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The detection of circular RNA molecules (circRNAs) is typically based on short-read RNA sequencing data processed by computational detection tools. During the last decade, a plethora of such tools have been developed, but a systematic comparison with orthogonal validation is missing. Here, we set up a circRNA detection tool benchmarking study, in which 16 tools were used and detected over 315,000 unique circRNAs in three deeply sequenced human cell types. Next, 1,516 predicted circRNAs were empirically validated using three orthogonal methods. Generally, tool-specific precision values are high and similar (median of 98.8%, 96.3%, and 95.5% for qPCR, RNase R, and amplicon sequencing, respectively) whereas the sensitivity and number of predicted circRNAs (ranging from 1,372 to 58,032) are the most significant tool differentiators. Furthermore, we demonstrate the complementarity of tools through the increase in detection sensitivity by considering the union of highly-precise tool combinations while keeping the number of false discoveries low. Finally, based on the benchmarking results, recommendations are put forward for circRNA detection and validation.

Circular RNA (circRNA) is a kind of new regulatory RNA with diverse biological functions. Numerous circRNAs have been identified in many plant species; however, evolution of plant circRNAs remains largely unknown. In this study, we assembled full-length sequences of 6,519 rice (Oryza sativa) circRNAs and analyzed their conservation in other 46 plant species based on comparison of sequences and expression patterns. We found that, at the genomic level, 8.7% of the 6,519 circRNAs were conserved in dicotyledonous plants and 49.1% in Oryza genus. Meanwhile, 57.8% of parental protein coding genes of the rice circRNAs originated recently after divergence of monocotyledonous plants, implying recent origin of the majority of rice circRNAs, a conclusion further supported by the results based on analysis of 4,663 full-length circRNAs in Arabidopsis thaliana. Accordingly, we proposed three models to address the origination of different types of circRNAs. Taken together, the results obtained in this study provide new insights for the evolutionary dynamics of plant circRNAs and candidate circRNAs for further functional exploration.

Exposure to cigarette smoke (CS) results in injury to the epithelial cells of the human respiratory tract and has been implicated as a causative factor in the development of chronic obstructive pulmonary disease and lung cancers. The application of omics-scale methodologies has improved the capacity to understand cellular signaling processes underlying response to CS exposure. We report here the development of an algorithm based on quantitative assessment of transcriptomic profiles and signaling pathway perturbation analysis (SPPA) of human bronchial epithelial cells (HBEC) exposed to the toxic components present in CS. HBEC were exposed to CS of different compositions and for different durations using an ISO3308 smoking regime and the impact of exposure was monitored in 2263 signaling pathways in the cell to generate a total effect score that reflects the quantitative degree of impact of external stimuli on the cells. These findings support the conclusion that the SPPA algorithm provides an objective, systematic, sensitive means to evaluate the biological impact of exposures to CS of different compositions making a powerful comparative tool for commercial product evaluation and potentially for other known or potentially toxic environmental smoke substances.

Secale strictum subsp. kuprijanovii is a perennial, hermaphrodite wild rye species and a progenitor of the modern cultivated rye, Secale cereale. With high adaptive capacity in stress conditions, it is valuable for enriching the germplasm resources of rye. Therefore, to elucidate its genetic and phylogenetic relationship is of great importance. We hereby sequenced, assembled and presented for the first time the complete chloroplast genome of this less studied species. The whole genome is 137,079 bp in size, including a large single copy region of 81,099 bp, a small single copy region of 12,820 bp and two separated inverted repeat regions of 43,160 bp. A total of 109 unique genes were annotated, including 67 protein-coding genes, 38 tRNA genes and 4 rRNA genes. Phylogenetic analysis showed that Secale strictum subsp. kuprijanovii clustered most closely with Secale cereal. A remarkably close evolutionary relationship of S.strictum subsp. kuprijanovii with various wheat varieties may indicate its usage as a genetic resource for the breeding of both the cultivated rye and wheat.

