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Studying gene family evolution strongly benefits from insightful visualisations. However, the ever-growing number of sequenced genomes is leading to increasingly larger gene families, which challenges existing gene tree visualisations. Indeed, most of them present users with a dilemma: display complete but intractable gene trees, or collapse subtrees, thereby hiding their children’s information. Here, we introduce Matreex, a new dynamic tool to scale-up the visualisation of gene families. Matreex’ key idea is to use “phylogenetic” profiles, which are dense representations of gene repertoires, to minimise the information loss when collapsing subtrees. We illustrate Matreex usefulness with three biological applications. First, using the textbook example of visual opsins, we show Matreex’ potential to create easily interpretable figures for teaching and outreach. Secondly, by displaying 22 intraflagellar transport gene families across 622 species cumulating 5’500 representatives, we show how Matreex can be used to automate large-scale analyses of gene presence-absence. Notably, we report for the first time the complete loss of intraflagellar transport in the myxozoanThelohanellus kitauei. Finally, we demonstrate on the MutS family the power of combining gene trees and phylogenetic profiles to delve into precise evolutionary analyses of large multi-copy gene families. Matreex is available from the Python Package Index (pip install matreex) with the source code and documentation available athttps://github.com/DessimozLab/matreex.

Assessing the quality of protein-coding gene repertoires is critical in an era of increasingly abundant genome sequences for a diversity of species. State-of-the-art genome annotation assessment tools measure the completeness of a gene repertoire, but are blind to other types of errors, such as gene over-prediction or contamination.We developed OMArk, a software relying on fast, alignment-free sequence comparisons between a query proteome and precomputed gene families across the tree of life. OMArk assesses not only the completeness, but also the consistency of the gene repertoire as a whole relative to closely related species. It also reports likely contamination events.We validated OMArk with simulated data, then performed an analysis of the 1805 UniProt Eukaryotic Reference Proteomes, illustrating its usefulness for comparing and prioritizing proteomes based on their quality measures. In particular, we found strong evidence of contamination in 59 proteomes, and identified error propagation in avian gene annotation resulting from the use of a fragmented zebra finch proteome as reference.OMArk is available on GitHub (https://github.com/DessimozLab/OMArk), as a Python package on PyPi, and as an interactive online tool athttps://omark.omabrowser.org/.

Motivation Alternative splicing is a ubiquitous process in eukaryotes that allows distinct transcripts to be produced from the same gene. Yet, the study of transcript evolution within a gene family is still in its infancy. One prerequisite for this study is the availability of methods to compare sets of transcripts while accounting for their splicing structure. In this context, we generalize the concept of pairwise spliced alignments (PSpAs) to multiple spliced alignments (MSpAs). MSpAs have several important purposes in addition to empowering the study of the evolution of transcripts. For instance, it is a key to improving the prediction of gene models, which is important to solve the growing problem of genome annotation. Despite its essentialness, a formal definition of the concept and methods to compute MSpAs are still lacking. Results We introduce the MSpA problem and the SplicedFamAlignMulti (SFAM) method, to compute the MSpA of a gene family. Like most multiple sequence alignment (MSA) methods that are generally greedy heuristic methods assembling pairwise alignments, SFAM combines all PSpAs of coding DNA sequences and gene sequences of a gene family into an MSpA. It produces a single structure that represents the superstructure and models of the gene family. Using real vertebrate and simulated gene family data, we illustrate the utility of SFAM for computing accurate gene family superstructures, MSAs, inferring splicing orthologous groups and improving gene-model annotations. Availability and implementation The supporting data and implementation of SFAM are freely available at https://github.com/UdeS-CoBIUS/SpliceFamAlignMulti. Supplementary information Supplementary data are available at Bioinformatics Advances online.

In every living species, the function of a protein depends on its organisation of structural domains, and the length of a protein is a direct reflection of this. Because every species evolved under different evolutionary pressures, the protein length distribution, much like other genomic features, is expected to vary across species. Here we evaluated this diversity by comparing protein length distribution across 2,326 species (1,688 bacteria, 153 archaea and 485 eukaryotes). We found that proteins tend to be on average slightly longer in eukaryotes than in bacteria or archaea, but that the variation of length distribution across species is low, especially compared to the variation of other genomic features (genome size, number of proteins, gene length, GC content, isoelectric points of proteins). Moreover, most cases of atypical protein length distribution appear to be due to artifactual gene annotation, suggesting the actual variation of protein length distribution across species is even smaller. These results open the way for developing a genome annotation quality metric based on protein length distribution to complement conventional quality measures. Overall, our findings show that protein length distribution between living species is more consistent than previously thought, and provide evidence for a universal purifying selection on protein length, whose mechanism and fitness effect remain intriguing open questions.

