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Bioinformatics is a field known for the numerous standards and formats that have been developed over the years. This plethora of formats, sometimes complementary, and often redundant, poses many challenges to bioinformatics data analysts. They constantly need to find the best tool to convert their data into the suitable format, which is often a complex, technical and time consuming task. Moreover, these small yet important tasks are often difficult to make reproducible. To over-come these difficulties, we initiatedBioConvert, a collaborative project to facilitate the conversion of life science data from one format to another.BioConvertaggregates existing software within a single framework and complemented them with original code when needed. It provides a common interface to make the user experience more streamlined instead of having to learn tens of them. Currently,BioConvertsupports about 50 formats and 100 direct conversions in areas such as alignment, sequencing, phylogeny, and variant calling. In addition to being useful for end-users,BioConvertcan also be utilized by developers as a universal benchmarking framework for evaluating and comparing numerous conversion tools. Additionally, we provide a web server implementing an online user-friendly interface toBioConvert, hence allowing direct use for the community.

The bootstrap method is based on resampling alignments and re-estimating trees. Felsenstein’s bootstrap proportions (FBP) is the most common approach to assess the reliability and robustness of sequence-based phylogenies. However, when increasing taxon-sampling (i.e., the number of sequences) to hundreds or thousands of taxa, FBP tends to return low supports for deep branches. The Transfer Bootstrap Expectation (TBE) has been recently suggested as an alternative to FBP. TBE is measured using a continuous transfer index in [0,1] for each bootstrap tree, instead of the {0,1} index used in FBP to measure the presence/absence of the branch of interest. TBE has been shown to yield higher and more informative supports, without inducing falsely supported branches. Nonetheless, it has been argued that TBE must be used with care due to sampling issues, especially in datasets with high number of closely related taxa. In this study, we conduct multiple experiments by varying taxon sampling and comparing FBP and TBE support values on different phylogenetic depth, using empirical datasets. Our results show that the main critic of TBE stands in extreme cases with shallow branches and highly unbalanced sampling among clades, but that TBE is still robust in most cases, while FBP is inescapably negatively impacted by high taxon sampling. We suggest guidelines and good practices in TBE (and FBP) computing and interpretation.

On May 6, 2022, a powerful outbreak of monkeypox virus (MPXV) had been reported outside of Africa, with many continuing new cases being reported around the world. Analysis of mutations among the two different lineages present in the 2021 and 2022 outbreaks revealed the presence of G->A mutations occurring in the 5’GpA context, indicative of APOBEC3 cytosine deaminase activity. By using a sensitive PCR (3D-PCR) method allowing differential amplification of AT-rich DNA, we demonstrate that G->A hypermutated MPXV genomes can be recovered experimentally from APOBEC3 transfection followed by MPXV infection. Here, among the 7 human APOBEC3 cytidine deaminases (A3A-A3C, A3DE, A3F-A3H), only APOBEC3F was capable of extensively deaminating cytidine residues in MPXV genomes. Hyperedited genomes were also recovered in ~42% of analyzed patients, indicating that editing is part of the natural cycle of MPXV infection. Moreover, we demonstrate that substantial repair of these mutations occurs. Upon selection, corrected G->A mutations escaping drift loss contribute to the MPXV evolution observed in the current epidemics. Thus, stochastic or transient overexpression ofAPOBEC3Fgene exposes the MPXV genome to a broad spectrum of mutations that may be modeling the mutational landscape after multiple cycles of viral replication.

Integrons are genetic elements found exclusively in bacteria. They are well known for their role in disseminating antibiotic resistance genes among pathogens and more generally for enabling bacteria to rapidly adapt to changing environmental conditions. Integrons constitute a natural system to capture, stockpile, shuffle, express and disseminate genes embedded in cassettes. All these events are governed by the integron integrase through site-specific recombination between integron att sites (attC and attI sites). Here, we demonstrate that integron integrase can efficiently catalyze the insertion of cassettes in bacterial genomes, outside the att sites carried by the integron system. Surprisingly, analysis of more than 5 × 10 to the power of 5 independent clones revealed that the genome recombination sites differ greatly, in terms of sequence and structure, from both classical attC and attI recombination sites. We named theses new sites attG. Notably, among these a few are driving integration at very high rates. We also showed that, once inserted in genomes, cassettes can be expressed if located near a bacterial promoter. Moreover, even if occurring at low frequency, genome inserted cassettes can be excised precisely or imprecisely, inducing in this latter case, chromosomal modifications. These results unveil a new route for antibiotic resistance dissemination and expand the role of integrons in bacterial genome evolution.

Evolutionary convergences are observed at all levels, from phenotype to DNA and protein sequences, and the changes observed at these different levels tend to be strongly correlated. Here we propose a simulation-based method to detect positions under convergent evolution in large protein alignments, without prior knowledge on the phenotype and environmental constraints. A phylogeny is inferred from the data and used in simulations to estimate the expected number of amino-acid changes in stable evolutionary constraints (null model) for each position. Similarly, we count the number of mutations towards the same amino acid in the data and test if they are occurring more often than expected. We applied our method to two real datasets: HIV reverse transcriptase and fish rhodopsin, and to HIV-like simulated data. On the latter, with known convergent events and substitution model, we detected on average two third of these events, with a low fraction of false positives. With HIV data, one knows that drug resistance mutations (DRMs) are convergent. Even without any knowledge of patient treatment status, we retrieved more than 70% of positions corresponding to known DRMs. On the rhodopsin dataset, four substitutions are supposed to be convergent, as they change the maximum wavelength absorption of the photoreceptor and occurred several times independently during evolution. We detected three of them. These results demonstrate the potential of the method to target specific mutations to be further studied experimentally or, for example, using a nonsynonymous/synonymous rate ratio approach. Our software named ConDor is available at http://condor.pasteur.cloud.

