 Hello, the subject of this video is Swiss Orthology, a tool and a website which combines Swiss Institute of Bioinformatics Orthology resources. It aims to provide orthologs inferred by both resources and to guide you in choosing the most appropriate for your analysis. Imagine the following scenarios. Your molecular biologists studying a particular gene in the laboratory. You mainly do wet lab experiments, but you want to know more about your protein and its evolutionary history. Where do you start? Or you're a bioinformatics PhD student working in a lab that just sequenced a rare species. You've been tasked with the annotation and functional prediction of its proteins. Where do you begin? When faced with these scenarios, generally the following thoughts might go through your mind. What's the difference between an orthologue and a paralogue again? Will I have to download, learn and run some software? How painful will that be? What's the fastest way I can get orthologs between my species of interest? And can I understand the basics of what's going on in the black box of this software? Enough to know if it's appropriate for my analysis, its limitations and enough of the method to describe it. One way to help solve these problems is Swiss Orthology. Swiss Orthology is a recent SIB funded initiative aimed at serving the orthology community, both users and method developers. OMA, which stands for Orthologist Matrix, and OrthoDB are two leading resources which provide pre-computed gene orthology and standalone software for custom orthology analysis. Swiss Orthology is a joint collaboration between both groups, but Swiss Orthology is more than just OMA and OrthoDB. It supports services such as BUSCA and the Quest for Orthologs benchmark service, as well as community activities such as the yearly philosyve meeting for the Swiss phylogenetic community. But before going more into what Swiss Orthology offers, it's necessary to answer that nagging little question. What exactly is orthology? We must first start from the beginning. Evolution is one of the fundamental principles of biology. A major concept in evolution is that of homology or the relationship between genes related by a common ancestry. From this general homologous relationship, pairs of genes might be classified into any various subgroups of homologs, including orthologue, paralogue, zanologue, or homeologue, among others. There are two main types of homologs we will discuss, orthologs and paralogs. In comparative genomics and phylogenetics, the fundamental concept of orthology relates corresponding genes in different species. Orthologs are pairs of genes which have evolved from the same gene in the last common ancestor. Thus, orthologs started diverging due to aspeciation events. In contrast, paralogs are pairs of genes which started diverging from a duplication event. All of this can be visualized on a gene tree. In this example, imagine there was a common ancestral gene A. A duplication event produces paralogous genes A and B. Later, aspeciation event occurs, which causes the A and B genes to further start diverging into species 1 and species 2. Thus, these definitions are based solely on the evolutionary event which gave rise to the pair of genes in question, either aspeciation event, orthologs, or a duplication event, paralogs. Sometimes it is useful to deal with groups of genes which are derived from a single gene in an ancestral species. This is what we refer to as hierarchical orthologous groups or hogs. Hogs are clusters of orthologs and paralogs defined at a given taxonomic level of interest. Here in this example gene tree we have three hogs nested and defined at particular taxonomic levels. What advantage do hogs have? Well, they relate genes from multiple species and also give a finer level of granularity depending on your specific taxonomic level of interest. A hog is another way of looking at a gene family. For more information on hogs, see our YouTube video, What Are Hogs? Orthologs are useful to a variety of applications in genetics, genomics, cell and molecular biology, and of course evolutionary biology. Among the many applications include prediction of gene function, verification of function conservation, phylogenetics and phylogenomics, finding the best model systems to study a gene of interest, phylogenetic profiling and elucidating a gene family's evolutionary history in terms of gene birth, death and duplication, among others. A wide range of methods have been developed to infer orthologs, including OMA and orthodepine. There is a lot more on orthology. If you're interested here are some starting resources. The links are provided in the description. There are many types of homologs and many methods to infer them. One thing that might be difficult to know when starting your analysis is what type of orthologue would be the most appropriate type for your study. There are many orthology databases out there. With a focus on OMA and orthodepine, we aim to provide guidance to help prospective users determine which platform is suited to their needs. For this, we provide the Swiss Orthology Guide. Here you can interactively find information to help you choose between different Swiss orthology tools. For some applications, either OMA or orthodv is better suited. For others, both orthology resources are fine. In the flow chart, you can interactively decide which resource to use based on the type of species, type of analysis or format of data. Let's take one of the examples from before. You're the molecular biologist wanting to study the evolutionary history of your gene by making a phylogenetic tree. Navigate to Swissorthology.ch. You can then follow the flow chart by choosing Guide in the upper right-hand corner, type of analysis, then build a gene tree. We can see that both OMA and orthodb have functions in their browser to help get the orthologs of a gene family and to build a tree. Links to the respective website and more information are provided. The main service that Swissorthology provides is actually a tool to find and download the orthologs of your favorite gene from both OMA and orthodb at the same time. This is made possible by storing the data in RDF format and querying both SIV databases using Sparkle in the back end. This way, with one simple search, one can get the hogs from either OMA, orthodb, or take the intersection of orthologs or the union. Back to the scenario where you're trying to make a phylogenetic tree of your favorite gene. In order to get the orthologs between the two species, predicted by both OMA and orthodb, first search the Unipro KB identifier, which will take you to a table where the rows are different taxonomic levels, and the columns show which resource detects and or computes orthologs at that level. In this example, we can see that both OMA and orthodb computed hogs at the embryo phyto level. We can click on this row to be taken to the set of genes inferred to be orthologs to our gene of interest. Each row is a protein from the species at the embryo phyto level or below. We can see whether the proteins were predicted to be orthologs by OMA or orthodb resource. One can then download the sequences in faster format from one or the other resource both or the intersection. In this example, some genes are predicted in either OMA or orthodb simply due to differences in genomes which each database uses. For example, orthodb inferred orthologs using data vulgaris while OMA does not. More information about which species are used in each resource can be found in the Swiss orthology guide. In summary, Swiss orthology is an SIB funded resource which supports the maintenance and development of both OMA and orthodb to leading methods for inferring orthologs. It provides orthology guidance to know the difference and to help choose the best resource for your particular analysis. It also allows users to query both resources simultaneously and download the orthologs. As Swiss orthology is still in development, we want to hear from you. Any feedback, comments or suggestions are welcome at info at Swissorthology.ch. Many thanks to the Swiss orthology team and to the Swiss Institute of Bioinformatics.