Significance in Scale Space for Hi-C Data.

Rui Liu, Zhengwu Zhang, Hyejung Won, J S Marron
{"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.

查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
求助全文
约1分钟内获得全文 去求助
来源期刊
自引率
0.00%
发文量
0
期刊最新文献
Predicting circRNA-disease associations with shared units and multi-channel attention mechanisms. Vcfgl: A flexible genotype likelihood simulator for VCF/BCF files. FlowPacker: protein side-chain packing with torsional flow matching. SP-DTI: subpocket-informed transformer for drug-target interaction prediction. Relative quantification of proteins and post-translational modifications in proteomic experiments with shared peptides: a weight-based approach.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1