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Abstract Background Substitution matrices are key parameters for the alignment of two protein sequences, and consequently for most comparative genomics studies. The composition of biological sequences can vary importantly between species and groups of species, and classical matrices such as those in the BLOSUM series fail to accurately estimate alignment scores and statistical significance with sequences sharing marked compositional biases. Results We present a general and simple methodology to build matrices that are especially fitted to the compositional bias of proteins. Our approach is inspired from the one used to build the BLOSUM matrices and is based on learning substitution and amino acid frequencies on real sequences with the corresponding compositional bias. We applied it to the large scale comparison of Mollicute AT-rich genomes. The new matrix, MOLLI60, was used to predict pairwise orthology relationships, as well as homolog families among 24 Mollicute genomes. We show that this new matrix enables to better discriminate between true and false orthologs and improves the clustering of homologous proteins, with respect to the use of the classical matrix BLOSUM62. Conclusions We show in this paper that well-fitted matrices can improve the predictions of orthologous and homologous relationships among proteins with a similar compositional bias. With the ever-increasing number of sequenced genomes, our approach could prove valuable in numerous comparative studies focusing on atypical genomes.

Background: Bacterial sRNA-mediated regulatory networks has been introduced as a powerful way to analyze the fast rewiring capabilities of a bacteria in response to changing environmental conditions. The identification of mRNA targets of bacterial sRNAs is essential to investigate their functional activities. However, this step remains challenging with the lack of knowledge of the topological and biological constraints behind the formation of sRNA-mRNA duplexes. Even with the most sophisticated bioinformatics target prediction tools, the large proportion of false predictions may be prohibitive for further analyses. To deal with this issue, sRNA target analyses can be carried out from the resulting gene lists given by RNA-SEQ experiments when available. However, the number of resulting target candidates may be still huge and cannot be easily interpreted by domain experts who need to confront various biological features to prioritize the target candidates. Therefore, novel strategies have to be carried out to improve the specificity of computational prediction results, before proposing new candidates for an expensive experimental validation stage. Result: To address this issue, we propose a new visualization tool rNAV 2.0, for detecting and filtering bacterial sRNA targets for regulatory networks. rNAV is designed to cope with a variety of biological constraints, including the gene annotations, the conserved regions of interaction or specific patterns of regulation. Depending on the application, these constraints can be variously combined to analyze the target candidates, prioritized for instance by a known conserved interaction region, or because of a common function. Conclusion: The standalone application implements a set of known algorithms and interaction techniques, and applies them to the new problem of identifying reasonable sRNA target candidates. [ABSTRACT FROM AUTHOR]

Life sciences are currently going through a great number of transformations raised by the in-going revolution in high-throughput technologies for the acquisition of data. The integration of their high dimensionality, ranging from omics to clinical data, is becoming one of the most challenging stages. It involves inter-disciplinary developments with the aim to move towards an enhanced understanding of human physiology for caring purposes. Biologists, bioinformaticians, physicians and other experts related to the healthcare domain have to accompany each step of the analysis process in order to investigate and expertise these various data. In this perspective, methods related to information visualization are gaining increasing attention within life sciences. The softwares based on these methods are now well recognized to facilitate expert users’ success in carrying out their data analysis tasks. This article aims at reviewing the current methods and techniques dedicated to information visualisation and their current use in software development related to omics or/and clinical data. [ABSTRACT FROM AUTHOR]

The bacterium Mycoplasma agalactiae is responsible for contagious agalactia (CA) in small domestic ruminants, a syndrome listed by the World Organization for Animal Health and responsible for severe damage to the dairy industry. Recently, we frequently isolated this pathogen from lung lesions of ibexes during a mortality episode in the French Alps. This situation was unusual in terms of host specificity and tissue tropism, raising the question of M. agalactiae emergence in wildlife. To address this issue, the ibex isolates were characterized using a combination of approaches that included antigenic profiles, molecular typing, optical mapping, and whole-genome sequencing. Genome analyses showed the presence of a new, large prophage containing 35 coding sequences (CDS) that was detected in most but not all ibex strains and has a homolog in Mycoplasma conjunctivae, a species causing keratoconjunctivitis in wild ungulates. This and the presence in all strains of large integrated conjugative elements suggested highly dynamic genomes. Nevertheless, M. agalactiae strains circulating in the ibex population were shown to be highly related, most likely originating from a single parental clone that has also spread to another wild ungulate species of t he same geographical area, the chamois. These strains clearly differ from strains described in Europe so far, including those found nearby, before CA eradication a few years ago. While M. agalactiae pathogenicity in ibexes remains unclear, our data showed the emergence of atypical strains in Alpine wild ungulates, raising the question of a role for the wild fauna as a potential reservoir of pathogenic mycoplasmas. [ABSTRACT FROM AUTHOR]

Clustered regularly interspaced short palindromic repeat (CRISPR)-Cas systems are bacterial defences that target bacteriophages and mobile genetic elements. How these defences evolve in novel host environments remains largely unknown. We studied the evolution of the CRISPR-Cas system in Mycoplasma gallisepticum (also named Mycoplasmoides gallisepticum ), a bacterial pathogen of poultry that jumped into a passerine host ~30 years ago. Over the decade following the host shift, all isolates displaying a functional CRISPR-Cas system were found not only to harbour completely new sets of spacers, but the DNA protospacer adjacent motif recognized by the main effector M. gallisepticum Cas9 (MgCas9) was also different. These changes in CRISPR-Cas diversity and specificity are consistent with a change in the community of phages and mobile elements infecting M. gallisepticum as it colonized the novel host. In the years following the host shift, we also detected a gradual rise in isolates displaying non-functional MgCas9. After 12 years, all circulating isolates harboured inactive forms only. This loss of CRISPR-Cas function comes at a time when the passerine host is known to have evolved widespread resistance, which in turn drove the evolution of increasing M. gallisepticum virulence through antagonistic coevolution. Such striking concordance in the rise of inactivated forms of CRISPR-Cas and the evolution of host resistance suggests that the inactivation of the CRISPR-Cas system was necessary for enabling adaptive bacterial responses to host-driven selection. We highlight the need to consider both host and pathogen selection pressures on bacteria for understanding the evolution of CRISPR-Cas systems and the key factors driving the emergence of a pathogenic bacterium in a novel host.

