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  1. Article ; Online: Inferring gene regulatory networks from single-cell multiome data using atlas-scale external data.

    Yuan, Qiuyue / Duren, Zhana

    Nature biotechnology

    2024  

    Abstract: Existing methods for gene regulatory network (GRN) inference rely on gene expression data alone or on lower resolution bulk data. Despite the recent integration of chromatin accessibility and RNA sequencing data, learning complex mechanisms from limited ... ...

    Abstract Existing methods for gene regulatory network (GRN) inference rely on gene expression data alone or on lower resolution bulk data. Despite the recent integration of chromatin accessibility and RNA sequencing data, learning complex mechanisms from limited independent data points still presents a daunting challenge. Here we present LINGER (Lifelong neural network for gene regulation), a machine-learning method to infer GRNs from single-cell paired gene expression and chromatin accessibility data. LINGER incorporates atlas-scale external bulk data across diverse cellular contexts and prior knowledge of transcription factor motifs as a manifold regularization. LINGER achieves a fourfold to sevenfold relative increase in accuracy over existing methods and reveals a complex regulatory landscape of genome-wide association studies, enabling enhanced interpretation of disease-associated variants and genes. Following the GRN inference from reference single-cell multiome data, LINGER enables the estimation of transcription factor activity solely from bulk or single-cell gene expression data, leveraging the abundance of available gene expression data to identify driver regulators from case-control studies.
    Language English
    Publishing date 2024-04-12
    Publishing country United States
    Document type Journal Article
    ZDB-ID 1311932-1
    ISSN 1546-1696 ; 1087-0156
    ISSN (online) 1546-1696
    ISSN 1087-0156
    DOI 10.1038/s41587-024-02182-7
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  2. Article: Continuous lifelong learning for modeling of gene regulation from single cell multiome data by leveraging atlas-scale external data.

    Yuan, Qiuyue / Duren, Zhana

    bioRxiv : the preprint server for biology

    2023  

    Abstract: Accurate context-specific Gene Regulatory Networks (GRNs) inference from genomics data is a crucial task in computational biology. However, existing methods face limitations, such as reliance on gene expression data alone, lower resolution from bulk data, ...

    Abstract Accurate context-specific Gene Regulatory Networks (GRNs) inference from genomics data is a crucial task in computational biology. However, existing methods face limitations, such as reliance on gene expression data alone, lower resolution from bulk data, and data scarcity for specific cellular systems. Despite recent technological advancements, including single-cell sequencing and the integration of ATAC-seq and RNA-seq data, learning such complex mechanisms from limited independent data points still presents a daunting challenge, impeding GRN inference accuracy. To overcome this challenge, we present LINGER (LIfelong neural Network for GEne Regulation), a novel deep learning-based method to infer GRNs from single-cell multiome data with paired gene expression and chromatin accessibility data from the same cell. LINGER incorporates both 1) atlas-scale external bulk data across diverse cellular contexts and 2) the knowledge of transcription factor (TF) motif matching to
    Language English
    Publishing date 2023-08-03
    Publishing country United States
    Document type Preprint
    DOI 10.1101/2023.08.01.551575
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  3. Article ; Online: Integration of single-cell multi-omics data by regression analysis on unpaired observations.

    Yuan, Qiuyue / Duren, Zhana

    Genome biology

    2022  Volume 23, Issue 1, Page(s) 160

    Abstract: Despite recent developments, it is hard to profile all multi-omics single-cell data modalities on the same cell. Thus, huge amounts of single-cell genomics data of unpaired observations on different cells are generated. We propose a method named ... ...

    Abstract Despite recent developments, it is hard to profile all multi-omics single-cell data modalities on the same cell. Thus, huge amounts of single-cell genomics data of unpaired observations on different cells are generated. We propose a method named UnpairReg for the regression analysis on unpaired observations to integrate single-cell multi-omics data. On real and simulated data, UnpairReg provides an accurate estimation of cell gene expression where only chromatin accessibility data is available. The cis-regulatory network inferred from UnpairReg is highly consistent with eQTL mapping. UnpairReg improves cell type identification accuracy by joint analysis of single-cell gene expression and chromatin accessibility data.
    MeSH term(s) Chromatin/genetics ; Genomics ; Regression Analysis ; Single-Cell Analysis
    Chemical Substances Chromatin
    Language English
    Publishing date 2022-07-19
    Publishing country England
    Document type Journal Article ; Research Support, N.I.H., Extramural
    ZDB-ID 2040529-7
    ISSN 1474-760X ; 1474-760X
    ISSN (online) 1474-760X
    ISSN 1474-760X
    DOI 10.1186/s13059-022-02726-7
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  4. Article ; Online: Joint inference of clonal structure using single-cell genome and transcriptome sequencing data.

