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Zhuo X, Hsu S, Purushotham D, Kuntala PK, Harrison JK, Du AY, Chen S, Li D, Wang T. Comparing genomic and epigenomic features across species using the WashU Comparative Epigenome Browser. Genome Res 2023; 33:824-835. [PMID: 37156621 PMCID: PMC10317122 DOI: 10.1101/gr.277550.122] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2022] [Accepted: 05/03/2023] [Indexed: 05/10/2023]
Abstract
Genome browsers have become an intuitive and critical tool to visualize and analyze genomic features and data. Conventional genome browsers display data/annotations on a single reference genome/assembly; there are also genomic alignment viewer/browsers that help users visualize alignment, mismatch, and rearrangement between syntenic regions. However, there is a growing need for a comparative epigenome browser that can display genomic and epigenomic data sets across different species and enable users to compare them between syntenic regions. Here, we present the WashU Comparative Epigenome Browser. It allows users to load functional genomic data sets/annotations mapped to different genomes and display them over syntenic regions simultaneously. The browser also displays genetic differences between the genomes from single-nucleotide variants (SNVs) to structural variants (SVs) to visualize the association between epigenomic differences and genetic differences. Instead of anchoring all data sets to the reference genome coordinates, it creates independent coordinates of different genome assemblies to faithfully present features and data mapped to different genomes. It uses a simple, intuitive genome-align track to illustrate the syntenic relationship between different species. It extends the widely used WashU Epigenome Browser infrastructure and can be expanded to support multiple species. This new browser function will greatly facilitate comparative genomic/epigenomic research, as well as support the recent growing needs to directly compare and benchmark the T2T CHM13 assembly and other human genome assemblies.
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Affiliation(s)
- Xiaoyu Zhuo
- Department of Genetics, Washington University School of Medicine, St. Louis, Missouri 63110, USA
- The Edison Family Center for Genome Sciences and Systems Biology, Washington University School of Medicine, St. Louis, Missouri 63110, USA
| | - Silas Hsu
- Department of Genetics, Washington University School of Medicine, St. Louis, Missouri 63110, USA
- The Edison Family Center for Genome Sciences and Systems Biology, Washington University School of Medicine, St. Louis, Missouri 63110, USA
| | - Deepak Purushotham
- Department of Genetics, Washington University School of Medicine, St. Louis, Missouri 63110, USA
- The Edison Family Center for Genome Sciences and Systems Biology, Washington University School of Medicine, St. Louis, Missouri 63110, USA
| | - Prashant Kumar Kuntala
- Department of Genetics, Washington University School of Medicine, St. Louis, Missouri 63110, USA
- The Edison Family Center for Genome Sciences and Systems Biology, Washington University School of Medicine, St. Louis, Missouri 63110, USA
| | - Jessica K Harrison
- Department of Genetics, Washington University School of Medicine, St. Louis, Missouri 63110, USA
- The Edison Family Center for Genome Sciences and Systems Biology, Washington University School of Medicine, St. Louis, Missouri 63110, USA
| | - Alan Y Du
- Department of Genetics, Washington University School of Medicine, St. Louis, Missouri 63110, USA
- The Edison Family Center for Genome Sciences and Systems Biology, Washington University School of Medicine, St. Louis, Missouri 63110, USA
| | - Samuel Chen
- Department of Genetics, Washington University School of Medicine, St. Louis, Missouri 63110, USA
- The Edison Family Center for Genome Sciences and Systems Biology, Washington University School of Medicine, St. Louis, Missouri 63110, USA
| | - Daofeng Li
- Department of Genetics, Washington University School of Medicine, St. Louis, Missouri 63110, USA
- The Edison Family Center for Genome Sciences and Systems Biology, Washington University School of Medicine, St. Louis, Missouri 63110, USA
| | - Ting Wang
- Department of Genetics, Washington University School of Medicine, St. Louis, Missouri 63110, USA;
- The Edison Family Center for Genome Sciences and Systems Biology, Washington University School of Medicine, St. Louis, Missouri 63110, USA
- McDonnell Genome Institute, Washington University School of Medicine, St. Louis, Missouri 63110, USA
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LYi S, Gehlenborg N. Multi-View Design Patterns and Responsive Visualization for Genomics Data. IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS 2023; 29:559-569. [PMID: 36166553 PMCID: PMC10040461 DOI: 10.1109/tvcg.2022.3209398] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 05/04/2023]
Abstract
A series of recent studies has focused on designing cross-resolution and cross-device visualizations, i.e., responsive visualization, a concept adopted from responsive web design. However, these studies mainly focused on visualizations with a single view to a small number of views, and there are still unresolved questions about how to design responsive multi-view visualizations. In this paper, we present a reusable and generalizable framework for designing responsive multi-view visualizations focused on genomics data. To gain a better understanding of existing design challenges, we review web-based genomics visualization tools in the wild. By characterizing tools based on a taxonomy of responsive designs, we find that responsiveness is rarely supported in existing tools. To distill insights from the survey results in a systematic way, we classify typical view composition patterns, such as "vertically long," "horizontally wide," "circular," and "cross-shaped" compositions. We then identify their usability issues in different resolutions that stem from the composition patterns, as well as discussing approaches to address the issues and to make genomics visualizations responsive. By extending the Gosling visualization grammar to support responsive constructs, we show how these approaches can be supported. A valuable follow-up study would be taking different input modalities into account, such as mouse and touch interactions, which was not considered in our study.
