Literature DB >> 17947329

ppdb: a plant promoter database.

Yoshiharu Y Yamamoto1, Junichi Obokata.   

Abstract

ppdb (http://www.ppdb.gene.nagoya-u.ac.jp) is a plant promoter database that provides promoter annotation of Arabidopsis and rice. The database contains information on promoter structures, transcription start sites (TSSs) that have been identified from full-length cDNA clones and also a vast amount of TSS tag data. In ppdb, the promoter structures are determined by sets of promoter elements identified by a position-sensitive extraction method called local distribution of short sequences (LDSS). By using this database, the core promoter structure, the presence of regulatory elements and the distribution of TSS clusters can be identified. Although no differentiation of promoter architecture among plant species has been reported, there is some divergence of utilized sequences for promoter elements. Therefore, ppdb is based on species-specific sets of promoter elements, rather than on general motifs for multiple species. Each regulatory sequence is hyperlinked to literary information, a PLACE entry served by a plant cis-element database, and a list of promoters containing the regulatory sequence.

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Year:  2007        PMID: 17947329      PMCID: PMC2238996          DOI: 10.1093/nar/gkm785

Source DB:  PubMed          Journal:  Nucleic Acids Res        ISSN: 0305-1048            Impact factor:   16.971


BACKGROUND

A promoter database can be generated from a combination of genome sequence, information of promoter positions and a list of cis-regulatory elements. Currently a major restriction on the quality of a promoter database is our limited knowledge of cis-elements. There are several established genome-wide plant promoter databases available today (RARGE: (1), http://www.rarge.gsc.riken.jp/; AGRIS: (2), http://www.arabidopsis.med.ohio-state.edu/; AthaMap: (3), http://www.athamap.de/), and which are based on cis-regulatory sequences from PlantCARE [(4), http://www.bioinformatics.psb.ugent.be/webtools/plantcare/html/], PLACE [(5), http://www.dna.affrc.go.jp/PLACE/] or TRANSFAC [(6), http://www.gene-regulation.com/pub/databases.html]. These three promoter databases focus on cis-regulatory elements rather than core promoter structure, aiming to reveal the regulatory machinery that give the expression profiles. Unfortunately, these databases provide information only for Arabidopsis, and there are no genome-wide plant promoter databases available for other plant species. Local distribution of short sequences (LDSS) analysis is a method to extract promoter constituents by genome-wide statistical analysis (7,8). We have applied this method to the Arabidopsis and rice genomes, and identified 1000 octamer sequences per genome as LDSS-positive promoter elements (8). According to their distribution profiles, the identified octamers have been classified into regulatory element group (REG), TATA box and Y Patch as three major promoter element groups. REG is a direction-insensitive element that is preferentially found around −100 bp relative to the major transcription start site (TSS), and contains many established cis-regulatory sequences. Y Patch is a direction-sensitive plant core promoter element that appears around TSS. We found that utilized sequences of all three groups, including TATA element, are moderately differentiated between Arabidopsis and rice, demonstrating the importance of individual preparation of promoter elements for each genome. The large collection of extracted promoter elements can be utilized as a tool to reveal precise promoter architecture. Therefore, here we present a novel searchable ppdb database, based on the LDSS-positive elements. Utilization of a genome-specific set of promoter elements and the detection of the core promoter structure are the two unique features of this database. Currently, ppdb is the only one plant promoter database with information about core promoter types on a genomic scale, and the first genome-wide database for rice promoters.

PROMOTER SELECTION AND OUTPUT WINDOWS

Major function of ppdb is to detect promoter elements in the genome sequence and to summarize promoter structures. Data source of ppdb is shown in Table 1. The database detects REG, TATA box and Y Patch. Promoters of interest can be identified by a word search (e.g. ‘photosystem’) or a gene number (e.g. At5g38410, Os01g0100700 or AK121523) on the front page (http://www.ppdb.gene.nagoya-u.ac.jp). Selection of a specific gene model gives the following information: (i) sequence data, (ii) TSS data, (iii) a summary of the core promoter structure and (iv) REG data (Figure 1).
Table 1.

Source of ppdb

SpecificationSourceSize
Arabidopsis
Genome sequence and gene annotationTAIR 6http://www.arabidopsis.org/
TSS informationCap signatured CT-MSS tagsYamamoto, Y. Y. et al., unpublished data158 237
Selected RAFL cDNAhttp://rarge.gsc.riken.jp/62 108
Promoter elementsLDSS-positive octamers(8)659
PLACE entries corresponding to LDSS elementshttp://www.dna.affrc.go.jp/PLACE/21 (only matched motifs)
Rice
Genome sequence and gene annotationRGSP build 4.0http://rapdb.lab.nig.ac.jp/
TSS informationSelected fl cDNAhttp://cdna01.dna.affrc.go.jp/cDNA/17 286
Promoter elementsLDSS-positive octamers(8)600
PLACE entries corresponding to LDSS elementshttp://www.dna.affrc.go.jp/PLACE/4 (only matched motifs)
Figure 1.

