Small noncoding RNAs (sRNA/sncRNAs) are generated from different genomic loci and play important roles in biological processes, such as cell proliferation and the regulation of gene expression. Next-generation sequencing (NGS) has provided an unprecedented opportunity to discover and quantify diverse kinds of sncRNA, such as tRFs (tRNA-derived small RNA fragments), phasiRNAs (phased, secondary, small-interfering RNAs), Piwi-interacting RNA (piRNAs) and plant-specific 24-nt short interfering RNAs (siRNAs). However, currently available web-based tools do not provide approaches to comprehensively analyze all of these diverse sncRNAs. This study presents a novel integrated platform, sRNAtools (https://bioinformatics.caf.ac.cn/sRNAtools), that can be used in conjunction with high-throughput sequencing to identify and functionally annotate sncRNAs, including profiling microRNAss, piRNAs, tRNAs, small nuclear RNAs, small nucleolar RNAs and rRNAs and discovering isomiRs, tRFs, phasiRNAs and plant-specific 24-nt siRNAs for up to 21 model organisms. Different modules, including single case, batch case, group case and target case, are developed to provide users with flexible ways of studying sncRNA. In addition, sRNAtools supports different ways of uploading small RNA sequencing data in a very interactive queue system, while local versions based on the program package/Docker/virtureBox are also available. We believe that sRNAtools will greatly benefit the scientific community as an integrated tool for studying sncRNAs.

ABSTRACT: In plants, 24 nucleotide small interfering RNAs (24-nt siRNAs) account for a large percentage of the total siRNA pool, and they play an important role in guiding plant-specific RNA-directed DNA methylation (RdDM), which transcriptionally silences transposon elements, transgenes, repetitive sequences and some endogenous genes. Several loci in plant genomes produce clusters of 24-nt RNAs, and these loci are receiving increasing attention from the research community. However, at present there is no bioinformatics resource dedicated to 24-nt siRNA loci and their derived 24-nt siRNAs. Thus, in this study, Pln24NT, a freely available web resource, was created to centralize 24-nt siRNA loci and 24-nt siRNA information, including fundamental locus information, expression profiles and annotation of transposon elements, from next-generation sequencing (NGS) data for 10 popular plant species. An intuitive web interface was also developed for convenient searching and browsing, and analytical tools were included to help users flexibly analyze their own siRNA NGS data. Pln24NT will help the plant research community to discover and characterize 24-nt siRNAs, and may prove useful for studying the roles of siRNA in RNA-directed DNA methylation in plants. AVAILABILITY AND IMPLEMENTATION: http://bioinformatics.caf.ac.cn/Pln24NT . CONTACT: [email protected]. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Despite our expanding knowledge about the biochemistry of gene regulation involved in host-pathogen interactions, a quantitative understanding of this process at a transcriptional level is still limited. We devise and assess a computational framework that can address this question. This framework is founded on a mixture model-based likelihood, equipped with functionality to cluster genes per dynamic and functional changes of gene expression within an interconnected system composed of the host and pathogen. If genes from the host and pathogen are clustered in the same group due to a similar pattern of dynamic profiles, they are likely to be reciprocally co-evolving. If genes from the two organisms are clustered in different groups, this means that they experience strong host-pathogen interactions. The framework can test the rates of change for individual gene clusters during pathogenic infection and quantify their impacts on host-pathogen interactions. The framework was validated by a pathological study of poplar leaves infected by fungal Marssonina brunnea in which co-evolving and interactive genes that determine poplar-fungus interactions are identified. The new framework should find its wide application to studying host-pathogen interactions for any other interconnected systems.

Quantum-dot infrared photodetectors have emerged as attractive devices for sensing long wavelength radiation. Their principle of operation is based on absorption of radiation via intersublevel transitions in quantum dots. Multiple layers of self-organized In(Ga)As/Ga(Al)As quantum dots are generally incorporated in the active region of these devices. Three-dimensional quantum confinement allows normal incidence operation. This paper describes a novel variation in the design of these devices which allows a significant reduction of the dark current, high temperature operation and extension of operation to terahertz frequencies. The principle of operation and operating characteristics of this device—the tunnel quantum-dot intersublevel detector—are described. Operation is demonstrated from 6–80$\mu{\hbox {m}}$at temperatures up to 300 K with acceptable values of peak responsivity (0.1–0.75 A/W) and specific detectivity ($\hbox{10}^{7}$–$ \hbox{10}^{11}\ {\hbox {cm}}\cdot \hbox{Hz}^{1/2}/ \hbox{W}^{-1}$, depending on temperature and wavelength).