Proteins are the basic building blocks of all living organisms. They play a central role in determining the structure of living beings and are required for essential chemical reactions. One of the main challenges in bioinformatics is to characterize the function of all proteins. The problem of understanding protein function can be approached by understanding their evolutionary history. Orthology analysis plays an important role in studying the evolutionary relation of proteins. Proteins are termed orthologs if they derive from a single gene in the species' last common ancestor, i.e. if they were separated by a speciation event. Orthologs are useful because they retain their function more often than other homologs. 

Inference of a complete set of orthologs for many species is computationally intensive. Currently, the fastest algorithms rely on graph-based approaches, which compare all-vs-all sequences and then cluster top hits into groups of orthologs. The initial step of performing all-vs-all comparisons is usually the primary computational challenge as it scales quadratically with the number of species. 

A new, more scalable and less computationally demanding method was developed to solve this problem without sacrificing accuracy. The Hieranoid 2 algorithm reduces computational complexity to almost linear by overcoming the necessity to perform all-vs-all similarity searches. The algorithm progresses along a known species tree, from leaves to root. Starting at the leaves, ortholog groups are predicted conventionally and then summarized at internal nodes to form pseudo-species. These pseudo-species are then re-used to search against other (pseudo-)species higher in the tree. This way the algorithm aggregates new ortholog groups hierarchically. The hierarchy is a natural structure to store and view large multi-species ortholog groups, and provides a complete picture of inferred evolutionary events. 

To facilitate explorative analysis of hierarchical groups of orthologs, a new online tool was created. The HieranoiDB website provides precomputed hierarchical groups of orthologs for a set of 66 species. It allows the user to search for orthology assignments using protein description, protein sequence, or species. Evolutionary events and meta information is added to the hierarchical groups of orthologs, which are shown graphically as interactive trees. This representation allows exploring, searching, and easier visual inspection of multi-species ortholog groups.

The majority of orthology prediction methods focus on treating the whole protein sequence as a single evolutionary unit. However, proteins are often composed of individual units, called protein domains, that can have different evolutionary histories. To extend the full sequence based methodology to a domain-aware method, a new approach called Domainoid is proposed. Here, domains are extracted from full-length sequences and subjected to orthology inference. This allows Domainoid to find orthology that would be missed by a full sequence approach.

Networks are a convenient graphical representation for showing a large number of functional associations between genes or proteins. They allow various analyses of graph properties, and can help visualize complex relationships. A framework for inferring comprehensive functional association networks was developed, called FunCoup. A major difference compared to other networks is FunCoup's extensive use of orthology relationships between species, which significantly boosts its coverage. Using naïve Bayesian classifiers to integrate 10 different evidence types and orthology transfer, FunCoup captures functional associations of many types, and provides comprehensive networks for 17 species across five gold-standards.

Understanding biological systems in complex organisms is important in life science in order to comprehend the interplay of genes, proteins, and compounds causing complex diseases. As biological systems are intricate, bioinformatics tools, models, and algorithms are of the utmost importance to understand the bigger picture and decipher biological meaning from the vast amounts of information available from biological experiments and predictions. Bioinformatics programs and algorithms do not only depend on information from experiments, but also on information generated from other tools in order to draw accurate conclusions and make predictions. 

Prediction of orthologs, genes having a common ancestry, separated by a speciation event, are important building blocks for a wide variety of tools and analysis pipelines, as they can be used to transfer gene function between species. Orthologs can for example be used to map genes of model organisms to genes in humans in studies of drug targets. They are extensively used in functional association networks in order to transfer information between species. Functional association networks are models of associations between genes or proteins, where associations can be derived from experimental evidence of different types, from the species itself, or transferred from other species using orthologs. The networks can be used to explore the context and neighbors of a gene, but also for a variety of higher-level analyses, e.g. network-based pathway enrichment analysis. In pathway enrichment analysis the networks can be utilized to contextualize experimental gene sets and annotate them with biological functions. As these tools depend on each other, it is of great importance that the networks used in pathway enrichment analysis are comprehensive and accurate, and that the orthologs used in the networks are relevant and significant. 

In this thesis, the development and improvement of five bioinformatics tools within three areas of bioinformatics are presented. Despite the tools residing within slightly different areas, they all rely on each other, and can all on different levels improve our understanding of biological functions and biological meaning, from the level of orthology analysis to functional association networks to pathway enrichment analysis.