Vai ai contenuti. | Spostati sulla navigazione | Spostati sulla ricerca | Vai al menu | Contatti | Accessibilità

| Crea un account

Nicoletti, Chiara (2017) Genome conformation and transcription regulation: methods and applications. [Tesi di dottorato]

Full text disponibile come:

[img]
Anteprima
Documento PDF - Versione accettata
19Mb

Abstract (inglese)

The 3D organization of chromatin within the nucleus is crucial for genome functionality. This is true at multiple levels of resolution: on a large scale, with chromosomes occupying distinct volumes (chromosome territories), at the level of
individual chromatin fibers, organized in compartmentalized domains (as the Topologically Associating Domains, TADs), and down to the formation of short range chromatin interactions (as enhancer-promoter loops). The widespread adoption of high-throughput techniques derived from Chromosome Conformation Capture (3C) has been instrumental in advancing the knowledge of chromatin nuclear organization. In particular, Hi-C has the potential to achieve the most comprehensive characterization of chromatin 3D interactions, as in principle it can detect any pair of restriction fragments connected as a result of ligation by proximity. The analysis of the enormous amount of genomic data produced by Hi-C required the development of ad hoc algorithms and computational procedures. Despite the increasing number of available bioinformatics pipelines, no consensus on the optimal approach to analyze Hi-C data has been reached yet. Therefore, we quantitatively compared several Hi-C data analysis methods for the identification of multi-scale chromatin structures to highlight strengths and weaknesses of the various methods and propose application guidelines and good practices. Specifically, we compared different computational approaches (6 for the characterization of chromatin loops and 7 to identify TADs) using publicly available Hi-C datasets, comprising data from different species and cell lines, Hi-C protocol variations and data resolution. Additionally, the algorithms were tested on simulated Hi-C data to assess sensitivity and precision of each method. The tools differed in terms of implemented analysis steps and strategies adopted for alignment, filtering, normalization, and feature identification (global or local looping interactions calling and single-scale or multi-scale TAD discovery). Results of this comparison indicate that performances of the methods considerably vary, both in quantitative and qualitative terms, and that the tools need extensive optimization of the parameters in order to work properly. Despite, in general, TAD callers resulted riper than algorithms to call interactions, still most of them are characterized by crucial limitations, as for instance the inability to investigate how the 3D organization of chromatin structures
evolves over time (as e.g., during differentiation). Although the molecular mechanisms underlying TADs formation are still debated, it is evident that distinct interaction patterns can be observed within individual TADs. In particular, some domains appear to have a very compact structure, while others have a less uniform or weaker interaction frequency within the domain, while showing a strong interaction between the borders.
To address these limitations, I developed TAD-AH (TADs Advanced Hierarchy), a four-step sequential procedure coded in R, for the characterization of both static and dynamically changing chromatin domains. As a case study, I analyzed Hi-C data generated prior and post human fibroblasts (IMR90) trans-differentiation into skeletal muscle cells (myoblasts, and, when put in differentiation media, myotubes) by overexpression of muscle stem cells master regulator MyoD. I integrated Hi-C with epigenomic and transcriptomic data from the same conditions and confirmed that the identified genomic features are consistent with the biological scenario under scrutiny.

Abstract (italiano)

