TY - GEN
T1 - Inferring diploid 3D chromatin structures from Hi-C data
AU - Cauer, Alexandra Gesine
AU - Yardımcı, Gürkan
AU - Vert, Jean Philippe
AU - Varoquaux, Nelle
AU - Noble, William Stafford
N1 - Funding Information:
Funding WSN acknowledges support from the National Institutes of Health Common Fund 4D Nucleome Program (Grant U54 DK107979). NV was supported by a BIDS fellowship from the Gordon and Betty Moore Foundation (Grant GBMF3834) and by the Alfred P. Sloan Foundation (Grant 2013-10-27).
Publisher Copyright:
© Alexandra Gesine Cauer, Gürkan Yardımcı, Jean-Philippe Vert, Nelle Varoquaux, and William Stafford Noble;
Copyright:
Copyright 2019 Elsevier B.V., All rights reserved.
PY - 2019/9
Y1 - 2019/9
N2 - The 3D organization of the genome plays a key role in many cellular processes, such as gene regulation, differentiation, and replication. Assays like Hi-C measure DNA-DNA contacts in a high-throughput fashion, and inferring accurate 3D models of chromosomes can yield insights hidden in the raw data. For example, structural inference can account for noise in the data, disambiguate the distinct structures of homologous chromosomes, orient genomic regions relative to nuclear landmarks, and serve as a framework for integrating other data types. Although many methods exist to infer the 3D structure of haploid genomes, inferring a diploid structure from Hi-C data is still an open problem. Indeed, the diploid case is very challenging, because Hi-C data typically does not distinguish between homologous chromosomes. We propose a method to infer 3D diploid genomes from Hi-C data. We demonstrate the accuracy of the method on simulated data, and we also use the method to infer 3D structures for mouse chromosome X, confirming that the active homolog exhibits a bipartite structure, whereas the active homolog does not.
AB - The 3D organization of the genome plays a key role in many cellular processes, such as gene regulation, differentiation, and replication. Assays like Hi-C measure DNA-DNA contacts in a high-throughput fashion, and inferring accurate 3D models of chromosomes can yield insights hidden in the raw data. For example, structural inference can account for noise in the data, disambiguate the distinct structures of homologous chromosomes, orient genomic regions relative to nuclear landmarks, and serve as a framework for integrating other data types. Although many methods exist to infer the 3D structure of haploid genomes, inferring a diploid structure from Hi-C data is still an open problem. Indeed, the diploid case is very challenging, because Hi-C data typically does not distinguish between homologous chromosomes. We propose a method to infer 3D diploid genomes from Hi-C data. We demonstrate the accuracy of the method on simulated data, and we also use the method to infer 3D structures for mouse chromosome X, confirming that the active homolog exhibits a bipartite structure, whereas the active homolog does not.
KW - 3D modeling
KW - Chromatin structure
KW - Genome 3D architecture
KW - Hi-C
UR - http://www.scopus.com/inward/record.url?scp=85072649597&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85072649597&partnerID=8YFLogxK
U2 - 10.4230/LIPIcs.WABI.2019.11
DO - 10.4230/LIPIcs.WABI.2019.11
M3 - Conference contribution
AN - SCOPUS:85072649597
T3 - Leibniz International Proceedings in Informatics, LIPIcs
BT - 19th International Workshop on Algorithms in Bioinformatics, WABI 2019
A2 - Huber, Katharina T.
A2 - Gusfield, Dan
PB - Schloss Dagstuhl- Leibniz-Zentrum fur Informatik GmbH, Dagstuhl Publishing
T2 - 19th International Workshop on Algorithms in Bioinformatics, WABI 2019
Y2 - 8 September 2019 through 10 September 2019
ER -