The power to detect two linked QTLs under the two-locus model.

<p>(<b>a</b>) <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0046545#s3" target="_blank">Results</a> when the two QTLs have a coupling effect. (<b>b</b>) <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0046545#s3" target="_blank">Results</a> when the two QTLs have a decoupling effect. The power is shown for each QTL. See text for details.</p

The power of QTL mapping as a function of the number of inbreeding generations.

<p><b>a </b> and <b>b </b> are assumed. The red and blue lines are for the results of Method I and Method II, respectively. See text for details.</p

The effects of recombination rate (a), genome size (b) and marker density (c) on the power of QTL mapping.

<p>The panels in broken squares are identical to <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0046545#pone-0046545-g002" target="_blank">Fig. 2a</a>.</p

The power of QTL mapping with over-dominance.

<p>(<b>a</b>) Asymmetric over-dominance. (<b>b</b>) Symmetric over-dominance. See text for details.</p

Improved performance of NESmapper by optimization the profiles.

a<p>Three test datasets were used for the performance evaluation. (1) The artificial NES set: 163 positive and 60 negative experimentally verified NESs; (2) the ValidNES-test set containing 92 proteins (100 NESs) from the ValidNES database of positive and negative data; (3) the Sp-test set, a negative test set containing 50 proteins from the Sp-protein dataset.</p>b<p>Profiles optimization was conducted using training data sets, excluding the corresponding test data.</p>c<p>Profiles optimization was conducted using training data sets, including the corresponding test data.</p><p>N.T.: not tested.</p><p>Improved performance of NESmapper by optimization the profiles.</p

Positive and negative NES datasets obtained from four different data resources.

<p>(<b>A</b>) Artificial NES datasets. (<b>B</b>) DUB NES datasets. (<b>C</b>) Valid NES datasets. (<b>D</b>) Sp-protein datasets. The positive and negative datasets (B-P2 and B-N2) of the DUB datasets and the negative training dataset (D-N2) of the Sp-protein datasets were always included in the training data for the profile optimization, whereas the other training datasets were used only when they were not contained in a test dataset to be used. For example, when we conducted the prediction test with the test datasets, A-P1 and A-N1, we used the optimized profiles for NESmapper, that were trained with C-N2, in addition to B-P2, B-N2, and D-N2.</p

NESmapper: Accurate Prediction of Leucine-Rich Nuclear Export Signals Using Activity-Based Profiles

<div><p>The nuclear export of proteins is regulated largely through the exportin/CRM1 pathway, which involves the specific recognition of leucine-rich nuclear export signals (NESs) in the cargo proteins, and modulates nuclear–cytoplasmic protein shuttling by antagonizing the nuclear import activity mediated by importins and the nuclear import signal (NLS). Although the prediction of NESs can help to define proteins that undergo regulated nuclear export, current methods of predicting NESs, including computational tools and consensus-sequence-based searches, have limited accuracy, especially in terms of their specificity. We found that each residue within an NES largely contributes independently and additively to the entire nuclear export activity. We created activity-based profiles of all classes of NESs with a comprehensive mutational analysis in mammalian cells. The profiles highlight a number of specific activity-affecting residues not only at the conserved hydrophobic positions but also in the linker and flanking regions. We then developed a computational tool, NESmapper, to predict NESs by using profiles that had been further optimized by training and combining the amino acid properties of the NES-flanking regions. This tool successfully reduced the considerable number of false positives, and the overall prediction accuracy was higher than that of other methods, including NESsential and Wregex. This profile-based prediction strategy is a reliable way to identify functional protein motifs. NESmapper is available at <a href="http://sourceforge.net/projects/nesmapper" target="_blank">http://sourceforge.net/projects/nesmapper</a>.</p></div

ROC analyses for five NES prediction methods.

<p>(<b>A</b>) ROC curve generated with artificial NES datasets. For the artificial NES sets, 163 positive and 60 negative experimentally verified NESs were used to plot the ROC curves for the traditional consensus-based prediction, NetNES, NESmapper, Wregex, and NESsential. The true positive rates (TPRs) and false positive rates (FPRs) for each tool were measured by changing the threshold scores for Wregex and NESmapper or the threshold probability values for NESsential. The curves for the NESmapper predictions with the optimized and unoptimized profiles are shown with solid lines with red circles and with dotted lines with orange triangles, respectively, those for Wregex with solid lines with green squares, and those for NESsential with solid lines with blue diamonds. The results for the traditional consensus-based prediction and NetNES are shown with green and blue asterisks, respectively. (<b>B</b>) ROC curve generated with ValidNES/Sp-test datasets. We measured the false positives by counting NESs called from regions other than the ranges corresponding to true NESs. To calculate the FPRs for the ValidNES and Sp-test datasets, only called NESs that matched the traditional consensus sequence were counted as false positives and divided by the number of sequences that matched the traditional consensus sequence in each dataset (841 for ValidNES and 231 for Sp-test). The mean FPRs for both datasets were used for the analysis. (<b>C</b>) ROC curve generated with the artificial NES and ValidNES/Sp-test datasets.</p

Prediction accuracies of NetNES, Wregex, NESsential, NESmapper, and consensus-based NES predictions using artificial NES test data.

<p>Prediction accuracies of the indicated methods and tools were determined with the artificial NES sets, as in <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003841#pcbi-1003841-t001" target="_blank">Table 1</a>.</p>a<p>Traditional NES consensus sequence, Φ–X2,3–Φ–X2,3–Φ–X–Φ.</p>b<p>Class 1a, 1b, 1c, 1d, 2, and 3 NES consensus sequences, not allowing A, C, T, or W at positions Φ3 and Φ4 (see <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003841#s2" target="_blank">Introduction</a> for detail).</p>c<p>Prediction with recommended PSSM configuration.</p>d<p>Prediction with relaxed PSSM configuration.</p>e<p>NESs with the probability values of ≥0.1 selected.</p>f<p>NESs with the probability values of ≥0.5 selected.</p>g<p>Prediction with unoptimized NES profiles, NESs with a score of ≥2 were selected.</p>h<p>Prediction with optimized NES profiles. NESs with a score of ≥2 were selected.</p><p>Prediction accuracies of NetNES, Wregex, NESsential, NESmapper, and consensus-based NES predictions using artificial NES test data.</p

Independent and additive contributions of amino acids at the conserved hydrophobic positions to the entire NES activity.

<p>One or two leucine residues of a class 1a NES at the Φ1, Φ3, or Φ4 conserved hydrophobic positions, indicated on the top line, were replaced with cysteine, phenylalanine, threonine or tryptophan, as highlighted in blue, and the nuclear export activity was assayed in NIH3T3 cells. The indicated activity scores were determined as in <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003841#pcbi.1003841.s001" target="_blank">Figure S1</a>. Note that the effects of the substituted residues on the NES activity scores were roughly independent and additive.</p