L’organizzazione tridimensionale della cromatina all’interno del nucleo è alla base della regolazione funzionale del genoma, sia a livello macroscopico, dove i cromosomi occupano spazi distinti (territori cromosomici), sia a livello di singole fibre, dove la cromatina si organizza in domini compartimentalizzati (Topologically Associating Domains, TADs), dentro i quali avviene la formazione di interazioni a corto raggio (come quelle che sussistono tra promotori e regioni regolatrici). Le tecniche denominate Chromosome Conformation Capture (3C) hanno permesso di investigare e caratterizzare i diversi livelli dell’organizzazione strutturale della cromatina all’interno del nucleo. In particolare, l’Hi-C, attraverso la combinazione del protocollo di 3C e del sequenziamento massivo, è in grado di restituire un’immagine completa dell’architettura della cromatina e dei contatti all’interno del genoma. Nonostante in questi ultimi anni siano stati resi disponibili diversi strumenti computazionali per l’analisi dei dati di Hi-C, non esiste tuttora un consenso su quale sia il metodo ottimale da usare. Una valutazione comparativa dei software per l'analisi dei dati Hi-C è quindi necessaria non solo per evidenziare i punti di forza e le debolezze dei vari metodi, ma anche per proporre linee guida utili all’utente medio. Per questo motivo ho applicato diversi approcci computazionali (6 per la caratterizzazione delle interazioni e 7 per identificare i TAD) a 6 set di dati pubblici di Hi-C, relativi a diverse specie e linee cellulari (H1-hESC, IMR90, linee cellulari linfoblastoidi ed embrioni di D. melanogaster), a differenti metodiche sperimentali (standard Hi-C, simplified Hi-C e In situ Hi-C) e analizzati a diverse risoluzioni. Inoltre, gli algoritmi sono stati applicati a dati simulati per determinare sensibilità e precisione di ogni metodo. I software differiscono sia per le fasi di analisi implementate sia per le strategie adottate in ciascun passaggio: l'allineamento della sequenza completa contro quello della sequenza “spezzata”, i filtri applicati, la normalizzazione implicita contro quella esplicita, l’arricchimento di interazione locale contro quello globale e l’individuazione di TAD ad uno o più livelli. I metodi variano molto a livello di prestazioni sia in termini quantitativi sia qualitativi, e richiedono di ottimizzare un’ampia gamma di parametri per funzionare correttamente. Nonostante, in generale, gli algoritmi per identificare i TAD si siano dimostrati più affidabili di quelli per trovare le interazioni, ci sono ancora dei limiti fondamentali nell’identificazione dei TAD, ad esempio nello studio dell’evoluzione di queste strutture nel tempo. Sebbene i meccanismi alla base della formazione dei TAD siano tuttora dibattuti, è innegabile che questi siano caratterizzati da pattern distintivi di interazione: in alcuni TAD possiamo osservare un segnale di interazione più omogeneo, mentre in altri l’interazione è più che altro evidente tra le regioni che lo delimitano. Per superare questi limiti, ho sviluppato un nuovo metodo per l’analisi dei TAD a partire da dati di Hi-C (TAD-AH), atto ad indagare un aspetto finora inesplorato dell'architettura del genoma: la quarta dimensione, ovvero come la struttura si evolve nel tempo in base a stimoli di varia natura (ad esempio durante il differenziamento). Per testare TAD-AH ho analizzato dati di Hi-C generati prima e dopo il trans-differenziamento di fibroblasti umani (IMR90) in cellule muscolari (mioblasti e miotubi) ad opera del principale regolatore delle cellule staminali muscolari, MYOD. L’integrazione dei dati di Hi-C con altri dati epigenomici e trascrittomici ha confermato che la caratterizzazione delle strutture identificate è coerente con lo scenario biologico in esame.

Statistiche Download
Tipo di EPrint:Tesi di dottorato
Relatore:Piccolo, Stefano
Dottorato (corsi e scuole):Ciclo 30 > Corsi 30 > MEDICINA MOLECOLARE
Data di deposito della tesi:08 Gennaio 2018
Anno di Pubblicazione:30 Ottobre 2017
Parole chiave (italiano / inglese):Hi-C Bioinformatics Chromosome Conformation Capture Benchmarking
Settori scientifico-disciplinari MIUR:Area 05 - Scienze biologiche > BIO/11 Biologia molecolare
Struttura di riferimento:Dipartimenti > Dipartimento di Medicina Molecolare
Codice ID:10568
Depositato il:16 Nov 2018 09:40
Simple Metadata
Full Metadata
EndNote Format

Bibliografia

I riferimenti della bibliografia possono essere cercati con Cerca la citazione di AIRE, copiando il titolo dell'articolo (o del libro) e la rivista (se presente) nei campi appositi di "Cerca la Citazione di AIRE".
Le url contenute in alcuni riferimenti sono raggiungibili cliccando sul link alla fine della citazione (Vai!) e tramite Google (Ricerca con Google). Il risultato dipende dalla formattazione della citazione.