    Bai, Xiangqi / Duren, Zhana / Wan, Lin / Xia, Li C

    NAR genomics and bioinformatics

    2024  Volume 6, Issue 1, Page(s) lqae017

    Abstract: Latest advancements in the high-throughput single-cell genome (scDNA) and transcriptome (scRNA) sequencing technologies enabled cell-resolved investigation of tissue clones. However, it remains challenging to cluster and couple single cells for ... ...

    Abstract Latest advancements in the high-throughput single-cell genome (scDNA) and transcriptome (scRNA) sequencing technologies enabled cell-resolved investigation of tissue clones. However, it remains challenging to cluster and couple single cells for heterogeneous scRNA and scDNA data generated from the same specimen. In this study, we present a computational framework called CCNMF, which employs a novel Coupled-Clone Non-negative Matrix Factorization technique to jointly infer clonal structure for matched scDNA and scRNA data. CCNMF couples multi-omics single cells by linking copy number and gene expression profiles through their general concordance. It successfully resolved the underlying coexisting clones with high correlations between the clonal genome and transcriptome from the same specimen. We validated that CCNMF can achieve high accuracy and robustness using both simulated benchmarks and real-world applications, including an ovarian cancer cell lines mixture, a gastric cancer cell line, and a primary gastric cancer. In summary, CCNMF provides a powerful tool for integrating multi-omics single-cell data, enabling simultaneous resolution of genomic and transcriptomic clonal architecture. This computational framework facilitates the understanding of how cellular gene expression changes in conjunction with clonal genome alternations, shedding light on the cellular genomic difference of subclones that contributes to tumor evolution.
    Language English
    Publishing date 2024-02-13
    Publishing country England
    Document type Journal Article
    ISSN 2631-9268
    ISSN (online) 2631-9268
    DOI 10.1093/nargab/lqae017
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  5. Article: Integration of single-cell multi-omics data by regression analysis on unpaired observations

    Yuan, Qiuyue / Duren, Zhana

    Genome biology. 2022 Dec., v. 23, no. 1

    2022  

    Abstract: Despite recent developments, it is hard to profile all multi-omics single-cell data modalities on the same cell. Thus, huge amounts of single-cell genomics data of unpaired observations on different cells are generated. We propose a method named ... ...

    Abstract Despite recent developments, it is hard to profile all multi-omics single-cell data modalities on the same cell. Thus, huge amounts of single-cell genomics data of unpaired observations on different cells are generated. We propose a method named UnpairReg for the regression analysis on unpaired observations to integrate single-cell multi-omics data. On real and simulated data, UnpairReg provides an accurate estimation of cell gene expression where only chromatin accessibility data is available. The cis-regulatory network inferred from UnpairReg is highly consistent with eQTL mapping. UnpairReg improves cell type identification accuracy by joint analysis of single-cell gene expression and chromatin accessibility data.
    Keywords chromatin ; gene expression ; genome ; genomics ; multiomics ; regression analysis
    Language English
    Dates of publication 2022-12
    Size p. 160.
    Publishing place BioMed Central
    Document type Article
    ZDB-ID 2040529-7
    ISSN 1474-760X
    ISSN 1474-760X
    DOI 10.1186/s13059-022-02726-7
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  6. Article ; Online: Human Genetic Variants Associated with COVID-19 Severity are Enriched in Immune and Epithelium Regulatory Networks.

    Feng, Zhanying / Ren, Xianwen / Duren, Zhana / Wang, Yong

    Phenomics (Cham, Switzerland)

    2022  Volume 2, Issue 6, Page(s) 389–403

    Abstract: Human genetic variants can influence the severity of symptoms infected with SARS-COV-2. Several genome-wide association studies have identified human genomic risk single nucleotide polymorphisms (SNPs) associated with coronavirus disease 2019 (COVID-19) ... ...