Selection of a specific gene model gives the following information: (i) sequence data, (ii) TSs data, (iii) a summary of the core promoter structure and (iv) REG data. Peak TSS is highlighted. TPM means tag per million and this is an indication of expression level at each TSS.

Selection of a specific gene model gives the following information: (i) sequence data, (ii) TSs data, (iii) a summary of the core promoter structure and (iv) REG data. Peak TSS is highlighted. TPM means tag per million and this is an indication of expression level at each TSS. Source of ppdb At the sequence window, octamer elements identified by the LDSS analysis (8) are highlighted. There are two modes for detection, ‘Reliable’ and ‘ALL. Reliable’ is a default setting where only elements at appropriate positions relative to the peak TSS are detected. Promoters without any TSS information do not show any elements. In this case, selecting ALL allows global detection without any positional restriction. The sensitive area in the Reliable mode for each element group is described on the front page. The ‘TSS information’ table provides the expressional strength of each TSS. Tag per million (TPM) in the window shows the relative counts of TSS tags in a tag library, and this information comes from CT-MPSS analysis (Yamamoto,Y.Y. et al., unpublished data). The methods and quality assessment of the data will be described elsewhere. ‘The table of Core promoter information’ shows the presence and absence of TATA box and Y Patch. Currently, a search for Inr (Initiator for the consensus around TSS) is not executed, thus all promoters will show ‘Not Available’ for it. We have a plan to add Inr information in a near future as a minor update. The ‘REG information’ table shows a REG list of a promoter and its corresponding PPDB and PLACE motifs. For example, the table in Figure 1 shows that AtREG379 belongs to the ACGT group of PPDB and corresponds to ACGT, ACGTG, GCCAC and ACGTGKC motifs of PLACE (5). PPDB motifs have been extracted from REG sequences with the aid of a two-dimensional (2D) REG-promoter clustering (8). REG sequences, as well as PPDB and PLACE motifs, are linked to other pages for biological information. REG information is shown under the category of ‘Promoter Summary’. Selection of ALL adds another category, ‘Not Reliable Promoter Summary’. This category can be used when searching for regulatory elements (REG) from wider regions or when there is no TSS information on the promoter of interest.

ADDITIONAL PAGES

Some biological information of REGs is also provided by ppdb. As shown in Figure 2, there is a page with a whole list of REGs, the ‘ALL REG List’. This list presents the relationship between REG sequences, PPDB motifs and PLACE motifs.
Figure 2.

Page showing the whole list of REGs, the ‘All REG List’, ‘Summary of the REG’ followed by ‘Hit Gene List of the REG’. This also shows the relationship between REG Sequences, PPDB motifs and PLACE motifs.

Page showing the whole list of REGs, the ‘All REG List’, ‘Summary of the REG’ followed by ‘Hit Gene List of the REG’. This also shows the relationship between REG Sequences, PPDB motifs and PLACE motifs. Selection of a specific REG sequence leads to ‘Summary of the REG’, followed by ‘Hit Gene List of the REG’. The ‘Summary of the REG’ section shows corresponding PPDB and PLACE motifs and a brief description of the motifs. Selection of each motif leads to PLACE ID and also to the original article(s) for the motif. The ‘Hit Gene List’ section gives information about promoters sharing the REG.
  8 in total

1.  PlantCARE, a database of plant cis-acting regulatory elements and a portal to tools for in silico analysis of promoter sequences.

Authors:  Magali Lescot; Patrice Déhais; Gert Thijs; Kathleen Marchal; Yves Moreau; Yves Van de Peer; Pierre Rouzé; Stephane Rombauts
Journal:  Nucleic Acids Res       Date:  2002-01-01       Impact factor: 16.971

2.  Functional annotation of a full-length Arabidopsis cDNA collection.

Authors:  Motoaki Seki; Mari Narusaka; Asako Kamiya; Junko Ishida; Masakazu Satou; Tetsuya Sakurai; Maiko Nakajima; Akiko Enju; Kenji Akiyama; Youko Oono; Masami Muramatsu; Yoshihide Hayashizaki; Jun Kawai; Piero Carninci; Masayoshi Itoh; Yoshiyuki Ishii; Takahiro Arakawa; Kazuhiro Shibata; Akira Shinagawa; Kazuo Shinozaki
Journal:  Science       Date:  2002-03-21       Impact factor: 47.728

3.  Plant cis-acting regulatory DNA elements (PLACE) database: 1999.

Authors:  K Higo; Y Ugawa; M Iwamoto; T Korenaga
Journal:  Nucleic Acids Res       Date:  1999-01-01       Impact factor: 16.971

