Literature DB >> 32421805

IRIS3: integrated cell-type-specific regulon inference server from single-cell RNA-Seq.

Anjun Ma1, Cankun Wang1, Yuzhou Chang1, Faith H Brennan2, Adam McDermaid3,4, Bingqiang Liu5, Chi Zhang6, Phillip G Popovich2, Qin Ma1.   

Abstract

A group of genes controlled as a unit, usually by the same repressor or activator gene, is known as a regulon. The ability to identify active regulons within a specific cell type, i.e., cell-type-specific regulons (CTSR), provides an extraordinary opportunity to pinpoint crucial regulators and target genes responsible for complex diseases. However, the identification of CTSRs from single-cell RNA-Seq (scRNA-Seq) data is computationally challenging. We introduce IRIS3, the first-of-its-kind web server for CTSR inference from scRNA-Seq data for human and mouse. IRIS3 is an easy-to-use server empowered by over 20 functionalities to support comprehensive interpretations and graphical visualizations of identified CTSRs. CTSR data can be used to reliably characterize and distinguish the corresponding cell type from others and can be combined with other computational or experimental analyses for biomedical studies. CTSRs can, therefore, aid in the discovery of major regulatory mechanisms and allow reliable constructions of global transcriptional regulation networks encoded in a specific cell type. The broader impact of IRIS3 includes, but is not limited to, investigation of complex diseases hierarchies and heterogeneity, causal gene regulatory network construction, and drug development. IRIS3 is freely accessible from https://bmbl.bmi.osumc.edu/iris3/ with no login requirement.
© The Author(s) 2020. Published by Oxford University Press on behalf of Nucleic Acids Research.

Entities:  

Year:  2020        PMID: 32421805      PMCID: PMC7319566          DOI: 10.1093/nar/gkaa394

Source DB:  PubMed          Journal:  Nucleic Acids Res        ISSN: 0305-1048            Impact factor:   16.971


  56 in total

1.  Comprehensive Integration of Single-Cell Data.

Authors:  Tim Stuart; Andrew Butler; Paul Hoffman; Christoph Hafemeister; Efthymia Papalexi; William M Mauck; Yuhan Hao; Marlon Stoeckius; Peter Smibert; Rahul Satija
Journal:  Cell       Date:  2019-06-06       Impact factor: 41.582

2.  HOCOMOCO: towards a complete collection of transcription factor binding models for human and mouse via large-scale ChIP-Seq analysis.

Authors:  Ivan V Kulakovskiy; Ilya E Vorontsov; Ivan S Yevshin; Ruslan N Sharipov; Alla D Fedorova; Eugene I Rumynskiy; Yulia A Medvedeva; Arturo Magana-Mora; Vladimir B Bajic; Dmitry A Papatsenko; Fedor A Kolpakov; Vsevolod J Makeev
Journal:  Nucleic Acids Res       Date:  2018-01-04       Impact factor: 16.971

3.  Bias, robustness and scalability in single-cell differential expression analysis.

Authors:  Charlotte Soneson; Mark D Robinson
Journal:  Nat Methods       Date:  2018-02-26       Impact factor: 28.547

4.  QUBIC2: a novel and robust biclustering algorithm for analyses and interpretation of large-scale RNA-Seq data.

Authors:  Juan Xie; Anjun Ma; Yu Zhang; Bingqiang Liu; Sha Cao; Cankun Wang; Jennifer Xu; Chi Zhang; Qin Ma
Journal:  Bioinformatics       Date:  2020-02-15       Impact factor: 6.937

5.  DMINDA 2.0: integrated and systematic views of regulatory DNA motif identification and analyses.

Authors:  Jinyu Yang; Xin Chen; Adam McDermaid; Qin Ma
Journal:  Bioinformatics       Date:  2017-08-15       Impact factor: 6.937

6.  Mapping Gene Expression in Excitatory Neurons during Hippocampal Late-Phase Long-Term Potentiation.

Authors:  Patrick B Chen; Riki Kawaguchi; Charles Blum; Jennifer M Achiro; Giovanni Coppola; Thomas J O'Dell; Kelsey C Martin
Journal:  Front Mol Neurosci       Date:  2017-02-22       Impact factor: 5.639

7.  DrImpute: imputing dropout events in single cell RNA sequencing data.

Authors:  Wuming Gong; Il-Youp Kwak; Pruthvi Pota; Naoko Koyano-Nakagawa; Daniel J Garry
Journal:  BMC Bioinformatics       Date:  2018-06-08       Impact factor: 3.169

8.  iRegulon: from a gene list to a gene regulatory network using large motif and track collections.

