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Article ; Online: Machine and deep learning methods for predicting 3D genome organization.

Wall, Brydon P G / Nguyen, My / Harrell, J Chuck / Dozmorov, Mikhail G

ArXiv

2024

Abstract: Three-Dimensional (3D) chromatin interactions, such as enhancer-promoter interactions (EPIs), loops, Topologically Associating Domains (TADs), and A/B compartments play critical roles in a wide range of cellular processes by regulating gene expression. ... ...

Abstract	Three-Dimensional (3D) chromatin interactions, such as enhancer-promoter interactions (EPIs), loops, Topologically Associating Domains (TADs), and A/B compartments play critical roles in a wide range of cellular processes by regulating gene expression. Recent development of chromatin conformation capture technologies has enabled genome-wide profiling of various 3D structures, even with single cells. However, current catalogs of 3D structures remain incomplete and unreliable due to differences in technology, tools, and low data resolution. Machine learning methods have emerged as an alternative to obtain missing 3D interactions and/or improve resolution. Such methods frequently use genome annotation data (ChIP-seq, DNAse-seq, etc.), DNA sequencing information (k-mers, Transcription Factor Binding Site (TFBS) motifs), and other genomic properties to learn the associations between genomic features and chromatin interactions. In this review, we discuss computational tools for predicting three types of 3D interactions (EPIs, chromatin interactions, TAD boundaries) and analyze their pros and cons. We also point out obstacles of computational prediction of 3D interactions and suggest future research directions.
Language	English
Publishing date	2024-03-04
Publishing country	United States
Document type	Preprint
ISSN	2331-8422
ISSN (online)	2331-8422
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Article: Inducible deletion of

Zheng, Xiaoqing / Dozmorov, Mikhail G / Espinoza, Luis / Bowes, Mckenna M / Bastacky, Sheldon / Sawalha, Amr H

bioRxiv : the preprint server for biology

2024

Abstract: Systemic lupus erythematosus is a remitting relapsing autoimmune disease characterized by autoantibody production and multi-organ involvement. T cell epigenetic dysregulation plays an important role in the pathogenesis of lupus. We have previously ... ...

Abstract	Systemic lupus erythematosus is a remitting relapsing autoimmune disease characterized by autoantibody production and multi-organ involvement. T cell epigenetic dysregulation plays an important role in the pathogenesis of lupus. We have previously demonstrated upregulation of the key epigenetic regulator EZH2 in CD4+ T cells isolated from lupus patients. To further investigate the role of EZH2 in the pathogenesis of lupus, we generated a tamoxifen-inducible CD4+ T cell
Language	English
Publishing date	2024-03-07
Publishing country	United States
Document type	Preprint
DOI	10.1101/2024.03.04.583401
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Article: Reforming disease classification system-are we there yet?

Dozmorov, Mikhail G

Annals of translational medicine

2017 Volume 6, Issue Suppl 1, Page(s) S30

Language	English
Publishing date	2017-11-21
Publishing country	China
Document type	Editorial ; Comment
ZDB-ID	2893931-1
ISSN	2305-5847 ; 2305-5839
ISSN (online)	2305-5847
ISSN	2305-5839
DOI	10.21037/atm.2018.09.36
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Article ; Online: Disease classification: from phenotypic similarity to integrative genomics and beyond.

Dozmorov, Mikhail G

Briefings in bioinformatics

2018 Volume 20, Issue 5, Page(s) 1769–1780

Abstract: A fundamental challenge of modern biomedical research is understanding how diseases that are similar on the phenotypic level are similar on the molecular level. Integration of various genomic data sets with the traditionally used phenotypic disease ... ...

