MOTIVATION: Protein sequence comparison is a fundamental element in the bioinformatics toolkit. When sequences are annotated with features such as functional domains, transmembrane domains, low complexity regions or secondary structure elements, the resulting feature architectures allow better informed comparisons. However, many existing schemes for scoring architecture similarities cannot cope with features arising from multiple annotation sources. Those that do fall short in the resolution of overlapping and redundant feature annotations. RESULTS: Here, we introduce FAS, a scoring method that integrates features from multiple annotation sources in a directed acyclic architecture graph. Redundancies are resolved as part of the architecture comparison by finding the paths through the graphs that maximize the pair-wise architecture similarity. In a large-scale evaluation on more than 10 000 human-yeast ortholog pairs, architecture similarities assessed with FAS are consistently more plausible than those obtained using e-values to resolve overlaps or leaving overlaps unresolved. Three case studies demonstrate the utility of FAS on architecture comparison tasks: benchmarking of orthology assignment software, identification of functionally diverged orthologs, and diagnosing protein architecture changes stemming from faulty gene predictions. With the help of FAS, feature architecture comparisons can now be routinely integrated into these and many other applications. AVAILABILITY AND IMPLEMENTATION: FAS is available as python package: https://pypi.org/project/greedyFAS/.

The Quest for Orthologs (QfO) is an open collaboration framework for experts in comparative phylogenomics and related research areas who have an interest in highly accurate orthology predictions and their applications. We here report highlights and discussion points from the QfO meeting 2015 held in Barcelona. Achievements in recent years have established a basis to support developments for improved orthology prediction and to explore new approaches. Central to the QfO effort is proper benchmarking of methods and services, as well as design of standardized datasets and standardized formats to allow sharing and comparison of results. Simultaneously, analysis pipelines have been improved, evaluated and adapted to handle large datasets. All this would not have occurred without the long-term collaboration of Consortium members. Meeting regularly to review and coordinate complementary activities from a broad spectrum of innovative researchers clearly benefits the community. Highlights of the meeting include addressing sources of and legitimacy of disagreements between orthology calls, the context dependency of orthology definitions, special challenges encountered when analyzing very anciently rooted orthologies, orthology in the light of whole-genome duplications, and the concept of orthologous versus paralogous relationships at different levels, including domain-level orthology. Furthermore, particular needs for different applications (e.g. plant genomics, ancient gene families and others) and the infrastructure for making orthology inferences available (e.g. interfaces with model organism databases) were discussed, with several ongoing efforts that are expected to be reported on during the upcoming 2017 QfO meeting.

Summary: Phylogenetic profiles form the basis for tracing proteins and their functions across species and through time. Novel genome sequences nowadays often represent species from the remotest corner of the tree of life. Thus, phylogenetic profiling becomes increasingly important for functionally annotating this data and to integrate it into a comprehensive view on organismal evolution. To strengthen the link between the sharing of a gene across species and of the corresponding function, it is meanwhile common to complement phylogenetic profiles with additional information, such as domain architecture similarities between orthologs, or pairwise similarities of other protein features. However, there are few visualization tools that facilitate an intuitive integration of these various information layers. Here, we present PhyloProfile, an R-based tool to visualize, explore and analyze multi-layered phylogenetic profiles. Availability and implementation: PhyloProfile is available as open source code under the MIT license at https://github.com/BIONF/phyloprofile. An online version for testing PhyloProfile and for small to medium-scale analyses is available at http://applbio.biologie.uni-frankfurt.de/phyloprofile.

BACKGROUND: The increasing number of sequenced genomes provides the basis for exploring the genetic and functional diversity within the tree of life. Only a tiny fraction of the encoded proteins undergoes a thorough experimental characterization. For the remainder, bioinformatics annotation tools are the only means to infer their function. Exploiting significant sequence similarities to already characterized proteins, commonly taken as evidence for homology, is the prevalent method to deduce functional equivalence. Such methods fail when homologs are too diverged, or when they have assumed a different function. Finally, due to convergent evolution, functional equivalence is not necessarily linked to common ancestry. Therefore complementary approaches are required to identify functional equivalents. RESULTS: We present the Feature Architecture Comparison Tool http://www.cibiv.at/FACT to search for functionally equivalent proteins. FACT uses the similarity between feature architectures of two proteins, i.e., the arrangements of functional domains, secondary structure elements and compositional properties, as a proxy for their functional equivalence. A scoring function measures feature architecture similarities, which enables searching for functional equivalents in entire proteomes. Our evaluation of 9,570 EC classified enzymes revealed that FACT, using the full feature, set outperformed the existing architecture-based approaches by identifying significantly more functional equivalents as highest scoring proteins. We show that FACT can identify functional equivalents that share no significant sequence similarity. However, when the highest scoring protein of FACT is also the protein with the highest local sequence similarity, it is in 99% of the cases functionally equivalent to the query. We demonstrate the versatility of FACT by identifying a missing link in the yeast glutathione metabolism and also by searching for the human GolgA5 equivalent in Trypanosoma brucei. CONCLUSIONS: FACT facilitates a quick and sensitive search for functionally equivalent proteins in entire proteomes. FACT is complementary to approaches using sequence similarity to identify proteins with the same function. Thus, FACT is particularly useful when functional equivalents need to be identified in evolutionarily distant species, or when functional equivalents are not homologous. The most reliable annotation transfers, however, are achieved when feature architecture similarity and sequence similarity are jointly taken into account.

In order to shed light on the rate and mode of evolution of DNA sequence evolution in the germ line of humans and chimpanzees, we have sequenced 136 kb around the human ZFY gene as well as a total of 180 kb of genomic DNA surrounding the ZFX and ZFY genes on the chimpanzee X and Y chromosome, respectively. The comparison of the orthologous sequences on the human and chimpanzee sex chromosomes show that whereas the ZFX region display 0.8% substitutional differences between the two species, the ZFY region display 1.5% differences. By contrast, insertions and deletions show no difference between the two regions. Interestingly, transversions show a much more drastic preponderance in the male germ line than transitions, and the insertion of retroviral-like elements seem to occur more frequently in the male than in the female germ line.