Robust strategies to identify patients at high risk for tumor metastasis, such as those frequently observed in intrahepatic cholangiocarcinoma (ICC), remain limited. While gene/protein expression profiling holds great potential as an approach to cancer diagnosis and prognosis, previously developed protocols using multiple diagnostic signatures for expression-based metastasis prediction have not been widely applied successfully because batch effects and different data types greatly decreased the predictive performance of gene/protein expression profile-based signatures in interlaboratory and data type dependent validation. To address this problem and assist in more precise diagnosis, we performed a genome-wide integrative proteome and transcriptome analysis and developed an ensemble machine learning-based integration algorithm for metastasis prediction (EMLI-Metastasis) and risk stratification (EMLI-Prognosis) in ICC. Based on massive proteome (216) and transcriptome (244) data sets, 132 feature (biomarker) genes were selected and used to train the EMLI-Metastasis algorithm. To accurately detect the metastasis of ICC patients, we developed a weighted ensemble machine learning method based on k-Top Scoring Pairs (k-TSP) method. This approach generates a metastasis classifier for each bootstrap aggregating training data set. Ten binary expression rank-based classifiers were generated for detection of metastasis separately. To further improve the accuracy of the method, the 10 binary metastasis classifiers were combined by weighted voting based on the score from the prediction results of each classifier. The prediction accuracy of the EMLI-Metastasis algorithm achieved 97.1% and 85.0% in proteome and transcriptome datasets, respectively. Among the 132 feature genes, 21 gene-pair signatures were developed to establish a metastasis-related prognosis risk-stratification model in ICC (EMLI-Prognosis). Based on EMLI-Prognosis algorithm, patients in the high-risk group had significantly dismal overall survival relative to the low-risk group in the clinical cohort (P-value

Circular RNA (circRNA) is a special type of non-coding RNA that participates in diverse biological processes in both animals and plants. Five years ago, we developed a comprehensive plant circRNA database (PlantcircBase), which has attracted much attention from the plant circRNA community. Here, we report an updated PlantcircBase (v.7.0), which contains 171,118 circRNAs from 21 plant species. Over 31,000 of the circRNAs have full-length sequences constructed based on analysis of 749 bulk RNA sequencing (RNA-seq) datasets downloaded from the public domain and Nanopore long-read sequencing results of rice RNAs newly generated in this study. A plant multiple conservation score (PMCS), based on the conservation of both sequence and expression profiles, was calculated for each circRNA to quantify and compare the conservation of all circRNAs. A new parameter, plant circRNA confidence level (PCCL), is introduced to measure the identity reliability of each circRNA based on experimental validation results and the number of references that support the circRNA. All this information and other details of circRNAs can be browsed, searched, and downloaded from PlantcircBase 7.0, which also provides online bioinformatics tools for visualization and sequence alignment. PlantcircBase 7.0 is publicly and freely accessible at http://ibi.zju.edu.cn/plantcircbase/.

Single-cell RNA (scRNA) profiling or scRNA-sequencing (scRNA-seq) makes it possible to parallelly investigate diverse molecular features of multiple types of cells in a given plant tissue and discover cell developmental processes. In this study, we evaluated the effects of sample size (i.e., cell number) on the outcome of single-cell transcriptome analysis by sampling different numbers of cells from a pool of ~57,000 Arabidopsis thaliana root cells integrated from five published studies. Our results indicated that the most significant principal components could be achieved when 20,000–30,000 cells were sampled, a relatively high reliability of cell clustering could be achieved by using ~20,000 cells with little further improvement by using more cells, 96% of the differentially expressed genes could be successfully identified with no more than 20,000 cells, and a relatively stable pseudotime could be estimated in the subsample with 5000 cells. Finally, our results provide a general guide for optimizing sample size to be used in plant scRNA-seq studies.

Background: Single-cell RNA (scRNA) profiling or scRNA-sequencing (scRNA-seq) is a rapidly developing technology and an important frontier of molecular biology science. scRNA profiling makes it possible to parallelly investigate diverse molecular features of multiple types of cells in a given plant tissue, and promotes elucidation of cellular heterogeneity and discovery of developmental processes underpinning cell differentiation. While it is assumed that the power of scRNA profiling in uncovering cellular heterogeneity largely depends on the depth of scRNA-seq, no study about the effect of the sequenced cell numbers on the power of plant scRNA-seq has ever been reported. Results: In this study, on the basis of analyzing the sample coverage of 1,244 available scRNA-seq studies (including 30 in plants) and the effect of sample coverage on cell clustering and identification of cell types, we evaluated the effects of sample size (i.e., cell number) on the outcome of single cell transcriptome analysis by sampling different number of cells from a pool of ~57,000 Arabidopsis thaliana root cells integrated from five published studies. Our results indicated that the most significant principle components could be achieved when 20,000-30,000 cells were sampled, a relatively high reliability of cell clustering could be achieved by using ~20,000 cells with little further improvement by using more cells, 96% of the differentially expressed genes could be successfully identified with no more than 20,000 cells, and a relatively stable pseudotime could be estimated in the sub-sample with 5,000 cells. Conclusions: Our results imply that ~20,000 (or 10,000 - 30,000[1] ) cells are enough for profiling Arabidopsis root cells using scRNA-seq, although the applicability of this number to other Arabidopsis tissues and other plants is yet to be further determined by analyzing scRNA-seq data generated from diverse tissues of different plant species. Nevertheless, our results provide a general guide for optimizing sample size to be used in plant scRNA-seq studies. Change to “or up to 300000”?

