Nucleic Acids Research Advance Access originally published online on September 19, 2008
Nucleic Acids Research 2009 37(Database issue):D37-D40; doi:10.1093/nar/gkn597
Nucleic Acids Research, 2009, Vol. 37, Database issue D37-D40
© 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.
DiProDB: a database for dinucleotide properties
Maik Friedel1,
Swetlana Nikolajewa2,
Jürgen Sühnel1 and
Thomas Wilhelm3,*
1Biocomputing Group, Leibniz Institute for Age Research - Fritz Lipmann Institute, Beutenbergstrasse 11, 07745 Jena, 2Department of Bioinformatics, Friedrich-Schiller-University Jena, Ernst-Abbe-Platz 2, 07743 Jena, Germany and 3Theoretical Systems Biology, Institute of Food Research, Norwich Research Park, Colney, Norwich NR4 7UA, UK
*To whom correspondence should be addressed. Tel: +44 1603 255313; Fax: +44 1603 255128; Email: thomas.wilhelm{at}bbsrc.ac.uk
Received August 1, 2008. Revised September 3, 2008. Accepted September 3, 2008.
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ABSTRACT
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DiProDB (
http://diprodb.fli-leibniz.de) is a database of conformational
and thermodynamic dinucleotide properties. It includes datasets
both for DNA and RNA, as well as for single and double strands.
The data have been shown to be important for understanding different
aspects of nucleic acid structure and function, and they can
also be used for encoding nucleic acid sequences. The database
is intended to facilitate further applications of dinucleotide
properties. A number of property datasets is highly correlated.
Therefore, the database comes with a correlation analysis facility.
Authors having determined new sets of dinucleotide property
values are invited to submit these data to DiProDB.
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INTRODUCTION
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Nucleic acid properties are governed by the corresponding nucleotide
sequence. More specifically, many properties such as nucleic
acid stability, for example, seem to depend primarily on the
identity of nearest-neighbour nucleotides (
1). The corresponding
nearest-neighbour model is also the basis for RNA secondary
structure prediction by free-energy minimization (
2). It is
known that not only thermodynamic but also conformational nucleotide
properties may play a role. It has been shown, for example,
that promoter locations can be predicted adopting dinucleotide
stiffness parameters derived from molecular dynamic simulations
(
3). Also, curved DNA is known to play a role in prokaryotic
gene expression (
4). In addition, physical DNA profiles have
been used for an improved promoter prediction (
5,
6). There are
numerous other examples. It is, however, beyond the scope of
this brief database description to provide a comprehensive overview.
Currently, we are developing a Genome Browser that encodes complete
eukaryotic or prokaryotic genomes by thermodynamic and conformational
dinucleotide properties. In this context, we have collected
more than 100 sets of dinucleotide properties from the literature.
Currently, there are two related data collections, the PROPERTY
DB (srs6.bionet.nsc.ru/srs6bin/cgi-bin/wgetz?-page+LibInfo+-id+1pFZP1TuQpU+-lib+PROPERTY)
with about 30 property sets (
7) and plot.it (hydra.icgeb.trieste.it/dna/plot_it.html)
with about 50 sets (Vlahovicek,K. and Pongor,S., unpublished
data). Both of these databases do not include many of the existing
datasets and, in addition, it is difficult to trace back the
original data sources. Also, both of them are not included in
the NAR Database Collection. Therefore, we have set up the database
DiProDB, which is aimed to be a one-stop resource for these
properties. With DiProDB we want to provide reliable, easily
accessible and comprehensive information on dinucleotide properties
that may stimulate the application of these data to a diversity
of biological problems.
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DATABASE CONTENT
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DiProDB currently includes 115 dinucleotide datasets. They were
collected from the literature and are classified according to
nucleic acid type (DNA and RNA), strand information (double
or single), how the data were obtained (experimental, theoretical/calculated)
and also according to the general type of the dinucleotide property:
thermodynamical (e.g. free energy), conformational (e.g. twist)
or letter-based (e.g. GC content). We include the letter-based
data to demonstrate relations to thermodynamical and conformational
properties. Moreover, most of the current motif discovery approaches
are letter-based. An example from our work refers to the identification
of significant purine–pyrimidine patterns in restriction
enzyme binding sites (
8). The number of datasets for each category
is shown in
Table 1. For each dataset, the 16 dinucleotide values,
the unit of measurement, the reference, the classification features
as well as comments are provided. If a dataset refers to RNA,
it is mentioned in the corresponding property name, if the name
does not mention a nucleic acid, it always refers to DNA.
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USER INTERFACE
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DiProDB displays all data in a single table, see
Figure 1. The
number and type of columns shown can be customized by the user.
