---
OA_place: publisher
_id: '18642'
abstract:
- lang: eng
text: "This thesis consists of two pieces of work in the broader feld of computational
biology,\r\nboth of which are methods for the analysis of large scale biological
data, implemented in\r\nefcient 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 fne-map
genetic efects, fnd the correct causal structure among tens of traits and\r\nmillions
of genetic markers, and infer the causal efect 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
efects 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 specifc
genetic efects, and how the error rates of this recovery are infuenced\r\nby missing
data due to diferent 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
diferences 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
fnally how it can detect alterations in 3D genome structure due to genetic mutations\r\nin
samples of medical relevance.\r\n"
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."
alternative_title:
- ISTA Thesis
article_processing_charge: No
author:
- first_name: Nick N
full_name: Machnik, Nick N
id: 3591A0AA-F248-11E8-B48F-1D18A9856A87
last_name: Machnik
orcid: 0000-0001-6617-9742
citation:
ama: Machnik NN. Algorithms for causal learning and comparative analysis for genomic
data. 2024. doi:10.15479/at:ista:18642
apa: Machnik, N. N. (2024). Algorithms for causal learning and comparative analysis
for genomic data. Institute of Science and Technology Austria. https://doi.org/10.15479/at:ista:18642
chicago: Machnik, Nick N. “Algorithms for Causal Learning and Comparative Analysis
for Genomic Data.” Institute of Science and Technology Austria, 2024. https://doi.org/10.15479/at:ista:18642.
ieee: N. N. Machnik, “Algorithms for causal learning and comparative analysis for
genomic data,” Institute of Science and Technology Austria, 2024.
ista: Machnik NN. 2024. Algorithms for causal learning and comparative analysis
for genomic data. Institute of Science and Technology Austria.
mla: Machnik, Nick N. Algorithms for Causal Learning and Comparative Analysis
for Genomic Data. Institute of Science and Technology Austria, 2024, doi:10.15479/at:ista:18642.
short: N.N. Machnik, Algorithms for Causal Learning and Comparative Analysis for
Genomic Data, Institute of Science and Technology Austria, 2024.
corr_author: '1'
date_created: 2024-12-10T13:49:15Z
date_published: 2024-12-11T00:00:00Z
date_updated: 2026-04-07T13:23:06Z
day: '11'
ddc:
- '576'
degree_awarded: PhD
department:
- _id: GradSch
- _id: MaRo
doi: 10.15479/at:ista:18642
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checksum: d45e4d170f9a70a1f69b44b99bd058e4
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creator: nmachnik
date_created: 2024-12-11T11:59:54Z
date_updated: 2025-06-12T22:30:02Z
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creator: nmachnik
date_created: 2024-12-11T11:59:34Z
date_updated: 2025-06-12T22:30:02Z
embargo_to: open_access
file_id: '18650'
file_name: thesis.zip
file_size: 14189810
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language:
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month: '12'
oa: 1
oa_version: Published Version
page: '138'
project:
- _id: 9B8D11D6-BA93-11EA-9121-9846C619BF3A
grant_number: PCEGP3_181181
name: Improving estimation and prediction of common complex disease risk
publication_identifier:
issn:
- 2663-337X
publication_status: published
publisher: Institute of Science and Technology Austria
related_material:
record:
- id: '18648'
relation: part_of_dissertation
status: public
- id: '8707'
relation: part_of_dissertation
status: public
status: public
supervisor:
- first_name: Matthew Richard
full_name: Robinson, Matthew Richard
id: E5D42276-F5DA-11E9-8E24-6303E6697425
last_name: Robinson
orcid: 0000-0001-8982-8813
title: Algorithms for causal learning and comparative analysis for genomic data
type: dissertation
user_id: ba8df636-2132-11f1-aed0-ed93e2281fdd
year: '2024'
...