Anders, S., Pyl, P.T., and Huber, W. (2015). HTSeq--a Python framework to work with high-throughput sequencing data. Bioinformatics 31, 166–169. Cerca con Google

Andrey, G., Montavon, T., Mascrez, B., Gonzalez, F., Noordermeer, D., Leleu, M., Trono,D., Spitz, F., and Duboule, D. (2013). A switch between topological domains underlies HoxD genes collinearity in mouse limbs. Science 340, 1234167. Cerca con Google

Apostolou, E., Ferrari, F., Walsh, R.M., Bar-Nur, O., Stadtfeld, M., Cheloufi, S., Stuart,H.T., Polo, J.M., Ohsumi, T.K., Borowsky, M.L., et al. (2013). Genome-wide chromatin interactions of the Nanog locus in pluripotency, differentiation, and reprogramming. Cell Stem Cell 12, 699–712. Cerca con Google

Ay, F., and Noble, W.S. (2015). Analysis methods for studying the 3D architecture of the genome. Genome Biol. 16, 183. Ay, F., Bailey, T.L., and Noble, W.S. (2014). Statistical confidence estimation for Hi-C data reveals regulatory chromatin contacts. Genome Res. 24, 999–1011 Cerca con Google

Bolzer, A., Kreth, G., Solovei, I., Koehler, D., Saracoglu, K., Fauth, C., Müller, S., Eils, R., Cremer, C., Speicher, M.R., et al. (2005). Three-dimensional maps of all chromosomes in human male fibroblast nuclei and prometaphase rosettes. PLoS Biol. 3, e157. Cerca con Google

Bonev, B., and Cavalli, G. (2016). Organization and function of the 3D genome. Nature Reviews Genetics 17, nrg.2016.112. Cerca con Google

Cavalli, G., and Misteli, T. (2013). Functional implications of genome topology. Nat. Struct. Mol. Biol. 20, 290–299. Cerca con Google

Celniker, S.E., Dillon, L.A.L., Gerstein, M.B., Gunsalus, K.C., Henikoff, S., Karpen, G.H., Kellis, M., Lai, E.C., Lieb, J.D., MacAlpine, D.M., et al. (2009). Unlocking the secrets of the Cerca con Google

genome. Nature 459, 927–930. Cerca con Google

Cournac, A., Marie-Nelly, H., Marbouty, M., Koszul, R., and Mozziconacci, J. (2012). Normalization of a chromosomal contact map. BMC Genomics 13, 436. Cerca con Google

Crane, E., Bian, Q., McCord, R.P., Lajoie, B.R., Wheeler, B.S., Ralston, E.J., Uzawa, S., Dekker, J., and Meyer, B.J. (2015). Condensin-driven remodelling of X chromosome topology during dosage compensation. Nature 523, 240–244. Cerca con Google

Dali, R., and Blanchette, M. (2017). A critical assessment of topologically associating domain prediction tools. Nucleic Acids Res. Cerca con Google

Dekker, J., Rippe, K., Dekker, M., and Kleckner, N. (2002). Capturing chromosome conformation. Science 295, 1306–1311. Cerca con Google

Dekker, J., Marti-Renom, M.A., and Mirny, L.A. (2013). Exploring the three-dimensional organization of genomes: interpreting chromatin interaction data. Nat. Rev. Genet. 14,390–403. Cerca con Google

Dekker, J., Belmont, A.S., Guttman, M., Leshyk, V.O., Lis, J.T., Lomvardas, S., Mirny, L.A.,O’Shea, C.C., Park, P.J., Ren, B., et al. (2017). The 4D nucleome project. Nature 549, 219–226. Cerca con Google

