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While phylogenetic trees and associated data have been getting easier to generate, it has been difficult to reuse, combine, and synthesize the information they provided, because published trees are often only available as image files and associated data are often stored in incompatible formats. To increase the reproducibility and reusability of phylogenetic data, the ggtree object was designed for storing phylogenetic tree and associated data, as well as visualization directives. The ggtree object itself is a graphic object and can be rendered as a static image. More importantly, the input tree and associated data that are used in visualization can be extracted from the graphic object, making it an ideal data structure for publishing tree (image, tree, and data in one single object) and thus enhancing data reuse and analytical reproducibility, as well as facilitating integrative and comparative studies. The ggtree package is freely available at https://www.bioconductor.org/packages/ggtree. The ggtree object is designed to store phylogenetic tree and associated data, and the object itself is a graphic object that can be rendered as an image file. This work will increase the reproducibility and reusability of phylogenetic data, as well as facilitate integrative and comparative studies. Highlights The phylogenetic tree and diverse accompanying data can be stored in a ggtree graph object, which improves the reproducibility and reusability of phylogenetic data. The phylogenetic tree and associated data can be extracted from the ggtree object, which can be reanalyzed and help various scientific disciplines synthesize their comparative studies and phylogenetic information. The ggtree graph object can be rendered as a static image, and the visualization directives that were previously saved in the object can be reused to display a different tree object in a manner akin to Microsoft Word Format Painter.

The network analysis has attracted increasing attention and interest from ecological academics, thus it is of great necessity to develop more convenient and powerful tools. For that reason, we have developed an R package, named “ggClusterNet,” to complete and display the network analysis in an easier manner. In that package, ten network layout algorithms are designed to better display the modules of microbiome network (randomClusterG, PolygonClusterG, PolygonRrClusterG, ArtifCluster, randSNEClusterG, PolygonModsquareG, PolyRdmNotdCirG, model_Gephi.2, model_igraph, and model_maptree). For the convenience of the users, many functions related to microbial network analysis, such as corMicor(), net_properties(), node_properties(), ZiPiPlot(), random_Net_compate(), are integrated to complete the network mining. Furthermore, the pipeline function named network.2() and corBionetwork() are also added for the quick achievement of the network or bipartite network analysis as well as their in‐depth mining. The ggClusterNet is publicly available via GitHub (https://github.com/taowenmicro/ggClusterNet/) or Gitee (https://gitee.com/wentaomicro/ggClusterNet) for users' access. A complete description of the usages can be found on the manuscript's GitHub page (https://github.com/taowenmicro/ggClusterNet/wiki). Highlights ggClusterNet is an R package for microbial networks. Analysis functions could help the user to easily complete network analysis and interpretation. Ten network layout algorithms allow users more alternatives to plot the network and generate published‐ready figures. It is free to access on GitHub and Gitee. ggClusterNet is an R package for microbial networks analysis and interpretation. Ten network layout algorithms  allow users more alternatives to plot the network and generate published‐ready figures. It is free to access on GitHub and Gitee.

Data visualization plays a crucial role in illustrating results and sharing knowledge among researchers. Though many types of visualization tools are widely used, most of them require enough coding experience or are designed for specialized usages, or are not free. Here, we present ImageGP, a specialized visualization platform designed for biology and chemistry data illustration. ImageGP could generate generalized plots like lines, bars, scatters, boxes, sets, heatmaps, and histograms with the most common input content in a user‐friendly interface. Normally plotting using ImageGP only needs a few mouse clicks. For some plots, one only needs to just paste data and click submit to get the visualization results. Additionally, ImageGP supplies up to 26 parameters to meet customizable requirements. ImageGP also contains specialized plots like volcano plot, functional enrichment plot for most omics‐data analysis, and other four specialized functions for microbiome analysis. Since 2017, ImageGP has been running for nearly 5 years and serving 336,951 visits from all over the world. Together, ImageGP (http://www.ehbio.com/ImageGP/) is an effective and efficient tool for experimental researchers to comprehensively visualize and interpret data generated from wet‐lab and dry‐lab. Representative visualization results of ImageGP. ImageGP supports 16 types of images (including heatmap, volcano plot, enrichment bubble plot) and four types of online analysis with up to 26 parameters for customization. Highlights Publication‐quality visualization results. Easy to use and customize. Reproducible results with scripts.

Exosomes are extracellular nanovesicles that mediate a number of cellular processes, including intracellular signalling. There are many published examples of exosome–exosome dimers; however, their relevance has not been explored. Here, we propose that cells release exosomes to physically interact with incoming exosomes, forming dimers that we hypothesize attenuate incoming exosome‐mediated signalling. We discuss experiments to test this hypothesis and potential relevance in health and disease.