A large variety of pairwise measures of similarity or dissimilarity have been developed for comparing phylogenetic trees, for example, species trees or gene trees. Due to its intuitive definition in terms of tree clades and bipartitions and its computational efficiency, the Robinson–Foulds (RF) distance is the most widely used for trees with unweighted edges and labels restricted to leaves (representing the genetic elements being compared). However, in the case of gene trees, an important information revealing the nature of the homologous relation between gene pairs (orthologs, paralogs, and xenologs) is the type of event associated to each internal node of the tree, typically speciations or duplications, but other types of events may also be considered, such as horizontal gene transfers. This labeling of internal nodes is usually inferred from a gene tree/species tree reconciliation method. Here, we address the problem of comparing such event-labeled trees. The problem differs from the classical problem of comparing uniformly labeled trees (all labels belonging to the same alphabet) that may be done using the Tree Edit Distance (TED) mainly due to the fact that, in our case, two different alphabets are considered for the leaves and internal nodes of the tree, and leaves are not affected by edit operations. We propose an extension of the RF distance to event-labeled trees, based on edit operations comparable to those considered for TED: node insertion, node deletion, and label substitution. We show that this new Labeled Robinson–Foulds (LRF) distance can be computed in linear time, in addition of maintaining other desirable properties: being a metric, reducing to RF for trees with no labels on internal nodes and maintaining an intuitive interpretation. The algorithm for computing the LRF distance enables novel analyses on event-label trees such as reconciled gene trees. Here, we use it to study the impact of taxon sampling on labeled gene tree inference and conclude that denser taxon sampling yields trees with better topology but worse labeling. [Algorithms; combinatorics; gene trees; phylogenetics; Robinson–Foulds; tree distance.]

Multiciliogenesis is a complex process that allows the generation of hundreds of motile cilia on the surface of specialized cells, to create fluid flow across epithelial surfaces. Dysfunction of human multiciliated cells is associated with diseases of the brain, airway and reproductive tracts. Despite recent efforts to characterize the transcriptional events responsible for the differentiation of multiciliated cells, a lot of actors remain to be identified. In this work, we capitalize on the ever-growing quantity of high-throughput data to search for new candidate genes involved in multiciliation. After performing a large-scale screening using 10 transcriptomics datasets dedicated to multiciliation, we established a specific evolutionary signature involving Otomorpha fish to use as a criterion to select the most likely targets. Combining both approaches highlighted a list of 114 potential multiciliated candidates. We characterized these genes first by generating protein interaction networks, which showed various clusters of ciliated and multiciliated genes, and then by computing phylogenetic profiles. In the end, we selected 11 poorly characterized genes that seem like particularly promising multiciliated candidates. By combining functional and comparative genomics methods, we developed a novel type of approach to study biological processes and identify new promising candidates linked to that process.

New genomic tools open doors to study ecology, evolution, and population genomics of wild animals. For the Barn owl species complex, a cosmopolitan nocturnal raptor, a very fragmented draft genome was assembled for the American species (Tyto furcata pratincola) (Jarvis et al. 2014). To improve the genome, we assembled de novo Illumina and Pacific Biosciences (PacBio) long reads sequences of its European counterpart (Tyto alba alba). This genome assembly of 1.219 Gbp comprises 21,509 scaffolds and results in a N50 of 4,615,526 bp. BUSCO (Universal Single‐Copy Orthologs) analysis revealed an assembly completeness of 94.8% with only 1.8% of the genes missing out of 4,915 avian orthologs searched, a proportion similar to that found in the genomes of the zebra finch (Taeniopygia guttata) or the collared flycatcher (Ficedula albicollis). By mapping the reads of the female American barn owl to the male European barn owl reads, we detected several structural variants and identified 70 Mbp of the Z chromosome. The barn owl scaffolds were further mapped to the chromosomes of the zebra finch. In addition, the completeness of the European barn owl genome is demonstrated with 94 of 128 proteins missing in the chicken genome retrieved in the European barn owl transcripts. This improved genome will help future barn owl population genomic investigations.