The revolution in new sequencing technologies is greatly leading to new understandings of the relations between genotype and phenotype. To interpret and analyze data that are grouped according to a phenotype of interest, methods based on statistical enrichment became a standard in biology. However, these methods synthesize the biological information by a priori selecting the over-represented terms and may suffer from focusing on the most studied genes that represent a limited coverage of annotated genes within a gene set. Semantic similarity measures have shown great results within the pairwise gene comparison by making advantage of the underlying structure of the Gene Ontology. We developed GSAn, a novel gene set annotation method that uses semantic similarity measures to synthesize a priori Gene Ontology annotation terms. The originality of our approach is to identify the best compromise between the number of retained annotation terms that has to be drastically reduced and the number of related genes that has to be as large as possible. Moreover, GSAn offers interactive visualization facilities dedicated to the multi-scale analysis of gene set annotations. Compared to enrichment analysis tools, GSAn has shown excellent results in terms of maximizing the gene coverage while minimizing the number of terms., (© The Author(s) 2019. Published by Oxford University Press on behalf of NAR Genomics and Bioinformatics.)

Motivation: The recent revolution in new sequencing technologies, as a part of the continuous process of adopting new innovative protocols has strongly impacted the interpretation of relations between phenotype and genotype. Thus, understanding the resulting gene sets has become a bottleneck that needs to be addressed. Automatic methods have been proposed to facilitate the interpretation of gene sets. While statistical functional enrichment analyses are currently well known, they tend to focus on well-known genes and to ignore new information from less-studied genes. To address such issues, applying semantic similarity measures is logical if the knowledge source used to annotate the gene sets is hierarchically structured. In this work, we propose a new method for analyzing the impact of different semantic similarity measures on gene set annotations., Results: We evaluated the impact of each measure by taking into consideration the two following features that correspond to relevant criteria for a "good" synthetic gene set annotation: (i) the number of annotation terms has to be drastically reduced and the representative terms must be retained while annotating the gene set, and (ii) the number of genes described by the selected terms should be as large as possible. Thus, we analyzed nine semantic similarity measures to identify the best possible compromise between both features while maintaining a sufficient level of details. Using Gene Ontology to annotate the gene sets, we obtained better results with node-based measures that use the terms' characteristics than with measures based on edges that link the terms. The annotation of the gene sets achieved with the node-based measures did not exhibit major differences regardless of the characteristics of terms used., Competing Interests: The authors have declared that no competing interests exist.

We report here the draft genome sequences of Mycoplasma auris and Mycoplasma yeatsii, two species commonly isolated from the external ear canal of Caprinae.

We report here the draft genome sequences of Mycoplasma alkalescens, Mycoplasma arginini, and Mycoplasma bovigenitalium. These three species are regularly isolated from bovine clinical specimens, although their role in disease is unclear.

Mycoplasma putrefaciens is one of the etiologic agents of contagious agalactia in goats. We report herein the complete genome sequence of Mycoplasma putrefaciens strain 9231.

Trypanosoma brucei is a protozoan parasite of major of interest in discovering new genes for drug targets. This parasite alternates its life cycle between the mammal host(s) (bloodstream form) and the insect vector (procyclic form), with two divergent glucose metabolism amenable to in vitro culture. While the metabolic network of the bloodstream forms has been well characterized, the flux distribution between the different branches of the glucose metabolic network in the procyclic form has not been addressed so far. We present a computational analysis (called Metaboflux) that exploits the metabolic topology of the procyclic form, and allows the incorporation of multipurpose experimental data to increase the biological relevance of the model. The alternatives resulting from the structural complexity of networks are formulated as an optimization problem solved by a metaheuristic where experimental data are modeled in a multiobjective function. Our results show that the current metabolic model is in agreement with experimental data and confirms the observed high metabolic flexibility of glucose metabolism. In addition, Metaboflux offers a rational explanation for the high flexibility in the ratio between final products from glucose metabolism, thsat is, flux redistribution through the malic enzyme steps.

There has been a renewed interest for metabolism in the computational biology community, leading to an avalanche of papers coming from methodological network analysis as well as experimental and theoretical biology. This paper is meant to serve as an initial guide for both the biologists interested in formal approaches and the mathematicians or computer scientists wishing to inject more realism into their models. The paper is focused on the structural aspects of metabolism only. The literature is vast enough already, and the thread through it difficult to follow even for the more experienced worker in the field. We explain methods for acquiring data and reconstructing metabolic networks, and review the various models that have been used for their structural analysis. Several concepts such as modularity are introduced, as are the controversies that have beset the field these past few years, for instance, on whether metabolic networks are small-world or scale-free, and on which model better explains the evolution of metabolism. Clarifying the work that has been done also helps in identifying open questions and in proposing relevant future directions in the field, which we do along the paper and in the conclusion.