Partitioning of Minimotifs Based on Function with Improved Prediction Accuracy
Minimotifs are short contiguous peptide sequences in proteins that are known to have a function in at least one other protein. One of the principal limitations in minimotif prediction is that false positives limit the usefulness of this approach. As a step toward resolving this problem we have built, implemented, and tested a new data-driven algorithm that reduces false-positive predictions. Certain domains and minimotifs are known to be strongly associated with a known cellular process or molecular function. Therefore, we hypothesized that by restricting minimotif predictions to those where the minimotif containing protein and target protein have a related cellular or molecular function, the prediction is more likely to be accurate. This filter was implemented in Minimotif Miner using function annotations from the Gene Ontology. We have also combined two filters that are based on entirely different principles and this combined filter has a better predictability than the individual components. Testing these functional filters on known and random minimotifs has revealed that they are capable of separating true motifs from false positives. In particular, for the cellular function filter, the percentage of known minimotifs that are not removed by the filter is approximately 4.6 times that of random minimotifs. For the molecular function filter this ratio is approximately 2.9. These results, together with the comparison with the published frequency score filter, strongly suggest that the new filters differentiate true motifs from random background with good confidence. A combination of the function filters and the frequency score filter performs better than these two individual filters.
Partitioning of Minimotifs Based on Function with
Improved Prediction Accuracy
, Tian Mi
, Jerlin Camilus Merlin
, Aaron Oommen
, Patrick Gradie
Martin R. Schiller
1 Department of Computer Science and Engineering, University of Connecticut, Storrs, Connecticut, United States of America, 2 School of Life Sciences, University of
Nevada Las Vegas, Las Vegas, Nevada, United States of America
Minimotifs are short contiguous peptide sequences in proteins that are known to have a function in at least
one other protein. One of the principal limitations in minimotif prediction is that false positives limit the usefulness of this
approach. As a step toward resolving this problem we have built, implemented, and tested a new data-driven algorithm
that reduces false-positive predictions.
Certain domains and minimotifs are known to be strongly associated with a known
cellular process or molecular function. Therefore, we hypothesized that by restricting minimotif predictions to those where
the minimotif containing protein and target protein have a related cellular or molecular function, the prediction is more
likely to be accurate. This filter was implemented in Minimotif Miner using function annotations from the Gene Ontology.
We have also combined two filters that are based on entirely different principles and this combined filter has a better
predictability than the individual components.
Testing these functional filters on known and random minimotifs has revealed that they are
capable of separating true motifs from false positives. In particular, for the cellular function filter, the percentage of known
minimotifs that are not removed by the filter is ,4.6 times that of random minimotifs. For the molecular function filter this
ratio is ,2.9. These results, together with the comparison with the published frequency score filter, strongly suggest that
the new filters differentiate true motifs from random background with good confidence. A combination of the function
filters and the frequency score filter performs better than these two individual filters.
Citation: Rajasekaran S, Mi T, Merlin JC, Oommen A, Gradie P, et al. (2010) Partitioning of Minimotifs Based on Function with Improved Prediction Accuracy. PLoS
ONE 5(8): e12276. doi:10.1371/journal.pone.0012276
Editor: Vladimir Brusic, Dana-Farber Cancer Institute, United States of America
Received July 11, 2010; Accepted July 27, 2010; Published August 19, 2010
Copyright: ß 2010 Rajasekaran et al. This is an open-access article distributed under the terms of the Cre ative Commons Attribution License, which permits
unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Funding: This work was supported by National Institutes of Health grant number GM079689 to M.R.S., National Science Foundation (NSF) Grant 0829916 to S.R.,
by the University of Nevada Las Vegas, and by the University of Connecticut. The funders had no role in study design, data collection and analysis, decision to
publish, or preparation of the manuscript.
Competing Interests: The authors have declared that no competing interests exist.
* E-mail: email@example.com (SR); firstname.lastname@example.org (MRS)
. These authors contributed equally to this work.
Minimotifs are short contiguous peptide pieces of proteins
that have a known biological function. These functions can be
categorized into binding, posttranslational modification of the
minimotif, and protein trafficking. While there are many known
functional minimotifs, predicting a minimotif in a new protein
based on a consensus sequence, position-specific scoring matrix, or
other algorithms produces many false-positive predictions. This
limits the usefulness of minimotif prediction programs such as
Minimotif Miner (MnM) [1,2], Eukaryotic Linear Motif (ELM)
[3,4], and ScanSite [5,6]. These programs all use different
approaches to reduce false positive predictions.
