185 related articles for article (PubMed ID: 32787954)
1. Modeling and analysis of Hi-C data by HiSIF identifies characteristic promoter-distal loops.
Zhou Y; Cheng X; Yang Y; Li T; Li J; Huang TH; Wang J; Lin S; Jin VX
Genome Med; 2020 Aug; 12(1):69. PubMed ID: 32787954
[TBL] [Abstract][Full Text] [Related]
2. Computational Processing and Quality Control of Hi-C, Capture Hi-C and Capture-C Data.
Hansen P; Gargano M; Hecht J; Ibn-Salem J; Karlebach G; Roehr JT; Robinson PN
Genes (Basel); 2019 Jul; 10(7):. PubMed ID: 31323892
[TBL] [Abstract][Full Text] [Related]
3. HiC-bench: comprehensive and reproducible Hi-C data analysis designed for parameter exploration and benchmarking.
Lazaris C; Kelly S; Ntziachristos P; Aifantis I; Tsirigos A
BMC Genomics; 2017 Jan; 18(1):22. PubMed ID: 28056762
[TBL] [Abstract][Full Text] [Related]
4. LASCA: loop and significant contact annotation pipeline.
Luzhin AV; Golov AK; Gavrilov AA; Velichko AK; Ulianov SV; Razin SV; Kantidze OL
Sci Rep; 2021 Mar; 11(1):6361. PubMed ID: 33737718
[TBL] [Abstract][Full Text] [Related]
5. Chrom-Lasso: a lasso regression-based model to detect functional interactions using Hi-C data.
Lu J; Wang X; Sun K; Lan X
Brief Bioinform; 2021 Nov; 22(6):. PubMed ID: 34013331
[TBL] [Abstract][Full Text] [Related]
6. SnapHiC: a computational pipeline to identify chromatin loops from single-cell Hi-C data.
Yu M; Abnousi A; Zhang Y; Li G; Lee L; Chen Z; Fang R; Lagler TM; Yang Y; Wen J; Sun Q; Li Y; Ren B; Hu M
Nat Methods; 2021 Sep; 18(9):1056-1059. PubMed ID: 34446921
[TBL] [Abstract][Full Text] [Related]
7. Mustache: multi-scale detection of chromatin loops from Hi-C and Micro-C maps using scale-space representation.
Roayaei Ardakany A; Gezer HT; Lonardi S; Ay F
Genome Biol; 2020 Sep; 21(1):256. PubMed ID: 32998764
[TBL] [Abstract][Full Text] [Related]
8. hicGAN infers super resolution Hi-C data with generative adversarial networks.
Liu Q; Lv H; Jiang R
Bioinformatics; 2019 Jul; 35(14):i99-i107. PubMed ID: 31510693
[TBL] [Abstract][Full Text] [Related]
9. An integrated model for detecting significant chromatin interactions from high-resolution Hi-C data.
Carty M; Zamparo L; Sahin M; González A; Pelossof R; Elemento O; Leslie CS
Nat Commun; 2017 May; 8():15454. PubMed ID: 28513628
[TBL] [Abstract][Full Text] [Related]
10. Joint annotation of chromatin state and chromatin conformation reveals relationships among domain types and identifies domains of cell-type-specific expression.
Libbrecht MW; Ay F; Hoffman MM; Gilbert DM; Bilmes JA; Noble WS
Genome Res; 2015 Apr; 25(4):544-57. PubMed ID: 25677182
[TBL] [Abstract][Full Text] [Related]
11. Characteristic arrangement of nucleosomes is predictive of chromatin interactions at kilobase resolution.
Zhang H; Li F; Jia Y; Xu B; Zhang Y; Li X; Zhang Z
Nucleic Acids Res; 2017 Dec; 45(22):12739-12751. PubMed ID: 29036650
[TBL] [Abstract][Full Text] [Related]
12. MSTD: an efficient method for detecting multi-scale topological domains from symmetric and asymmetric 3D genomic maps.
Ye Y; Gao L; Zhang S
Nucleic Acids Res; 2019 Jun; 47(11):e65. PubMed ID: 30941409
[TBL] [Abstract][Full Text] [Related]
13. HiC-DC+ enables systematic 3D interaction calls and differential analysis for Hi-C and HiChIP.
Sahin M; Wong W; Zhan Y; Van Deynze K; Koche R; Leslie CS
Nat Commun; 2021 Jun; 12(1):3366. PubMed ID: 34099725
[TBL] [Abstract][Full Text] [Related]
14. SpectralTAD: an R package for defining a hierarchy of topologically associated domains using spectral clustering.
Cresswell KG; Stansfield JC; Dozmorov MG
BMC Bioinformatics; 2020 Jul; 21(1):319. PubMed ID: 32689928
[TBL] [Abstract][Full Text] [Related]
15. cLoops2: a full-stack comprehensive analytical tool for chromatin interactions.
Cao Y; Liu S; Ren G; Tang Q; Zhao K
Nucleic Acids Res; 2022 Jan; 50(1):57-71. PubMed ID: 34928392
[TBL] [Abstract][Full Text] [Related]
16. Leveraging three-dimensional chromatin architecture for effective reconstruction of enhancer-target gene regulatory interactions.
Salviato E; Djordjilović V; Hariprakash JM; Tagliaferri I; Pal K; Ferrari F
Nucleic Acids Res; 2021 Sep; 49(17):e97. PubMed ID: 34197622
[TBL] [Abstract][Full Text] [Related]
17. Rich Chromatin Structure Prediction from Hi-C Data.
Malik L; Patro R
IEEE/ACM Trans Comput Biol Bioinform; 2019; 16(5):1448-1458. PubMed ID: 29994683
[TBL] [Abstract][Full Text] [Related]
18. Characterizing chromatin interactions of regulatory elements and nucleosome positions, using Hi-C, Micro-C, and promoter capture Micro-C.
Lee BH; Wu Z; Rhie SK
Epigenetics Chromatin; 2022 Dec; 15(1):41. PubMed ID: 36544209
[TBL] [Abstract][Full Text] [Related]
19. Identification and utilization of copy number information for correcting Hi-C contact map of cancer cell lines.
Khalil AIS; Muzaki SRBM; Chattopadhyay A; Sanyal A
BMC Bioinformatics; 2020 Nov; 21(1):506. PubMed ID: 33160308
[TBL] [Abstract][Full Text] [Related]
20. peakC: a flexible, non-parametric peak calling package for 4C and Capture-C data.
Geeven G; Teunissen H; de Laat W; de Wit E
Nucleic Acids Res; 2018 Sep; 46(15):e91. PubMed ID: 29800273
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]