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Background Venn diagrams are commonly used to display list comparison. In biology, they are widely used to show the differences between gene lists originating from different differential analyses, for instance. They thus allow the comparison between different experimental conditions or between different methods. However, when the number of input lists exceeds four, the diagram becomes difficult to read. Alternative layouts and dynamic display features can improve its use and its readability. Results jvenn is a new JavaScript library. It processes lists and produces Venn diagrams. It handles up to six input lists and presents results using classical or Edwards-Venn layouts. User interactions can be controlled and customized. Finally, jvenn can easily be embeded in a web page, allowing to have dynamic Venn diagrams. Conclusions jvenn is an open source component for web environments helping scientists to analyze their data. The library package, which comes with full documentation and an example, is freely available at http://bioinfo.genotoul.fr/jvenn .

Although the use of metabarcoding to identify taxa in DNA mixtures is widely approved, its reliability in quantifying taxon abundance is still the subject of debate. In this study we investigated the relationships between the amount of pollen grains in mock solutions and the abundance of high-throughput sequence reads and how the relationship was affected by the pollen counting methodology, the number of PCR cycles, the type of markers and plant species whose pollen grains have different characteristics. We found a significant positive relationship between the number of DNA sequences and the number of pollen grains in the mock solutions. However, better relationships were obtained with light microscopy as a pollen grain counting method compared with flow cytometry, with the chloroplastic trnL marker compared with ribosomal ITS1 and with 30 when compared with 25 or 35 PCR cycles. We provide a list of recommendations to improve pollen quantification.

Given the ongoing decline of both pollinators and plants, it is crucial to implement effective methods to describe complex pollination networks across time and space in a comprehensive and high-throughput way. Here we tested if metabarcoding may circumvent the limits of conventional methodologies in detecting and quantifying plant-pollinator interactions. Metabarcoding experiments on pollen DNA mixtures described a positive relationship between the amounts of DNA from focal species and the number of trnL and ITS1 sequences yielded. The study of pollen loads of insects captured in plant communities revealed that as compared to the observation of visits, metabarcoding revealed 2.5 times more plant species involved in plant-pollinator interactions. We further observed a tight positive relationship between the pollen-carrying capacities of insect taxa and the number of trnL and ITS1 sequences. The number of visits received per plant species also positively correlated to the number of their ITS1 and trnL sequences in insect pollen loads. By revealing interactions hard to observe otherwise, metabarcoding significantly enlarges the spatiotemporal observation window of pollination interactions. By providing new qualitative and quantitative information, metabarcoding holds great promise for investigating diverse facets of interactions and will provide a new perception of pollination networks as a whole.

Tomato (Solanum lycopersicum) is a major crop plant and a model system for fruit development. Solanum is one of the largest angiosperm genera(1) and includes annual and perennial plants from diverse habitats. Here we present a high-quality genome sequence of domesticated tomato, a draft sequence of its closest wild relative, Solanum pimpinellifolium(2), and compare them to each other and to the potato genome (Solanum tuberosum). The two tomato genomes show only 0.6% nucleotide divergence and signs of recent admixture, but show more than 8% divergence from potato, with nine large and several smaller inversions. In contrast to Arabidopsis, but similar to soybean, tomato and potato small RNAs map predominantly to gene-rich chromosomal regions, including gene promoters. The Solanum lineage has experienced two consecutive genome triplications: one that is ancient and shared with rosids, and a more recent one. These triplications set the stage for the neofunctionalization of genes controlling fruit characteristics, such as colour and fleshiness.

ObjectiveThe gut microbiota, which is considered a causal factor in metabolic diseases as shown best in animals, is under the dual influence of the host genome and nutritional environment. This study investigated whether the gut microbiota per se, aside from changes in genetic background and diet, could sign different metabolic phenotypes in mice.MethodsThe unique animal model of metabolic adaptation was used, whereby C57Bl/6 male mice fed a high-fat carbohydrate-free diet (HFD) became either diabetic (HFD diabetic, HFD-D) or resisted diabetes (HFD diabetes-resistant, HFD-DR). Pyrosequencing of the gut microbiota was carried out to profile the gut microbial community of different metabolic phenotypes. Inflammation, gut permeability, features of white adipose tissue, liver and skeletal muscle were studied. Furthermore, to modify the gut microbiota directly, an additional group of mice was given a gluco-oligosaccharide (GOS)-supplemented HFD (HFD+GOS).ResultsDespite the mice having the same genetic background and nutritional status, a gut microbial profile specific to each metabolic phenotype was identified. The HFD-D gut microbial profile was associated with increased gut permeability linked to increased endotoxaemia and to a dramatic increase in cell number in the stroma vascular fraction from visceral white adipose tissue. Most of the physiological characteristics of the HFD-fed mice were modulated when gut microbiota was intentionally modified by GOS dietary fibres.ConclusionsThe gut microbiota is a signature of the metabolic phenotypes independent of differences in host genetic background and diet.

