SuPreMo: a computational tool for streamlining <i>in silico</i> perturbation using sequence-based predictive models

Gjoni, Ketrin; Pollard, Katherine S

doi:10.1093/bioinformatics/btae340

Cited by 2 publications

(1 citation statement)

References 35 publications

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“…To quantify the influence of short DNA sequences on genome folding, we defined a disruption score as the square root of the sum of squared differences between predicted maps before and after local sequence perturbations (Fig 1), as previously used to interpret Akita's predictions [19,27]. This disruption score is sensitive to gain or loss of boundaries, as well as changes in TAD substructures [28,29]. We leveraged the ensemble of models to validate sequence perturbation approaches at CTCF sites by their cross-model stability.…”

Section: Cross Species Modelmentioning

confidence: 99%

Interpreting the CTCF-mediated sequence grammar of genome folding with AkitaV2

Smaruj,

Kamulegeya,

Kelley

et al. 2024

Preprint

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Interphase mammalian genomes are folded in 3D with complex locus-specific patterns that impact gene regulation. CTCF (CCCTC-binding factor) is a key architectural protein that binds specific DNA sites, halts cohesin-mediated loop extrusion, and enables long-range chromatin interactions. There are hundreds of thousands of annotated CTCF-binding sites in mammalian genomes; disruptions of some result in distinct phenotypes, while others have no visible effect. Despite their importance, the determinants of which CTCF sites are necessary for genome folding and gene regulation remain unclear. Here, we update and utilize Akita, a convolutional neural network model, to extract the sequence preferences and grammar of CTCF contributing to genome folding. Our analyses of individual CTCF sites reveal four predictions: (i) only a small fraction of genomic sites are impactful, (ii) insulation strength is highly dependent on sequences flanking the core CTCF binding motif, (iii) core and flanking sequences are broadly compatible, and (iv) core and flanking nucleotides contribute largely additively to overall strength. Our analysis of collections of CTCF sites make two predictions for multi-motif grammar: (i) insulation strength depends on the number of CTCF sites within a cluster, and (ii) pattern formation is governed by the orientation and spacing of these sites, rather than any inherent specialization of the CTCF motifs themselves. In sum, we present a framework for using neural network models to probe the sequences instructing genome folding and provide a number of predictions to guide future experimental inquiries.Author SummaryMammalian genomes are spatially organized in 3D with profound consequences for all processes involving DNA. CTCF is a key genome organizer, recognizing numerous sites and creating a variety of contact patterns across the genome. Despite the importance of CTCF, the sequence determinants and grammar of how individual sites collectively instruct genome folding remain unclear. This work leverages the ability of Akita, a deep neural network, to make high-throughput predictions for genome folding after DNA sequence perturbations. Using Akita, we make several experimentally testable predictions. First, only a minority of annotated sites individually impact folding, and flanking DNA sequences greatly modulate their impact. Second, multiple sites together influence folding based on their number, orientation, and spacing. In sum, we provide a roadmap for interpreting neural networks to better understand genome folding and important considerations for the design of experiments.

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Section: Cross Species Modelmentioning

confidence: 99%

Interpreting the CTCF-mediated sequence grammar of genome folding with AkitaV2

Smaruj,

Kamulegeya,

Kelley

et al. 2024

Preprint

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An integrated view of the structure and function of the human 4D nucleome

Dekker,

Oksuz,

Zhang

et al. 2024

Preprint

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The dynamic three-dimensional (3D) organization of the human genome (the 4D Nucleome) is closely linked to genome function. Here, we integrate a wide variety of genomic data generated by the 4D Nucleome Project to provide a detailed view of human 3D genome organization in widely used embryonic stem cells (H1-hESCs) and immortalized fibroblasts (HFFc6). We provide extensive benchmarking of 3D genome mapping assays and integrate these diverse datasets to annotate spatial genomic features across scales. The data reveal a rich complexity of chromatin domains and their sub-nuclear positions, and over one hundred thousand structural loops and promoter-enhancer interactions. We developed 3D models of population-based and individual cell-to-cell variation in genome structure, establishing connections between chromosome folding, nuclear organization, chromatin looping, gene transcription, and DNA replication. We demonstrate the use of computational methods to predict genome folding from DNA sequence, uncovering potential effects of genetic variants on genome structure and function. Together, this comprehensive analysis contributes insights into human genome organization and enhances our understanding of connections between the regulation of genome function and 3D genome organization in general.

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SuPreMo: a computational tool for streamlining in silico perturbation using sequence-based predictive models

Cited by 2 publications

References 35 publications

Interpreting the CTCF-mediated sequence grammar of genome folding with AkitaV2

Interpreting the CTCF-mediated sequence grammar of genome folding with AkitaV2

An integrated view of the structure and function of the human 4D nucleome

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