Abstract	A fundamental challenge of modern biomedical research is understanding how diseases that are similar on the phenotypic level are similar on the molecular level. Integration of various genomic data sets with the traditionally used phenotypic disease similarity revealed novel genetic and molecular mechanisms and blurred the distinction between monogenic (Mendelian) and complex diseases. Network-based medicine has emerged as a complementary approach for identifying disease-causing genes, genetic mediators, disruptions in the underlying cellular functions and for drug repositioning. The recent development of machine and deep learning methods allow for leveraging real-life information about diseases to refine genetic and phenotypic disease relationships. This review describes the historical development and recent methodological advancements for studying disease classification (nosology).
MeSH term(s)	Comorbidity ; Genetic Diseases, Inborn/classification ; Genetic Diseases, Inborn/complications ; Genetic Diseases, Inborn/genetics ; Genomics ; Humans ; Machine Learning ; Phenotype ; Terminology as Topic
Language	English
Publishing date	2018-05-28
Publishing country	England
Document type	Journal Article ; Research Support, Non-U.S. Gov't ; Review
ZDB-ID	2068142-2
ISSN	1477-4054 ; 1467-5463
ISSN (online)	1477-4054
ISSN	1467-5463
DOI	10.1093/bib/bby049
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Article: GitHub Statistics as a Measure of the Impact of Open-Source Bioinformatics Software.

Dozmorov, Mikhail G

Frontiers in bioengineering and biotechnology

2018 Volume 6, Page(s) 198

Abstract: Modern research is increasingly data-driven and reliant on bioinformatics software. Publication is a common way of introducing new software, but not all bioinformatics tools get published. Giving there are competing tools, it is important not merely to ... ...

Abstract	Modern research is increasingly data-driven and reliant on bioinformatics software. Publication is a common way of introducing new software, but not all bioinformatics tools get published. Giving there are competing tools, it is important not merely to find the appropriate software, but have a metric for judging its usefulness. Journal's impact factor has been shown to be a poor predictor of software popularity; consequently, focusing on publications in high-impact journals limits user's choices in finding useful bioinformatics tools. Free and open source software repositories on popular code sharing platforms such as GitHub provide another venue to follow the latest bioinformatics trends. The open source component of GitHub allows users to bookmark and copy repositories that are most useful to them. This Perspective aims to demonstrate the utility of GitHub "stars," "watchers," and "forks" (GitHub statistics) as a measure of software impact. We compiled lists of impactful bioinformatics software and analyzed commonly used impact metrics and GitHub statistics of 50 genomics-oriented bioinformatics tools. We present examples of community-selected best bioinformatics resources and show that GitHub statistics are distinct from the journal's impact factor (JIF), citation counts, and alternative metrics (Altmetrics, CiteScore) in capturing the level of community attention. We suggest the use of GitHub statistics as an unbiased measure of the usability of bioinformatics software complementing the traditional impact metrics.
Language	English
Publishing date	2018-12-18
Publishing country	Switzerland
Document type	Journal Article
ZDB-ID	2719493-0
ISSN	2296-4185
ISSN	2296-4185
DOI	10.3389/fbioe.2018.00198
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Article ; Online: Analysis of Liver Responses to Non-alcoholic Steatohepatitis by mRNA-Sequencing.

Green, Christopher D / Dozmorov, Mikhail G / Spiegel, Sarah

Methods in molecular biology (Clifton, N.J.)

2022 Volume 2455, Page(s) 163–179

Abstract: Non-alcoholic steatohepatitis (NASH) is a major cause of chronic liver disease that can ultimately lead to cirrhosis and hepatocellular carcinoma. Although NASH is associated with excessive liver lipid accumulation, hepatocyte injury, inflammation, and ... ...

Abstract	Non-alcoholic steatohepatitis (NASH) is a major cause of chronic liver disease that can ultimately lead to cirrhosis and hepatocellular carcinoma. Although NASH is associated with excessive liver lipid accumulation, hepatocyte injury, inflammation, and fibrosis, its etiology remains incompletely understood. These can be characterized by determining transcriptional changes in specific genes previously found to be involved in these processes. As an inherently multifaceted disease, studies of NASH often require unbiased examination of major genes and pathways to identify the mechanisms involved in this disorder. To address this need, quantitative approaches such as mRNA-sequencing have been developed for the global assessment of gene expression. Here, we describe a protocol for bulk mRNA-sequencing that can be utilized for both liver samples and specific cell types isolated from the liver. This approach provides an important resource to further understand the molecular changes that occur during the development of NASH that can be utilized to design better therapeutic treatments.
MeSH term(s)	Humans ; Liver/metabolism ; Liver Cirrhosis/pathology ; Liver Neoplasms/pathology ; Non-alcoholic Fatty Liver Disease/genetics ; Non-alcoholic Fatty Liver Disease/metabolism ; RNA, Messenger/genetics
Chemical Substances	RNA, Messenger
Language	English
Publishing date	2022-02-25
Publishing country	United States
Document type	Journal Article ; Research Support, N.I.H., Extramural
ISSN	1940-6029
ISSN (online)	1940-6029
DOI	10.1007/978-1-0716-2128-8_14
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Article ; Online: Epigenomic annotation-based interpretation of genomic data: from enrichment analysis to machine learning.