---
OA_place: repository
OA_type: free access
_id: '18648'
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"
acknowledged_ssus:
- _id: ScienComp
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). "
article_processing_charge: No
author:
- first_name: Nick N
full_name: Machnik, Nick N
id: 3591A0AA-F248-11E8-B48F-1D18A9856A87
last_name: Machnik
orcid: 0000-0001-6617-9742
- first_name: Seyed Mahdi
full_name: Mahmoudi, Seyed Mahdi
id: b9f6d5ef-7774-11eb-a47f-df2c75c02ee7
last_name: Mahmoudi
- first_name: Malgorzata
full_name: Borczyk, Malgorzata
last_name: Borczyk
- first_name: Ilse
full_name: Krätschmer, Ilse
id: 30d4014e-7753-11eb-b44b-db6d61112e73
last_name: Krätschmer
orcid: 0000-0002-5636-9259
- first_name: Markus J.
full_name: Bauer, Markus J.
last_name: Bauer
- first_name: Matthew Richard
full_name: Robinson, Matthew Richard
id: E5D42276-F5DA-11E9-8E24-6303E6697425
last_name: Robinson
orcid: 0000-0001-8982-8813
citation:
ama: Machnik NN, Mahmoudi SM, Borczyk M, Krätschmer I, Bauer MJ, Robinson MR. Causal
inference for multiple risk factors and diseases from genomics data. bioRxiv.
2024. doi:10.1101/2023.12.06.570392
apa: Machnik, N. N., Mahmoudi, S. M., Borczyk, M., Krätschmer, I., Bauer, M. J.,
& Robinson, M. R. (2024). Causal inference for multiple risk factors and diseases
from genomics data. bioRxiv. https://doi.org/10.1101/2023.12.06.570392
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.” BioRxiv, 2024. https://doi.org/10.1101/2023.12.06.570392.
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,” bioRxiv. 2024.
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,
10.1101/2023.12.06.570392.
mla: Machnik, Nick N., et al. “Causal Inference for Multiple Risk Factors and Diseases
from Genomics Data.” BioRxiv, 2024, doi:10.1101/2023.12.06.570392.
short: N.N. Machnik, S.M. Mahmoudi, M. Borczyk, I. Krätschmer, M.J. Bauer, M.R.
Robinson, BioRxiv (2024).
corr_author: '1'
date_created: 2024-12-11T10:42:59Z
date_published: 2024-08-10T00:00:00Z
date_updated: 2026-04-28T22:30:26Z
day: '10'
department:
- _id: MaRo
doi: 10.1101/2023.12.06.570392
language:
- iso: eng
license: https://creativecommons.org/licenses/by-nc/4.0/
main_file_link:
- open_access: '1'
url: https://doi.org/10.1101/2023.12.06.570392
month: '08'
oa: 1
oa_version: Preprint
project:
- _id: 9B8D11D6-BA93-11EA-9121-9846C619BF3A
grant_number: PCEGP3_181181
name: Improving estimation and prediction of common complex disease risk
- _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
publication: bioRxiv
publication_status: published
related_material:
record:
- id: '18642'
relation: dissertation_contains
status: public
status: public
title: Causal inference for multiple risk factors and diseases from genomics data
tmp:
image: /images/cc_by_nc.png
legal_code_url: https://creativecommons.org/licenses/by-nc/4.0/legalcode
name: Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0)
short: CC BY-NC (4.0)
type: preprint
user_id: 8b945eb4-e2f2-11eb-945a-df72226e66a9
year: '2024'
...
---
_id: '14689'
article_processing_charge: No
article_type: letter_note
author:
- first_name: Elizabeth
full_name: Ing-Simmons, Elizabeth
last_name: Ing-Simmons
- first_name: Nick N
full_name: Machnik, Nick N
id: 3591A0AA-F248-11E8-B48F-1D18A9856A87
last_name: Machnik
orcid: 0000-0001-6617-9742
- first_name: Juan M.
full_name: Vaquerizas, Juan M.
last_name: Vaquerizas
citation:
ama: 'Ing-Simmons E, Machnik NN, Vaquerizas JM. Reply to: Revisiting the use of
structural similarity index in Hi-C. Nature Genetics. 2023;55(12):2053-2055.
doi:10.1038/s41588-023-01595-5'
apa: 'Ing-Simmons, E., Machnik, N. N., & Vaquerizas, J. M. (2023). Reply to:
Revisiting the use of structural similarity index in Hi-C. Nature Genetics.