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Lu J, Cao X, Zhong S. EpiAlignment: alignment with both DNA sequence and epigenomic data. Nucleic Acids Res 2019; 47:W11-W19. [PMID: 31114924 PMCID: PMC6602515 DOI: 10.1093/nar/gkz426] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2019] [Revised: 05/06/2019] [Accepted: 05/13/2019] [Indexed: 12/01/2022] Open
Abstract
Comparative epigenomics, which subjects both epigenome and genome to interspecies comparison, has become a powerful approach to reveal regulatory features of the genome. Thus elucidated regulatory features surpass the information derived from comparison of genomic sequences alone. Here, we present EpiAlignment, a web-based tool to align genomic regions with both DNA sequence and epigenomic data. EpiAlignment takes DNA sequence and epigenomic profiles derived by ChIP-seq from two species as input data, and outputs the best semi-global alignments. These alignments are based on EpiAlignment scores, computed by a dynamic programming algorithm that accounts for both sequence alignment and epigenome similarity. For timely response, the EpiAlignment web server automatically initiates up to 140 computing threads depending on the size of user input data. For users’ convenience, we have pre-compiled the comparable human and mouse epigenome datasets in matched cell types and tissues from the Roadmap Epigenomics and ENCODE consortia. Users can either upload their own data or select pre-compiled datasets as inputs for EpiAlignment analyses. Results are presented in graphical and tabular formats where the entries can be interactively expanded to visualize additional features of these aligned regions. EpiAlignment is available at https://epialign.ucsd.edu/.
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Affiliation(s)
- Jia Lu
- Department of Bioengineering, University of California San Diego, La Jolla, CA 92093, USA
| | - Xiaoyi Cao
- Department of Bioengineering, University of California San Diego, La Jolla, CA 92093, USA
| | - Sheng Zhong
- Department of Bioengineering, University of California San Diego, La Jolla, CA 92093, USA
- To whom correspondence should be addressed. Tel: +1 858 246 1118; Fax: +1 858 244 4543;
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Lin Y, Li Y, Zhu X, Huang Y, Li Y, Li M. Genetic Contexts Characterize Dynamic Histone Modification Patterns Among Cell Types. Interdiscip Sci 2019; 11:698-710. [PMID: 31165438 DOI: 10.1007/s12539-019-00338-7] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2019] [Revised: 05/18/2019] [Accepted: 05/27/2019] [Indexed: 11/29/2022]
Abstract
Histone modifications play critical roles in mammalian development, regulating chromatin structure and gene expression. Dynamic histone modifications among cell types have been shown to associate with changes in mammalian development. However, how to quantitatively measure the histone modification alterations and how histone modifications vary across cell types under different genetic contexts remain largely unexplored and whether these changes are related to the primary DNA sequence remains limited. Here, we employed an entropy-based method to measure histone modification alterations in six definite genomic regions across five cell types and identified lineage-specific histone modification genes. We observed that histone modification alterations prefer to enrich in 5'-UTR exons, and also in 3'-UTR exons and its downstream. Then we built a model to predict the histone modification patterns from the primary DNA sequence. We found that the frequencies of k-mer sequence compositions are predictive of histone modification patterns, suggesting that the primary DNA sequence correlated with the histone modification alterations among cell types. Additionally, the lineage-specific histone modification genes display a higher conservation and lower GC-content. Together, we performed a systematic analysis for histone modification alterations and demonstrated how to identify genomic region-specific elements of epigenetic and genetic regulation and histone modification patterns across different cell types.
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Affiliation(s)
- Yanmei Lin
- College of Chemistry, Sichuan University, Chengdu, 610064, China
| | - Yan Li
- College of Chemistry, Sichuan University, Chengdu, 610064, China
| | - Xingyong Zhu
- College of Chemistry, Sichuan University, Chengdu, 610064, China
| | - Yuyao Huang
- College of Chemistry, Sichuan University, Chengdu, 610064, China
| | - Yizhou Li
- College of Chemistry, Sichuan University, Chengdu, 610064, China. .,College of Cybersecurity, Sichuan University, Chengdu, 610064, Sichuan, China.
| | - Menglong Li
- College of Chemistry, Sichuan University, Chengdu, 610064, China.