Dixon, J.R., Selvaraj, S., Yue, F., Kim, A., Li, Y., Shen, Y., Hu, M., Liu, J.S., and Ren, B. (2012). Topological domains in mammalian genomes identified by analysis of chromatin interactions. Nature 485, 376–380. Cerca con Google

Dixon, J.R., Jung, I., Selvaraj, S., Shen, Y., Antosiewicz-Bourget, J.E., Lee, A.Y., Ye, Z., Kim, A., Rajagopal, N., Xie, W., et al. (2015). Chromatin architecture reorganization during stem cell differentiation. Nature 518, 331–336. Cerca con Google

Dobin, A., Davis, C.A., Schlesinger, F., Drenkow, J., Zaleski, C., Jha, S., Batut, P., Chaisson, M., and Gingeras, T.R. (2013). STAR: ultrafast universal RNA-seq aligner. Bioinformatics 29, 15–21. Cerca con Google

Dostie, J., Richmond, T.A., Arnaout, R.A., Selzer, R.R., Lee, W.L., Honan, T.A., Rubio, E.D., Krumm, A., Lamb, J., Nusbaum, C., et al. (2006). Chromosome Conformation Capture Carbon Copy (5C): a massively parallel solution for mapping interactions between genomic elements. Genome Res. 16, 1299–1309. Cerca con Google

Durand, N.C., Shamim, M.S., Machol, I., Rao, S.S.P., Huntley, M.H., Lander, E.S., and Aiden, E.L. (2016). Juicer provides a one-click system for analyzing loop-resolution Hi-C experiments. Cell Syst 3, 95–98. Cerca con Google

Eagen, K.P., Aiden, E.L., and Kornberg, R.D. (2017). Polycomb-mediated chromatin loops revealed by a subkilobase-resolution chromatin interaction map. Proc. Natl. Acad. Sci.U.S.A. 114, 8764–8769. Cerca con Google

ENCODE Project Consortium (2012). An integrated encyclopedia of DNA elements in the human genome. Nature 489, 57–74. Cerca con Google

Ferraiuolo, M.A., Rousseau, M., Miyamoto, C., Shenker, S., Wang, X.Q.D., Nadler, M., Blanchette, M., and Dostie, J. (2010). The three-dimensional architecture of Hox cluster silencing. Nucleic Acids Res. 38, 7472–7484. Cerca con Google

Filippova, D., Patro, R., Duggal, G., and Kingsford, C. (2014). Identification of alternative topological domains in chromatin. Algorithms Mol Biol 9, 14. Cerca con Google

Forcato, M., Nicoletti, C., Pal, K., Livi, C.M., Ferrari, F., and Bicciato, S. (2017). Comparison of computational methods for Hi-C data analysis. Nature Methods 14, 679–685. Cerca con Google

Fudenberg, G., Imakaev, M., Lu, C., Goloborodko, A., Abdennur, N., and Mirny, L.A. (2016). Formation of Chromosomal Domains by Loop Extrusion. Cell Rep 15, 2038–2049. Cerca con Google

Fullwood, M.J., Liu, M.H., Pan, Y.F., Liu, J., Xu, H., Mohamed, Y.B., Orlov, Y.L., Velkov, S., Ho, A., Mei, P.H., et al. (2009). An oestrogen-receptor-alpha-bound human chromatin interactome. Nature 462, 58–64. Cerca con Google

Gruber, S. (2017). Shaping chromosomes by DNA capture and release: gating the SMC rings. Curr. Opin. Cell Biol. 46, 87–93. Cerca con Google

Haarhuis, J.H.I., van der Weide, R.H., Blomen, V.A., Yáñez-Cuna, J.O., Amendola, M., van Ruiten, M.S., Krijger, P.H.L., Teunissen, H., Medema, R.H., van Steensel, B., et al. (2017). The Cohesin Release Factor WAPL Restricts Chromatin Loop Extension. Cell 169, 693–707.e14. Cerca con Google