Dozmorov, Mikhail G

Bioinformatics (Oxford, England)

2017 Volume 33, Issue 20, Page(s) 3323–3330

Abstract: ... guide for the interpretation of genome-wide ROIs within an epigenomic context.: Contact: mikhail ... dozmorov@vcuhealth.org.: Supplementary information: Supplementary data are available at Bioinformatics ...

Abstract	Motivation: One of the goals of functional genomics is to understand the regulatory implications of experimentally obtained genomic regions of interest (ROIs). Most sequencing technologies now generate ROIs distributed across the whole genome. The interpretation of these genome-wide ROIs represents a challenge as the majority of them lie outside of functionally well-defined protein coding regions. Recent efforts by the members of the International Human Epigenome Consortium have generated volumes of functional/regulatory data (reference epigenomic datasets), effectively annotating the genome with epigenomic properties. Consequently, a wide variety of computational tools has been developed utilizing these epigenomic datasets for the interpretation of genomic data. Results: The purpose of this review is to provide a structured overview of practical solutions for the interpretation of ROIs with the help of epigenomic data. Starting with epigenomic enrichment analysis, we discuss leading tools and machine learning methods utilizing epigenomic and 3D genome structure data. The hierarchy of tools and methods reviewed here presents a practical guide for the interpretation of genome-wide ROIs within an epigenomic context. Contact: mikhail.dozmorov@vcuhealth.org. Supplementary information: Supplementary data are available at Bioinformatics online.
MeSH term(s)	Epigenomics/methods ; Guidelines as Topic ; Humans ; Machine Learning ; Molecular Sequence Annotation/methods ; Software
Language	English
Publishing date	2017-10-13
Publishing country	England
Document type	Comparative Study ; Journal Article
ZDB-ID	1422668-6
ISSN	1367-4811 ; 1367-4803
ISSN (online)	1367-4811
ISSN	1367-4803
DOI	10.1093/bioinformatics/btx414
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Article: TADCompare: An R Package for Differential and Temporal Analysis of Topologically Associated Domains.

Cresswell, Kellen G / Dozmorov, Mikhail G

Frontiers in genetics

2020 Volume 11, Page(s) 158

Abstract: Recent research using chromatin conformation capture technologies, such as Hi-C, has demonstrated the importance of topologically associated domains (TADs) and smaller chromatin loops, collectively referred hereafter as "interacting domains." Many such ... ...

Abstract	Recent research using chromatin conformation capture technologies, such as Hi-C, has demonstrated the importance of topologically associated domains (TADs) and smaller chromatin loops, collectively referred hereafter as "interacting domains." Many such domains change during development or disease, and exhibit cell- and condition-specific differences. Quantification of the dynamic behavior of interacting domains will help to better understand genome regulation. Methods for comparing interacting domains between cells and conditions are highly limited. We developed TADCompare, a method for differential analysis of boundaries of interacting domains between two or more Hi-C datasets. TADCompare is based on a spectral clustering-derived measure called the eigenvector gap, which enables a loci-by-loci comparison of boundary differences. Using this measure, we introduce methods for identifying differential and consensus boundaries of interacting domains and tracking boundary changes over time. We further propose a novel framework for the systematic classification of boundary changes. Colocalization- and gene enrichment analysis of different types of boundary changes demonstrated distinct biological functionality associated with them. TADCompare is available on https://github.com/dozmorovlab/TADCompare and Bioconductor (submitted).
Language	English
Publishing date	2020-03-10
Publishing country	Switzerland
Document type	Journal Article
ZDB-ID	2606823-0
ISSN	1664-8021
ISSN	1664-8021
DOI	10.3389/fgene.2020.00158
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Article ; Online: matchRanges: generating null hypothesis genomic ranges via covariate-matched sampling.

