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

Evaluating the power of QTL mapping by simulations.

<p><b>a</b> The distributions of the LOD values at markers along chromosomes (left: the chromosome with the QTL, right: a chromosome representing the other chromosomes without the QTL). The QTL is located at the middle of the chromosome (left panel). The red and blue lines show the LOD scores of Method I and Method II, respectively. The result is from a single replication of the simulation with  = 200,  = 0, and  = 2. The 5% cutoff values for the two methods are shown by broken lines. <b>b</b> The distributions of the power of the two methods, which were obtained by 10,000 replications.</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

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

Role of NGS in genomics-assisted breeding.

<p>NGS occupies a critical position in a genomics-assisted breeding pipeline; it helps improve the speed and precision of trait mapping to identify genes and QTLs that are the targets of MAS, and it underlies the ability to calculate GEBVs based on genome-wide prediction that predict the breeding value of individuals in a breeding population using GS.</p

Prediction accuracies of NetNES, Wregex, NESsential, NESmapper, and consensus-based NES predictions using the ValidNES/SpNES test data.

<p>Prediction accuracies were determined with the ValidNES dataset consisting of 185 proteins containing 205 LMB-sensitive NESs, as positive and negative data and the Sp-test negative dataset, containing 60 proteins from the Sp-protein dataset, as in <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003841#pcbi-1003841-t001" target="_blank">Tables 1</a> and <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003841#pcbi-1003841-t002" target="_blank">2</a>.</p>a<p>Prediction with recommended PSSM configuration.</p>b<p>Prediction with relaxed PSSM configuration.</p>c<p>NESs with the probability values of ≥0.1 selected.</p>d<p>NESs with the probability values of ≥0.5 selected.</p>e<p>Percentage of proteins containing predicted NESs is indicated with parentheses.</p><p>Prediction accuracies of NetNES, Wregex, NESsential, NESmapper, and consensus-based NES predictions using the ValidNES/SpNES test data.</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

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

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