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      MPRAnator: a web-based tool for the design of massively parallel reporter assay experiments

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          Abstract

          Motivation: With the rapid advances in DNA synthesis and sequencing technologies and the continuing decline in the associated costs, high-throughput experiments can be performed to investigate the regulatory role of thousands of oligonucleotide sequences simultaneously. Nevertheless, designing high-throughput reporter assay experiments such as massively parallel reporter assays (MPRAs) and similar methods remains challenging.

          Results: We introduce MPRAnator, a set of tools that facilitate rapid design of MPRA experiments. With MPRA Motif design, a set of variables provides fine control of how motifs are placed into sequences, thereby allowing the investigation of the rules that govern transcription factor (TF) occupancy. MPRA single-nucleotide polymorphism design can be used to systematically examine the functional effects of single or combinations of single-nucleotide polymorphisms at regulatory sequences. Finally, the Transmutation tool allows for the design of negative controls by permitting scrambling, reversing, complementing or introducing multiple random mutations in the input sequences or motifs.

          Availability and implementation: MPRAnator tool set is implemented in Python, Perl and Javascript and is freely available at www.genomegeek.com and www.sanger.ac.uk/science/tools/mpranator. The source code is available on www.github.com/hemberg-lab/MPRAnator/ under the MIT license. The REST API allows programmatic access to MPRAnator using simple URLs.

          Contact: igs@ 123456sanger.ac.uk or mh26@ 123456sanger.ac.uk

          Supplementary information: Supplementary data are available at Bioinformatics online.

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          Most cited references2

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          Probing the effect of promoters on noise in gene expression using thousands of designed sequences

          Genetically identical cells exhibit large variability (noise) in gene expression, with important consequences for cellular function. Although the amount of noise decreases with and is thus partly determined by the mean expression level, the extent to which different promoter sequences can deviate away from this trend is not fully known. Here, we present a high-throughput method for measuring promoter-driven noise for thousands of designed synthetic promoters in parallel. We use it to investigate how promoters encode different noise levels and find that the noise levels of promoters with similar mean expression levels can vary more than one order of magnitude, with nucleosome-disfavoring sequences resulting in lower noise and more transcription factor binding sites resulting in higher noise. We propose a kinetic model of gene expression that takes into account the nonspecific DNA binding and one-dimensional sliding along the DNA, which occurs when transcription factors search for their target sites. We show that this assumption can improve the prediction of the mean-independent component of expression noise for our designed promoter sequences, suggesting that a transcription factor target search may affect gene expression noise. Consistent with our findings in designed promoters, we find that binding-site multiplicity in native promoters is associated with higher expression noise. Overall, our results demonstrate that small changes in promoter DNA sequence can tune noise levels in a manner that is predictable and partly decoupled from effects on the mean expression levels. These insights may assist in designing promoters with desired noise levels.
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            High throughput technologies for the functional discovery of mammalian enhancers: new approaches for understanding transcriptional regulatory network dynamics.

            Completion of the human and mouse genomes has inspired new initiatives to obtain a global understanding of the functional regulatory networks governing gene expression. Enhancers are primary regulatory DNA elements determining precise spatio- and temporal gene expression patterns, but the observation that they can function at any distance from the gene(s) they regulate has made their genome-wide characterization challenging. Since traditional, single reporter approaches would be unable to accomplish this enormous task, high throughput technologies for mapping chromatin features associated with enhancers have emerged as an effective surrogate for enhancer discovery. However, the last few years have witnessed the development of several new innovative approaches that can effectively screen for and discover enhancers based on their functional activation of transcription using massively parallel reporter systems. In addition to their application for genome annotation, these new high throughput functional approaches open new and exciting avenues for modeling gene regulatory networks.
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              Author and article information

              Journal
              Bioinformatics
              Bioinformatics
              bioinformatics
              bioinfo
              Bioinformatics
              Oxford University Press
              1367-4803
              1367-4811
              01 January 2017
              06 September 2016
              06 September 2016
              : 33
              : 1
              : 137-138
              Affiliations
              [1 ]Department of Computational Genomics, Wellcome Trust Sanger Institute, Wellcome Genome Campus, Hinxton, CB10 1SA, UK
              [2 ]Department of Life Sciences, Imperial College London, London, SW7 2AZ, UK
              [3 ]Department of Genetics, Harvard Medical School, Boston, MA 02135, USA
              Author notes
              [* ]To whom correspondence should be addressed.

              Associate Editor: Janet Kelso

              Article
              btw584
              10.1093/bioinformatics/btw584
              5198521
              27605100
              f996017e-6058-465b-93e2-eadcd103a01a
              © The Author 2016. Published by Oxford University Press.

              This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( http://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.

              History
              : 20 April 2016
              : 13 July 2016
              : 2 September 2016
              Page count
              Pages: 2
              Categories
              Applications Notes
              Gene Expression

              Bioinformatics & Computational biology
              Bioinformatics & Computational biology

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