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  1. Article ; Online: hichipper: a preprocessing pipeline for calling DNA loops from HiChIP data.

    Lareau, Caleb A / Aryee, Martin J

    Nature methods

    2018  Volume 15, Issue 3, Page(s) 155–156

    MeSH term(s) DNA ; Software
    Chemical Substances DNA (9007-49-2)
    Language English
    Publishing date 2018-02-13
    Publishing country United States
    Document type Journal Article ; Research Support, Non-U.S. Gov't ; Research Support, U.S. Gov't, Non-P.H.S. ; Comment
    ZDB-ID 2169522-2
    ISSN 1548-7105 ; 1548-7091
    ISSN (online) 1548-7105
    ISSN 1548-7091
    DOI 10.1038/nmeth.4583
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  2. Article ; Online: diffloop: a computational framework for identifying and analyzing differential DNA loops from sequencing data.

    Lareau, Caleb A / Aryee, Martin J

    Bioinformatics (Oxford, England)

    2017  Volume 34, Issue 4, Page(s) 672–674

    Abstract: Summary: The 3D architecture of DNA within the nucleus is a key determinant of interactions between genes, regulatory elements, and transcriptional machinery. As a result, differences in DNA looping structure are associated with variation in gene ... ...

    Abstract Summary: The 3D architecture of DNA within the nucleus is a key determinant of interactions between genes, regulatory elements, and transcriptional machinery. As a result, differences in DNA looping structure are associated with variation in gene expression and cell state. To systematically assess changes in DNA looping architecture between samples, we introduce diffloop, an R/Bioconductor package that provides a suite of functions for the quality control, statistical testing, annotation, and visualization of DNA loops. We demonstrate this functionality by detecting differences between ENCODE ChIA-PET samples and relate looping to variability in epigenetic state.
    Availability and implementation: Diffloop is implemented as an R/Bioconductor package available at https://bioconductor.org/packages/release/bioc/html/diffloop.html.
    Contact: aryee.martin@mgh.harvard.edu.
    Supplementary information: Supplementary data are available at Bioinformatics online.
    MeSH term(s) Chromatin/metabolism ; Chromatin/ultrastructure ; Chromatin Immunoprecipitation/methods ; Epigenomics ; Humans ; K562 Cells ; MCF-7 Cells ; Molecular Sequence Annotation/methods ; Neoplasms/genetics ; Sequence Analysis, DNA/methods ; Software
    Chemical Substances Chromatin
    Language English
    Publishing date 2017-10-12
    Publishing country England
    Document type Journal Article ; Research Support, Non-U.S. Gov't ; Research Support, U.S. Gov't, Non-P.H.S.
    ZDB-ID 1422668-6
    ISSN 1367-4811 ; 1367-4803
    ISSN (online) 1367-4811
    ISSN 1367-4803
    DOI 10.1093/bioinformatics/btx623
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  3. Article ; Online: Reply.

    Bhan, Irun / Aryee, Martin / Ting, David T

    Gastroenterology

    2019  Volume 156, Issue 6, Page(s) 1933–1934

    MeSH term(s) Chimerism ; Epithelial Cells ; Humans ; Liquid Biopsy ; Liver Diseases
    Language English
    Publishing date 2019-03-27
    Publishing country United States
    Document type Letter ; Comment
    ZDB-ID 80112-4
    ISSN 1528-0012 ; 0016-5085
    ISSN (online) 1528-0012
    ISSN 0016-5085
    DOI 10.1053/j.gastro.2019.03.042
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  4. Article ; Online: Preprocessing and Computational Analysis of Single-Cell Epigenomic Datasets.

    Lareau, Caleb / Kangeyan, Divy / Aryee, Martin J

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

    2019  Volume 1935, Page(s) 187–202

    Abstract: Recent technological developments have enabled the characterization of the epigenetic landscape of single cells across a range of tissues in normal and diseased states and under various biological and chemical perturbations. While analysis of these ... ...

    Abstract Recent technological developments have enabled the characterization of the epigenetic landscape of single cells across a range of tissues in normal and diseased states and under various biological and chemical perturbations. While analysis of these profiles resembles methods from single-cell transcriptomic studies, unique challenges are associated with bioinformatics processing of single-cell epigenetic data, including a much larger (10-1,000×) feature set and significantly greater sparsity, requiring customized solutions. Here, we discuss the essentials of the computational methodology required for analyzing common single-cell epigenomic measurements for DNA methylation using bisulfite sequencing and open chromatin using ATAC-Seq.
    MeSH term(s) Animals ; Chromatin/genetics ; Computational Biology/methods ; DNA Methylation/genetics ; Epigenesis, Genetic/genetics ; Epigenomics/methods ; High-Throughput Nucleotide Sequencing/methods ; Humans ; Mice ; Sequence Analysis, DNA/methods ; Single-Cell Analysis/methods ; Software
    Chemical Substances Chromatin
    Language English
    Publishing date 2019-02-13
    Publishing country United States
    Document type Journal Article
    ISSN 1940-6029
    ISSN (online) 1940-6029
    DOI 10.1007/978-1-4939-9057-3_13
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  5. Article ; Online: Author Correction: Feature selection and dimension reduction for single-cell RNA-Seq based on a multinomial model.