When clicking on the ID button in the first column a new page
pops up containing all relevant information about the corresponding
property. The database entries can be sorted according to three
different criteria. There is also a search option for all or
for specific columns. The complete table or parts of it can
be saved as text file or in a format directly importable into
the Genome Browser mentioned in the Introduction section. The
DiProDB website contains a Submit button, where users can submit
new property datasets.
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DATA ANALYSES
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The DiProDB website contains a Correlate option, where users
can calculate Pearson's or Spearman's rank correlation coefficients
for all or selected properties. This allows easy identification
of dependencies between different dinucleotide properties. As
an example in
Figure 2, Spearman's correlation data are shown
for five different datasets quantifying the twist in B-DNA.
All datasets are clearly correlated to each other. However,
the extent of correlation is rather different. Correlation coefficients
>0.58 are considered as statistically significant (
P <
0.01,
t-test).
Based on these correlations, we have done different hierarchical
clustering analyses to get a deeper insight into the overall
correlation of the datasets.
Figure 3 shows a single linkage
hierarchical clustering of all 23 B-DNA double-strand thermodynamical
properties together with the three-dinucleotide letter-based
quantities GC content, purine (GA) content and keto (GT) content.
This clustering is based on the distance measure 1–|
rPearson|,
because it is just the absolute value of the correlation, which
indicates whether two properties contain similar information.
Other correlation measures like Spearman or Kendall-Tau give
very similar results. It can be seen that all free-energy data
contain more or less the same information and that this is basically
equivalent to the GC content. This is very likely due to the
simple fact that GC pairs have three H-bonds instead of two
in AT base pairs. The complete single-linkage hierarchical clustering
of all 115 properties is given in the
Supplementary Material (
Table 2), where also a corresponding Ward clustering (
14) is
shown. The latter one shows a separation between a free energy/entropy/enthalpy/stacking
energy/melting temperature cluster and another cluster containing
all the conformational datasets. The complete single linkage
clustering reveals that the most uncorrelated dinucleotide properties
are direction, inclination, twist–rise (conformational),
stacking energy, tilt, shift, propeller twist and rise.
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Figure 3. Hierarchical clustering of all 23 B-DNA double-strand physicochemical properties and the three-dinucleotide letter-based quantities GC content, purine (GA) content and keto (GT) content. The property sets are designated by their IDs and names.
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In order to gain more insights into the data, we performed two
principal component analyses (PCA) (
15). The complete data of
115 properties for 16 dinucleotides corresponds to 115 points
in 16-dimensional space (or 16 points in 115-dimensional space).
PCA helps to reveal the internal structure of such high-dimensional
data by providing lower dimensional pictures of the ‘cloud’
in coordinates corresponding to maximum variance of the data
(
http://en.wikipedia.org/wiki/Principal_components_analysis).
The cloud of all 115 properties in the first two principal components
(PCs, the new coordinates) is shown in
Figure 4. Only the most
uncorrelated property ‘direction’ lies outside the
shown region: (PC1,PC2)
Direction = (0.1,1.6) (the complete figure
containing direction and a PC1–PC3 projection are given
in the
Supplementary Material; note also that only the first
three PCs carry relevant information: PC1 78.5%, PC2 16.9%,
PC3 3.3%). The other two outliers are melting temperature and
persistence length. This indicates that especially these three
properties carry information quite different from the others.
Note that the latter two properties are not amongst the outliers
according to the above mentioned single linkage clustering,
because each one has (at least) one better correlation to other
datasets (melting temperature to stacking energy, and persistence
length to tilt–shift).
Figure 4 also indicates three clusters
containing all other properties, one stacking energy/entropy
cluster, a twist cluster and the central main cluster.
Finally, we also performed a PCA calculating the 115 principal
components for the 16 dinucleotides. The first 15 PCs carry
information (23%, 21%, 14%, 12%, 6%, etc.), roughly indicating
that about this number of low correlated properties is needed
to represent all information of the complete set of 115 properties.
The
Supplementary Material also contains a corresponding PC1–PC2
plot, together with all detailed information about the performed
PCAs.
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OUTLOOK
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So far the DiProDB database contains 115 sets of dinucleotide
properties. In the future, this number is to be increased. We
also invite other authors to submit their measured or calculated
dinucleotide properties to DiProDB.
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SUPPLEMENTARY DATA
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Supplementary data are available at NAR Online.
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FUNDING
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Funding for open access charge: Biotechnology and Biological
Sciences Research Council (BBSRC)IFR Core Strategic Grant.
Conflict of interest statement. None declared.
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ACKNOWLEDGEMENTS
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We are grateful to Friedrich Haubensak for setting up the database
and to Rolf Hühne for helpful comments on the database
layout.
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INTRODUCTION