He, B., Chen, C., Teng, L., and Tan, K. (2014). Global view of enhancer-promoter interactome in human cells. Proc. Natl. Acad. Sci. U.S.A. 111, E2191-2199. Cerca con Google

Heinz, S., Benner, C., Spann, N., Bertolino, E., Lin, Y.C., Laslo, P., Cheng, J.X., Murre, C., Singh, H., and Glass, C.K. (2010). Simple combinations of lineage-determining transcription factors prime cis-regulatory elements required for macrophage and B cell identities. Mol. Cell 38, 576–589. Cerca con Google

Ho, J.W.K., Jung, Y.L., Liu, T., Alver, B.H., Lee, S., Ikegami, K., Sohn, K.-A., Minoda, A., Tolstorukov, M.Y., Appert, A., et al. (2014). Comparative analysis of metazoan chromatin organization. Nature 512, 449–452. Cerca con Google

Hou, C., Li, L., Qin, Z.S., and Corces, V.G. (2012). Gene density, transcription, and insulators contribute to the partition of the Drosophila genome into physical domains. Mol.Cell 48, 471–484. Cerca con Google

Hu, M., Deng, K., Selvaraj, S., Qin, Z., Ren, B., and Liu, J.S. (2012). HiCNorm: removing biases in Hi-C data via Poisson regression. Bioinformatics 28, 3131–3133. Cerca con Google

Hughes, J.R., Roberts, N., McGowan, S., Hay, D., Giannoulatou, E., Lynch, M., De Gobbi, M., Taylor, S., Gibbons, R., and Higgs, D.R. (2014). Analysis of hundreds of cis-regulatory landscapes at high resolution in a single, high-throughput experiment. Nat. Genet. 46, 205–212. Cerca con Google

Hwang, Y.C., Lin, C.F., Valladares, O., Malamon, J., Kuksa, P.P., Zheng, Q., Gregory, B.D., and Wang, L.-S. (2015). HIPPIE: a high-throughput identification pipeline for promoter interacting enhancer elements. Bioinformatics 31, 1290–1292. Cerca con Google

Imakaev, M., Fudenberg, G., McCord, R.P., Naumova, N., Goloborodko, A., Lajoie, B.R., Dekker, J., and Mirny, L.A. (2012). Iterative correction of Hi-C data reveals hallmarks of chromosome organization. Nat. Methods 9, 999–1003. Cerca con Google

Imakaev, M.V., Fudenberg, G., and Mirny, L.A. (2015). Modeling chromosomes: Beyond pretty pictures. FEBS Lett. 589, 3031–3036. Cerca con Google

Ji, X., Dadon, D.B., Powell, B.E., Fan, Z.P., Borges-Rivera, D., Shachar, S., Weintraub, A.S., Hnisz, D., Pegoraro, G., Lee, T.I., et al. (2016). 3D chromosome regulatory landscape of human pluripotent cells. Cell Stem Cell 18, 262–275. Cerca con Google

Jin, F., Li, Y., Dixon, J.R., Selvaraj, S., Ye, Z., Lee, A.Y., Yen, C.-A., Schmitt, A.D., Espinoza, C.A., and Ren, B. (2013). A high-resolution map of the three-dimensional chromatin interactome in human cells. Nature 503, 290–294. Cerca con Google

Kim, D., Pertea, G., Trapnell, C., Pimentel, H., Kelley, R., and Salzberg, S.L. (2013). TopHat2: accurate alignment of transcriptomes in the presence of insertions, deletions and gene fusions. Genome Biol. 14, R36. Cerca con Google

Knight, P.A., and Ruiz, D. (2013). A fast algorithm for matrix balancing. IMA J Numer Anal 33, 1029–1047. Cerca con Google

Langmead, B., and Salzberg, S.L. (2012). Fast gapped-read alignment with Bowtie 2. Nat.Methods 9, 357–359. Cerca con Google

Langmead, B., Trapnell, C., Pop, M., and Salzberg, S.L. (2009). Ultrafast and memoryefficient alignment of short DNA sequences to the human genome. Genome Biol. 10, R25. Cerca con Google