To reduce false positive minimotif predictions, three approaches
have been used in MnM [1,2]. In frequency analysis, the complexity
of minimotif sequence definitions can be used to rank-order
minimotifs. A surface prediction algorithm can identify minimotifs
likely to be on the surface of a protein. The third approach selects
minimotifs that have conserved minimotif sequences in many
species. ELM has also implemented several filters for: 1) Cell
compartments, 2) Globular domains, 3) Taxonomy, and 4)
Structure [3,4,7]. The cell compartment filter selects minimotifs
where both the ligand and its target are in the same cellular
compartment. The globular domain filter selects minimotifs in
intrinsically disordered regions. The taxonomy filter eliminates
minimotifs that are not in the same species. The structure filter
selects for minimotifs that have exposure to solvent or similar
secondary structural features. In ScanSite [5,6], minimotifs are
described as position-specific scoring matrices (PSSMs) that indicate
the frequency of each amino acid at each position using data derived
from peptide library and phage display experiments [8,9]. ScanSite
provides different stringencies of predictions.
Despite these inter-related approaches, false positives remain a
concern, thus new types of filters are needed. In considering new
strategies that might be used to refine minimotif predictions, we
have noticed that some proteins which contain a particular
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domain are thought to have functions related to similar cellular
processes. For example, proteins, which contain PTB or SH2
domain that binds to phospho-tyrosine containing proteins, are
typically involved in signaling . Likewise, BRCT domains
generally bind to phosphopeptides and are often in proteins
associated with DNA repair and cell cycle checkpoints [11,12].
Therefore, we hypothesize that knowledge about molecular and
cellular functions can be used to refine minimotif predictions.
The Gene Ontology (GO) database  contains structured
information about biological processes, cellular components, and
molecular functions. Proteins are associated with terms in each of these
ontologies. Each ontology is structured as a directed acylic graph that
maps the relationships between terms. We can take advantage of GO
because it references protein accession numbers and thus, can be cross
referenced to the Minimotif Miner database[1,2].
We have built a syntactical and semantic structure for
minimotifs that enables easy integration of minimotif and GO
data . Briefly, a minimotif is contained in a protein, which is
called the ‘source protein’. A minimotif in the source protein has
an action, designated ‘activity’. The protein which recognizes the
minimotif to induce the activity is the ‘target protein’. For
example, in [yytm in Jak2] [binds] [the SH2 domain of SHB], yytm
is the minimotif sequence, Jak2 is the source protein, binds is the
activity, and SHB is the target protein. SH2 domain specifies the
region where the minimotif binds the target SHB.
For predictions of new minimotifs, the source protein query
contains multiple minimotif short sequences that may encode new
activities with all of the target proteins. In this paper, the source
protein and the set of putative target proteins can be mapped to
cellular and molecular functions derived from the GO database to
determine whether the source and target proteins share at least one
common or similar cellular function and/or molecular function.
This approach was first tested on the Minimotif Miner database of
experimentally verified minimotifs. Analysis of several variations of
the algorithm demonstrates that this approach can reduce false
positive minimotif predictions in the test dataset and eliminate many
predictions in a set of randomly selected query proteins.
Since there are many false positives in minimotif prediction
programs, any means of selecting minimotifs with a higher probability
of being true is desired. The molecular function filter does provide this
advantage. Another important aspect of the filters presented in this
paper is that they segregate minimotifs into groups for uses. With the
cell function algorithm users can choose minimotifs for target proteins
that are involved in the same cellular process or in a different cellular
process. For example if the query protein is involved in cell division,
one user may want to only look for minimotif predictions for
other proteins involved in cell division or may want to identify
predictions that are involved in other cellular processes. We have also
implemented this for molecular functions as well.
Another important contribution of this paper is the novel
conclusion that it may be possible to combine more than one filters
to get another filter whose performance is better than that of the
individual filters. In particular, we have devised two combinations.
The first combination has the molecular function and the frequency
score filter and the second combination has the cellular function
filter and the frequency score filter. The new combination filters
have much better p-values than all the component filters involved.
Data sources for evaluating the cellular and molecular
function filter algorithms
To reduce the false positives in the minimotif predictions by
MnM with cellular/molecular function information, we obtained
this functional data from the GO database. We selected the GO
database for this purpose because it has the largest ontologies for
these functions and has relationships between functions. The GO
ontology (4/09 release) has 16,698 terms and 32,719 edges for
biological processes/cellular functions and 9309 terms and 9,924
edges for molecular functions. The edges for functional relation-
ships are directed from the juxtaposed node to the larger node for
two neighboring terms. Because identical proteins in the MnM
and GO databases may have different accession numbers, we used
an alias table to map these accession numbers to the cellular/
molecular functions of each protein.