    Townes, F William / Hicks, Stephanie C / Aryee, Martin J / Irizarry, Rafael A

    Genome biology

    2020  Volume 21, Issue 1, Page(s) 179

    Abstract: An amendment to this paper has been published and can be accessed via the original article. ...

    Abstract An amendment to this paper has been published and can be accessed via the original article.
    Language English
    Publishing date 2020-07-22
    Publishing country England
    Document type Published Erratum
    ZDB-ID 2040529-7
    ISSN 1474-760X ; 1465-6906
    ISSN (online) 1474-760X
    ISSN 1465-6906
    DOI 10.1186/s13059-020-02109-w
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  6. Article: diffloop: a computational framework for identifying and analyzing differential DNA loops from sequencing data

    Lareau, Caleb A / Aryee, Martin J / Berger, Bonnie

    Bioinformatics. 2018 Feb. 15, v. 34, no. 4

    2018  

    Abstract: The 3D architecture of DNA within the nucleus is a key determinant of interactions between genes, regulatory elements, and transcriptional machinery. As a result, differences in DNA looping structure are associated with variation in gene expression and ... ...

    Abstract The 3D architecture of DNA within the nucleus is a key determinant of interactions between genes, regulatory elements, and transcriptional machinery. As a result, differences in DNA looping structure are associated with variation in gene expression and cell state. To systematically assess changes in DNA looping architecture between samples, we introduce diffloop, an R/Bioconductor package that provides a suite of functions for the quality control, statistical testing, annotation, and visualization of DNA loops. We demonstrate this functionality by detecting differences between ENCODE ChIA-PET samples and relate looping to variability in epigenetic state. Diffloop is implemented as an R/Bioconductor package available at https://bioconductor.org/packages/release/bioc/html/diffloop.html Supplementary data are available at Bioinformatics online.
    Keywords DNA ; bioinformatics ; epigenetics ; gene expression ; genes ; quality control ; regulatory sequences ; transcription (genetics)
    Language English
    Dates of publication 2018-0215
    Size p. 672-674.
    Publishing place Oxford University Press
    Document type Article
    ZDB-ID 1468345-3
    ISSN 1460-2059 ; 1367-4811 ; 1367-4803
    ISSN (online) 1460-2059 ; 1367-4811
    ISSN 1367-4803
    DOI 10.1093/bioinformatics/btx623
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  7. Article: Author Correction: Feature selection and dimension reduction for single-cell RNA-Seq based on a multinomial model

    Townes, F. William / Hicks, Stephanie C / Aryee, Martin J / Irizarry, Rafael A

    Genome biology. 2020 Dec., v. 21, no. 1

    2020  

    Abstract: An amendment to this paper has been published and can be accessed via the original article. ...

    Abstract An amendment to this paper has been published and can be accessed via the original article.
    Keywords RNA ; sequence analysis ; statistical models
    Language English
    Dates of publication 2020-12
    Size p. 179.
    Publishing place BioMed Central
    Document type Article
    Note Published Erratum
    ZDB-ID 2040529-7
    ISSN 1474-760X ; 1465-6906
    ISSN (online) 1474-760X
    ISSN 1465-6906
    DOI 10.1186/s13059-020-02109-w
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  8. Article ; Online: Feature selection and dimension reduction for single-cell RNA-Seq based on a multinomial model.

    Townes, F William / Hicks, Stephanie C / Aryee, Martin J / Irizarry, Rafael A

    Genome biology

    2019  Volume 20, Issue 1, Page(s) 295

    Abstract: Single-cell RNA-Seq (scRNA-Seq) profiles gene expression of individual cells. Recent scRNA-Seq datasets have incorporated unique molecular identifiers (UMIs). Using negative controls, we show UMI counts follow multinomial sampling with no zero inflation. ...

