In recent decades, with the continuous development of high‐throughput sequencing technology, data volume in medical research has increased, at the same time, almost all clinical researchers have their own independent omics data, which provided a better condition for data mining and a deeper understanding of gene functions. However, for these large amounts of data, many common and cutting‐edge effective bioinformatics research methods still cannot be widely used. This has encouraged the establishment of many analytical platforms, a portion of databases or platforms were designed to solve the special analysis needs of users, for instance, MG RAST, IMG/M, Qiita, BIGSdb, and TRAPR were developed for specific omics research, and some databases or servers provide solutions for special problems solutions. Metascape was designed to only provide functional annotations of genes as well as function enrichment analysis; BioNumerics and RidomSeqSphere+ perform multilocus sequence typing; CARD provides only antimicrobial resistance annotations. Additionally, some web services are outdated, and inefficient interaction often fails to meet the needs of researchers, such as our previous versions of the platform. Therefore, the demand to complete massive data processing tasks urgently requires a comprehensive bioinformatics analysis platform. Hence, we have developed a website platform, Sangerbox 3.0 (http://vip.sangerbox.com/), a web‐based tool platform. On a user‐friendly interface that also supports differential analysis, the platform provides interactive customizable analysis tools, including various kinds of correlation analyses, pathway enrichment analysis, weighted correlation network analysis, and other common tools and functions, users only need to upload their own corresponding data into Sangerbox 3.0, select required parameters, submit, and wait for the results after the task has been completed. We have also established a new interactive plotting system that allows users to adjust the parameters in the image; moreover, optimized plotting performance enables users to adjust large‐capacity vector maps on the web site. At the same time, we have integrated GEO, TCGA, ICGC, and other databases and processed data in batches, greatly reducing the difficulty to obtain data and improving the efficiency of bioimformatics study for users. Finally, we also provide users with rich sources of bioinformatics analysis courses, offering a platform for researchers to share and exchange knowledge.

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Altitude is a determining factor of ecosystem properties and processes in mountains. This study investigated the changes in the concentrations of carbon (C), nitrogen (N), and phosphorus (P) and their ratios in four key ecosystem components (forest floor litter, fine roots, soil, and soil microorganisms) along an altitudinal gradient (from 50 m to 950 m a.s.l.) in subtropical China. The results showed that soil organic C and microbial biomass C concentrations increased linearly with increasing altitude. Similar trends were observed for concentrations of total soil N and microbial biomass N. In contrast, the N concentration of litter and fine roots decreased linearly with altitude. With increasing altitude, litter, fine roots, and soil C:N ratios increased linearly, while the C:N ratio of soil microbial biomass did not change significantly. Phosphorus concentration and C:P and N:P ratios of all ecosystem components generally had nonlinear relationships with altitude. Our results indicate that the altitudinal pattern of plant and soil nutrient status differs among ecosystem components and that the relative importance of P vs. N limitation for ecosystem functions and processes shifts along altitudinal gradients.

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