Authors:  Rekin's Janky; Annelien Verfaillie; Hana Imrichová; Bram Van de Sande; Laura Standaert; Valerie Christiaens; Gert Hulselmans; Koen Herten; Marina Naval Sanchez; Delphine Potier; Dmitry Svetlichnyy; Zeynep Kalender Atak; Mark Fiers; Jean-Christophe Marine; Stein Aerts
Journal:  PLoS Comput Biol       Date:  2014-07-24       Impact factor: 4.475

9.  Clustergrammer, a web-based heatmap visualization and analysis tool for high-dimensional biological data.

Authors:  Nicolas F Fernandez; Gregory W Gundersen; Adeeb Rahman; Mark L Grimes; Klarisa Rikova; Peter Hornbeck; Avi Ma'ayan
Journal:  Sci Data       Date:  2017-10-10       Impact factor: 6.444

10.  Linking transcriptional and genetic tumor heterogeneity through allele analysis of single-cell RNA-seq data.

Authors:  Jean Fan; Hae-Ock Lee; Soohyun Lee; Da-Eun Ryu; Semin Lee; Catherine Xue; Seok Jin Kim; Kihyun Kim; Nikolaos Barkas; Peter J Park; Woong-Yang Park; Peter V Kharchenko
Journal:  Genome Res       Date:  2018-06-13       Impact factor: 9.043

View more
  9 in total

1.  Androgen conspires with the CD8+ T cell exhaustion program and contributes to sex bias in cancer.

Authors:  Hyunwoo Kwon; Johanna M Schafer; No-Joon Song; Satoshi Kaneko; Anqi Li; Tong Xiao; Anjun Ma; Carter Allen; Komal Das; Lei Zhou; Brian Riesenberg; Yuzhou Chang; Payton Weltge; Maria Velegraki; David Y Oh; Lawrence Fong; Qin Ma; Debasish Sundi; Dongjun Chung; Xue Li; Zihai Li
Journal:  Sci Immunol       Date:  2022-07-01

2.  IRIS-FGM: an integrative single-cell RNA-Seq interpretation system for functional gene module analysis.

Authors:  Yuzhou Chang; Carter Allen; Changlin Wan; Dongjun Chung; Chi Zhang; Zihai Li; Qin Ma
Journal:  Bioinformatics       Date:  2021-02-17       Impact factor: 6.937

3.  Use of scREAD to explore and analyze single-cell and single-nucleus RNA-seq data for Alzheimer's disease.

Authors:  Cankun Wang; Yujia Xiang; Hongjun Fu; Qin Ma
Journal:  STAR Protoc       Date:  2021-05-03

4.  Assessing deep learning methods in cis-regulatory motif finding based on genomic sequencing data.

Authors:  Shuangquan Zhang; Anjun Ma; Jing Zhao; Dong Xu; Qin Ma; Yan Wang
Journal:  Brief Bioinform       Date:  2022-01-17       Impact factor: 13.994

5.  scGNN is a novel graph neural network framework for single-cell RNA-Seq analyses.

Authors:  Juexin Wang; Anjun Ma; Yuzhou Chang; Jianting Gong; Yuexu Jiang; Ren Qi; Cankun Wang; Hongjun Fu; Qin Ma; Dong Xu
Journal:  Nat Commun       Date:  2021-03-25       Impact factor: 17.694

6.  Single nucleus multi-omics regulatory landscape of the murine pituitary.

Authors:  Frederique Ruf-Zamojski; Zidong Zhang; Michel Zamojski; Gregory R Smith; Natalia Mendelev; Hanqing Liu; German Nudelman; Mika Moriwaki; Hanna Pincas; Rosa Gomez Castanon; Venugopalan D Nair; Nitish Seenarine; Mary Anne S Amper; Xiang Zhou; Luisina Ongaro; Chirine Toufaily; Gauthier Schang; Joseph R Nery; Anna Bartlett; Andrew Aldridge; Nimisha Jain; Gwen V Childs; Olga G Troyanskaya; Joseph R Ecker; Judith L Turgeon; Corrine K Welt; Daniel J Bernard; Stuart C Sealfon
Journal:  Nat Commun       Date:  2021-05-11       Impact factor: 14.919

Review 7.  Single-cell RNA sequencing technologies and applications: A brief overview.

Authors:  Dragomirka Jovic; Xue Liang; Hua Zeng; Lin Lin; Fengping Xu; Yonglun Luo
Journal:  Clin Transl Med       Date:  2022-03

8.  Microglia coordinate cellular interactions during spinal cord repair in mice.

Authors:  Faith H Brennan; Yang Li; Cankun Wang; Anjun Ma; Qi Guo; Yi Li; Nicole Pukos; Warren A Campbell; Kristina G Witcher; Zhen Guan; Kristina A Kigerl; Jodie C E Hall; Jonathan P Godbout; Andy J Fischer; Dana M McTigue; Zhigang He; Qin Ma; Phillip G Popovich
Journal:  Nat Commun       Date:  2022-07-14       Impact factor: 17.694

9.  scREAD: A Single-Cell RNA-Seq Database for Alzheimer's Disease.

Authors:  Jing Jiang; Cankun Wang; Ren Qi; Hongjun Fu; Qin Ma
Journal:  iScience       Date:  2020-11-05
  9 in total

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