Summary: The Rab family of small GTPases regulates and provides specificity to the endomembrane trafficking system; each Rab subfamily is associated with specific pathways. Thus, characterization of Rab repertoires provides functional information about organisms and evolution of the eukaryotic cell. Yet, the complex structure of the Rab family limits the application of existing methods for protein classification. Here, we present a major redesign of the Rabifier, a bioinformatic pipeline for detection and classification of Rab GTPases. It is more accurate, significantly faster than the original version and is now open source, both the code and the data, allowing for community participation. Availability and Implementation: Rabifier and RabDB are freely available through the web at http://rabdb.org . The Rabifier package can be downloaded from the Python Package Index at https://pypi.python.org/pypi/rabifier , the source code is available at Github https://github.com/evocell/rabifier . Contact: [email protected] or [email protected]. Supplementary information: Supplementary data are available at Bioinformatics online.

Rab proteins are small GTPases that act as essential regulators of vesicular trafficking. 44 subfamilies are known in humans, performing specific sets of functions at distinct subcellular localisations and tissues. Rab function is conserved even amongst distant orthologs. Hence, the annotation of Rabs yields functional predictions about the cell biology of trafficking. So far, annotating Rabs has been a laborious manual task not feasible for current and future genomic output of deep sequencing technologies. We developed, validated and benchmarked the Rabifier, an automated bioinformatic pipeline for the identification and classification of Rabs, which achieves up to 90% classification accuracy. We cataloged roughly 8.000 Rabs from 247 genomes covering the entire eukaryotic tree. The full Rab database and a web tool implementing the pipeline are publicly available at www.RabDB.org. For the first time, we describe and analyse the evolution of Rabs in a dataset covering the whole eukaryotic phylogeny. We found a highly dynamic family undergoing frequent taxon-specific expansions and losses. We dated the origin of human subfamilies using phylogenetic profiling, which enlarged the Rab repertoire of the Last Eukaryotic Common Ancestor with Rab14, 32 and RabL4. Furthermore, a detailed analysis of the Choanoflagellate Monosiga brevicollis Rab family pinpointed the changes that accompanied the emergence of Metazoan multicellularity, mainly an important expansion and specialisation of the secretory pathway. Lastly, we experimentally establish tissue specificity in expression of mouse Rabs and show that neo-functionalisation best explains the emergence of new human Rab subfamilies. With the Rabifier and RabDB, we provide tools that easily allows non-bioinformaticians to integrate thousands of Rabs in their analyses. RabDB is designed to enable the cell biology community to keep pace with the increasing number of fully-sequenced genomes and change the scale at which we perform comparative analysis in cell biology.

In comparative genomics, gene order data is often modeled as signed permutations. A classical problem for genome comparison is to detect common intervals in permutations, that is, genes that are colocalized in several species, indicating that they remained grouped during evolution. A second largely studied problem related to gene order is to compute a minimum scenario of reversals that transforms a signed permutation into another. Several studies began to mix the two problems and it was observed that their results are not always compatible: Often, parsimonious scenarios of reversals break common intervals. If a scenario does not break any common interval, it is called perfect. In two recent studies, Bérard et al. defined a class of permutations for which building a perfect scenario of reversals sorting a permutation was achieved in polynomial time and stated as an open question whether it is possible to decide, given a permutation, if there exists a minimum scenario of reversals that is perfect. In this paper, we give a solution to this problem and prove that this widens the class of permutations addressed by the aforementioned studies. We implemented and tested this algorithm on gene order data of chromosomes from several mammal species and we compared it to other methods. The algorithm helps to choose among several possible scenarios of reversals and indicates that the minimum scenario of reversals is not always the most plausible.