$\textit{BlockFormer}$: Transformer-based inference from genomic contact maps

Abstract:Locating genomic features from genomic contact maps, such as centromere identification from genome-wide chromosome conformation capture techniques, notably Hi-C, can be formulated as an inverse problem: infer one parameter per entity given a map summarizing pairwise interactions through blocks of variable numbers and sizes. In this work, we introduce a data-driven approach that leverages shared structure between these contact maps, such as global alignment between localized patterns, while handling the variability in number and size of chromosomes arising in real-world data. Our approach relies on a transformer architecture capable of handling such variability and a custom simulator to generate abundant, yet computationally cheap synthetic data for training. Applied to the problem of centromere localization, the method recovers genomic positions at or below the map resolution across species with various genome sizes using a coarse-to-fine refinement step when chromosome sizes fall outside the training range. We concentrate our evaluation on Hi-C data, where the block structure is well characterized and ground truth is available.

Machine Learning (cs.LG); Quantitative Methods (q-bio.QM)

arXiv:2605.21617 [cs.LG]

(or arXiv:2605.21617v3 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2605.21617 Focus to learn more arXiv-issued DOI via DataCite

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