Lévy-Leduc, C., Delattre, M., Mary-Huard, T., and Robin, S. (2014). Two-dimensional segmentation for analyzing Hi-C data. Bioinformatics 30, i386-392. Cerca con Google

Li, H., and Durbin, R. (2009). Fast and accurate short read alignment with Burrows-Wheeler transform. Bioinformatics 25, 1754–1760. Cerca con Google

Li, H., Handsaker, B., Wysoker, A., Fennell, T., Ruan, J., Homer, N., Marth, G., Abecasis, G., Durbin, R., and 1000 Genome Project Data Processing Subgroup (2009). The Sequence Alignment/Map format and SAMtools. Bioinformatics 25, 2078–2079. Cerca con Google

Lieberman-Aiden, E., van Berkum, N.L., Williams, L., Imakaev, M., Ragoczy, T., Telling, A., Amit, I., Lajoie, B.R., Sabo, P.J., Dorschner, M.O., et al. (2009). Comprehensive mapping of long-range interactions reveals folding principles of the human genome. Science 326, 289–293. Cerca con Google

Lonfat, N., Montavon, T., Darbellay, F., Gitto, S., and Duboule, D. (2014). Convergent evolution of complex regulatory landscapes and pleiotropy at Hox loci. Science 346, 1004–1006. Cerca con Google

Lun, A.T.L., and Smyth, G.K. (2015). diffHic: a Bioconductor package to detect differential genomic interactions in Hi-C data. BMC Bioinformatics 16, 258. Cerca con Google

Lupiáñez, D.G., Spielmann, M., and Mundlos, S. (2016). Breaking TADs: How Alterations of Chromatin Domains Result in Disease. Trends Genet. 32, 225–237. Cerca con Google

Lupiáñez, D.G., Kraft, K., Heinrich, V., Krawitz, P., Brancati, F., Klopocki, E., Horn, D., Kayserili, H., Opitz, J.M., Laxova, R., et al. (2015). Disruptions of topological chromatin domains cause pathogenic rewiring of gene-enhancer interactions. Cell 161, 1012–1025. Cerca con Google

Ma, W., Ay, F., Lee, C., Gulsoy, G., Deng, X., Cook, S., Hesson, J., Cavanaugh, C., Ware, C.B., Krumm, A., et al. (2015). Fine-scale chromatin interaction maps reveal the cisregulatory landscape of human lincRNA genes. Nat. Methods 12, 71–78. Cerca con Google

Marco-Sola, S., Sammeth, M., Guigó, R., and Ribeca, P. (2012). The GEM mapper: fast, accurate and versatile alignment by filtration. Nat. Methods 9, 1185–1188. Cerca con Google

Mifsud, B., Martincorena, I., Darbo, E., Sugar, R., Schoenfelder, S., Fraser, P., and Luscombe, N.M. (2017). GOTHiC, a probabilistic model to resolve complex biases and to identify real interactions in Hi-C data. PLoS ONE 12, e0174744. Cerca con Google

Mora, A., Sandve, G.K., Gabrielsen, O.S., and Eskeland, R. (2016). In the loop: promoterenhancer interactions and bioinformatics. Brief. Bioinformatics 17, 980–995. Cerca con Google

Nagano, T., Lubling, Y., Stevens, T.J., Schoenfelder, S., Yaffe, E., Dean, W., Laue, E.D., Tanay, A., and Fraser, P. (2013). Single-cell Hi-C reveals cell-to-cell variability in chromosome structure. Nature 502, 59–64. Cerca con Google

Nagano, T., Lubling, Y., Várnai, C., Dudley, C., Leung, W., Baran, Y., Mendelson Cohen, N., Wingett, S., Fraser, P., and Tanay, A. (2017). Cell-cycle dynamics of chromosomal organization at single-cell resolution. Nature 547, 61–67. Cerca con Google