To test the effectiveness of the filter algorithms, we ideally
needed to compare a dataset of verified minimotifs to known
negatives. For experimentally verified minimotifs we used the
Minimotif Miner 2 database (MnM 2), for which the total number
of minimotifs is ,5300 [1,2]. 2,926 of these entries encoded
minimotifs where accession numbers for both the minimotif source
and target proteins were known. Of these 1,739 entries had at least
one cellular function and 2,018 had at least one molecular
function in the GO database. These entries were treated as the
‘‘Validated’’ positive dataset containing experimentally confirmed
We did not have access to known negative minimotifs, so we
generated a dataset that will serve as ‘‘negative’’ interactions that
are comprised of proteins that are most likely not to interact.
There are ,500,000 known protein-protein interactions for
.5,000 total proteins, but if all possible pairwise interactions are
considered, then the number of true minimotifs is a very small
fraction of the total possible number of all interacting protein
pairs. For example, if there are ,30,000 proteins for the
,500,000 interactions, then the total number of possible pairing
is 449,985,000. Thus, it is safe to assume that choosing randomly
generated protein pairings represents ‘‘negative’’ minimotifs.
Therefore, 20,000 entries of random pairs of source proteins
and target proteins were sampled. Of these pairs, 3,153 had at
least one cellular function and 3,706 of the pairs had at least one
molecular function in the GO dataset. These entries were used as
the ‘‘Negative’’ datasets. We then tested if any of these negative data points
was in the positive dataset. In particular, for every minimotif in our database
we generated all the (source, target) pairs. We assembled all of these pairs into
a collection C. Followed by this, for every pair (A, B) in the negative dataset we
checked if (A, B) was in C. None of these 20,000 pairs was in C. This is
again a validation of the way we have picked the negative dataset.
Design and evaluation of the basic function filter
The basic function filter algorithms test whether at least one
common or similar cellular/molecular function is shared by the
given minimotif source protein and target protein. Given the
minimotif source protein S, which contains the putative minimotif
p, and a known or predicted associated target protein T, with the
protein accession number alias table, find the list of cellular
functions of each S, and T. Compare the two function lists to
identify a set C of common cellular functions. The molecular
function filter algorithm is identical except that it utilizes molecular
functions F, instead of C.
This algorithm was applied to the above datasets for molecular
and cellular functions. To evaluate the efficacy of the algorithms
we used two metrics. The percentage for the experimentally
verified minimotifs not removed by the filter is the sensitivity, while
the percentage of negative minimotifs not removed by the filter is
the selectivity. The ratio of sensitivity/selectivity is a Discrimination
Ratio (DR) that measures the preference for verified minimotifs
over that of negative minimotifs. The choice of DR is quite natural.
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Please note that sensitivity is the % of true positives and selectivity is the % of
false positives. Clearly, we want the true positives to be large and the false
positives to be low and hence we want this ratio (i.e., DR) to be large. Ideally,
if an ROC curve could be plotted, that will bring out the statistical significance
nicely. For an ROC curve to be plotted there has to be an underlying parameter
that changes. For some of the datasets in our analysis, there is no relevant
underlying parameter that changes and hence we could not plot ROC curves for
them. Also, note that the ROC curve is nothing but a plot of false positives
versus true positives. In some sense we can think of the DR as a (single number)
summary of the ROC curve.
Sensitivity and selectivity both range from 0% to 100% with
100 % indicating complete recovery of the experimental mini-
motifs or of negative minimotifs. A DR above 1 indicates a
Figure 1. ROC curves for minimotif filters. ROC curves for the molecular (A) and cellular (B) function filters, as well as the frequency score filter
are shown. Analysis was with the minimotifs in the MnM 2 database that have known molecular and cellular functions in the GO database (A,B).
Table 1. Evaluation of the cellular function filter algorithm.
