In silico prediction of deleterious single amino acid polymorphisms from amino acid sequence

Bioinformatics tools for discovery and functional analysis of single nucleotide polymorphisms

With the high speed DNA sequencing of genome, databases of genome data continue to grow, and the understanding of genetic variation between individuals grows as well. Single nucleotide polymorphisms (SNPs), a main type of genetic variation, are increasingly important resource for understanding the structure and function of the human genome and become a valuable resource for investigating the genetic basis of disease. During the past years, in addition to experimental approaches to characterize specific variants, intense bioinformatics techniques were applied to understand effects of these genetic changes. In the genetics studies, one intends to understand the molecular basis of disease, and computational methods are becoming increasingly important for SNPs selection, prediction and understanding the downstream effects of genetic variation. The review provides systematic information on the available resources and methods for SNPs discovery and analysis. We also report some new results on DNA sequence-based prediction of SNPs in human cytochrome P450, which serves as an example of computational methods to predict and discovery SNPs. Additionally, annotation and prediction of functional SNPs, as well as a comprehensive list of existing tools and online recourses, are reviewed and described.

The role of balanced training and testing data sets for binary classifiers in bioinformatics

Training and testing of conventional machine learning models on binary classification problems depend on the proportions of the two outcomes in the relevant data sets. This may be especially important in practical terms when real-world applications of the classifier are either highly imbalanced or occur in unknown proportions. Intuitively, it may seem sensible to train machine learning models on data similar to the target data in terms of proportions of the two binary outcomes. However, we show that this is not the case using the example of prediction of deleterious and neutral phenotypes of human missense mutations in human genome data, for which the proportion of the binary outcome is unknown. Our results indicate that using balanced training data (50% neutral and 50% deleterious) results in the highest balanced accuracy (the average of True Positive Rate and True Negative Rate), Matthews correlation coefficient, and area under ROC curves, no matter what the proportions of the two phenotypes are in the testing data. Besides balancing the data by undersampling the majority class, other techniques in machine learning include oversampling the minority class, interpolating minority-class data points and various penalties for misclassifying the minority class. However, these techniques are not commonly used in either the missense phenotype prediction problem or in the prediction of disordered residues in proteins, where the imbalance problem is substantial. The appropriate approach depends on the amount of available data and the specific problem at hand.

SySAP: a system-level predictor of deleterious single amino acid polymorphisms

Single amino acid polymorphisms (SAPs), also known as non-synonymous single nucleotide polymorphisms (nsSNPs), are responsible for most of human genetic diseases. Discriminate the deleterious SAPs from neutral ones can help identify the disease genes and understand the mechanism of diseases. In this work, a method of deleterious SAP prediction at system level was established. Unlike most existing methods, our method not only considers the sequence and structure information, but also the network information. The integration of network information can improve the performance of deleterious SAP prediction. To make our method available to the public, we developed SySAP (a System-level predictor of deleterious Single Amino acid Polymorphisms), an easy-to-use and high accurate web server. SySAP is freely available at http://www.biosino.org/ SySAP/ and http://lifecenter.sgst.cn/SySAP/.