Structural Variant Impact Prediction

Identifies how structural variants create ectopic enhancer-promoter interactions by bringing regulatory elements into proximity with non-target genes. Models compare predicted Hi-C contact maps from wild-type and variant-bearing sequences to flag neo-TADs where enhancers cross domain boundaries. Key outputs include:

• Disruption scores for existing loops
• Gain-of-interaction predictions for new contacts
• Quantitative estimates of gene expression dysregulation This is critical for interpreting non-coding variants in congenital disorders and cancer.

Quantifies the degree to which deletions, inversions, or duplications compromise topologically associating domain boundaries. The model computes an insulation score delta between reference and variant genomes, measuring boundary strength loss. When boundary insulation drops below a threshold, previously separated regulatory domains merge, enabling enhancer hijacking. Features include:

• Boundary strength quantification via directionality index shifts
• Cross-boundary interaction frequency analysis
• CTCF motif disruption assessment at anchor sites

Models how structural variants alter the processivity of cohesin-mediated loop extrusion. By simulating extrusion dynamics on variant-bearing sequences, the model predicts:

• Premature loop stall events at deleted CTCF sites
• Bypass of boundary elements due to inversion of motif orientation
• Formation of ectopic chromatin loops spanning rearranged segments These simulations integrate polymer physics constraints with sequence-based CTCF binding predictions to generate physically plausible folding outcomes.

Performs haplotype-resolved 3D genome prediction to compare folding patterns between wild-type and variant alleles within the same cell. This approach controls for trans-acting factors and isolates cis-effects of the structural variant. The pipeline:

• Phases heterozygous variants to separate maternal and paternal reads
• Predicts allele-specific Hi-C contact maps independently
• Computes differential interaction matrices highlighting variant-driven changes Essential for understanding dominant disease mechanisms where one allele disrupts normal architecture.

Combines signals across genomic scales to predict variant impact on folding. Input features include:

• Base-pair level: CTCF motif scores, transcription factor binding site predictions
• Nucleosome level: Histone modification ChIP-seq signals, DNA accessibility
• Domain level: Replication timing, lamina-associated domain annotations
• Whole-locus level: GC content, repetitive element density Graph neural networks aggregate these heterogeneous features at variant breakpoints to predict whether a rearrangement will propagate structural changes beyond the immediate breakpoint region.

Integrates 3D folding predictions into a variant effect classifier that assigns pathogenicity scores to structural variants of uncertain significance. The framework combines:

• Predicted contact frequency changes at disease-relevant loci
• Conservation scores of disrupted boundary elements
• Gene dosage sensitivity metrics for rewired targets
• Population frequency of similar rearrangements in control databases Output is a calibrated probability of pathogenicity suitable for clinical reporting pipelines and prioritization of variants for functional validation.

The computational task of identifying physical contacts between distal enhancer elements and gene promoters. Structural variants like deletions or inversions can rewire these interactions, causing gene misexpression.

• Mechanism: Relies on 3D genome folding models to infer spatial proximity.
• Impact: A deletion removing a TAD boundary can allow an enhancer to ectopically activate a non-target promoter.
• Key Model: Akita and DeepC predict these interactions from sequence alone.

A self-interacting genomic region where DNA sequences physically interact more frequently with each other than with sequences outside the domain. TADs act as fundamental structural units of chromosome folding.

• Boundary Disruption: Structural variants that delete TAD boundaries cause ectopic enhancer-promoter contacts.
• Insulation Score: A quantitative metric used to identify TAD boundaries from Hi-C data.
• Loop Extrusion: The mechanistic process that forms TADs, blocked by CTCF boundary elements.

A mechanistic model of chromatin organization wherein cohesin complexes actively reel DNA to form progressively larger loops until blocked by CTCF boundary elements. This process explains the formation of TADs and chromatin loops.

• Variant Impact: A structural variant that deletes a convergent CTCF site disrupts the loop extrusion barrier, causing domain fusion.
• Simulation: Cohesin complex simulation models predict how variants alter extrusion dynamics.
• Polymer Physics: Physically informed neural networks integrate loop extrusion principles for structural prediction.

A quantitative metric calculated from Hi-C contact maps that measures the degree to which a genomic locus is insulated from interactions with neighboring regions. It is the primary computational method for identifying TAD boundaries.

• Calculation: Computed by sliding a square window along the Hi-C matrix diagonal and summing contacts within vs. across the window.
• Variant Analysis: A sharp drop in the insulation score at a deleted boundary indicates a domain fusion event.
• Directionality Index: A complementary metric that quantifies upstream or downstream interaction bias.

The computational identification of DNA sequence motifs bound by the CCCTC-binding factor (CTCF) , a critical architectural protein that defines domain boundaries and anchors chromatin loops.

• Motif Orientation: The convergent orientation of CTCF motifs is essential for loop extrusion blocking.
• Variant Disruption: A structural variant that deletes or inverts a CTCF site can abolish boundary function.
• Deep Learning: Convolutional neural networks predict CTCF occupancy from DNA sequence and epigenomic context.

The prediction or analysis of 3D genome organization separately for maternal and paternal chromosomes. This approach reveals how heterozygous structural variants influence chromatin structure in a haplotype-resolved manner.

• Phasing Requirement: Requires haplotype-phased Hi-C data or deep learning models that accept phased genotypes.
• Variant Interpretation: Distinguishes the structural impact of a variant on one allele while the other allele serves as an internal control.
• DNA FISH Validation: Gold-standard experimental method to confirm allele-specific distance predictions.