Background Saprophytic filamentous fungi are ubiquitous micro-organisms that play an essential role in photosynthetic carbon recycling. The wood-decayer Pycnoporus cinnabarinus is a model fungus for the study of plant cell wall decomposition and is used for a number of applications in green and white biotechnology. Results The 33.6 megabase genome of P. cinnabarinus was sequenced and assembled, and the 10,442 predicted genes were functionally annotated using a phylogenomic procedure. In-depth analyses were carried out for the numerous enzyme families involved in lignocellulosic biomass breakdown, for protein secretion and glycosylation pathways, and for mating type. The P. cinnabarinus genome sequence revealed a consistent repertoire of genes shared with wood-decaying basidiomycetes. P. cinnabarinus is thus fully equipped with the classical families involved in cellulose and hemicellulose degradation, whereas its pectinolytic repertoire appears relatively limited. In addition, P. cinnabarinus possesses a complete versatile enzymatic arsenal for lignin breakdown. We identified several genes encoding members of the three ligninolytic peroxidase types, namely lignin peroxidase, manganese peroxidase and versatile peroxidase. Comparative genome analyses were performed in fungi displaying different nutritional strategies (white-rot and brown-rot modes of decay). P. cinnabarinus presents a typical distribution of all the specific families found in the white-rot life style. Growth profiling of P. cinnabarinus was performed on 35 carbon sources including simple and complex substrates to study substrate utilization and preferences. P. cinnabarinus grew faster on crude plant substrates than on pure, mono- or polysaccharide substrates. Finally, proteomic analyses were conducted from liquid and solid-state fermentation to analyze the composition of the secretomes corresponding to growth on different substrates. The distribution of lignocellulolytic enzymes in the secretomes was strongly dependent on growth conditions, especially for lytic polysaccharide mono-oxygenases. Conclusions With its available genome sequence, P. cinnabarinus is now an outstanding model system for the study of the enzyme machinery involved in the degradation or transformation of lignocellulosic biomass.

In 2020, the world faced the Severe Acute Respiratory Syndrome Coronavirus 2 (SAU : Pleasenotetha ARS-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 \"Journé es Ouvertes en Biologie, Informatique et Mathé matiques\" (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.

AIMS/HYPOTHESIS: Evidence suggests that bacterial components in blood could play an early role in events leading to diabetes. To test this hypothesis, we studied the capacity of a broadly specific bacterial marker (16S rDNA) to predict the onset of diabetes and obesity in a general population. METHODS: Data from an Epidemiological Study on the Insulin Resistance Syndrome (D.E.S.I.R.) is a longitudinal study with the primary aim of describing the history of the metabolic syndrome. The 16S rDNA concentration was measured in blood at baseline and its relationship with incident diabetes and obesity over 9 years of follow-up was assessed. In addition, in a nested case-control study in which participants later developed diabetes, bacterial phylotypes present in blood were identified by pyrosequencing of the overall 16S rDNA gene content. RESULTS: We analysed 3,280 participants without diabetes or obesity at baseline. The 16S rDNA concentration was higher in those destined to have diabetes. No difference was observed regarding obesity. However, the 16S rDNA concentration was higher in those who had abdominal adiposity at the end of follow-up. The adjusted OR (95% CIs) for incident diabetes and for abdominal adiposity were 1.35 (1.11, 1.60), p = 0.002 and 1.18 (1.03, 1.34), p = 0.01, respectively. Moreover, pyrosequencing analyses showed that participants destined to have diabetes and the controls shared a core blood microbiota, mostly composed of the Proteobacteria phylum (85-90%). CONCLUSIONS/INTERPRETATION: 16S rDNA was shown to be an independent marker of the risk of diabetes. These findings are evidence for the concept that tissue bacteria are involved in the onset of diabetes in humans.

Background Next generation sequencing platforms are now well implanted in sequencing centres and some laboratories. Upcoming smaller scale machines such as the 454 junior from Roche or the MiSeq from Illumina will increase the number of laboratories hosting a sequencer. In such a context, it is important to provide these teams with an easily manageable environment to store and process the produced reads. Results We describe a user-friendly information system able to manage large sets of sequencing data. It includes, on one hand, a workflow environment already containing pipelines adapted to different input formats (sff, fasta, fastq and qseq), different sequencers (Roche 454, Illumina HiSeq) and various analyses (quality control, assembly, alignment, diversity studies,…) and, on the other hand, a secured web site giving access to the results. The connected user will be able to download raw and processed data and browse through the analysis result statistics. The provided workflows can easily be modified or extended and new ones can be added. Ergatis is used as a workflow building, running and monitoring system. The analyses can be run locally or in a cluster environment using Sun Grid Engine. Conclusions NG6 is a complete information system designed to answer the needs of a sequencing platform. It provides a user-friendly interface to process, store and download high-throughput sequencing data.

Transcriptome analysis based on a de novo assembly of next generation RNA sequences is now performed routinely in many laboratories. The generated results, including contig sequences, quantification figures, functional annotations and variation discovery outputs are usually bulky and quite diverse. This article presents a user oriented storage and visualisation environment permitting to explore the data in a top-down manner, going from general graphical views to all possible details. The software package is based on biomart, easy to install and populate with local data. The software package is available under the GNU General Public License (GPL) at http://bioinfo.genotoul.fr/RNAbrowse.