4.  AthaMap: from in silico data to real transcription factor binding sites.

Authors:  Lorenz Bülow; Nils Ole Steffens; Claudia Galuschka; Martin Schindler; Reinhard Hehl
Journal:  In Silico Biol       Date:  2006

5.  Clustering of DNA sequences in human promoters.

Authors:  Peter C FitzGerald; Andrey Shlyakhtenko; Alain A Mir; Charles Vinson
Journal:  Genome Res       Date:  2004-07-15       Impact factor: 9.043

6.  TRANSFAC and its module TRANSCompel: transcriptional gene regulation in eukaryotes.

Authors:  V Matys; O V Kel-Margoulis; E Fricke; I Liebich; S Land; A Barre-Dirrie; I Reuter; D Chekmenev; M Krull; K Hornischer; N Voss; P Stegmaier; B Lewicki-Potapov; H Saxel; A E Kel; E Wingender
Journal:  Nucleic Acids Res       Date:  2006-01-01       Impact factor: 16.971

7.  Identification of plant promoter constituents by analysis of local distribution of short sequences.

Authors:  Yoshiharu Y Yamamoto; Hiroyuki Ichida; Minami Matsui; Junichi Obokata; Tetsuya Sakurai; Masakazu Satou; Motoaki Seki; Kazuo Shinozaki; Tomoko Abe
Journal:  BMC Genomics       Date:  2007-03-08       Impact factor: 3.969

8.  AGRIS: Arabidopsis gene regulatory information server, an information resource of Arabidopsis cis-regulatory elements and transcription factors.

Authors:  Ramana V Davuluri; Hao Sun; Saranyan K Palaniswamy; Nicole Matthews; Carlos Molina; Mike Kurtz; Erich Grotewold
Journal:  BMC Bioinformatics       Date:  2003-06-23       Impact factor: 3.169

  8 in total
  35 in total

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Authors:  Cristina Ruiz; Maria Pla; Nuri Company; Jordi Riudavets; Anna Nadal
Journal:  Plant Mol Biol       Date:  2015-12-19       Impact factor: 4.076

Review 2.  Web-queryable large-scale data sets for hypothesis generation in plant biology.

Authors:  Siobhan M Brady; Nicholas J Provart
Journal:  Plant Cell       Date:  2009-04-28       Impact factor: 11.277

3.  Simple database to select promoters for plant transgenesis.

Authors:  Olga G Smirnova; Salmaz S Ibragimova; Alex V Kochetov
Journal:  Transgenic Res       Date:  2011-08-03       Impact factor: 2.788

4.  Characterization and identification of cis-regulatory elements in Arabidopsis based on single-nucleotide polymorphism information.

Authors:  Paula Korkuc; Jos H M Schippers; Dirk Walther
Journal:  Plant Physiol       Date:  2013-11-07       Impact factor: 8.340

Review 5.  Genomics and bioinformatics resources for crop improvement.

Authors:  Keiichi Mochida; Kazuo Shinozaki
Journal:  Plant Cell Physiol       Date:  2010-03-05       Impact factor: 4.927

6.  Identification and application of a rice senescence-associated promoter.

Authors:  Li Liu; Yong Zhou; Mark W Szczerba; Xianghua Li; Yongjun Lin
Journal:  Plant Physiol       Date:  2010-05-03       Impact factor: 8.340

7.  Synthetic circuit of inositol phosphorylceramide synthase in Leishmania : a chemical biology approach.

Authors:  Vineetha Mandlik; Dixita Limbachiya; Sonali Shinde; Milsee Mol; Shailza Singh
Journal:  J Chem Biol       Date:  2013-01-03

8.  A γ-glutamyl cyclotransferase protects Arabidopsis plants from heavy metal toxicity by recycling glutamate to maintain glutathione homeostasis.

Authors:  Bibin Paulose; Sudesh Chhikara; Joshua Coomey; Ha-Il Jung; Olena Vatamaniuk; Om Parkash Dhankher
Journal:  Plant Cell       Date:  2013-11-08       Impact factor: 11.277

9.  GRASSIUS: a platform for comparative regulatory genomics across the grasses.

Authors:  Alper Yilmaz; Milton Y Nishiyama; Bernardo Garcia Fuentes; Glaucia Mendes Souza; Daniel Janies; John Gray; Erich Grotewold
Journal:  Plant Physiol       Date:  2008-11-05       Impact factor: 8.340

10.  A small intergenic region drives exclusive tissue-specific expression of the adjacent genes in Arabidopsis thaliana.

Authors:  Hernán G Bondino; Estela M Valle
Journal:  BMC Mol Biol       Date:  2009-10-16       Impact factor: 2.946

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