Noordermeer, D., and Duboule, D. (2013). Chromatin looping and organization at developmentally regulated gene loci. Wiley Interdiscip Rev Dev Biol 2, 615–630. Cerca con Google

Nora, E.P., Lajoie, B.R., Schulz, E.G., Giorgetti, L., Okamoto, I., Servant, N., Piolot, T., van Berkum, N.L., Meisig, J., Sedat, J., et al. (2012). Spatial partitioning of the regulatory landscape of the X-inactivation centre. Nature 485, 381–385. Cerca con Google

Phillips-Cremins, J.E., Sauria, M.E.G., Sanyal, A., Gerasimova, T.I., Lajoie, B.R., Bell,J.S.K., Ong, C.-T., Hookway, T.A., Guo, C., Sun, Y., et al. (2013). Architectural protein subclasses shape 3D organization of genomes during lineage commitment. Cell 153, 1281–1295. Cerca con Google

Pombo, A., and Dillon, N. (2015). Three-dimensional genome architecture: players and mechanisms. Nat. Rev. Mol. Cell Biol. 16, 245–257. Cerca con Google

Pope, B.D., Ryba, T., Dileep, V., Yue, F., Wu, W., Denas, O., Vera, D.L., Wang, Y., Hansen, R.S., Canfield, T.K., et al. (2014). Topologically associating domains are stable units of replication-timing regulation. Nature 515, 402–405. Cerca con Google

Rao, S.S.P., Huntley, M.H., Durand, N.C., Stamenova, E.K., Bochkov, I.D., Robinson, J.T., Sanborn, A.L., Machol, I., Omer, A.D., Lander, E.S., et al. (2014). A 3D map of the human genome at kilobase resolution reveals principles of chromatin looping. Cell 159, 1665–1680. Cerca con Google

Rao, S.S.P., Huang, S.-C., Glenn St Hilaire, B., Engreitz, J.M., Perez, E.M., Kieffer-Kwon, K.-R., Sanborn, A.L., Johnstone, S.E., Bascom, G.D., Bochkov, I.D., et al. (2017). Cohesin Loss Eliminates All Loop Domains. Cell 171, 305–320.e24. Cerca con Google

Roadmap Epigenomics Consortium, Kundaje, A., Meuleman, W., Ernst, J., Bilenky, M., Yen, A., Heravi-Moussavi, A., Kheradpour, P., Zhang, Z., Wang, J., et al. (2015). Integrative analysis of 111 reference human epigenomes. Nature 518, 317–330. Cerca con Google

Rowley, M.J., Nichols, M.H., Lyu, X., Ando-Kuri, M., Rivera, I.S.M., Hermetz, K., Wang, P., Ruan, Y., and Corces, V.G. (2017). Evolutionarily Conserved Principles Predict 3D Chromatin Organization. Mol. Cell 67, 837–852.e7. Cerca con Google

Sanborn, A.L., Rao, S.S.P., Huang, S.-C., Durand, N.C., Huntley, M.H., Jewett, A.I., Bochkov, I.D., Chinnappan, D., Cutkosky, A., Li, J., et al. (2015). Chromatin extrusion explains key features of loop and domain formation in wild-type and engineered genomes. Proc. Natl. Acad. Sci. U.S.A. 112, E6456-6465. Cerca con Google

Sanyal, A., Lajoie, B.R., Jain, G., and Dekker, J. (2012). The long-range interaction landscape of gene promoters. Nature 489, 109–113. Cerca con Google

Sauria, M.E.G., Phillips-Cremins, J.E., Corces, V.G., and Taylor, J. (2015). HiFive: a tool suite for easy and efficient HiC and 5C data analysis. Genome Biol. 16, 237. Cerca con Google

Schmitt, A.D., Hu, M., Jung, I., Xu, Z., Qiu, Y., Tan, C.L., Li, Y., Lin, S., Lin, Y., Barr, C.L., et al. (2016a). A compendium of chromatin contact maps reveals spatially active regions in the human genome. Cell Rep 17, 2042–2059. Cerca con Google