    Abstract Single-cell RNA-Seq (scRNA-Seq) profiles gene expression of individual cells. Recent scRNA-Seq datasets have incorporated unique molecular identifiers (UMIs). Using negative controls, we show UMI counts follow multinomial sampling with no zero inflation. Current normalization procedures such as log of counts per million and feature selection by highly variable genes produce false variability in dimension reduction. We propose simple multinomial methods, including generalized principal component analysis (GLM-PCA) for non-normal distributions, and feature selection using deviance. These methods outperform the current practice in a downstream clustering assessment using ground truth datasets.
    MeSH term(s) Models, Statistical ; Sequence Analysis, RNA ; Single-Cell Analysis
    Language English
    Publishing date 2019-12-23
    Publishing country England
    Document type Journal Article ; Research Support, N.I.H., Extramural ; Research Support, Non-U.S. Gov't ; Validation Study
    ZDB-ID 2040529-7
    ISSN 1474-760X ; 1474-760X
    ISSN (online) 1474-760X
    ISSN 1474-760X
    DOI 10.1186/s13059-019-1861-6
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  9. Article: High-resolution CTCF footprinting reveals impact of chromatin state on cohesin extrusion dynamics.

    Sept, Corriene E / Tak, Y Esther / Cerda-Smith, Christian G / Hutchinson, Haley M / Goel, Viraat / Blanchette, Marco / Bhakta, Mital S / Hansen, Anders S / Joung, J Keith / Johnstone, Sarah / Eyler, Christine E / Aryee, Martin J

    bioRxiv : the preprint server for biology

    2023  

    Abstract: DNA looping is vital for establishing many enhancer-promoter interactions. While CTCF is known to anchor many cohesin-mediated loops, the looped chromatin fiber appears to predominantly exist in a poorly characterized actively extruding state. To better ... ...

    Abstract DNA looping is vital for establishing many enhancer-promoter interactions. While CTCF is known to anchor many cohesin-mediated loops, the looped chromatin fiber appears to predominantly exist in a poorly characterized actively extruding state. To better characterize extruding chromatin loop structures, we used CTCF MNase HiChIP data to determine both CTCF binding at high resolution and 3D contact information. Here we present
    Language English
    Publishing date 2023-10-28
    Publishing country United States
    Document type Preprint
    DOI 10.1101/2023.10.20.563340
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  10. Article ; Online: Spatial transcriptomics reveals distinct tissue niches linked with steroid responsiveness in acute gastrointestinal GVHD.

    Patel, Bidish K / Raabe, Michael J / Lang, Evan R / Song, Yuhui / Lu, Chenyue / Deshpande, Vikram / Nieman, Linda T / Aryee, Martin J / Chen, Yi-Bin / Ting, David T / DeFilipp, Zachariah

    Blood

    2023  Volume 142, Issue 21, Page(s) 1831–1844

    Abstract: Severe acute graft-versus-host disease (aGVHD) is associated with significant mortality and morbidity, especially in steroid-resistant (SR) cases. Spatial transcriptomic technology can elucidate tissue-based interactions in vivo and possibly identify ... ...

    Abstract Severe acute graft-versus-host disease (aGVHD) is associated with significant mortality and morbidity, especially in steroid-resistant (SR) cases. Spatial transcriptomic technology can elucidate tissue-based interactions in vivo and possibly identify predictors of treatment response. Tissue sections from 32 treatment-naïve patients with biopsy-confirmed lower gastrointestinal (GI) aGVHD were obtained. The GeoMx digital spatial profiler was used to capture transcriptome profiles of >18 000 genes from different foci of immune infiltrates, colonic epithelium, and vascular endothelium. Each tissue compartment sampled showed 2 distinct clusters that were analyzed for differential expression and spatially resolved correlation of gene signatures. Classic cell-mediated immunity signatures, normal differentiated epithelial cells, and inflamed vasculature dominated foci sampled from steroid-sensitive cases. In contrast, a neutrophil predominant noncanonical inflammation with regenerative epithelial cells and some indication of angiogenic endothelial response was overrepresented in areas from SR cases. Evaluation of potential prognostic biomarkers identified ubiquitin specific peptidase 17-like (USP17L) family of genes as being differentially expressed in immune cells from patients with worsened survival. In summary, we demonstrate distinct tissue niches with unique gene expression signatures within lower GI tissue from patients with aGVHD and provide evidence of a potential prognostic biomarker.
    MeSH term(s) Humans ; Transcriptome ; Graft vs Host Disease/drug therapy ; Graft vs Host Disease/genetics ; Immunity, Cellular ; Steroids/therapeutic use ; Intestinal Mucosa ; Acute Disease ; Hematopoietic Stem Cell Transplantation
    Chemical Substances Steroids
    Language English
    Publishing date 2023-09-11
    Publishing country United States
    Document type Journal Article ; Research Support, Non-U.S. Gov't ; Research Support, N.I.H., Extramural
    ZDB-ID 80069-7
    ISSN 1528-0020 ; 0006-4971
    ISSN (online) 1528-0020
    ISSN 0006-4971
    DOI 10.1182/blood.2023020644
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