    Abstract Human genetic variants can influence the severity of symptoms infected with SARS-COV-2. Several genome-wide association studies have identified human genomic risk single nucleotide polymorphisms (SNPs) associated with coronavirus disease 2019 (COVID-19) severity. However, the causal tissues or cell types underlying COVID-19 severity are uncertain. In addition, candidate genes associated with these risk SNPs were investigated based on genomic proximity instead of their functional cellular contexts. Here, we compiled regulatory networks of 77 human contexts and revealed those risk SNPs' enriched cellular contexts and associated risk SNPs with transcription factors, regulatory elements, and target genes. Twenty-one human contexts were identified and grouped into two categories: immune cells and epithelium cells. We further aggregated the regulatory networks of immune cells and epithelium cells. These two aggregated regulatory networks were investigated to reveal their association with risk SNPs' regulation. Two genomic clusters, the chemokine receptors cluster and the oligoadenylate synthetase (OAS) cluster, showed the strongest association with COVID-19 severity, and they had different regulatory programs in immune and epithelium contexts. Our findings were supported by analysis of both SNP array and whole genome sequencing-based genome wide association study (GWAS) summary statistics.
    Supplementary information: The online version contains supplementary material available at 10.1007/s43657-022-00066-x.
    Language English
    Publishing date 2022-08-13
    Publishing country Switzerland
    Document type Journal Article
    ISSN 2730-5848
    ISSN (online) 2730-5848
    DOI 10.1007/s43657-022-00066-x
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  7. Article ; Online: Modeling regulatory network topology improves genome-wide analyses of complex human traits.

    Zhu, Xiang / Duren, Zhana / Wong, Wing Hung

    Nature communications

    2021  Volume 12, Issue 1, Page(s) 2851

    Abstract: Genome-wide association studies (GWAS) have cataloged many significant associations between genetic variants and complex traits. However, most of these findings have unclear biological significance, because they often have small effects and occur in non- ... ...

    Abstract Genome-wide association studies (GWAS) have cataloged many significant associations between genetic variants and complex traits. However, most of these findings have unclear biological significance, because they often have small effects and occur in non-coding regions. Integration of GWAS with gene regulatory networks addresses both issues by aggregating weak genetic signals within regulatory programs. Here we develop a Bayesian framework that integrates GWAS summary statistics with regulatory networks to infer genetic enrichments and associations simultaneously. Our method improves upon existing approaches by explicitly modeling network topology to assess enrichments, and by automatically leveraging enrichments to identify associations. Applying this method to 18 human traits and 38 regulatory networks shows that genetic signals of complex traits are often enriched in interconnections specific to trait-relevant cell types or tissues. Prioritizing variants within enriched networks identifies known and previously undescribed trait-associated genes revealing biological and therapeutic insights.
    MeSH term(s) Algorithms ; Bayes Theorem ; Computer Simulation ; Data Mining ; Gene Regulatory Networks ; Genome, Human ; Genome-Wide Association Study/methods ; Genome-Wide Association Study/statistics & numerical data ; Humans ; Models, Genetic ; Multifactorial Inheritance/genetics ; Polymorphism, Single Nucleotide ; Transcription Factors/genetics
    Chemical Substances Transcription Factors
    Language English
    Publishing date 2021-05-14
    Publishing country England
    Document type Journal Article ; Research Support, N.I.H., Extramural ; Research Support, Non-U.S. Gov't
    ZDB-ID 2553671-0
    ISSN 2041-1723 ; 2041-1723
    ISSN (online) 2041-1723
    ISSN 2041-1723
    DOI 10.1038/s41467-021-22588-0
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  8. Article: Regulatory analysis of single cell multiome gene expression and chromatin accessibility data with scREG

    Duren, Zhana / Chang, Fengge / Naqing, Fnu / Xin, Jingxue / Liu, Qiao / Wong, Wing Hung

    Genome biology. 2022 Dec., v. 23, no. 1

    2022  

    Abstract: Technological development has enabled the profiling of gene expression and chromatin accessibility from the same cell. We develop scREG, a dimension reduction methodology, based on the concept of cis-regulatory potential, for single cell multiome data. ... ...

    Abstract Technological development has enabled the profiling of gene expression and chromatin accessibility from the same cell. We develop scREG, a dimension reduction methodology, based on the concept of cis-regulatory potential, for single cell multiome data. This concept is further used for the construction of subpopulation-specific cis-regulatory networks. The capability of inferring useful regulatory network is demonstrated by the two-fold increment on network inference accuracy compared to the Pearson correlation-based method and the 27-fold enrichment of GWAS variants for inflammatory bowel disease in the cis-regulatory elements. The R package scREG provides comprehensive functions for single cell multiome data analysis.
    Keywords chromatin ; gene expression ; genome ; inflammatory bowel disease
    Language English
    Dates of publication 2022-12
    Size p. 114.
    Publishing place BioMed Central
    Document type Article
    ZDB-ID 2040529-7
    ISSN 1474-760X
    ISSN 1474-760X
    DOI 10.1186/s13059-022-02682-2
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  9. Article ; Online: GuidingNet: revealing transcriptional cofactor and predicting binding for DNA methyltransferase by network regularization.

