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Publications
Reference panel-guided super-resolution inference of Hi-C data
Abstract Motivation Accurately assessing contacts between DNA fragments inside the nucleus with Hi-C experiment is crucial for understanding… (voir plus) the role of 3D genome organization in gene regulation. This challenging task is due in part to the high sequencing depth of Hi-C libraries required to support high-resolution analyses. Most existing Hi-C data are collected with limited sequencing coverage, leading to poor chromatin interaction frequency estimation. Current computational approaches to enhance Hi-C signals focus on the analysis of individual Hi-C datasets of interest, without taking advantage of the facts that (i) several hundred Hi-C contact maps are publicly available and (ii) the vast majority of local spatial organizations are conserved across multiple cell types. Results Here, we present RefHiC-SR, an attention-based deep learning framework that uses a reference panel of Hi-C datasets to facilitate the enhancement of Hi-C data resolution of a given study sample. We compare RefHiC-SR against tools that do not use reference samples and find that RefHiC-SR outperforms other programs across different cell types, and sequencing depths. It also enables high-accuracy mapping of structures such as loops and topologically associating domains. Availability and implementation https://github.com/BlanchetteLab/RefHiC.
Disentanglement aims to recover meaningful latent ground-truth factors from the observed distribution solely, and is formalized through the … (voir plus)theory of identifiability. The identifiability of independent latent factors is proven to be impossible in the unsupervised i.i.d. setting under a general nonlinear map from factors to observations. In this work, however, we demonstrate that it is possible to recover quantized latent factors under a generic nonlinear diffeomorphism. We only assume that the latent factors have independent discontinuities in their density, without requiring the factors to be statistically independent. We introduce this novel form of identifiability, termed quantized factor identifiability, and provide a comprehensive proof of the recovery of the quantized factors.
Genomic (DNA) sequences encode an enormous amount of information for gene regulation and protein synthesis. Similar to natural language mode… (voir plus)ls, researchers have proposed foundation models in genomics to learn generalizable features from unlabeled genome data that can then be fine-tuned for downstream tasks such as identifying regulatory elements. Due to the quadratic scaling of attention, previous Transformer-based genomic models have used 512 to 4k tokens as context (0.001% of the human genome), significantly limiting the modeling of long-range interactions in DNA. In addition, these methods rely on toke
Genomic (DNA) sequences encode an enormous amount of information for gene regulation and protein synthesis. Similar to natural language mode… (voir plus)ls, researchers have proposed foundation models in genomics to learn generalizable features from unlabeled genome data that can then be fine-tuned for downstream tasks such as identifying regulatory elements. Due to the quadratic scaling of attention, previous Transformer-based genomic models have used 512 to 4k tokens as context (0.001% of the human genome), significantly limiting the modeling of long-range interactions in DNA. In addition, these methods rely on toke
Genomic (DNA) sequences encode an enormous amount of information for gene regulation and protein synthesis. Similar to natural language mode… (voir plus)ls, researchers have proposed foundation models in genomics to learn generalizable features from unlabeled genome data that can then be fine-tuned for downstream tasks such as identifying regulatory elements. Due to the quadratic scaling of attention, previous Transformer-based genomic models have used 512 to 4k tokens as context (0.001% of the human genome), significantly limiting the modeling of long-range interactions in DNA. In addition, these methods rely on toke
Genomic (DNA) sequences encode an enormous amount of information for gene regulation and protein synthesis. Similar to natural language mode… (voir plus)ls, researchers have proposed foundation models in genomics to learn generalizable features from unlabeled genome data that can then be fine-tuned for downstream tasks such as identifying regulatory elements. Due to the quadratic scaling of attention, previous Transformer-based genomic models have used 512 to 4k tokens as context (0.001% of the human genome), significantly limiting the modeling of long-range interactions in DNA. In addition, these methods rely on toke
Genomic (DNA) sequences encode an enormous amount of information for gene regulation and protein synthesis. Similar to natural language mode… (voir plus)ls, researchers have proposed foundation models in genomics to learn generalizable features from unlabeled genome data that can then be fine-tuned for downstream tasks such as identifying regulatory elements. Due to the quadratic scaling of attention, previous Transformer-based genomic models have used 512 to 4k tokens as context (0.001% of the human genome), significantly limiting the modeling of long-range interactions in DNA. In addition, these methods rely on toke
Genomic (DNA) sequences encode an enormous amount of information for gene regulation and protein synthesis. Similar to natural language mode… (voir plus)ls, researchers have proposed foundation models in genomics to learn generalizable features from unlabeled genome data that can then be fine-tuned for downstream tasks such as identifying regulatory elements. Due to the quadratic scaling of attention, previous Transformer-based genomic models have used 512 to 4k tokens as context (0.001% of the human genome), significantly limiting the modeling of long-range interactions in DNA. In addition, these methods rely on toke
Genomic (DNA) sequences encode an enormous amount of information for gene regulation and protein synthesis. Similar to natural language mode… (voir plus)ls, researchers have proposed foundation models in genomics to learn generalizable features from unlabeled genome data that can then be fine-tuned for downstream tasks such as identifying regulatory elements. Due to the quadratic scaling of attention, previous Transformer-based genomic models have used 512 to 4k tokens as context (0.001% of the human genome), significantly limiting the modeling of long-range interactions in DNA. In addition, these methods rely on toke
Modeling strong gravitational lenses in order to quantify distortions in the images of background sources and to reconstruct the mass densit… (voir plus)y in foreground lenses has been a difficult computational challenge. As the quality of gravitational lens images increases, the task of fully exploiting the information they contain becomes computationally and algorithmically more difficult. In this work, we use a neural network based on the recurrent inference machine to reconstruct simultaneously an undistorted image of the background source and the lens mass density distribution as pixelated maps. The method iteratively reconstructs the model parameters (the image of the source and a pixelated density map) by learning the process of optimizing the likelihood given the data using the physical model (a ray-tracing simulation), regularized by a prior implicitly learned by the neural network through its training data. When compared to more traditional parametric models, the proposed method is significantly more expressive and can reconstruct complex mass distributions, which we demonstrate by using realistic lensing galaxies taken from the IllustrisTNG cosmological hydrodynamic simulation.
