Literature DB >> 16899491

Identification of regulatory modules by co-clustering latent variable models: stem cell differentiation.

Je-Gun Joung1, Dongho Shin, Rho Hyun Seong, Byoung-Tak Zhang.   

Abstract

MOTIVATION: An important issue in stem cell biology is to understand how to direct differentiation towards a specific cell type. To elucidate the mechanism, previous studies have focused on identifying the responsible gene regulators, which have, however, failed to provide a systemic view of regulatory modules. To obtain a unified description of the regulatory modules, we characterized major stem cell species by employing a co-clustering latent variable model (LVM). The LVM-based method allowed us to elucidate the cell type-specific transcription factors, using genomic sequences as well as expression profiles.
RESULTS: We used a list of genes enriched in each of 21 stem cell subpopulations, and their upstream genomic sequences. The LVM-based study allowed us to uncover the regulatory modules for each stem cell cluster, e.g. GABP and E2F for the proliferation phase, and Ap2alpha and Ap2gamma for the quiescence phase. Furthermore, the identities of the stem cell clusters were well revealed by the constituent genes that were directly targeted by the modules. Consequently, our analytical framework was demonstrated to be useful through a detailed case study of stem cell differentiation and can be applied to problems with similar characteristics.

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Year:  2006        PMID: 16899491     DOI: 10.1093/bioinformatics/btl343

Source DB:  PubMed          Journal:  Bioinformatics        ISSN: 1367-4803            Impact factor:   6.937


  10 in total

1.  Transcription factors expressed in olfactory bulb local progenitor cells revealed by genome-wide transcriptome profiling.

Authors:  Gordon R O Campbell; Ariane Baudhuin; Karen Vranizan; John Ngai
Journal:  Mol Cell Neurosci       Date:  2010-12-29       Impact factor: 4.314

2.  The grapevine expression atlas reveals a deep transcriptome shift driving the entire plant into a maturation program.

Authors:  Marianna Fasoli; Silvia Dal Santo; Sara Zenoni; Giovanni Battista Tornielli; Lorenzo Farina; Anita Zamboni; Andrea Porceddu; Luca Venturini; Manuele Bicego; Vittorio Murino; Alberto Ferrarini; Massimo Delledonne; Mario Pezzotti
Journal:  Plant Cell       Date:  2012-09-04       Impact factor: 11.277

3.  Motif-guided sparse decomposition of gene expression data for regulatory module identification.

Authors:  Ting Gong; Jianhua Xuan; Li Chen; Rebecca B Riggins; Huai Li; Eric P Hoffman; Robert Clarke; Yue Wang
Journal:  BMC Bioinformatics       Date:  2011-03-22       Impact factor: 3.169

4.  Decreased expression of BRCA1 in SK-BR-3 cells is the result of aberrant activation of the GABP Beta promoter by an NRF-1-containing complex.

Authors:  Crista Thompson; Gwen MacDonald; Christopher R Mueller
Journal:  Mol Cancer       Date:  2011-05-24       Impact factor: 27.401

5.  Lineage-based identification of cellular states and expression programs.

Authors:  Tatsunori Hashimoto; Tommi Jaakkola; Richard Sherwood; Esteban O Mazzoni; Hynek Wichterle; David Gifford
Journal:  Bioinformatics       Date:  2012-06-15       Impact factor: 6.937

6.  Oncogenic ETS fusions deregulate E2F3 target genes in Ewing sarcoma and prostate cancer.

Authors:  Sven Bilke; Raphaela Schwentner; Fan Yang; Maximilian Kauer; Gunhild Jug; Robert L Walker; Sean Davis; Yuelin J Zhu; Marbin Pineda; Paul S Meltzer; Heinrich Kovar
Journal:  Genome Res       Date:  2013-08-12       Impact factor: 9.043

7.  Unraveling transcriptional regulatory programs by integrative analysis of microarray and transcription factor binding data.

Authors:  Huai Li; Ming Zhan
Journal:  Bioinformatics       Date:  2008-06-27       Impact factor: 6.937

8.  Knowledge-guided multi-scale independent component analysis for biomarker identification.

Authors:  Li Chen; Jianhua Xuan; Chen Wang; Ie-Ming Shih; Yue Wang; Zhen Zhang; Eric Hoffman; Robert Clarke
Journal:  BMC Bioinformatics       Date:  2008-10-06       Impact factor: 3.169

9.  Robust Co-clustering to Discover Toxicogenomic Biomarkers and Their Regulatory Doses of Chemical Compounds Using Logistic Probabilistic Hidden Variable Model.

Authors:  Mohammad Nazmol Hasan; Md Masud Rana; Anjuman Ara Begum; Moizur Rahman; Md Nurul Haque Mollah
Journal:  Front Genet       Date:  2018-11-01       Impact factor: 4.599

10.  Computational Model for Predicting the Relationship Between Micro-RNAs and Their Target Messenger RNAs in Breast and Colon Cancers.

Authors:  Shinuk Kim
Journal:  Cancer Inform       Date:  2018-07-02
  10 in total

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