Pan‐genomics is one of the most powerful means to study genomic variation and obtain a sketch of genes within a defined clade of species. Though there are a lot of computational tools to achieve this, an integrated framework to evaluate their performance and offer the best choice to users has never been achieved. To ease the process of large‐scale prokaryotic genome analysis, we introduce Integrated Prokaryotes Genome and pan‐genome Analysis (IPGA), a one‐stop web service to analyze, compare, and visualize pan‐genome as well as individual genomes, that rids users of installing any specific tools. IPGA features a scoring system that helps users to evaluate the reliability of pan‐genome profiles generated by different packages. Thus, IPGA can help users ascertain the profiling method that is most suitable for their data set for the following analysis. In addition, IPGA integrates several downstream comparative analysis and genome analysis modules to make users achieve diverse targets. Integrated Prokaryotes Genome and pan‐genome Analysis (IPGA) serves as a free and easy‐to‐use web‐based system that could provide up‐to‐date pan‐genome analysis service for non‐bioinformaticians. IPGA offers users the most reliable pan‐genome profile which enables users to perform additional comparative genomic analysis. IPGA provides a series of downstream analysis modules such as phylogenetic inference, synteny inference, and target genome annotation. Highlights IPGA serves as a free and easy‐to‐use web‐based system that could provide up‐to‐date pan‐genome analysis service for non‐bioinformaticians. IPGA offers users the most reliable pan‐genome profile which enables users to perform additional comparative genomic analysis. IPGA provides a series of downstream analysis modules such as phylogenetic inference, synteny inference, and target genome annotation.

The number of microbial genomes sequenced each year is expanding rapidly, in part due to genome-resolved metagenomic studies that routinely recover hundreds of draft-quality genomes. Rapid algorithms have been developed to comprehensively compare large genome sets, but they are not accurate with draft-quality genomes. Here we present dRep, a program that reduces the computational time for pairwise genome comparisons by sequentially applying a fast, inaccurate estimation of genome distance, and a slow, accurate measure of average nucleotide identity. dRep achieves a 28 × increase in speed with perfect recall and precision when benchmarked against previously developed algorithms. We demonstrate the use of dRep for genome recovery from time-series datasets. Each metagenome was assembled separately, and dRep was used to identify groups of essentially identical genomes and select the best genome from each replicate set. This resulted in recovery of significantly more and higher-quality genomes compared to the set recovered using co-assembly.

The serious coronavirus disease‐2019 (COVID‐19) was first reported in December 2019 in Wuhan, China. COVID‐19 is an infectious disease caused by severe acute respiratory syndrome‐coronavirus 2 (SARS‐CoV‐2). Angiotensin converting enzyme 2(ACE2) is the cellular receptor for SARS‐CoV‐2. Considering the critical roles of testicular cells for the transmission of genetic information between generations, we analyzed single‐cell RNA‐sequencing (scRNA‐seq) data of adult human testis. The mRNA expression of ACE2 was expressed in both germ cells and somatic cells. Moreover, the positive rate of ACE2 in testes of infertile men was higher than normal, which indicates that SARS‐CoV‐2 may cause reproductive disorders through pathway activated by ACE2 and the men with reproductive disorder may easily to be infected by SARS‐CoV‐2. The expression level of ACE2 was related to the age, and the mid‐aged with higher positive rate than young men testicular cells. Taken together, this research provides a biological background of the potential route for infection of SARS‐CoV‐2 and may enable rapid deciphering male‐related reproductive disorders induced by COVID‐19.

COVID‐19 has significant case fatality. Glucocorticoids are the only treatment shown to improve survival, but only among patients requiring supplemental oxygen. WHO advises patients to seek medical care for “trouble breathing,” but hypoxemic patients frequently have no respiratory symptoms. Our cohort study of hospitalized COVID‐19 patients shows that respiratory symptoms are uncommon and not associated with mortality. By contrast, objective signs of respiratory compromise—oxygen saturation and respiratory rate—are associated with markedly elevated mortality. Our findings support expanding guidelines to include at‐home assessment of oxygen saturation and respiratory rate in order to expedite life‐saving treatments patients to high‐risk COVID‐19 patients.

Frailty is a geriatric syndrome that results from multisystem impairment caused by age‐associated accumulation of deficits. The frailty index is used to define the level of frailty. Several studies have searched for molecular biomarkers associated with frailty, to meet the needs for personalized care. Cyclase‐associated protein 2 (CAP2) is a multifunctional actin‐binding protein involved in various physiological and pathological processes, that might reflect frailty's intrinsic complexity. This study aimed to investigate the association between frailty index and circulating CAP2 concentration in 467 community‐dwelling older adults (median age: 79; range: 65–92 years) from Milan, Italy. The selected robust regression model showed that circulating CAP2 concentration was not associated with chronological age, as well as sex and education. However, circulating CAP2 concentration was significantly and inversely associated with the frailty index: a 0.1‐unit increase in frailty index leads to ~0.5‐point mean decrease in CAP2 concentration. Furthermore, mean CAP2 concentration was significantly lower in frail participants (i.e., frailty index ≥0.25) than in non‐frail participants. This study shows the association between serum CAP2 concentration and frailty status for the first time, highlighting the potential of CAP2 as a biomarker for age‐associated accumulation of deficits.