Biology subjects

Hristov, B.

Publications and source records attributed to Hristov, B..

3 recordsLinked to original sources

bioRxiv2023

Enhancing Hi-C contact matrices for loop detection with Capricorn, a multi-view diffusion model

High-resolution Hi-C contact matrices reveal the detailed three-dimensional architecture of the genome, but high-coverage experimental Hi-C data are expensive to generate. On the other hand, chromatin structure analyses struggle with extremely sparse contact matrices. To address this problem, computational methods to enhance low-coverage contact matrices have been developed, but existing methods are largely based on resolution enhancement methods for natural images and hence often employ models that do not distinguish between biologically meaningful contacts, such as loops, and other stochastic contacts. We present Capricorn, a machine learning model for Hi-C resolution enhancement that incorporates small-scale chromatin features as additional views of the input Hi-C contact matrix and leverages a diffusion probability model backbone to generate a high-coverage matrix. We show that Capricorn outperforms the state of the art in a cross-cell-line setting, improving on existing methods by 17.8% in mean squared error and 22.9% in F1 score for chromatin loop identification from the generated high-coverage data. We also demonstrate that Capricorn performs well in the cross-chromosome setting and cross-chromosome, cross-cell-line setting, improving the downstream loop F1 score by 15.7% relative to existing methods. We further show that our multi-view idea can also be used to improve several existing methods, Hi-CARN and HiCNN, indicating the wide applicability of this approach. Finally, we use DNA sequence to validate discovered loops and find that the fraction of CTCF-supported loops from Capricorn is similar to those identified from the high-coverage data. Capricorn is a powerful Hi-C resolution enhancement method that enables scientists to find chromatin features that cannot be identified in the low-coverage contact matrix. Implementation of Capricorn and source code for reproducing all figures in this paper are available at https://github.com/CHNFTQ/Capricorn.

bioinformatics↗

bioRxiv2022

Deciphering the non-coding code of pathogenicity and sexual differentiation in the human malaria parasite.

The complex life cycle of Plasmodium falciparum requires coordinated gene expression regulation to allow host cell invasion, transmission, and immune evasion. However, this cascade of transcripts is unlikely to be regulated by the limited number of identified parasite-specific transcription factors. Increasing evidence now suggests a major role for epigenetic mechanisms in gene expression in the parasite. In eukaryotes, many lncRNAs have been identified and shown to be pivotal regulators of genome structure and gene expression. To investigate the regulatory roles of lncRNAs in P. falciparum we explored the intergenic lncRNA distribution in nuclear and cytoplasmic subcellular locations. Using nascent RNA expression profiles, we identified a total of 1,768 lncRNAs, of which 58% were identified as novel lncRNAs in P. falciparum. The subcellular localization and stage-specific expression of several putative lncRNAs were validated using RNA fluorescence in situ hybridization (RNA-FISH). Additionally, the genome-wide occupancy of several candidate nuclear lncRNAs was explored using Chromatin Isolation by RNA Purification (ChIRP). ChIRP-seq of candidate lncRNAs revealed that lncRNA occupancy sites within the parasite genome are focal and sequence-specific with a particular enrichment for several parasite-specific gene families, including those involved in pathogenesis, erythrocyte remodeling, and regulation of sexual differentiation. We further validated the function of one specific lncRNA (lncRNA-ch14) using the CRISPR-Cas9 genome editing tool. Genomic and phenotypic analysis of the {bigtriangleup}lncRNA-ch14 line demonstrated the importance of this lncRNA in sexual differentiation and sexual reproduction. Our findings bring a new level of insight into the role of lncRNAs in pathogenicity, gene regulation and sexual differentiation. These findings also open new avenues for targeted approaches towards therapeutic strategies against the deadly malaria parasite.

microbiology↗

bioRxiv2022

Linking cells across single-cell modalities by synergistic matching of neighborhood structure

A wide variety of experimental methods are available to characterize different properties of single cells in a complex biosample. However, because these measurement techniques are typically destructive, researchers are often presented with complementary measurements from disjoint subsets of cells, providing a fragmented view of the cells biological processes. This creates a need for computational tools capable of integrating disjoint multi-omics data. Because different measurements typically do not share any features, the problem requires the integration to be done in unsupervised fashion. Recently, several methods have been proposed that project the cell measurements into a common latent space and attempt to align the corresponding low-dimensional manifolds. In this study we present an approach, Synmatch, which produces a direct matching of the cells between modalities by exploiting information about neighborhood structure in each modality. Synmatch relies on the intuition that cells which are close in one measurement space should be close in the other as well. This allows us to formulate the matching problem as a constrained supermodular optimization problem over neighborhood structures that can be solved efficiently. We show that our approach successfully matches cells in small real multi-omics datasets and performs favorably when compared to recently published state-of-the-art methods. Further, we demonstrate that Synmatch is capable of scaling to large datasets of thousands of cells. The Synmatch code and data used in this manuscript are available at https://github.com/orgs/Noble-Lab/synmatch

genomics↗