In 2020, the world faced the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) pandemic that drastically altered people's lives. Since then, many countries have been forced to suspend public gatherings, leading to many conference cancellations, postponements, or reorganizations. Switching from a face-to-face to a remote conference became inevitable and the ultimate solution to sustain scientific exchanges at the national and the international levels. The same year, as a committee, we were in charge of organizing the major French annual conference that covers all computational biology areas: The "Journées Ouvertes en Biologie, Informatique et Mathématiques" (JOBIM). Despite the health crisis, we succeeded in changing the conference format from face to face to remote in a very short amount of time. Here, we propose 10 simple rules based on this experience to modify a conference format in an optimized and cost-effective way. In addition to the suggested rules, we decided to emphasize an unexpected benefit of this situation: a significant reduction in greenhouse gas (GHG) emissions related to travel for scientific conference attendance. We believe that even once the SARS-CoV-2 crisis is over, we collectively will have an opportunity to think about the way we approach such scientific events over the longer term.

Summary: WAVES is a web application dedicated to bioinformatic tool integration. It provides an efficient way to implement a service for any bioinformatic software. Such services are automatically made available in three ways: web pages, web forms to include in remote websites and a RESTful web services single application programing interface to access remotely from applications. In order to fulfill the service's computational needs, WAVES can perform computation on various resources and environments, such as Galaxy instances. Availability and implementation: WAVES was developed with Django, a Python-based web framework. It was designed as a reusable web application. It is fully portable, as only a Python installation is required to run Django. It is licensed under GNU General Public License. Source code, documentation with examples and demo are available from http://www.atgc-montpellier.fr/waves/. Supplementary information: Supplementary data are available at Bioinformatics online.

MOTIVATION: The reconstruction of ancestral genetic sequences from the analysis of contemporaneous data is a powerful tool to improve our understanding of molecular evolution. Various statistical criteria defined in a phylogenetic framework can be used to infer nucleotide, amino-acid or codon states at internal nodes of the tree, for every position along the sequence. These criteria generally select the state that maximizes (or minimizes) a given criterion. Although it is perfectly sensible from a statistical perspective, that strategy fails to convey useful information about the level of uncertainty associated to the inference. RESULTS: The present study introduces a new criterion for ancestral sequence reconstruction, the minimum posterior expected error (MPEE), that selects a single state whenever the signal conveyed by the data is strong, and a combination of multiple states otherwise. We also assess the performance of a criterion based on the Brier scoring scheme which, like MPEE, does not rely on any tuning parameters. The precision and accuracy of several other criteria that involve arbitrarily set tuning parameters are also evaluated. Large scale simulations demonstrate the benefits of using the MPEE and Brier-based criteria with a substantial increase in the accuracy of the inference of past sequences compared to the standard approach and realistic compromises on the precision of the solutions returned. AVAILABILITY AND IMPLEMENTATION: The software package PhyML (https://github.com/stephaneguindon/phyml) provides an implementation of the Maximum A Posteriori (MAP) and MPEE criteria for reconstructing ancestral nucleotide and amino-acid sequences.

SUMMARY: Amino acid replacement rate matrices are an essential basis of protein studies (e.g. in phylogenetics and alignment). A number of general purpose matrices have been proposed (e.g. JTT, WAG, LG) since the seminal work of Margaret Dayhoff and co-workers. However, it has been shown that matrices specific to certain protein groups (e.g. mitochondrial) or life domains (e.g. viruses) differ significantly from general average matrices, and thus perform better when applied to the data to which they are dedicated. This Web server implements the maximum-likelihood estimation procedure that was used to estimate LG, and provides a number of tools and facilities. Users upload a set of multiple protein alignments from their domain of interest and receive the resulting matrix by email, along with statistics and comparisons with other matrices. A non-parametric bootstrap is performed optionally to assess the variability of replacement rate estimates. Maximum-likelihood trees, inferred using the estimated rate matrix, are also computed optionally for each input alignment. Finely tuned procedures and up-to-date ML software (PhyML 3.0, XRATE) are combined to perform all these heavy calculations on our clusters. AVAILABILITY: http://www.atgc-montpellier.fr/ReplacementMatrix/ CONTACT: [email protected] SUPPLEMENTARY INFORMATION: Supplementary data are available at http://www.atgc-montpellier.fr/ReplacementMatrix/

BACKGROUND: Supertree methods combine phylogenies with overlapping sets of taxa into a larger one. Topological conflicts frequently arise among source trees for methodological or biological reasons, such as long branch attraction, lateral gene transfers, gene duplication/loss or deep gene coalescence. When topological conflicts occur among source trees, liberal methods infer supertrees containing the most frequent alternative, while veto methods infer supertrees not contradicting any source tree, i.e. discard all conflicting resolutions. When the source trees host a significant number of topological conflicts or have a small taxon overlap, supertree methods of both kinds can propose poorly resolved, hence uninformative, supertrees. RESULTS: To overcome this problem, we propose to infer non-plenary supertrees, i.e. supertrees that do not necessarily contain all the taxa present in the source trees, discarding those whose position greatly differs among source trees or for which insufficient information is provided. We detail a variant of the PhySIC veto method called PhySIC_IST that can infer non-plenary supertrees. PhySIC_IST aims at inferring supertrees that satisfy the same appealing theoretical properties as with PhySIC, while being as informative as possible under this constraint. The informativeness of a supertree is estimated using a variation of the CIC (Cladistic Information Content) criterion, that takes into account both the presence of multifurcations and the absence of some taxa. Additionally, we propose a statistical preprocessing step called STC (Source Trees Correction) to correct the source trees prior to the supertree inference. STC is a liberal step that removes the parts of each source tree that significantly conflict with other source trees. Combining STC with a veto method allows an explicit trade-off between veto and liberal approaches, tuned by a single parameter.Performing large-scale simulations, we observe that STC+PhySIC_IST infers much more informative supertrees than PhySIC, while preserving low type I error compared to the well-known MRP method. Two biological case studies on animals confirm that the STC preprocess successfully detects anomalies in the source trees while STC+PhySIC_IST provides well-resolved supertrees agreeing with current knowledge in systematics. CONCLUSION: The paper introduces and tests two new methodologies, PhySIC_IST and STC, that demonstrate the interest in inferring non-plenary supertrees as well as preprocessing the source trees. An implementation of the methods is available at: http://www.atgc-montpellier.fr/physic_ist/.