Nucleic Acids Research Advance Access originally published online on November 4, 2008
Nucleic Acids Research 2009 37(Database issue):D72-D76; doi:10.1093/nar/gkn763
Nucleic Acids Research, 2009, Vol. 37, Database issue D72-D76
© 2008 The Author(s)
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/2.0/uk/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
Transterm: a database to aid the analysis of regulatory sequences in mRNAs
Grant H. Jacobs1,2,
Augustine Chen1,
Stewart G. Stevens1,
Peter A. Stockwell1,
Michael A. Black1,
Warren P. Tate1 and
Chris M. Brown1,*
1Biochemistry Department and Webster Centre, University of Otago, PO Box 56 and 2Bioinfotools, PO Box 6129, Dunedin, New Zealand
*To whom correspondence should be addressed. Tel: +643 4795201; Fax: +643 4797866; Email: chris.brown{at}otago.ac.nz
Received September 16, 2008. Revised October 6, 2008. Accepted October 6, 2008.
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ABSTRACT
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Messenger RNAs, in addition to coding for proteins, may contain
regulatory elements that affect how the protein is translated.
These include protein and microRNA-binding sites. Transterm
(
http://mRNA.otago.ac.nz/Transterm.html) is a database of regions
and elements that affect translation with two major unique components.
The first is integrated results of analysis of general features
that affect translation (initiation, elongation, termination)
for species or strains in Genbank, processed through a standard
pipeline. The second is curated descriptions of experimentally
determined regulatory elements that function as translational
control elements in mRNAs. Transterm focuses on protein binding
sites, particularly those in 3'-untranslated regions (3'-UTR).
For this release the interface has been extensively updated
based on user feedback. The data is now accessible by strain
rather than species, for example there are 10
Escherichia coli strains (genomes) analysed separately. In addition to providing
a repository of data, the database also provides tools for users
to query their own mRNA sequences. Users can search sequences
for Transterm or user defined regulatory elements, including
protein or miRNA targets. Transterm also provides a central
core of links to related resources for complementary analyses.
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INTRODUCTION
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Messenger RNAs are translated into proteins, directed by specific
signals in the mRNA. The genetic code and codon usage may differ
between species. Translation in specific organisms may also
require that they make efficient use of elements around the
initiation and termination codons and also use a codon bias
for that organism's set of tRNAs. The preferred, often most
efficient set of signals, in a particular organism can often
be inferred from that most commonly used in that organism. For
example,
Homo sapiens has a strong bias prior to initiation
codons (Kozak's consensus) (
1), whereas
Escherichia coli has
a G/U bias following termination codons. These have been associated
with efficiency of initiation and termination respectively (
2,
3).
In addition to this general bias reflecting overall translation, individual mRNAs may contain regulatory elements within the mRNA that affect mRNA localization, stability or translation of the associated coding region (4–6). These function most frequently in the 3'-UTR but also in 5'-UTRs or coding regions (7,8). Key known elements are protein and miRNA-binding sites (9,10). Mutations and variations in these regulatory elements have been shown experimentally to affect their function and to be underlying contributors to genetic disease (11).
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DATABASE GENERATION AND CONTENT
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Transterm sequences and summaries
The detail of how Transterm 2008 was generated, and software
used is available on the web site. A summary including major
changes in this release is presented below. Data is parsed from
NCBI Genbank or NCBI Genomes entries using CDS (coding sequence)
fields, and mRNA fields when available. Key regions (CDS, 5'-UTRs
and 3'-UTR, Init, Term) or flanks are extracted using this CDS
or mRNA information. Eight sets of data are provided for each
taxonomic strain with over 40 CDS or mRNAs. The strains are
identified from the TaxID (NCBI taxonomy database identifier)
in the Genbank entry. Data collected can differ in experimental
support and redundancy.
For ‘Genomes’ sets reducing redundancy is not done, as genomes are considered to be complete datasets, but for Genbank data redundancy is removed according to our published procedure (12). This results in redundant and non-redundant sets of regions: users choose which is appropriate to their needs. These sets of data are processed to generate summary data for each TaxID.
In previous releases of Transterm, data was ‘mapped up’ to the species level. With the increasing number of specific strains of a particular species now present in Genbank, we now use the strain as the taxonomic unit to collate and organize the data. For example, the 10 complete E. coli strains are processed separately, rather than combined. The sets of data are then processed as described previously to give a comprehensive set of analyses for each dataset. A view of part of the new interface is shown in Figure 1.
