Predicting three-dimensional genome organization with chromatin states

Qi, Yuanming; Zhang, Bin

doi:10.1101/282095

Cited by 28 publications

(57 citation statements)

References 69 publications

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“…Methods that use information at the base pair level do not capture large scale continuous context and rather focus on window to window based predictions [8][9][10] . Methods that integrate a large genomic context do so by coarse segregation into genomic features [11][12][13] or polymer beads [14][15][16] , thus compromising on the base resolution. Here we present deepC, a deep neuronal network approach that can accurately predict Hi-C chromatin interactions from DNA sequence at megabase scale.…”

Section: Introductionmentioning

confidence: 99%

DeepC: Predicting chromatin interactions using megabase scaled deep neural networks and transfer learning

Gosden

Downes

Brown

et al. 2019

Preprint

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Understanding 3D genome structure requires high throughput, genome-wide approaches. However, assays for all vs. all chromatin interaction mapping are expensive and time consuming, which severely restricts their usage for large-scale mutagenesis screens or for mapping the impact of sequence variants. Computational models sophisticated enough to grasp the determinants of chromatin folding provide a unique window into the functional determinants of 3D genome structure as well as the effects of genome variation. A chromatin interaction predictor should work at the base pair level but also incorporate large-scale genomic context to simultaneously capture the large scale and intricate structures of chromatin architecture. Similarly, to be a flexible and generalisable approach it should also be applicable to data it has not been explicitly trained on. To develop a model with these properties, we designed a deep neuronal network (deepC) that utilizes transfer learning to accurately predict chromatin interactions from DNA sequence at megabase scale. The model generalizes well to unseen chromosomes and works across cell types, Hi-C data resolutions and a range of sequencing depths. DeepC integrates DNA sequence context on an unprecedented scale, bridging the different levels of resolution from base pairs to TADs. We demonstrate how this model allows us to investigate sequence determinants of chromatin folding at genome-wide scale and to predict the importance of regulatory elements and the impact of sequence variations.

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Section: Introductionmentioning

confidence: 99%

DeepC: Predicting chromatin interactions using megabase scaled deep neural networks and transfer learning

Gosden

Downes

Brown

et al. 2019

Preprint

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“…It is not clear how well general noncoding impact methods will work on such variants, and they may be very far from genes, requiring a much larger total number of variants to be considered, with an accompanying rise in false positives. Advances in resolving three‐dimensional chromatin structure and how it varies (Kishi & Gotoh, ; Marti‐Renom et al, ; Qi & Zhang, ) hold long‐term hope for progress here.…”

Section: Discussionmentioning

confidence: 99%

Matching whole genomes to rare genetic disorders: Identification of potential causative variants using phenotype‐weighted knowledge in the CAGI SickKids5 clinical genomes challenge

et al. 2019

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Precise identification of causative variants from whole-genome sequencing data, including both coding and noncoding variants, is challenging. The Critical Assessment of Genome Interpretation 5 SickKids clinical genome challenge provided an opportunity to assess our ability to extract such information. Participants in the challenge were required to match each of the 24 whole-genome sequences to the correct phenotypic profile and to identify the disease class of each genome. These are all rare disease cases that have resisted genetic diagnosis in a state-of-the-art pipeline.The patients have a range of eye, neurological, and connective-tissue disorders. We used a gene-centric approach to address this problem, assigning each gene a multiphenotype-matching score. Mutations in the top-scoring genes for each phenotype profile were ranked on a 6-point scale of pathogenicity probability, resulting in an approximately equal number of top-ranked coding and noncoding candidate variants overall. We were able to assign the correct disease class for 12 cases and the correct genome to a clinical profile for five cases. The challenge assessor found genes in three of these five cases as likely appropriate. In the postsubmission phase, after careful screening of the genes in the correct genome, we identified additional potential diagnostic variants, a high proportion of which are noncoding.

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“…What are the physicochemical interactions that stabilize the folded WT-like structures in cohesin-depleted cells? Numerous studies have demonstrated the importance of phase separation or compartmentalization in genome organization (63)(64)(65)(66)(67)(68)(69)(70)(71). Different regions of the chromatin could adopt distinct post-translational modifications on histone proteins.…”

Section: Conclusion and Discussionmentioning

confidence: 99%

“…U b (r) is the harmonic bonding potential between neighboring beads with an equilibrium distance of 2.0σ and a spring constant of 1.0 /σ 2 . U sc (r) is a soft-core potential applied to all the non-bonded pairs to account for the excluded volume effect and to allow for chain crossing (53,68). It is equivalent to a capped off Lennard-Jones potential and only incurs a finite energetic cost for overlapping beads.…”

Section: Methodsmentioning

confidence: 99%

Characterizing chromatin folding coordinate and landscape with deep learning

Xie

Zhang

2019

Preprint

Self Cite

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Genome organization is critical for setting up the spatial environment of gene transcription, and substantial progress has been made towards its high-resolution characterization. The underlying molecular mechanism for its establishment is much less understood. We applied a deep-learning approach, variational autoencoder (VAE), to analyze the fluctuation and heterogeneity of chromatin structures revealed by single-cell super-resolution imaging and to identify a reaction coordinate for chromatin folding. This coordinate monitors the progression of topologically associating domain (TAD) formation and connects the seemingly random structures observed in individual cohesin-depleted cells as intermediate states along the folding pathway. Analysis of the folding landscape derived from VAE suggests that wellfolded structures similar to those found in wild-type cells remain energetically favorable in cohesin-depleted cells. The interaction energies, however, are not strong enough to overcome the entropic penalty, leading to the formation of only partially folded structures and the disappearance of TADs from contact maps upon averaging. Implications of these results for the molecular driving forces of chromatin folding are discussed.

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Predicting three-dimensional genome organization with chromatin states

Cited by 28 publications

References 69 publications

DeepC: Predicting chromatin interactions using megabase scaled deep neural networks and transfer learning

DeepC: Predicting chromatin interactions using megabase scaled deep neural networks and transfer learning

Matching whole genomes to rare genetic disorders: Identification of potential causative variants using phenotype‐weighted knowledge in the CAGI SickKids5 clinical genomes challenge

Characterizing chromatin folding coordinate and landscape with deep learning

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