Springer Nature. https://doi.org/10.1038/s41588-023-01595-5'
chicago: 'Ing-Simmons, Elizabeth, Nick N Machnik, and Juan M. Vaquerizas. “Reply
to: Revisiting the Use of Structural Similarity Index in Hi-C.” Nature Genetics.
Springer Nature, 2023. https://doi.org/10.1038/s41588-023-01595-5.'
ieee: 'E. Ing-Simmons, N. N. Machnik, and J. M. Vaquerizas, “Reply to: Revisiting
the use of structural similarity index in Hi-C,” Nature Genetics, 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.'
mla: 'Ing-Simmons, Elizabeth, et al. “Reply to: Revisiting the Use of Structural
Similarity Index in Hi-C.” Nature Genetics, vol. 55, no. 12, Springer Nature,
2023, pp. 2053–55, doi:10.1038/s41588-023-01595-5.'
short: E. Ing-Simmons, N.N. Machnik, J.M. Vaquerizas, Nature Genetics 55 (2023)
2053–2055.
date_created: 2023-12-17T23:00:53Z
date_published: 2023-12-01T00:00:00Z
date_updated: 2025-09-09T14:00:46Z
day: '01'
department:
- _id: MaRo
doi: 10.1038/s41588-023-01595-5
external_id:
isi:
- '001169777400004'
pmid:
- '38052961'
intvolume: ' 55'
isi: 1
issue: '12'
language:
- iso: eng
month: '12'
oa_version: None
page: 2053-2055
pmid: 1
publication: Nature Genetics
publication_identifier:
eissn:
- 1546-1718
issn:
- 1061-4036
publication_status: published
publisher: Springer Nature
quality_controlled: '1'
scopus_import: '1'
status: public
title: 'Reply to: Revisiting the use of structural similarity index in Hi-C'
type: journal_article
user_id: 317138e5-6ab7-11ef-aa6d-ffef3953e345
volume: 55
year: '2023'
...
---
_id: '8151'
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".
article_processing_charge: No
author:
- first_name: Mikhail
full_name: Maslov, Mikhail
id: 2E65BB0E-F248-11E8-B48F-1D18A9856A87
last_name: Maslov
orcid: 0000-0003-4074-2570
- first_name: Fyodor
full_name: Kondrashov, Fyodor
id: 44FDEF62-F248-11E8-B48F-1D18A9856A87
last_name: Kondrashov
orcid: 0000-0001-8243-4694
- first_name: Christina
full_name: Artner, Christina
id: 45DF286A-F248-11E8-B48F-1D18A9856A87
last_name: Artner
- first_name: Mike
full_name: Hennessey-Wesen, Mike
id: 3F338C72-F248-11E8-B48F-1D18A9856A87
last_name: Hennessey-Wesen
- first_name: Bor
full_name: Kavcic, Bor
id: 350F91D2-F248-11E8-B48F-1D18A9856A87
last_name: Kavcic
orcid: 0000-0001-6041-254X
- first_name: Nick N
full_name: Machnik, Nick N
id: 3591A0AA-F248-11E8-B48F-1D18A9856A87
last_name: Machnik
orcid: 0000-0001-6617-9742
- first_name: Roshan K
full_name: Satapathy, Roshan K
id: 46046B7A-F248-11E8-B48F-1D18A9856A87
last_name: Satapathy
- first_name: Isabella
full_name: Tomanek, Isabella
id: 3981F020-F248-11E8-B48F-1D18A9856A87
last_name: Tomanek
orcid: 0000-0001-6197-363X
citation:
ama: Maslov M, Kondrashov F, Artner C, et al. Core Project Proceedings. IST
Austria; 2020.
apa: Maslov, M., Kondrashov, F., Artner, C., Hennessey-Wesen, M., Kavcic, B., Machnik,
N. N., … Tomanek, I. (2020). Core Project Proceedings. IST Austria.
chicago: Maslov, Mikhail, Fyodor Kondrashov, Christina Artner, Mike Hennessey-Wesen,
Bor Kavcic, Nick N Machnik, Roshan K Satapathy, and Isabella Tomanek. Core
Project Proceedings. IST Austria, 2020.
