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      Determining promoter location based on DNA structure first-principles calculations

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      Author(s): 1 , 2 , 3 , 1 , 2 ,   4 , 5 , 1 , 4 , 6 ,
      Publication date (Electronic):
      Journal: Genome Biology
      Publisher: BioMed Central

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          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          A new method is presented which predicts promoter regions based on atomistic molecular dynamics simulations of small oligonucleotides, without requiring information on sequence conservation or features.

          Abstract

          A new method for the prediction of promoter regions based on atomic molecular dynamics simulations of small oligonucleotides has been developed. The method works independently of gene structure conservation and orthology and of the presence of detectable sequence features. Results obtained with our method confirm the existence of a hidden physical code that modulates genome expression.

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          Most cited references55

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          The transcriptional landscape of the mammalian genome.

          P Carninci, T Kasukawa, S. Katayama … (2005)
          This study describes comprehensive polling of transcription start and termination sites and analysis of previously unidentified full-length complementary DNAs derived from the mouse genome. We identify the 5' and 3' boundaries of 181,047 transcripts with extensive variation in transcripts arising from alternative promoter usage, splicing, and polyadenylation. There are 16,247 new mouse protein-coding transcripts, including 5154 encoding previously unidentified proteins. Genomic mapping of the transcriptome reveals transcriptional forests, with overlapping transcription on both strands, separated by deserts in which few transcripts are observed. The data provide a comprehensive platform for the comparative analysis of mammalian transcriptional regulation in differentiation and development.
          Article 0 comments Cited 1280 times – based on 0 reviews     Review now
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            Genome-wide analysis of mammalian promoter architecture and evolution.

            Piero Carninci, Albin Sandelin, Boris Lenhard … (2006)
            Mammalian promoters can be separated into two classes, conserved TATA box-enriched promoters, which initiate at a well-defined site, and more plastic, broad and evolvable CpG-rich promoters. We have sequenced tags corresponding to several hundred thousand transcription start sites (TSSs) in the mouse and human genomes, allowing precise analysis of the sequence architecture and evolution of distinct promoter classes. Different tissues and families of genes differentially use distinct types of promoters. Our tagging methods allow quantitative analysis of promoter usage in different tissues and show that differentially regulated alternative TSSs are a common feature in protein-coding genes and commonly generate alternative N termini. Among the TSSs, we identified new start sites associated with the majority of exons and with 3' UTRs. These data permit genome-scale identification of tissue-specific promoters and analysis of the cis-acting elements associated with them.
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              Application of a time-delay neural network to promoter annotation in the Drosophila melanogaster genome.

              Martin Reese (2001)
              Computational methods for automated genome annotation are critical to understanding and interpreting the bewildering mass of genomic sequence data presently being generated and released. A neural network model of the structural and compositional properties of a eukaryotic core promoter region has been developed and its application for analysis of the Drosophila melanogaster genome is presented. The model uses a time-delay architecture, a special case of a feed-forward neural network. The structure of this model allows for variable spacing between functional binding sites, which is known to play a key role in the transcription initiation process. Application of this model to a test set of core promoters not only gave better discrimination of potential promoter sites than previous statistical or neural network models, but also revealed indirectly subtle properties of the transcription initiation signal. When tested in the Adh region of 2.9 Mbases of the Drosophila genome, the neural network for promoter prediction (NNPP) program that incorporates the time-delay neural network model gives a recognition rate of 75% (69/92) with a false positive rate of 1/547 bases. The present work can be regarded as one of the first intensive studies that applies novel gene regulation technologies to the identification of the complex gene regulation sites in the genome of Drosophila melanogaster.
              Article 0 comments Cited 262 times – based on 0 reviews     Review now
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                Author and article information

                Journal
                Journal ID (nlm-ta): Genome Biol
                Title: Genome Biology
                Publisher: BioMed Central
                ISSN (Print): 1465-6906
                ISSN (Electronic): 1465-6914
                Publication date (Print): 2007
                Publication date (Electronic): 11 December 2007
                Volume: 8
                Issue: 12
                Page: R263
                Affiliations
                [1 ]Institute for Research in Biomedicine, Parc Científic de Barcelona, Josep Samitier, Barcelona 08028, Spain
                [2 ]Departament de Bioquímica i Biología Molecular, Facultat de Biología, Avgda Diagonal, Barcelona 08028, Spain
                [3 ]Grup de recerca en Bioinformàtica i Estadística Mèdica, Departament de Biologia de Sistemes, Universitat de Vic. Laura, 13 08500 VIC, Spain
                [4 ]Computational Biology Program, Barcelona Supercomputer Center, Jordi Girona, Edifici Torre Girona, Barcelona 08028, Spain
                [5 ]Institut Català per la Recerca i Estudis Avançats (ICREA), Passeig Lluís Companys, 23. Barcelona 08010, Spain
                [6 ]Instituto Nacional de Bioinformática, Structural Bioinformatics Unit, Parc Cientific de Barcelona, Josep Samitier, Barcelona 08028, USA
                Article
                Publisher ID: gb-2007-8-12-r263
                DOI: 10.1186/gb-2007-8-12-r263
                PMC ID: 2246265
                PubMed ID: 18072969
                SO-VID: cfd309f3-130f-4914-ab4e-c13e89a2efce
                Copyright © Copyright © 2007 Goñi et al.; licensee BioMed Central Ltd.
                License:

                This is an open access article distributed under the terms of the Creative Commons Attribution License ( http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                Date received : 12 September 2007
                Date revision received : 24 November 2007
                Date accepted : 11 December 2007
                Categories
                Subject: Method

                ScienceOpen disciplines: Genetics
                Data availability:
                ScienceOpen disciplines: Genetics

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