Extracellular microRNAs (miRNAs) have been proposed to function in cross-kingdom gene regulation. Among these, plant-derived miRNAs of dietary origin have been reported to survive the harsh conditions of the human digestive system, enter the circulatory system, and regulate gene expression and metabolic function. However, definitive evidence supporting the presence of plant-derived miRNAs of dietary origin in mammals has been difficult to obtain due to limited sample sizes. We have developed a bioinformatics pipeline (ePmiRNA_finder) that provides strident miRNA classification and applied it to analyze 421 small RNA sequencing data sets from 10 types of human body fluids and tissues and comparative samples from carnivores and herbivores. A total of 35 miRNAs were identified that map to plants typically found in the human diet and these miRNAs were found in at least one human blood sample and their abundance was significantly different when compared to samples from human microbiome or cow. The plant-derived miRNA profiles were body fluid/tissue-specific and highly abundant in the brain and the breast milk samples, indicating selective absorption and/or the ability to be transported across tissue/organ barriers. Our data provide conclusive evidence for the presence of plant-derived miRNAs as a consequence of dietary intake and their cross-kingdom regulatory function within human circulating system.

Gene expression profiling holds great potential as a new approach to histological diagnosis and precision medicine of cancers of unknown primary (CUP). Batch effects and different data types greatly decrease the predictive performance of biomarker-based algorithms, and few methods have been widely applied to identify tissue origin of CUP up to now. To address this problem and assist in more precise diagnosis, we have developed a gene expression rank-based majority vote algorithm for tissue origin diagnosis of CUP (TOD-CUP) of most common cancer types. Based on massive tissue-specific RNA-seq data sets (10 553) found in The Cancer Genome Atlas (TCGA), 538 feature genes (biomarkers) were selected based on their gene expression ranks and used to predict tissue types. The top scoring pairs (TSPs) classifier of the tumor type was optimized by the TCGA training samples. To test the prediction accuracy of our TOD-CUP algorithm, we analyzed (1) two microarray data sets (1029 Agilent and 2277 Affymetrix/Illumina chips) and found 91% and 94% prediction accuracy, respectively, (2) RNA-seq data from five cancer types derived from 141 public metastatic cancer tumor samples and achieved 94% accuracy and (3) a total of 25 clinical cancer samples (including 14 metastatic cancer samples) were able to classify 24/25 samples correctly (96.0% accuracy). Taken together, the TOD-CUP algorithm provides a powerful and robust means to accurately identify the tissue origin of 24 cancer types across different data platforms. To make the TOD-CUP algorithm easily accessible for clinical application, we established a Web-based server for tumor tissue origin diagnosis (http://ibi. zju.edu.cn/todcup/).

Circular RNAs (circRNAs) are covalently closed single-stranded loop RNA molecules with or without protein coding capability. CircRNAs were previously considered to be splicing intermediates or artifacts but are now found to be pervasively expressed in all eukaryotes studied with some demonstrated to have important molecular functions in various biological processes. CircRNA is now a hot study topic of molecular biology. In this review, we summarize the progress achieved so far on plant circRNAs, including identification and functional characterization, compare the similarities and differences of circRNAs between plants and animals, and discuss the challenges for confident detection and functional investigation of plant circRNAs. Similar to what have been found in animals, plant genomes contain a large number of circRNAs that potentially regulate a wide range of biological progresses related to plant development and biotic/abiotic responses. Despite only a few plant circRNAs have been functionally characterized, novel function/mechanism that has not been reported in animals was revealed, implying more exciting findings about plant circRNAs are expected in future studies.

