Literature DB >> 18651798

Design of compact, universal DNA microarrays for protein binding microarray experiments.

Anthony A Philippakis1, Aaron M Qureshi, Michael F Berger, Martha L Bulyk.   

Abstract

Our group has recently developed a compact, universal protein binding microarray (PBM) that can be used to determine the binding preferences of transcription factors (TFs). This design represents all possible sequence variants of a given length k (i.e., all k-mers) on a single array, allowing a complete characterization of the binding specificities of a given TF. Here, we present the mathematical foundations of this design based on de Bruijn sequences generated by linear feedback shift registers. We show that these sequences represent the maximum number of variants for any given set of array dimensions (i.e., number of spots and spot lengths), while also exhibiting desirable pseudo-randomness properties. Moreover, de Bruijn sequences can be selected that represent gapped sequence patterns, further increasing the coverage of the array. This design yields a powerful experimental platform that allows the binding preferences of TFs to be determined with unprecedented resolution.

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Year:  2008        PMID: 18651798      PMCID: PMC3203512          DOI: 10.1089/cmb.2007.0114

Source DB:  PubMed          Journal:  J Comput Biol        ISSN: 1066-5277            Impact factor:   1.479


  17 in total

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6.  Quantitative high-throughput analysis of transcription factor binding specificities.

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7.  An Eulerian path approach to local multiple alignment for DNA sequences.

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8.  Defining the sequence-recognition profile of DNA-binding molecules.

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Review 10.  Computational prediction of transcription-factor binding site locations.

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Journal:  Genome Biol       Date:  2003-12-23       Impact factor: 13.583

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  29 in total

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4.  Rapid and systematic analysis of the RNA recognition specificities of RNA-binding proteins.

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5.  Optimized Sequence Library Design for Efficient In Vitro Interaction Mapping.

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6.  Joker de Bruijn: Covering k-Mers Using Joker Characters.

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Journal:  J Comput Biol       Date:  2018-08-17       Impact factor: 1.479

7.  Bayesian hierarchical model of protein-binding microarray k-mer data reduces noise and identifies transcription factor subclasses and preferred k-mers.

Authors:  Bo Jiang; Jun S Liu; Martha L Bulyk
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8.  5-Hydroxymethylcytosine in E-box motifs ACAT|GTG and ACAC|GTG increases DNA-binding of the B-HLH transcription factor TCF4.

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9.  Efficient Design of Compact Unstructured RNA Libraries Covering All k-mers.

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Journal:  J Comput Biol       Date:  2015-12-29       Impact factor: 1.479

10.  Evaluation of methods for modeling transcription factor sequence specificity.

Authors:  Matthew T Weirauch; Atina Cote; Raquel Norel; Matti Annala; Yue Zhao; Todd R Riley; Julio Saez-Rodriguez; Thomas Cokelaer; Anastasia Vedenko; Shaheynoor Talukder; Harmen J Bussemaker; Quaid D Morris; Martha L Bulyk; Gustavo Stolovitzky; Timothy R Hughes
Journal:  Nat Biotechnol       Date:  2013-01-27       Impact factor: 54.908

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