Browsing by Author "Harris, Hannah L."

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    Chromatin alternates between A and B compartments at kilobase scale for subgenic organization
    (Springer Nature, 2023) Harris, Hannah L.; Gu, Huiya; Olshansky, Moshe; Wang, Ailun; Farabella, Irene; Eliaz, Yossi; Kalluchi, Achyuth; Krishna, Akshay; Jacobs, Mozes; Cauer, Gesine; Pham, Melanie; Rao, Suhas S. P.; Dudchenko, Olga; Omer, Arina; Mohajeri, Kiana; Kim, Sungjae; Nichols, Michael H.; Davis, Eric S.; Gkountaroulis, Dimos; Udupa, Devika; Aiden, Aviva Presser; Corces, Victor G.; Phanstiel, Douglas H.; Noble, William Stafford; Nir, Guy; Di Pierro, Michele; Seo, Jeong-Sun; Talkowski, Michael E.; Aiden, Erez Lieberman; Rowley, M. Jordan; Center for Theoretical Biological Physics
    Nuclear compartments are prominent features of 3D chromatin organization, but sequencing depth limitations have impeded investigation at ultra fine-scale. CTCF loops are generally studied at a finer scale, but the impact of looping on proximal interactions remains enigmatic. Here, we critically examine nuclear compartments and CTCF loop-proximal interactions using a combination of in situ Hi-C at unparalleled depth, algorithm development, and biophysical modeling. Producing a large Hi-C map with 33 billion contacts in conjunction with an algorithm for performing principal component analysis on sparse, super massive matrices (POSSUMM), we resolve compartments to 500 bp. Our results demonstrate that essentially all active promoters and distal enhancers localize in the A compartment, even when flanking sequences do not. Furthermore, we find that the TSS and TTS of paused genes are often segregated into separate compartments. We then identify diffuse interactions that radiate from CTCF loop anchors, which correlate with strong enhancer-promoter interactions and proximal transcription. We also find that these diffuse interactions depend on CTCF’s RNA binding domains. In this work, we demonstrate features of fine-scale chromatin organization consistent with a revised model in which compartments are more precise than commonly thought while CTCF loops are more protracted.
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    Predicting A/B compartments from histone modifications using deep learning
    (Elsevier, 2024) Zheng, Suchen; Thakkar, Nitya; Harris, Hannah L.; Liu, Susanna; Zhang, Megan; Gerstein, Mark; Aiden, Erez Lieberman; Rowley, M. Jordan; Noble, William Stafford; Gürsoy, Gamze; Singh, Ritambhara
    The three-dimensional organization of genomes plays a crucial role in essential biological processes. The segregation of chromatin into A and B compartments highlights regions of activity and inactivity, providing a window into the genomic activities specific to each cell type. Yet, the steep costs associated with acquiring Hi-C data, necessary for studying this compartmentalization across various cell types, pose a significant barrier in studying cell type specific genome organization. To address this, we present a prediction tool called compartment prediction using recurrent neural networks (CoRNN), which predicts compartmentalization of 3D genome using histone modification enrichment. CoRNN demonstrates robust cross-cell-type prediction of A/B compartments with an average AuROC of 90.9%. Cell-type-specific predictions align well with known functional elements, with H3K27ac and H3K36me3 identified as highly predictive histone marks. We further investigate our mispredictions and found that they are located in regions with ambiguous compartmental status. Furthermore, our model’s generalizability is validated by predicting compartments in independent tissue samples, which underscores its broad applicability.