Rice (Oryza sativa L.) is one of the most important crops worldwide. Its relatives, including phylogenetically related species of rice and paddy weeds with a similar ecological niche, can provide crucial genetic resources (such as resistance to biotic and abiotic stresses and high photosynthetic efficiency) for rice research. Although many rice genomic databases have been constructed, a database providing large-scale curated genomic data from rice relatives and offering specific gene resources is still lacking. Here, we present RiceRelativesGD, a user-friendly genomic database of rice relatives. RiceRelativesGD integrates large-scale genomic resources from 2 cultivated rice and 11 rice relatives, including 208 321 specific genes and 13 643 genes related to photosynthesis and responsive to external stimuli. Diverse bioinformatics tools are embedded in the database, which allow users to search, visualize and download the information of interest. To our knowledge, this is the first genomic database providing a centralized genetic resource of rice relatives. RiceRelativesGD will serve as a significant and comprehensive knowledgebase for the rice community.

Circular RNAs (circRNAs) are a kind of non-coding RNAs; they were first found over 35 years ago but have only become a research hotspot in recent years. CircRNAs are covalently closed loops derived from precursor mRNAs by non-sequential back-splicing. Based on their genomic location, circRNAs can be classified into exonic, intronic, intergenic, and exon-intronic circRNAs (Chen, 2016). By taking advantages of recent advances in bioinformatics algorithms, a large number of circRNAs have been detected among diverse organisms, including plants such as Arabidopsis thaliana and Oryza sativa (Memczak et al., 2013; Ye et al., 2015; Chen, 2016).

Circular RNA (circRNA) is a kind of covalently closed single-stranded RNA molecules that have been proved to play important roles in transcriptional regulation of genes in diverse species. With the rapid development of bioinformatics tools, a huge number (95143) of circRNAs have been identified from different plant species, providing an opportunity for uncovering the overall characteristics of plant circRNAs. Here, based on publicly available circRNAs, we comprehensively analyzed characteristics of plant circRNAs with the help of various bioinformatics tools as well as in-house scripts and workflows, including the percentage of coding genes generating circRNAs, the frequency of alternative splicing events of circRNAs, the non-canonical splicing signals of circRNAs and the networks involving circRNAs, miRNAs and mRNAs. All this information has been integrated into an upgraded online database, PlantcircBase 3.0 (http://ibi.zju.edu.cn/plantcircbase/). In this database, we provided browse, search and visualization tools as well as a web-based blast tool, BLASTcirc, for prediction of circRNAs from query sequences based on searching against plant genomes and transcriptomes.

Barnyardgrass (Echinochloa crus-galli) is a pernicious weed in agricultural fields worldwide. The molecular mechanisms underlying its success in the absence of human intervention are presently unknown. Here we report a draft genome sequence of the hexaploid species E. crus-galli, i.e., a 1.27 Gb assembly representing 90.7% of the predicted genome size. An extremely large repertoire of genes encoding cytochrome P450 monooxygenases and glutathione S-transferases associated with detoxification are found. Two gene clusters involved in the biosynthesis of an allelochemical 2,4-dihydroxy-7-methoxy-1,4-benzoxazin-3-one (DIMBOA) and a phytoalexin momilactone A are found in the E. crus-galli genome, respectively. The allelochemical DIMBOA gene cluster is activated in response to co-cultivation with rice, while the phytoalexin momilactone A gene cluster specifically to infection by pathogenic Pyricularia oryzae. Our results provide a new understanding of the molecular mechanisms underlying the extreme adaptation of the weed., Barnyardgrass is a problematic agricultural weed. Here, via genomic analysis, Guo et al. identify genes potentially underlying its success, including a gene cluster activated in response to co-cultivation with rice that leads to synthesis of the allelochemical DIMBOA.

A direct procedure for the determination of total polysaccharides (TPS) in Camellia sinensis was set up based on the modified phenol-sulfuric acid method. The monosaccharide composition of TPS was analyzed by GC. Based on the results of GC, model monosaccharide mixtures were made which provided an adequate standard for this procedure. Through single-factor and orthogonal (L934) experiments, the experimental conditions such as the volume of phenol, the volume of concentrated sulfuric acid, the reaction time, and the incubation temperature, were optimized. The highest sensitivity of absorbance was obtained when the volume of concentrated sulfuric acid, the volume of phenol (6%), and the incubation temperature were 2.5 ml, 0.2 ml, and 50?C, respectively. Under optimum conditions, the prepared samples were determined satisfactorily, with the recovery from 100.2% to 103.7%, and a relative standard deviation (RSD) of 2.1%. Overall, the modified method is easily operated, rapid, sensitive and accurate. A similar procedure can be applied to the determination of other plant polysaccharides as well. .