Orthology is a cornerstone of comparative genomics and has numerous applications in current biology. In this chapter, we first introduce the concepts of orthology and paralogy. We then present the currently available orthology inference methods and the community-led efforts of standardization and benchmarking accompanying these developments. The large panel of available orthology resources is compared in terms of species coverage, access, contextual data and tools proposed to end-users to facilitate the analysis and exploitation of orthology data. We then review the importance of orthology applications, ranging from the study of protein families and information transfer to the comparison of genomes and genotype/phenotype correlations. Finally, we discuss the current challenges in the orthology field, faced with an ever-increasing number of proteomes of particularly heterogeneous quality. We highlight the urgent need of considering orthology at the protein domain and transcript levels and the conceptual and practical difficulties that this raises.

New genomic tools open doors to study ecology, evolution, and population genomics of wild animals. For the Barn owl species complex, a cosmopolitan nocturnal raptor, a very fragmented draft genome was assembled for the American species (Tyto furcata pratincola) (Jarvis et al. 2014). To improve the genome, we assembled de novo Illumina and Pacific Biosciences (PacBio) long reads sequences of its European counterpart (Tyto alba alba). This genome assembly of 1.219 Gbp comprises 21,509 scaffolds and results in a N50 of 4,615,526 bp. BUSCO (Universal Single‐Copy Orthologs) analysis revealed an assembly completeness of 94.8% with only 1.8% of the genes missing out of 4,915 avian orthologs searched, a proportion similar to that found in the genomes of the zebra finch (Taeniopygia guttata) or the collared flycatcher (Ficedula albicollis). By mapping the reads of the female American barn owl to the male European barn owl reads, we detected several structural variants and identified 70 Mbp of the Z chromosome. The barn owl scaffolds were further mapped to the chromosomes of the zebra finch. In addition, the completeness of the European barn owl genome is demonstrated with 94 of 128 proteins missing in the chicken genome retrieved in the European barn owl transcripts. This improved genome will help future barn owl population genomic investigations., We assemble the genome of the European barn owl (Tyto alba alba) of 1.219 Gbp by mixing Illumina (117×) and Pacific Biosciences (PacBio) long reads (12×) technologies. BUSCO (Universal Single‐Copy Orthologs) analysis revealed an assembly completeness of 94.8% with only 1.8% of the genes missing out of 4,915 avian orthologs searched, a proportion similar to that found in the genomes of the zebra finch (Taeniopygia guttata) or the collared flycatcher (Ficedula albicollis). In addition, we identify the Z chromosome' scaffolds, 73% of proteins thought to be missing in the chicken.

Background The constant and massive increase of biological data offers unprecedented opportunities to decipher the function and evolution of genes and their roles in human diseases. However, the multiplicity of sources and flow of data mean that efficient access to useful information and knowledge production has become a major challenge. This challenge can be addressed by taking inspiration from Web 2.0 and particularly social networks, which are at the forefront of big data exploration and human-data interaction. Objective MyGeneFriends is a Web platform inspired by social networks, devoted to genetic disease analysis, and organized around three types of proactive agents: genes, humans, and genetic diseases. The aim of this study was to improve exploration and exploitation of biological, postgenomic era big data. Methods MyGeneFriends leverages conventions popularized by top social networks (Facebook, LinkedIn, etc), such as networks of friends, profile pages, friendship recommendations, affinity scores, news feeds, content recommendation, and data visualization. Results MyGeneFriends provides simple and intuitive interactions with data through evaluation and visualization of connections (friendships) between genes, humans, and diseases. The platform suggests new friends and publications and allows agents to follow the activity of their friends. It dynamically personalizes information depending on the user’s specific interests and provides an efficient way to share information with collaborators. Furthermore, the user’s behavior itself generates new information that constitutes an added value integrated in the network, which can be used to discover new connections between biological agents. Conclusions We have developed MyGeneFriends, a Web platform leveraging conventions from popular social networks to redefine the relationship between humans and biological big data and improve human processing of biomedical data. MyGeneFriends is available at lbgi.fr/mygenefriends.