Nucleic Acids Research 2006 34(Web Server issue):W588-W590; doi:10.1093/nar/gkl313
© The Author 2006. Published by Oxford University Press. All rights reserved
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TICO: a tool for postprocessing the predictions of prokaryotic translation initiation sites
M. Tech*,
B. Morgenstern and
P. Meinicke
Abteilung Bioinformatik, Institut für Mikrobiologie und Genetik Georg-August-Universität Göttingen, Goldschmidtstrasse 1, 37077 Göttingen, Germany
*To whom correspondence should be addressed. Tel: +49 551 3914927; Fax: +49 551 3914929; E-mail: maike{at}gobics.de
Received February 14, 2006. Revised March 6, 2006. Accepted April 11, 2006.
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ABSTRACT
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Exact localization of the translation initiation sites (TIS)
in prokaryotic genomes is difficult to achieve using conventional
gene finders. We recently introduced the program TICO for postprocessing
TIS predictions based on a completely unsupervised learning
algorithm. The program can be utilized through our web interface
at
http://tico.gobics.de/ and it is also freely available as
a commandline version for Linux and Windows. The latest version
of our program provides a tool for visualization of the resulting
TIS model. Although the underlying method is not based on any
specific assumptions about characteristic sequence features
of prokaryotic TIS the prediction rates of our tool are competitive
on experimentally verified test data.
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INTRODUCTION
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The accuracy of translation initiation site (TIS) prediction
in prokaryotes is still not sufficient. Though existing gene
finders may reliably identify coding regions of significant
length, they usually show a poor performance in predicting the
correct TISs (
1–
3). For genomes with a high G+C content
in particular, the rate of inaccurate TIS predictions is usually
high (
3). Recently, several postprocessors have been proposed
(
1,
2,
4,
5) to improve the accuracy of TIS prediction in prokaryotic
genomes.
Our tool TICO (for ‘TIs COrrector’) is based on an unsupervised learning scheme for relocation of putative gene starts. The underlying TIS model is very general and does not include any specific assumptions about TIS-related sequence features. In particular, no a priori assumptions about Shine–Dalgarno (SD) motifs (6) and their positional and compositional variation are required. In addition, we avoided any kind of empirical thresholds which might also imply a severe bias towards certain genomes.
Despite the generality of the implemented method, the prediction performance of our program is competitive (7), with good results even on high-G+C genomes. The program is supplemented with a tool for visualizing relevant sequence features which have been learned by the algorithm. This extension provides an effective means for monitoring the resulting model and may also reveal unknown sequence characteristics associated with prokaryotic TISs.
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OUTLINE OF THE IMPLEMENTED ALGORITHM
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The algorithm implemented in the tool TICO is based on a constrained
clustering scheme which we described in detail in (
7). The clustering
starts with an initial gene annotation given as input. Within
a specified
search range around each annotated start candidate,
all possible gene starts are defined as additional TIS candidates.
These candidates have to share the reading frame of the annotated
open reading frame (ORF) without any in-frame stop codon occurring
between a potential start and the annotated stop. At the start
of the clustering, the initially annotated gene starts are labelled
as
strong TISs; all other candidates are labelled as
weak TISs.
Based on that labelling, a positional weight matrix (PWM) is
estimated from position-dependent trinucleotide frequencies.
Positional smoothing is applied to the estimated probabilities
in order to prevent vanishing entries. Finally, logarithms of
strong and weak probabilities are subtracted to build the PWM.
Then the PWM is used to score the candidates and, in turn, the
score is used to reassign the candidates according to strong
and weak TIS categories. Among all candidates associated with
an ORF, the candidate with the maximum positive score is labelled
strong; all other candidates of that ORF, are labelled weak.
Estimation of the PWM is repeated until the labels no longer
change or a maximum of 20 iterations has been reached.
As the clustering algorithm requires a suitable initialization, the resulting prediction to some degree depends on the prior annotation of TIS locations used for the initial labelling. In comparison with other tools our algorithm has proven to be rather robust with respect to low accuracy of the initial annotation (7). Nevertheless, the quality of the initial annotation can be too bad to serve as an appropriate starting point for our algorithm. Also, in cases where no TIS-related signals are actually present in the sequence, our algorithm is unlikely to improve the prediction.
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DESCRIPTION OF THE TOOL
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TICO can be accessed through our web interface and it is also
available as commandline tool for Linux and Windows. The tool
requires the input of a genome sequence in the FASTA format
and a valid gene annotation, as obtained, for example, using
the tool GLIMMER (
8). The current version accepts two kinds
of input format: GLIMMER2.x and our own format called ‘simple
coord’. In the download section, several Perl scripts
are available for conversion of other formats (e.g. the PTT
format provided by GenBank) into the TICO input format. The
output of the tool is provided in a GLIMMER-like format, in
our own format and in general feature format (GFF), according
to the specifications of the Sanger Institute (
http://www.sanger.ac.uk/).