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Figure 1. Part of the new Transterm user interface. Users select data to analyse from four datasets, e.g. ‘NCBI Genbank—One sequence for each coding sequence entry’. A taxomic group is selected by NCBI ‘TaxId’ number (e.g. 9606), then a particular type of output (listed in Table 1) can be selected by using the pull down menu (e.g. Consensus of initiation region, Figure 2). Data selected can be for all the sequences or a non-redundant set (for H. sapiens 96 417 versus 32 763 sequences). This data can also be searched using Blast or Scan for matches.
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Two files summarizing initiation codon context for two complete
bacterial genomes are shown in
Figure 2. This is a comparison
between a section of data from the context of two eubacteria,
Synechocystis PCC6803 (TaxID: 1148) and
Pseudomonas aeruginosa PAO1 (TaxID: 208964) initiation codons (*.initmatrix). The upper
panel shows a typical Shine-Dalgarno (SD) like pattern for a
high GC% genome (for example purines at –13 to –7,
whereas the lower panel PC6803 has an atypical pattern for a
bacterium (less purine bias at –13 to –7, pyrimidine
bias at –2, –1). Further investigation of this observation
using Transterm data could utilise alternative representations
of the same data, see
Table 1 (Panel C) (*.initnrttbit, *.initnrttcvs),
the aligned sequences themselves (*.init, *.dat) or summaries
of the data (*.sum). As suggested by this data cyanobacteria
have been shown to use a combination of SD-dependent and SD-independent
initiation (
13,
14).
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Figure 2. The ‘Consensus of initiation region’ files for Synechocystis PCC6803 (NBSynePCC_2-1148.initmatrix) and Pseudomonas aeruginosa PAO1 (NBPseuaeru-208964.initmatrix). A count of the percentage of each base in each position is shown (see text for analysis). The position (Pos) in the matrix is shown above –20 to +13, the ATG is at +1 to +3. The consensus (Cons) (>65%) is shown below. For these datasets the upper sequences were 41.7% GC3 and lower 65.8% GC3. More comprehensive descriptions of the data are also available (Table 1).
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Table 1. The key output files and a brief description of the contents of each. Further descriptions are available through the online help ‘Main Transterm Datafiles’
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A list of the key classes of output files are shown in
Table 1.
More detail of the content of each of these files in an online
help document on the website. Many of these analyses are newly
available in this release.
Transterm elements
Published literature was surveyed for descriptions of new elements. New elements would be included as they become available through published literature or feedback from users. Criteria for inclusion in Transterm are that it must be experimentally verified and published in a peer reviewed journal, and that it must be sufficiently well defined to be converted into a computer readable form (regular expression, matrix, secondary structure, or discrete sequence). Some elements, e.g. the Puf3-binding site from Saccharomyces cerevisiae are currently in this form in Transterm only. The format of an example (Puf3 protein-binding site) is shown in Figure 3.
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Figure 3. An example of Transterm element description (Puf3p-binding site). Elements may be described by strings, regular expressions, matrices or RNA secondary structure rules. In this case the element is simply described as a string. Users may construct more complex descriptions of the element based on the referenced literature, for example allowing mismatches, insertions or deletions.
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Where appropriate, elements reported in other databases, have
been included after an independent literature review. In a similar
fashion, several databases include reformatted Transterm elements
(
15,
16). Some elements e.g. the well-studied Iron Responsive
Element (IRE) are available as computer readable descriptor
in several online databases, in these cases hyperlinks are provided
from Transterm to allow the user to choose the most appropriate
tool for analysis. Large highly structured RNA elements (e.g.
riboswitches, IRESs) are not included, but are described in
Rfam, ncRNA and IRESsite (
17,
18). The focus of Transterm is
on protein-binding sites.
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COMPARISON WITH OTHER TRANSLATIONAL CONTROL DATABASES
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Several other databases provide some specific data, tools or
services that complement those of Transterm. There is a list
of resources referenced in the Transterm help online but the
most relevant are summarized here. Rfam—the database of
RNA families contains some
cis-regulatory elements common to
Transterm—these are cross-referenced. The elements are
described in a different way (covariation models) and therefore
are suitable for different types of analyses. RegRNA (
15), UTRdb
(
19), Recode (
20) all have related functionality but have not
been updated since 2006.
Update frequency
Translational control elements are updated regularly and the sequence datasets annually.
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FUNDING
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Health Research Council (HRC05/195 to W.P.T., C.M.B., L.P. and
R.T.P.); REANNZ and TelstraClear Capability build fund grant
(CB611 to C.M.B., M.A.B.); and utilizes the NZ Biomirror and
Bestgrid resources.
Conflict of interest statement. None declared.
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ACKNOWLEDGEMENTS
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Thanks to users who made suggestions for improvement or gave
feedback.
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REFERENCES
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