Distance sensitivity selectivity DR
0 11% 3% 3.8
1 26% 6% 4.6
2 48% 14% 3.4
3 65% 32% 2.0
4 82% 58% 1.4
5 90% 79% 1.2
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favorable filtering preference, while those below 1 show worse
performance in selecting the verified minimotifs rather than the
The cellular function algorithm had a sensitivity of ,11% and a
selectivity of ,3%, with a DR of 3.9, indicating that many motifs
were recovered and there was a ,4-fold preference for retaining a
verified instance over a randomly selected negative instance. The
molecular function algorithm had a sensitivity of ,29% and a
selectivity of ,13%, with a DR of 2.3. The test analysis shows that
there is value in using molecular and cellular function filters for
reducing false-positives in minimotif predictions.
Design and evaluation of an expanded algorithm based
on the function similarity
While the Cellular and Molecular function algorithms have
value in reducing false positives, the structure of the GO database
provided us with an opportunity to vary the stringency of function
assignment and optimize these algorithms. GO contains neigh-
borhood information of each cellular/molecular function term.
The nodes are cellular function terms or molecular function terms
in this case, and the edges go from the children nodes to parent
nodes. So the ‘‘at least one common function’’ becomes ‘‘at least
one similar enough function’’. That is to say, the predicted target
proteins are restricted to those for which at least one cellular/
molecular function is similar enough to one in minimotif source
protein, or the distance between at least one cellular/molecular
function of the target protein and that of the source protein is small
enough. We have introduced a distance threshold into the basic
The expended algorithm works as follows: given the distance
threshold t, for each pair of cellular/molecular functions, one from
the list of S and the other from the list of T following the basic
algorithm, examine their ancestors on the directed graph to see
whether there exists a common ancestor function such that
the total distance or the total number of edges between this
ancestor and the pair of functions is smaller than or equal to the
The basic algorithms used a distance threshold of 0; here we
tested 5 additional distance thresholds of 1, 2, 3, 4, and 5. Results
from the evaluation of the cellular function filter are shown in
Table 1. The sensitivity showed a linear increase with node
distance. The DR for verified minimotifs was the highest when the
distance threshold was one with a 4.6-fold preference for verified
minimotifs, but still showed a 3.4-fold preference for a threshold of
two nodes. The sensitivity significantly increased over the basic
filter by using a distance of one or two, rather than 0.
To test the statistical significance of the filters we have used
ROC curves and p-values. We have employed the programs of the
R project  for this purpose. In the case of the cellular function
filter, we have used the distance as the underlying parameter for
plotting the ROC curve (Figure 1A). The area under the ROC
curve is 0.7. and the p-value is 0.12. Note that p-value indicates
the probability of getting the same sensitivity and selectivity results
using a random predictor or filter.
Results from the evaluation of the molecular function filter are
shown in Table 2. Again sensitivity significantly increased with
distances of one or two without a major compromise in the DR. The
molecular function algorithm is more sensitive, but less selective
when compared to the cellular function filter. For the molecular
function filter also, we have plotted the ROC curve with distance as
the underlying parameter. Figure 1B shows this ROC curve. The
area under the ROC curve is 0.8 and the p-value is 0.03.
Both filters have value in reducing false-positives in the test
datasets and stringency of predictions can be controlled by
selecting distances between 0 and 3, whereas the performance of
the algorithms degrades at distance values above 3. The above
results indicate that the filters differentiate verified data from
negative data with a good confidence and strongly suggest when
predicting novel minimotifs these filters would help to decrease the
number of false-positive predictions.
A comparison with the frequency score filter
We wanted to compare the performance of the new filters with
one of the already existing MnM filters, namely, the frequency
score filter. To begin with we have plotted the ROC curve for the
frequency score filter. This ROC curve is shown in Figure 1C.
The area under this curve is 0.7 and the p-value is 0.08, which is
similar to that of the molecular and cellular functional filters.
Table 3 shows a comparison of the new filters with the
frequency score filter on various aspects. Consistent with the ROC
curves this table shows that the molecular function filter is
somewhat stronger than MnM Frequency score filter in discrim-
Table 2. Evaluation of the molecular function filter algorithm.
distance sensitivity selectivity DR
0 29% 12% 2.3
1 59% 21% 2.9
2 82% 35% 2.3
3 91% 50% 1.8
4 94% 61% 1.6
5 96% 72% 1.3
Table 3. Statistics for comparison of functional filters to the
Frequency Score filter.
Area 0.72 0.83 0.72 0.89 0.87
p-value 0.12 0.03 0.08 0.00 2 0.0002
Table 4. Evaluation of the molecular function – frequency
score combined filter.