All formats contain the gene coordinates as predicted by TICO
and a score which indicates the quality of the predicted TIS,
i.e. a measure of fit with respect to the model resulting from
the clustering algorithm. In GFF and the GLIMMER-like format
the relocations of the TIS are also represented by means of
the distance between the initially annotated position from the
input and the new position for the TIS as predicted by TICO.
The submission page of the web interface of TICO is shown in Figure 1. The user should upload the sequence file and the initial annotation. In addition, one or several output formats have to be selected and an email address has to be provided to receive the results. The commandline version is applicable to annotation pipelines and it is executable on a common desktop PC. The user interface has been implemented in Java and can be configured by means of a properties file or via commandline parameters.
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Figure 1 The submission page of the TICO web interface. The sequence and an annotation have to be uploaded. The user may choose one or more output formats and should give an email address to receive the results. In addition, some optional parameters can be adjusted, including the range of extracted sequences around the start candidates.
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Since the first version of TICO was published (
9) a number of
extensions have been added. The most important extensions include
an automatic adaptation of the smoothing parameter
sigma (
7)
and the visualization of the PWM features, which is detailed
in the next section.
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VISUALIZATION OF LEARNED CHARACTERISTICS
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The latest version of TICO provides a companion tool for visualization
of the discriminative features learned for the classification
of TIS candidates. The basis for the visualization is provided
by the PWM which results from the clustering algorithm. The
corresponding weights specify how trinucleotide occurrences
within the sequence window contribute to the score used for
labelling of TIS candidates. If a certain trinucleotide occurrence
within that window is associated with a high positive weight,
the occurrence provides evidence for a strong TIS. Similarly,
a high negative weight provides evidence for a weak TIS.
In the current implementation, the visualization of learned characteristics is realized by transforming the PWM into a colour image (Figure 2). In the resulting PWM image, colour represents the weight value associated with a certain trinucleotide (row) at a specific position (column). High positive and high negative weights result in red and deep blue spots, respectively. Intermediate weights produce orange, yellow or green areas in the image. A colour scale at the right of the PWM image displays the colours associated with certain weight values. To obtain more precise information about the weights, the numerical value can be accessed by clicking on the corresponding image location. In that context, a zoom function may be used for close inspection of the weights.
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Figure 2 Visualization of the PWM calculated for E.coli using the default settings. On the horizontal axis the position within the sequence window is denoted; on the vertical axis trinucleotides are denoted in alphabetical order. The potential start codon is located at position 0. The upstream and downstream regions are represented by negative and positive position indices, respectively. A colour scale indicates the numerical values of the weights.
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By means of the visualization of the PWM, the resulting TIS
model can be interpreted by the user. In combination with biological
background knowledge, the position-dependent trimer weights
can provide information about relevant TIS signals.
Figure 2,
for example, shows a visualization of the weights calculated
for
Escherichia coli. The Shine–Dalgarno sequence of
E.coli has been proposed to contain the pattern AGGAG (
6)
12 to 4 nt
upstream of the translation start (
10). In accordance with these
findings, the image shows high positive weights for the trimers
AGG, GAG, AGA and GGA at positions –12 to –7. Also,
a slight positive maximum for the trimer AAA immediately following
the start codon is observable. Such a triple-A downstream box
has been proposed to provide another TIS-related signal (
11),
which has been confirmed in (
12).
We should point out that the visualization of PWMs is only useful if strong TIS-related signals are actually present in the data. If statistically significant signals cannot be found in the sequence, no characteristic features will be observable. However, weak signals with bad visibility in the PWM image do not automatically imply a bad prediction of TIS locations. Although for several high-G+C genomes the resulting PWM images show a bad signal-to-noise ratio, in many cases the prediction of TIS can nevertheless be improved considerably using our algorithm (7).
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PERFORMANCE
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The performance of TICO has been tested on the genomes of
E.coli and
Bacillus subtilis, as well as on the high-G+C genomes of
Pseudomonas aeruginosa,
Burkholderia pseudomallei and
Ralstonia solanacearum. Using these genomes is reasonable because for
the corresponding organisms a reliable gene annotation is available.
The results in comparison with other recent tools for improvement
of TIS prediction can be found at
http://tico.gobics.de/results.jsp.
A detailed comparison of our tool with the tools RBSfinder (
4),
MED-Start (
2) and GS-Finder (
1) has been published in (
7).
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ACKNOWLEDGEMENTS
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The work was partially supported by the Bundesministerium für
Buildung und Forschung (BMBF) project MediGrid (01AK803G). Funding
to pay the Open Access publication charges for this article
was provided by the annual budget of the authors’ department.
Conflict of interest statement. None declared.
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ABSTRACT
INTRODUCTION