    Ren, Lixin / Gao, Caixia / Duren, Zhana / Wang, Yong

    Briefings in bioinformatics

    2020  Volume 22, Issue 4

    Abstract: The DNA methyltransferases (DNMTs) (DNMT3A, DNMT3B and DNMT3L) are primarily responsible for the establishment of genomic locus-specific DNA methylation patterns, which play an important role in gene regulation and animal development. However, this ... ...

    Abstract The DNA methyltransferases (DNMTs) (DNMT3A, DNMT3B and DNMT3L) are primarily responsible for the establishment of genomic locus-specific DNA methylation patterns, which play an important role in gene regulation and animal development. However, this important protein family's binding mechanism, i.e. how and where the DNMTs bind to genome, is still missing in most tissues and cell lines. This motivates us to explore DNMTs and TF's cooperation and develop a network regularized logistic regression model, GuidingNet, to predict DNMTs' genome-wide binding by integrating gene expression, chromatin accessibility, sequence and protein-protein interaction data. GuidingNet accurately predicted methylation experimental data validated DNMTs' binding, outperformed single data source based and sparsity regularized methods and performed well in within and across tissue prediction for several DNMTs in human and mouse. Importantly, GuidingNet can reveal transcription cofactors assisting DNMTs for methylation establishment. This provides biological understanding in the DNMTs' binding specificity in different tissues and demonstrate the advantage of network regularization. In addition to DNMTs, GuidingNet achieves good performance for other chromatin regulators' binding. GuidingNet is freely available at https://github.com/AMSSwanglab/GuidingNet.
    MeSH term(s) Animals ; Chromatin/genetics ; Chromatin/metabolism ; DNA (Cytosine-5-)-Methyltransferases/biosynthesis ; DNA (Cytosine-5-)-Methyltransferases/genetics ; DNA Methylation/genetics ; Databases, Genetic ; Gene Expression Regulation, Enzymologic ; Genome, Human ; Humans ; Mice ; Models, Biological ; Protein Interaction Maps ; Transcription Factors/genetics ; Transcription Factors/metabolism
    Chemical Substances Chromatin ; Transcription Factors ; DNA (Cytosine-5-)-Methyltransferases (EC 2.1.1.37)
    Language English
    Publishing date 2020-10-13
    Publishing country England
    Document type Journal Article ; Research Support, Non-U.S. Gov't
    ZDB-ID 2068142-2
    ISSN 1477-4054 ; 1467-5463
    ISSN (online) 1477-4054
    ISSN 1467-5463
    DOI 10.1093/bib/bbaa245
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  10. Article ; Online: Regulatory analysis of single cell multiome gene expression and chromatin accessibility data with scREG.

    Duren, Zhana / Chang, Fengge / Naqing, Fnu / Xin, Jingxue / Liu, Qiao / Wong, Wing Hung

    Genome biology

    2022  Volume 23, Issue 1, Page(s) 114

    Abstract: Technological development has enabled the profiling of gene expression and chromatin accessibility from the same cell. We develop scREG, a dimension reduction methodology, based on the concept of cis-regulatory potential, for single cell multiome data. ... ...

    Abstract Technological development has enabled the profiling of gene expression and chromatin accessibility from the same cell. We develop scREG, a dimension reduction methodology, based on the concept of cis-regulatory potential, for single cell multiome data. This concept is further used for the construction of subpopulation-specific cis-regulatory networks. The capability of inferring useful regulatory network is demonstrated by the two-fold increment on network inference accuracy compared to the Pearson correlation-based method and the 27-fold enrichment of GWAS variants for inflammatory bowel disease in the cis-regulatory elements. The R package scREG provides comprehensive functions for single cell multiome data analysis.
    MeSH term(s) Chromatin/genetics ; Gene Expression ; Gene Regulatory Networks ; Regulatory Sequences, Nucleic Acid ; Single-Cell Analysis
    Chemical Substances Chromatin
    Language English
    Publishing date 2022-05-16
    Publishing country England
    Document type Journal Article ; Research Support, N.I.H., Extramural
    ZDB-ID 2040529-7
    ISSN 1474-760X ; 1474-760X
    ISSN (online) 1474-760X
    ISSN 1474-760X
    DOI 10.1186/s13059-022-02682-2
    Database MEDical Literature Analysis and Retrieval System OnLINE

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