0.02 0.03 0.04
positive data distance = 0 28% 28% 28%
distance = 1 63% 63% 63%
distance = 2 88% 88% 88%
Negative data distance = 0 19% 16% 15%
distance = 1 27% 24% 23%
distance = 2 41% 39% 38%
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inating true positives from false positives. The cellular function
filter is similar to the MnM frequency score filter in performance.
Note: The above results indicate that the cellular function filter has a
poorer p-value than the frequency score and the molecular function filters. As a
result, one has to exercise caution while employing the cellular function filter.
Both the filters could be of value in clustering the motifs predicted by MnM.
A combination of molecular function and frequency
A novel contribution of this paper is the conclusion that a
combination of several filters can yield a better predictability than
the individual filters. In particular, we have devised two
combination filters. The first combination filter employs the
molecular function and the frequency score filters. Note that these
two filters are based on two different principles. The frequency
score is based on the number of occurrences of the predicted motif
whereas the molecular function filter is based on whether the
source and target proteins share a common molecular function.
Our tests of the combined filter indicate that the combined filter
has a better p-value than the two individual filters.
We have employed the either-or-based combination of the
molecular function filter and frequency score filter, in the
expectation that the two filters can complement each other in
some way, which is reasonable since they focus on different aspects
and therefore the combined filter may outperform any of the two.
Given a motif of some source protein, associated with its target
protein, the combined filter examines whether the source and
target proteins are retained by the molecular function filter, as well
as whether the motif and source are retained by the frequency
score filter. If either filter retains them, the combined filter retains
This idea was tested on the same positive dataset and negative
datasets. The positive datasets have already got experimentally
verified entries of motif, its source protein and the associated target
protein. For the negative datasets, which are 20,000 random
protein pairs, we threw one of each protein pair into Minimotif
Miner (MnM) [1,2] as the source query protein and found its motif
to form the triple of motif, source protein and target protein.
There are totally 463, 062 such triples, of which an unknown
molecular function can be found for both the source and target in
GO dataset. Then three thresholds (0.02, 0.03, 0.04) for frequency
score filter were picked up, together with three distances (0, 1, 2)
for molecular function filter, and the nine combinations of these
thresholds and distances are used as the threshold parameters of
the combined filter. The prediction of the combined filter is shown
in Table 4. To form a smooth curve, very small noises were
added to the sensitivity and selectivity, which is no more than
. The ROC curve is shown in Figure 2A, of which
the area under the curve (AUC) is 0.89 and the p-value is 0.002,
shown in Table 3.
Figure 2. ROC curve for the combined filters. Combination of molecular function and frequency score filters (A) and combination of cellular
function and frequency score filters (B) are shown. These ROC curves have been obtained by combining the two pairs of filters on an either-or basis.
Table 5. Evaluation of the cellular function – frequency score
0.02 0.03 0.04
positive data distance = 0 17% 17% 17%
distance = 1 44% 44% 44%
distance = 2 75% 75% 75%
distance = 3 88% 88% 88%
distance = 4 95% 95% 95%
negative data distance = 0 9% 6% 5%
distance = 1 12% 9% 8%
distance = 2 20% 17% 16%
distance = 3 37% 34% 34%
distance = 4 62% 60% 60%
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A combination of cellular function and frequency score
The second combination filter employs the cellular function
filter and frequency score filter in the same way. Considering the
cellular function filter is more stringent, five distances (0, 1, 2, 3, 4)
were used, together with the same three thresholds (0.02, 0.03,
0.04) for frequency score filter. As a result, fifteen threshold
parameters were formed for this combination of cellular function
and frequency score filters. To smoothen the ROC curve, very
small noises were also added, which is no more than
. The prediction of this combination is shown in
Table 5 and the ROC curve is shown in Figure 2B, for which
AUC is 0.87 and the p-value is 0.0002, shown in Table 3. Note
that even though the frequency score filter and the cellular
function filter on their own are not highly predictive, their
combination is very impressive.
Implementation of cellular and molecu lar function filters
We have implemented these new filters with the other filters on
the MnM 2 website (Figure 3). We allow the user to vary the
stringency by choosing different thresholds. We have added the
results of this analysis and a description to help users interpret the
results they should expect for different distance thresholds. We
have also designed the implementation so that this filter can be
used in combination with other MnM filters. We expect that when
used in combination with other MnM filters, this will increase the
specificity, but reduce the sensitivity of identifying true minimotifs.
