{"title":"Significance in Scale Space for Hi-C Data.","authors":"Rui Liu, Zhengwu Zhang, Hyejung Won, J S Marron","doi":"10.1093/bioinformatics/btaf026","DOIUrl":null,"url":null,"abstract":"<p><strong>Motivation: </strong>Hi-C technology has been developed to profile genome-wide chromosome conformation. So far Hi-C data has been generated from a large compendium of different cell types and different tissue types. Among different chromatin conformation units, chromatin loops were found to play a key role in gene regulation across different cell types. While many different loop calling algorithms have been developed, most loop callers identified shared loops as opposed to cell type specific loops.</p><p><strong>Results: </strong>We propose SSSHiC, a new loop calling algorithm based on significance in scale space, which can be used to understand data at different levels of resolution. By applying SSSHiCto neuronal and glial Hi-C data, we detected more loops that are potentially engaged in cell type specific gene regulation. Compared with other loop callers, such as Mustache, these loops were more frequently anchored to gene promoters of cellular marker genes and had better APA scores. Therefore, our results suggest that SSSHiCcan effectively capture loops that contain more gene regulatory information.</p><p><strong>Availability and implementation: </strong>The Hi-C data used in this study can be accessed through the PsychENCODE Knowledge Portal at https://www.synapse.org/\\#! Synapse: syn21760712. The code utilized for Curvature SSS cited in this study is available at https://github.com/jsmarron/MarronMatlabSoftware/blob/master/Matlab9/Matlab9Combined.zip. All custom code used in this research can be found in the GitHub repository: https://github.com/jerryliu01998/HiC. The code has also been submitted to Code Ocean with the DOI: 10.24433/CO.1912913.v1.</p><p><strong>Contact: </strong>For inquiries or support, please contact [Rui Liu, jerryliu@unc.edu]. Collaboration and feedback from the community are welcome.</p><p><strong>Supplementary information: </strong>Supplementary data supporting this study, including example datasets and detailed evaluations, are available at https://github.com/jerryliu01998/HiC/blob/main/SM_HiC_202408.pdf. Additional results are included in the Supplementary Materials section.</p>","PeriodicalId":93899,"journal":{"name":"Bioinformatics (Oxford, England)","volume":" ","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2025-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11879645/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Bioinformatics (Oxford, England)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1093/bioinformatics/btaf026","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
Motivation: Hi-C technology has been developed to profile genome-wide chromosome conformation. So far Hi-C data has been generated from a large compendium of different cell types and different tissue types. Among different chromatin conformation units, chromatin loops were found to play a key role in gene regulation across different cell types. While many different loop calling algorithms have been developed, most loop callers identified shared loops as opposed to cell type specific loops.
Results: We propose SSSHiC, a new loop calling algorithm based on significance in scale space, which can be used to understand data at different levels of resolution. By applying SSSHiCto neuronal and glial Hi-C data, we detected more loops that are potentially engaged in cell type specific gene regulation. Compared with other loop callers, such as Mustache, these loops were more frequently anchored to gene promoters of cellular marker genes and had better APA scores. Therefore, our results suggest that SSSHiCcan effectively capture loops that contain more gene regulatory information.
Availability and implementation: The Hi-C data used in this study can be accessed through the PsychENCODE Knowledge Portal at https://www.synapse.org/\#! Synapse: syn21760712. The code utilized for Curvature SSS cited in this study is available at https://github.com/jsmarron/MarronMatlabSoftware/blob/master/Matlab9/Matlab9Combined.zip. All custom code used in this research can be found in the GitHub repository: https://github.com/jerryliu01998/HiC. The code has also been submitted to Code Ocean with the DOI: 10.24433/CO.1912913.v1.
Contact: For inquiries or support, please contact [Rui Liu, jerryliu@unc.edu]. Collaboration and feedback from the community are welcome.
Supplementary information: Supplementary data supporting this study, including example datasets and detailed evaluations, are available at https://github.com/jerryliu01998/HiC/blob/main/SM_HiC_202408.pdf. Additional results are included in the Supplementary Materials section.