics</i>. 2020;52:1247-1255. doi:<a href=\"https://doi.org/10.1038/s41588-020-00712-y\">10.1038/s41588-020-00712-y</a>","apa":"Galan, S., Machnik, N. N., Kruse, K., Díaz, N., Marti-Renom, M. A., &#38; Vaquerizas, J. M. (2020). CHESS enables quantitative comparison of chromatin contact data and automatic feature extraction. <i>Nature Genetics</i>. Springer Nature. <a href=\"https://doi.org/10.1038/s41588-020-00712-y\">https://doi.org/10.1038/s41588-020-00712-y</a>","short":"S.  Galan, N.N. Machnik, K. Kruse, N. Díaz, M.A. Marti-Renom, J.M. Vaquerizas, Nature Genetics 52 (2020) 1247–1255.","chicago":"Galan, Silvia, Nick N Machnik, Kai Kruse, Noelia Díaz, Marc A Marti-Renom, and Juan M Vaquerizas. “CHESS Enables Quantitative Comparison of Chromatin Contact Data and Automatic Feature Extraction.” <i>Nature Genetics</i>. Springer Nature, 2020. <a href=\"https://doi.org/10.1038/s41588-020-00712-y\">https://doi.org/10.1038/s41588-020-00712-y</a>.","ista":"Galan S, Machnik NN, Kruse K, Díaz N, Marti-Renom MA, Vaquerizas JM. 2020. CHESS enables quantitative comparison of chromatin contact data and automatic feature extraction. Nature Genetics. 52, 1247–1255.","ieee":"S.  Galan, N. N. Machnik, K. Kruse, N. Díaz, M. A. Marti-Renom, and J. M. Vaquerizas, “CHESS enables quantitative comparison of chromatin contact data and automatic feature extraction,” <i>Nature Genetics</i>, vol. 52. Springer Nature, pp. 1247–1255, 2020."},"OA_type":"green","year":"2020","department":[{"_id":"FyKo"}],"status":"public","publication_status":"published","oa_version":"Submitted Version","page":"1247-1255","volume":52,"fulldoi":"https://doi.org/10.1038/s41588-020-00712-y","type":"journal_article","publisher":"Springer Nature","scopus_import":"1","main_file_link":[{"open_access":"1","url":"https://pmc.ncbi.nlm.nih.gov/articles/PMC7610641/"}],"publication":"Nature Genetics","abstract":[{"text":"Dynamic changes in the three-dimensional (3D) organization of chromatin are associated with central biological processes, such as transcription, replication and development. Therefore, the comprehensive identification and quantification of these changes is fundamental to understanding of evolutionary and regulatory mechanisms. Here, we present Comparison of Hi-C Experiments using Structural Similarity (CHESS), an algorithm for the comparison of chromatin contact maps and automatic differential feature extraction. We demonstrate the robustness of CHESS to experimental variability and showcase its biological applications on (1) interspecies comparisons of syntenic regions in human and mouse models; (2) intraspecies identification of conformational changes in Zelda-depleted Drosophila embryos; (3) patient-specific aberrant chromatin conformation in a diffuse large B-cell lymphoma sample; and (4) the systematic identification of chromatin contact differences in high-resolution Capture-C data. In summary, CHESS is a computationally efficient method for the comparison and classification of changes in chromatin contact data.","lang":"eng"}],"article_type":"original","quality_controlled":"1","article_processing_charge":"No","doi":"10.1038/s41588-020-00712-y","related_material":{"record":[{"id":"18642","relation":"dissertation_contains","status":"public"}]},"acknowledgement":"Work in the Vaquerizas laboratory is funded by the Max Planck Society, the Deutsche Forschungsgemeinschaft (DFG) Priority Programme SPP 2202 ‘Spatial Genome Architecture in Development and Disease’ (project no. 422857230 to J.M.V.), the DFG Clinical Research Unit CRU326 ‘Male Germ Cells: from Genes to Function’ (project no. 329621271 to J.M.V.), the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement no. 643062—ZENCODE-ITN to J.M.V.) and the Medical Research Council in the UK. This research was partially funded by the European Union’s H2020 Framework Programme through the European Research Council (grant no. 609989 to M.A.M.-R.). We thank the support of the Spanish Ministerio de Ciencia, Innovación y Universidades through grant no. BFU2017-85926-P to M.A.M.-R. The Centre for Genomic Regulation thanks the support of the Ministerio de Ciencia, Innovación y Universidades to the European Molecular Biology Laboratory partnership, the ‘Centro de Excelencia Severo Ochoa 2013–2017’, agreement no. SEV-2012-0208, the CERCA Programme/Generalitat de Catalunya, Spanish Ministerio de Ciencia, Innovación y Universidades through the Instituto de Salud Carlos III, the Generalitat de Catalunya through the Departament de Salut and Departament d’Empresa i Coneixement and cofinancing by the Spanish Ministerio de Ciencia, Innovación y Universidades with funds from the European Regional Development Fund corresponding to the 2014–2020 Smart Growth Operating Program. S.G. thanks the support from the Company of Biologists (grant no. JCSTF181158) and the European Molecular Biology Organization Short-Term Fellowship programme."}]