Widely applicable, accurate and fast inference methods in phylodynamics are needed to fully profit from the richness of genetic data in uncovering the dynamics of epidemics. Standard methods, including maximum-likelihood and Bayesian approaches, generally rely on complex mathematical formulae and approximations, and do not scale with dataset size. We develop a likelihood-free, simulation-based approach, which combines deep learning with (1) a large set of summary statistics measured on phylogenies or (2) a complete and compact representation of trees, which avoids potential limitations of summary statistics and applies to any phylodynamics model. Our method enables both model selection and estimation of epidemiological parameters from very large phylogenies. We demonstrate its speed and accuracy on simulated data, where it performs better than the state-of-the-art methods. To illustrate its applicability, we assess the dynamics induced by superspreading individuals in an HIV dataset of men-having-sex-with-men in Zurich. Our tool PhyloDeep is available ongithub.com/evolbioinfo/phylodeep.

Although the global response to COVID-19 has not been entirely unified, the opportunity arises to assess the impact of regional public health interventions and to classify strategies according to their outcome. Analysis of genetic sequence data gathered over the course of the pandemic allows us to link the dynamics associated with networks of connected individuals with specific interventions. In this study, clusters of transmission were inferred from a phylogenetic tree representing the relationships of patient sequences sampled from December 30, 2019 to April 17, 2020. Metadata comprising sampling time and location were used to define the global behavior of transmission over this earlier sampling period, but also the involvement of individual regions in transmission cluster dynamics. Results demonstrate a positive impact of international travel restrictions and nationwide lockdowns on global cluster dynamics. However, residual, localized clusters displayed a wide range of estimated initial secondary infection rates, for which uniform public health interventions are unlikely to have sustainable effects. Our findings highlight the presence of so-called “super-spreaders”, with the propensity to infect a larger-than-average number of people, in countries, such as the USA, for which additional mitigation efforts targeting events surrounding this type of spread are urgently needed to curb further dissemination of SARS-CoV-2.

MotivationThe first cases of the COVID-19 pandemic emerged in December 2019. Until the end of February 2020, the number of available genomes was below 1,000, and their multiple alignment was easily achieved using standard approaches. Subsequently, the availability of genomes has grown dramatically. Moreover, some genomes are of low quality with sequencing/assembly errors, making accurate re-alignment of all genomes nearly impossible on a daily basis. A more efficient, yet accurate approach was clearly required to pursue all subsequent bioinformatics analyses of this crucial data.ResultshCoV-19 genomes are highly conserved, with very few indels and no recombination. This makes the profile HMM approach particularly well suited to align new genomes, add them to an existing alignment and filter problematic ones. Using a core of ∼2,500 high quality genomes, we estimated a profile using HMMER, and implemented this profile in COVID-Align, a user-friendly interface to be used online or as standalone via Docker. The alignment of 1,000 genomes requires less than 20mn on our cluster. Moreover, COVID-Align provides summary statistics, which can be used to determine the sequencing quality and evolutionary novelty of input genomes (e.g. number of new mutations and indels).Availabilityhttps://covalign.pasteur.cloud, hub.docker.com/r/evolbioinfo/covid-alignContactsolivier.gascuel@pasteur.fr, frederic.lemoine@pasteur.frSupplementary informationSupplementary information is available at Bioinformatics online.

Felsenstein’s article describing the application of the bootstrap to evolutionary trees, is one of the most cited papers of all time. That statistical method, based on resampling and replications, is used extensively to assess the robustness of phylogenetic inferences. However, increasing numbers of sequences are now available for a wide variety of species, and phylogenies with hundreds or thousands of taxa are becoming routine. In that framework, Felsenstein’s bootstrap tends to yield very low supports, especially on deep branches. We propose a revised version, in which the presence of inferred branches in replications is measured using a gradual “transfer” distance, as opposed to the original version using a binary presence/absence index. The resulting supports are higher, while not inducing falsely supported branches. Our method is applied to large simulation, mammal and HIV datasets, for which it reveals the phylogenetic signal, while Felsenstein’s bootstrap fails to do so.

MicroRNAs (miRNAs) are major post-transcriptional regulators of gene expression, yet their origins and functional evolution in mammals remain little understood due to the lack of appropriate comparative data. Using RNA sequencing, we have generated extensive and comparable miRNA data for five organs in six species that represent all main mammalian lineages and birds (the evolutionary outgroup) with the aim to unravel the evolution of mammalian miRNAs. Our analyses reveal an overall expansion of miRNA repertoires in mammals, with threefold accelerated birth rates of miRNA families in placentals and marsupials, facilitated by the de novo emergence of miRNAs in host gene introns. Generally, our analyses suggest a high rate of miRNA family turnover in mammals with many newly emerged miRNA families being lost soon after their formation. Selectively preserved mammalian miRNA families gradually evolved higher expression levels, as well as altered mature sequences and target gene repertoires, and were apparently mainly recruited to exert regulatory functions in nervous tissues. However, miRNAs that originated on the X chromosome evolved high expression levels and potentially diverse functions during spermatogenesis, including meiosis, through selectively driven duplication-divergence processes. Overall, our study thus provides detailed insights into the birth and evolution of mammalian miRNA genes and the associated selective forces.