We anticipate that some users will want to look for new function of
proteins and exclude minimotif predictions that are related to the
known functions. Therefore, we have used a GUI checkbox that
allows users to only see minimotifs that were excluded from the
We wanted to examine how many predicted minimotifs were
filtered by the algorithms. We ran the filter on P53, Cyclin A, and
MSH2, which each have different molecular and cellular functions
(22 more proteins were tested and are shown in Supporting
Information S1). Statistics for predictions from this analysis are
shown in Table 6. The basic Cellular function filters eliminated
90–95 percent of the target predictions, retaining only those with
similar cell functions as expected. The Molecular function filter
was less robust eliminating 27–48 of the minimotif predictions.
Altering the GO term distance threshold also had the anticipated
result where the stringency of predictions was titrated as expected.
Figure 3. Image of the filter selector on the MnM website. All filters in this paper are now included as part of the MnM website. The option to
select minimotifs that have similar or dissimilar functions is implemented.
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It is important to increase the efficiency and specificity of
minimotif prediction. Many minimotif filters increase the specificity
of minimotif predictions. Over time the collective use of a set of well-
developed filters such as the ones we present here will lead to
accurate computational tools. This is not just true for minimotifs,
but for transcription factor binding sites as well. Incremental
development of algorithms is a standard in computational biology.
We have reported two new filters for the elimination of false
positives in minimotif predictions. Our testing results reveal that
these filters are indeed effective. The use of these filters seems to be
a logical approach for reducing false positives. If two proteins are
involved in the same cellular or molecular function, they may be in
the same or redundant pathways. However, if one contains a
minimotif that is the target of another protein in the pathway, then
this provides a second piece of data suggesting a functional
relationship between the two proteins.
The cell function filter, eliminated 90–95% of the predictions
for the 3 proteins we tested. This is the most stringent filter we
have come across in the other filters designed for MnM. The
frequency filter, surface prediction filter, and evolutionary
conservation filters all showed a preference for filtering false
positives, but not to the extent seen for the cellular function filter.
The molecular function filter, while not as stringent as the cellular
function filter, also performed better than previous filters
implemented in MnM. This suggests that other data-driven
minimotif filters used by themselves, or in combination may
provide a good approach for reducing false positives. This does
come at a cost, as a percentage of true minimotifs may be filtered.
We have been running Minimotif Miner for 4 years and one of
the major difficulties for users is that when a list of potential target
names is presented to them, most scientists do not have a
knowledge-base to understand all of the different functions in the
potential targets and this makes it difficult to select minimotifs for
experimental testing. The new functional filters help to alleviate
this problem, by restricting the predictions to those functions that
are related to the query protein. In the case where a user wants to
know new functions of their query, they can use the ‘‘exclude’’
filter, to identify only those functions that are not previously
related to the query. In conclusion, the functional filters provide a
valuable tool for reducing false-positive prediction of minimotifs.
Figure 3 shows a screenshot of the filter selection page in MnM.
Supporting Information S1 Supporting information document.
Found at: doi:10.1371/journal.pone.0012276.s001 (0.23 MB
We would like to thank the Minimotif Miner team for daily input in
preparation of the data for this paper.
Conceived and designed the experiments: SR MRS. Performed the
experiments: TM JCM. Analyzed the data: SR TM JCM PG MRS.
Contributed reagents/materials/analysis tools: AO PG. Wrote the paper:
SR TM MRS.
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Table 6. Analysis of novel queries with the cellular and
molecular function filters.
Cellular function Molec. function
Protein RefSeq Threshold *Total Retained *Total Retained
p53 NP_035770 0 64 10 67 46
p53 NP_035770 1 64 33 67 53
p53 NP_035770 2 64 52 67 63
p53 NP_035770 3 64 61 67 64
p53 NP_035770 4 64 64 67 65
p53 NP_035770 5 64 64 67 65
Cyclin A NP_003905 0 81 3 82 38
Cyclin A NP_003905 1 81 6 82 51
Cyclin A NP_003905 2 81 23 82 65
Cyclin A NP_003905 3 81 40 82 69
Cyclin A NP_003905 4 81 64 82 72
Cyclin A NP_003905 5 81 77 82 75
MSH2 NP_000242 0 76 8 80 25
MSH2 NP_000242 1 76 15 80 52
MSH2 NP_000242 2 76 34 80 66
MSH2 NP_000242 3 76 62 80 74
MSH2 NP_000242 4 76 73 80 76
MSH2 NP_000242 5 76 75 80 77
*Totals do not include minimotifs for which no GO terms are assigned to the
Improving MnM Predictions
PLoS ONE | www.plosone.org 7 August 2010 | Volume 5 | Issue 8 | e12276