Schmitt, A.D., Hu, M., and Ren, B. (2016b). Genome-wide mapping and analysis of chromosome architecture. Nat. Rev. Mol. Cell Biol. 17, 743–755. Cerca con Google

Schoenfelder, S., Furlan-Magaril, M., Mifsud, B., Tavares-Cadete, F., Sugar, R., Javierre, B.- M., Nagano, T., Katsman, Y., Sakthidevi, M., Wingett, S.W., et al. (2015). The pluripotent regulatory circuitry connecting promoters to their long-range interacting elements. Genome Res. 25, 582–597. Cerca con Google

Serra, F., Baù, D., Goodstadt, M., Castillo, D., Filion, G.J., and Marti-Renom, M.A. (2017). Cerca con Google

Automatic analysis and 3D-modelling of Hi-C data using TADbit reveals structural features of the fly chromatin colors. PLoS Comput. Biol. 13, e1005665. Cerca con Google

Servant, N., Varoquaux, N., Lajoie, B.R., Viara, E., Chen, C.-J., Vert, J.-P., Heard, E., Dekker, J., and Barillot, E. (2015). HiC-Pro: an optimized and flexible pipeline for Hi-C data processing. Genome Biol. 16, 259. Cerca con Google

Sexton, T., Yaffe, E., Kenigsberg, E., Bantignies, F., Leblanc, B., Hoichman, M., Parrinello, H., Tanay, A., and Cavalli, G. (2012). Three-dimensional folding and functional organization principles of the Drosophila genome. Cell 148, 458–472. Cerca con Google

Simonis, M., Klous, P., Splinter, E., Moshkin, Y., Willemsen, R., de Wit, E., van Steensel, B., and de Laat, W. (2006). Nuclear organization of active and inactive chromatin domains uncovered by chromosome conformation capture-on-chip (4C). Nat. Genet. 38, 1348–1354. Cerca con Google

Smith, E.M., Lajoie, B.R., Jain, G., and Dekker, J. (2016). Invariant TAD boundaries constrain cell-type-specific looping interactions between promoters and distal elements around the CFTR locus. Am. J. Hum. Genet. 98, 185–201. Cerca con Google

Teng, L., He, B., Wang, J., and Tan, K. (2015). 4DGenome: a comprehensive database of chromatin interactions. Bioinformatics 31, 2560–2564. Cerca con Google

Valton, A.-L., and Dekker, J. (2016). TAD disruption as oncogenic driver. Curr. Opin. Genet. Dev. 36, 34–40. Cerca con Google

Wang, H., Maurano, M.T., Qu, H., Varley, K.E., Gertz, J., Pauli, F., Lee, K., Canfield, T., Weaver, M., Sandstrom, R., et al. (2012). Widespread plasticity in CTCF occupancy linked to DNA methylation. Genome Res. 22, 1680–1688. Cerca con Google

Weinreb, C., and Raphael, B.J. (2016). Identification of hierarchical chromatin domains. Bioinformatics 32, 1601–1609. Cerca con Google

Woon Kim, Y., Kim, S., Geun Kim, C., and Kim, A. (2011). The distinctive roles of erythroid specific activator GATA-1 and NF-E2 in transcription of the human fetal γ-globin genes. Nucleic Acids Res. 39, 6944–6955. Cerca con Google

Yaffe, E., and Tanay, A. (2011). Probabilistic modeling of Hi-C contact maps eliminates systematic biases to characterize global chromosomal architecture. Nat. Genet. 43, 1059–1065. Cerca con Google

Zhang, Y., Liu, T., Meyer, C.A., Eeckhoute, J., Johnson, D.S., Bernstein, B.E., Nusbaum, C., Myers, R.M., Brown, M., Li, W., et al. (2008). Model-based analysis of ChIP-Seq (MACS). Genome Biol. 9, R137. Cerca con Google

Download statistics

Solo per lo Staff dell Archivio: Modifica questo record