ieee: M. Maslov et al., Core Project Proceedings. 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.
mla: Maslov, Mikhail, et al. Core Project Proceedings. 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.
date_created: 2020-07-22T14:48:14Z
date_published: 2020-01-28T00:00:00Z
date_updated: 2024-09-16T06:03:22Z
day: '28'
ddc:
- '510'
- '530'
- '570'
extern: '1'
file:
- access_level: local
content_type: application/pdf
creator: dernst
date_created: 2020-07-22T14:45:07Z
date_updated: 2020-07-22T14:45:07Z
file_id: '8152'
file_name: Core_Project_Proceedings_mod.pdf
file_size: 169620437
relation: main_file
file_date_updated: 2020-07-22T14:45:07Z
has_accepted_license: '1'
language:
- iso: eng
month: '01'
oa_version: None
page: '425'
publication_status: published
publisher: IST Austria
status: public
title: Core Project Proceedings
type: report
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
year: '2020'
...
---
OA_place: repository
OA_type: green
_id: '8707'
abstract:
- lang: eng
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.
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.'
article_processing_charge: No
article_type: original
author:
- first_name: Silvia
full_name: ' Galan, Silvia'
last_name: ' Galan'
- first_name: Nick N
full_name: Machnik, Nick N
id: 3591A0AA-F248-11E8-B48F-1D18A9856A87
last_name: Machnik
orcid: 0000-0001-6617-9742
- first_name: Kai
full_name: Kruse, Kai
last_name: Kruse
- first_name: Noelia
full_name: Díaz, Noelia
last_name: Díaz
- first_name: Marc A
full_name: Marti-Renom, Marc A
last_name: Marti-Renom
- first_name: Juan M
full_name: Vaquerizas, Juan M
last_name: Vaquerizas
citation:
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. Nature Genetics. 2020;52:1247-1255. doi:10.1038/s41588-020-00712-y
apa: Galan, S., Machnik, N. N., Kruse, K., Díaz, N., Marti-Renom, M. A., & Vaquerizas,
J. M. (2020). CHESS enables quantitative comparison of chromatin contact data
and automatic feature extraction. Nature Genetics. Springer Nature. https://doi.org/10.1038/s41588-020-00712-y
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.” Nature Genetics. Springer Nature,
2020. https://doi.org/10.1038/s41588-020-00712-y.
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,” Nature Genetics, vol. 52. Springer Nature,
pp. 1247–1255, 2020.
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.
mla: Galan, Silvia, et al. “CHESS Enables Quantitative Comparison of Chromatin Contact
Data and Automatic Feature Extraction.” Nature Genetics, vol. 52, Springer
Nature, 2020, pp. 1247–55, doi:10.1038/s41588-020-00712-y.
short: S. Galan, N.N. Machnik, K. Kruse, N. Díaz, M.A. Marti-Renom, J.M. Vaquerizas,
Nature Genetics 52 (2020) 1247–1255.
date_created: 2020-10-25T23:01:20Z
date_published: 2020-10-19T00:00:00Z
date_updated: 2026-04-28T22:30:26Z
day: '19'
department:
- _id: FyKo
doi: 10.1038/s41588-020-00712-y
external_id:
isi:
- '000579693500004'
pmid:
- '33077914'
intvolume: ' 52'
isi: 1
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://pmc.ncbi.nlm.nih.gov/articles/PMC7610641/
month: '10'
oa: 1
oa_version: Submitted Version
page: 1247-1255
pmid: 1
publication: Nature Genetics
publication_identifier:
eissn:
- 1546-1718
issn:
- 1061-4036
publication_status: published
publisher: Springer Nature
quality_controlled: '1'
related_material:
record:
- id: '18642'
relation: dissertation_contains
status: public
scopus_import: '1'
status: public
title: CHESS enables quantitative comparison of chromatin contact data and automatic
feature extraction
type: journal_article
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 52
year: '2020'
...