Detecting genetic regions under selection in structured populations is of great importance in ecology, evolutionary biology and breeding programmes. We recently proposed EigenGWAS, an unsupervised genomic scanning approach that is similar to F ST but does not require grouping information of the population, for detection of genomic regions under selection. The original EigenGWAS is designed for the random mating population, and here we extend its use to inbred populations. We also show in theory and simulation that eigenvalues, the previous corrector for genetic drift in EigenGWAS, are overcorrected for genetic drift, and the genomic inflation factor is a better option for this adjustment. Applying the updated algorithm, we introduce the new EigenGWAS online platform with highly efficient core implementation. Our online computational tool accepts plink data in a standard binary format that can be easily converted from the original sequencing data, provides the users with graphical results via the R-Shiny user-friendly interface. We applied the proposed method and tool to various data sets, and biologically interpretable results as well as caveats that may lead to an unsatisfactory outcome are given. The EigenGWAS online platform is available at www.eigen gwas.com, and can be localized and scaled up via R (recommended) or docker.

Predicting enzymes function is an important and difficult problem, particularly when enzymes may have the multiplex character, i.e., some enzymes simultaneously have two or three function classes. Most of the existing enzyme function predictor can only be used to deal with the mono-functional enzymes. Actually, multi-functional enzymes should not be ignored because they usually possess diverse biological functions worthy of our special notice. By introducing the ''improved Hybrid Multi-label Classifier'' and ''neighbor score'', a new predictor, called MF-EFP, has been developed that can be used to deal with the systems containing both mono-functional and multi-functional enzymes. As demonstration, the jackknife cross-validation was performed with MF-EFP on a benchmark dataset of enzymes classified into the following 7 functional classes: (1) EC 1 Oxidoreductase, (2) EC 2 Transferase, (3) EC 3 Hydrolase, (4) EC 4 Lyase, (5) EC 5 Isomerase, (6) EC 6 Ligase, (7) EC7 Translocases, where none of enzymes included has ≥90% pairwise sequence identity to any other in a same subset. The subset accuracy and average precision thus obtained by MF-EFP was 85.62% and 94.16% respectively. Extensive experiments also show that MF-EFP can outperform the existing predictors that also have the capacity to deal with such a complicated and stringent system. As a user-friendly web-server, MF-EFP is freely accessible to the public at the web-site http:// www.jci-bioinfo.cn/MF-EFP.