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Nusrat S, Harbig T, Gehlenborg N. Tasks, Techniques, and Tools for Genomic Data Visualization. COMPUTER GRAPHICS FORUM : JOURNAL OF THE EUROPEAN ASSOCIATION FOR COMPUTER GRAPHICS 2019; 38:781-805. [PMID: 31768085 PMCID: PMC6876635 DOI: 10.1111/cgf.13727] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/06/2023]
Abstract
Genomic data visualization is essential for interpretation and hypothesis generation as well as a valuable aid in communicating discoveries. Visual tools bridge the gap between algorithmic approaches and the cognitive skills of investigators. Addressing this need has become crucial in genomics, as biomedical research is increasingly data-driven and many studies lack well-defined hypotheses. A key challenge in data-driven research is to discover unexpected patterns and to formulate hypotheses in an unbiased manner in vast amounts of genomic and other associated data. Over the past two decades, this has driven the development of numerous data visualization techniques and tools for visualizing genomic data. Based on a comprehensive literature survey, we propose taxonomies for data, visualization, and tasks involved in genomic data visualization. Furthermore, we provide a comprehensive review of published genomic visualization tools in the context of the proposed taxonomies.
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Affiliation(s)
- S Nusrat
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | - T Harbig
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | - N Gehlenborg
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
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Yen A, Kellis M. Systematic chromatin state comparison of epigenomes associated with diverse properties including sex and tissue type. Nat Commun 2015; 6:7973. [PMID: 26282110 PMCID: PMC4557131 DOI: 10.1038/ncomms8973] [Citation(s) in RCA: 48] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2015] [Accepted: 07/01/2015] [Indexed: 01/06/2023] Open
Abstract
Epigenomic data sets provide critical information about the dynamic role of chromatin states in gene regulation, but a key question of how chromatin state segmentations vary under different conditions across the genome has remained unaddressed. Here we present ChromDiff, a group-wise chromatin state comparison method that generates an information-theoretic representation of epigenomes and corrects for external covariate factors to better isolate relevant chromatin state changes. By applying ChromDiff to the 127 epigenomes from the Roadmap Epigenomics and ENCODE projects, we provide novel group-wise comparative analyses across sex, tissue type, state and developmental age. Remarkably, we find that distinct sets of epigenomic features are maximally discriminative for different group-wise comparisons, in each case revealing distinct enriched pathways, many of which do not show gene expression differences. Our methodology should be broadly applicable for epigenomic comparisons and provides a powerful new tool for studying chromatin state differences at the genome scale.
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Affiliation(s)
- Angela Yen
- 1] Electrical Engineering and Computer Science Department, Computer Science and Artificial Intelligence Laboratory, MIT, 32 Vassar Street, 32D-524, Cambridge, Massachusetts 02139, USA [2] Broad Institute of MIT and Harvard, Cambridge, Massachusetts 02142, USA
| | - Manolis Kellis
- 1] Electrical Engineering and Computer Science Department, Computer Science and Artificial Intelligence Laboratory, MIT, 32 Vassar Street, 32D-524, Cambridge, Massachusetts 02139, USA [2] Broad Institute of MIT and Harvard, Cambridge, Massachusetts 02142, USA
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Xiao S, Cao X, Zhong S. Comparative epigenomics: defining and utilizing epigenomic variations across species, time-course, and individuals. WILEY INTERDISCIPLINARY REVIEWS-SYSTEMS BIOLOGY AND MEDICINE 2014; 6:345-52. [PMID: 25044241 DOI: 10.1002/wsbm.1274] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/05/2014] [Revised: 05/02/2014] [Accepted: 06/16/2014] [Indexed: 12/20/2022]
Abstract
UNLABELLED Epigenomic profiling, by revealing genome-wide distributions of epigenetic modifications, generated a large amount of structural information about the chromosomes. Epigenomic analysis has quickly become a big data science, posing tremendous challenges on its translation into knowledge. To meet this challenge, comparative analysis of epigenomes, dubbed comparative epigenomics, has emerged as an active research area. Here, we summarize the recent developments in comparative epigenomic analyses into three major directions, namely the comparisons across species, the time-course of a biological process, and individuals. We review the main ideas, methods, and findings in each direction, and discuss the implications to understanding the regulatory functions of the genomes. CONFLICT OF INTEREST The authors have declared no conflicts of interest for this article.
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Affiliation(s)
- Shu Xiao
- Department of Bioengineering, University of California San Diego, La Jolla, CA, USA
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