Virus-induced gene silencing database for phenomics and functional genomics in Nicotiana benthamiana

Virus-induced gene silencing (VIGS) is an important forward and reverse genetics method for the study of gene function in many plant species, especially Nicotiana benthamiana. However, despite the widespread use of VIGS, a searchable database compiling the phenotypes observed with this method is lacking. Such a database would allow researchers to know the phenotype associated with the silencing of a large number of individual genes without experimentation. We have developed a VIGS phenomics and functional genomics database (VPGD) that has DNA sequence information derived from over 4,000 N. benthamiana VIGS clones along with the associated silencing phenotype for approximately 1,300 genes. The VPGD has a built-in BLAST search feature that provides silencing phenotype information of specific genes. In addition, a keyword-based search function could be used to find a specific phenotype of interest with the corresponding gene, including its Gene Ontology descriptions. Query gene sequences from other plant species that have not been used for VIGS can also be searched for their homologs and silencing phenotype in N. benthamiana. VPGD is useful for identifying gene function not only in N. benthamiana but also in related Solanaceae plants such as tomato and potato. The database is accessible at http://vigs.noble.org.Noble Research Institute and NSF IOS-102564

The performance (coverage metric) of the HMM search on the main dataset using a five-fold cross-validation.

<p>The performance (coverage metric) of the HMM search on the main dataset using a five-fold cross-validation.</p

Prediction of Membrane Transport Proteins and Their Substrate Specificities Using Primary Sequence Information

<div><p>Background</p><p>Membrane transport proteins (transporters) move hydrophilic substrates across hydrophobic membranes and play vital roles in most cellular functions. Transporters represent a diverse group of proteins that differ in topology, energy coupling mechanism, and substrate specificity as well as sequence similarity. Among the functional annotations of transporters, information about their transporting substrates is especially important. The experimental identification and characterization of transporters is currently costly and time-consuming. The development of robust bioinformatics-based methods for the prediction of membrane transport proteins and their substrate specificities is therefore an important and urgent task.</p><p>Results</p><p>Support vector machine (SVM)-based computational models, which comprehensively utilize integrative protein sequence features such as amino acid composition, dipeptide composition, physico-chemical composition, biochemical composition, and position-specific scoring matrices (PSSM), were developed to predict the substrate specificity of seven transporter classes: amino acid, anion, cation, electron, protein/mRNA, sugar, and other transporters. An additional model to differentiate transporters from non-transporters was also developed. Among the developed models, the biochemical composition and PSSM hybrid model outperformed other models and achieved an overall average prediction accuracy of 76.69% with a Mathews correlation coefficient (MCC) of 0.49 and a receiver operating characteristic area under the curve (AUC) of 0.833 on our main dataset. This model also achieved an overall average prediction accuracy of 78.88% and MCC of 0.41 on an independent dataset.</p><p>Conclusions</p><p>Our analyses suggest that evolutionary information (i.e., the PSSM) and the AAIndex are key features for the substrate specificity prediction of transport proteins. In comparison, similarity-based methods such as BLAST, PSI-BLAST, and hidden Markov models do not provide accurate predictions for the substrate specificity of membrane transport proteins. <i>TrSSP: The Transporter Substrate Specificity Prediction Server</i>, a web server that implements the SVM models developed in this paper, is freely available at <a href="http://bioinfo.noble.org/TrSSP" target="_blank">http://bioinfo.noble.org/TrSSP</a>.</p></div

Variance inamino acid composition of transporter proteins.

<p>The variance in amino acid residues across amino acid transporters, anion transporters, cation transporters, electron transporters, protein/mRNA transporters, sugar transporters, other transporters, and non-transporters is plotted.</p

The performances of the best models on the main dataset for different substrate-specific transporter classes.

<p>The performances of the best models on the main dataset for different substrate-specific transporter classes.</p

The performance (coverage metric) of the BLAST search on the main dataset using the standard five-fold cross-validation.

<p>The performance (coverage metric) of the BLAST search on the main dataset using the standard five-fold cross-validation.</p

Amino acid composition of transporter proteins.

<p>The amino acid composition for each amino acid as a percentage of the total number of amino acids for amino acid transporters (blue diamonds), anion transporters (red squares), cation transporters (green triangles), electron transporters (purple x), protein/mRNA transporters (cyan asterisks), sugar transporters (orange circles), other transporters (plus signs), and non-transporters (orange dashes) are plotted.</p

A comparison of the coverage metric of our models with TTRBF.

<p>A comparison of the coverage metric of our models with TTRBF.</p

The performance (coverage metric) of the HMM search on the main dataset using a five-fold cross-validation.

<p>The performance (coverage metric) of the HMM search on the main dataset using a five-fold cross-validation.</p