Literature DB >> 28092692

SMiLE-seq identifies binding motifs of single and dimeric transcription factors.

Alina Isakova1,2, Romain Groux1,2,3, Michael Imbeault4, Pernille Rainer1, Daniel Alpern1,2, Riccardo Dainese1,2, Giovanna Ambrosini2,3, Didier Trono4, Philipp Bucher2,3, Bart Deplancke1,2.   

Abstract

Resolving the DNA-binding specificities of transcription factors (TFs) is of critical value for understanding gene regulation. Here, we present a novel, semiautomated protein-DNA interaction characterization technology, selective microfluidics-based ligand enrichment followed by sequencing (SMiLE-seq). SMiLE-seq is neither limited by DNA bait length nor biased toward strong affinity binders; it probes the DNA-binding properties of TFs over a wide affinity range in a fast and cost-effective fashion. We validated SMiLE-seq by analyzing 58 full-length human, mouse, and Drosophila TFs from distinct structural classes. All tested TFs yielded DNA-binding models with predictive power comparable to or greater than that of other in vitro assays. De novo motif discovery on all JUN-FOS heterodimers and several nuclear receptor-TF complexes provided novel insights into partner-specific heterodimer DNA-binding preferences. We also successfully analyzed the DNA-binding properties of uncharacterized human C2H2 zinc-finger proteins and validated several using ChIP-exo.

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Year:  2017        PMID: 28092692     DOI: 10.1038/nmeth.4143

Source DB:  PubMed          Journal:  Nat Methods        ISSN: 1548-7091            Impact factor:   28.547


  38 in total

1.  Technique: SMiLE-seq illuminates transcription factor motifs.

Authors:  Shimona Starling
Journal:  Nat Rev Genet       Date:  2017-01-31       Impact factor: 53.242

2.  Specificity landscapes unmask submaximal binding site preferences of transcription factors.

Authors:  Devesh Bhimsaria; José A Rodríguez-Martínez; Junkun Pan; Daniel Roston; Elif Nihal Korkmaz; Qiang Cui; Parameswaran Ramanathan; Aseem Z Ansari
Journal:  Proc Natl Acad Sci U S A       Date:  2018-10-19       Impact factor: 11.205

Review 3.  Organizing combinatorial transcription factor recruitment at cis-regulatory modules.

Authors:  Julie Dubois-Chevalier; Parisa Mazrooei; Mathieu Lupien; Bart Staels; Philippe Lefebvre; Jérôme Eeckhoute
Journal:  Transcription       Date:  2017-11-28

4.  Network-based approaches that exploit inferred transcription factor activity to analyze the impact of genetic variation on gene expression.

Authors:  Harmen J Bussemaker; Helen C Causton; Mina Fazlollahi; Eunjee Lee; Ivor Muroff
Journal:  Curr Opin Syst Biol       Date:  2017-04-17

5.  A parallelized, automated platform enabling individual or sequential ChIP of histone marks and transcription factors.

Authors:  Riccardo Dainese; Vincent Gardeux; Gerard Llimos; Daniel Alpern; Jia Yuan Jiang; Antonio Carlos Alves Meireles-Filho; Bart Deplancke
Journal:  Proc Natl Acad Sci U S A       Date:  2020-05-27       Impact factor: 11.205

Review 6.  Low-Affinity Binding Sites and the Transcription Factor Specificity Paradox in Eukaryotes.

Authors:  Judith F Kribelbauer; Chaitanya Rastogi; Harmen J Bussemaker; Richard S Mann
Journal:  Annu Rev Cell Dev Biol       Date:  2019-07-05       Impact factor: 13.827

7.  Sphingosine 1-Phosphate Receptor Signaling Establishes AP-1 Gradients to Allow for Retinal Endothelial Cell Specialization.

Authors:  Keisuke Yanagida; Eric Engelbrecht; Colin Niaudet; Bongnam Jung; Konstantin Gaengel; Kristina Holton; Steven Swendeman; Catherine H Liu; Michel V Levesque; Andrew Kuo; Zhongjie Fu; Lois E H Smith; Christer Betsholtz; Timothy Hla
Journal:  Dev Cell       Date:  2020-02-13       Impact factor: 12.270

Review 8.  Toward a Mechanistic Understanding of DNA Methylation Readout by Transcription Factors.

Authors:  Judith F Kribelbauer; Xiang-Jun Lu; Remo Rohs; Richard S Mann; Harmen J Bussemaker
Journal:  J Mol Biol       Date:  2019-11-02       Impact factor: 5.469

9.  Prediction of regulatory motifs from human Chip-sequencing data using a deep learning framework.

Authors:  Jinyu Yang; Anjun Ma; Adam D Hoppe; Cankun Wang; Yang Li; Chi Zhang; Yan Wang; Bingqiang Liu; Qin Ma
Journal:  Nucleic Acids Res       Date:  2019-09-05       Impact factor: 16.971

Review 10.  High throughput approaches to study RNA-protein interactions in vitro.

Authors:  Xuan Ye; Eckhard Jankowsky
Journal:  Methods       Date:  2019-09-05       Impact factor: 3.608

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