Davis, Eric S / Mu, Wancen / Lee, Stuart / Dozmorov, Mikhail G / Love, Michael I / Phanstiel, Douglas H

Bioinformatics (Oxford, England)

2023 Volume 39, Issue 5

Abstract: Motivation: Deriving biological insights from genomic data commonly requires comparing attributes of selected genomic loci to a null set of loci. The selection of this null set is non-trivial, as it requires careful consideration of potential covariates, ...

Abstract	Motivation: Deriving biological insights from genomic data commonly requires comparing attributes of selected genomic loci to a null set of loci. The selection of this null set is non-trivial, as it requires careful consideration of potential covariates, a problem that is exacerbated by the non-uniform distribution of genomic features including genes, enhancers, and transcription factor binding sites. Propensity score-based covariate matching methods allow the selection of null sets from a pool of possible items while controlling for multiple covariates; however, existing packages do not operate on genomic data classes and can be slow for large data sets making them difficult to integrate into genomic workflows. Results: To address this, we developed matchRanges, a propensity score-based covariate matching method for the efficient and convenient generation of matched null ranges from a set of background ranges within the Bioconductor framework. Availability and implementation: Package: https://bioconductor.org/packages/nullranges, Code: https://github.com/nullranges, Documentation: https://nullranges.github.io/nullranges.
MeSH term(s)	Software ; Genomics/methods ; Genome ; Regulatory Sequences, Nucleic Acid ; Research Design
Language	English
Publishing date	2023-04-20
Publishing country	England
Document type	Journal Article ; Research Support, N.I.H., Extramural ; Research Support, Non-U.S. Gov't
ZDB-ID	1422668-6
ISSN	1367-4811 ; 1367-4803
ISSN (online)	1367-4811
ISSN	1367-4803
DOI	10.1093/bioinformatics/btad197
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Article: A novel multi-omics mendelian randomization method for gene set enrichment and its application to psychiatric disorders.

Gedik, Huseyin / Peterson, Roseann / Chatzinakos, Christos / Dozmorov, Mikhail G / Vladimirov, Vladimir / Riley, Brien P / Bacanu, Silviu-Alin

medRxiv : the preprint server for health sciences

2024

Abstract: ... Some of the findings might be easier to adapt for treatment, e.g., our analyses suggest modest positive effects ...

Abstract	Genome-wide association studies (GWAS) of psychiatric disorders (PD) yield numerous loci with significant signals, but often do not implicate specific genes. Because GWAS risk loci are enriched in expression/protein/methylation quantitative loci (e/p/mQTL, hereafter xQTL), transcriptome/proteome/methylome-wide association studies (T/P/MWAS, hereafter XWAS) that integrate xQTL and GWAS information, can link GWAS signals to effects on specific genes. To further increase detection power, gene signals are aggregated within relevant gene sets (GS) by performing gene set enrichment (GSE) analyses. Often GSE methods test for enrichment of "signal" genes in curated GS while overlooking their linkage disequilibrium (LD) structure, allowing for the possibility of increased false positive rates. Moreover, no GSE tool uses xQTL information to perform mendelian randomization (MR) analysis. To make causal inference on association between PD and GS, we develop a novel MR GSE (MR-GSE) procedure. First, we generate a "synthetic" GWAS for each MSigDB GS by aggregating summary statistics for x-level (mRNA, protein or DNA methylation (DNAm) levels) from the largest xQTL studies available) of genes in a GS. Second, we use synthetic GS GWAS as exposure in a generalized summary-data-based-MR analysis of complex trait outcomes. We applied MR-GSE to GWAS of nine important PD. When applied to the underpowered opioid use disorder GWAS, none of the four analyses yielded any signals, which suggests a good control of false positive rates. For other PD, MR-GSE greatly increased the detection of GO terms signals (2,594) when compared to the commonly used (non-MR) GSE method (286). Some of the findings might be easier to adapt for treatment, e.g., our analyses suggest modest positive effects for supplementation with certain vitamins and/or omega-3 for schizophrenia, bipolar and major depression disorder patients. Similar to other MR methods, when applying MR-GSE researchers should be mindful of the confounding effects of horizontal pleiotropy on statistical inference.
Language	English
Publishing date	2024-04-15
Publishing country	United States
Document type	Preprint
DOI	10.1101/2024.04.14.24305811
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