Ribosome stalling is manifested by the local accumulation of ribosomes at specific codon positions of mRNAs. Here, we present ROSE, a deep learning framework to analyze high-throughput ribosome profiling data and estimate the probability of a ribosome stalling event occurring at each genomic location. Extensive validation tests on independent data demonstrated that ROSE possessed higher prediction accuracy than conventional prediction models, with an increase in the area under the receiver operating characteristic curve by up to 18.4%. In addition, genome-wide statistical analyses showed that ROSE predictions can be well correlated with diverse putative regulatory factors of ribosome stalling. Moreover, the genome-wide ribosome stalling landscapes of both human and yeast computed by ROSE recovered the functional interplays between ribosome stalling and cotranslational events in protein biogenesis, including protein targeting by the signal recognition particles and protein secondary structure formation. Overall, our study provides a novel method to complement the ribosome profiling techniques and further decipher the complex regulatory mechanisms underlying translation elongation dynamics encoded in the mRNA sequence.

SUMMARYInorganic phosphorus (Pi) is an essential element in numerous metabolic reactions and signaling pathways, but the molecular details of these pathways remain largely unknown. In this study, metabolite profiles of maize (Zea mays L.) leaves and roots were compared between six low‐Pi‐sensitive lines and six low‐Pi‐tolerant lines under Pi‐sufficient and Pi‐deficient conditions to identify pathways and genes associated with the low‐Pi stress response. Results showed that under Pi deprivation the concentrations of nucleic acids, organic acids and sugars were increased, but that the concentrations of phosphorylated metabolites, certain amino acids, lipid metabolites and nitrogenous compounds were decreased. The levels of secondary metabolites involved in plant immune reactions, including benzoxazinoids and flavonoids, were significantly different in plants grown under Pi‐deficient conditions. Among them, the 11 most stable metabolites showed significant differences under low‐ and normal‐Pi conditions based on the coefficient of variation (CV). Isoleucine and alanine were the most stable metabolites for the identification of Pi‐sensitive and Pi‐resistant maize inbred lines. With the significant correlation between morphological traits and metabolites, five low‐Pi‐responding consensus genes associated with morphological traits and simultaneously involved in metabolic pathways were mined by combining metabolites profiles and genome‐wide association study (GWAS). The consensus genes induced by Pi deficiency in maize seedlings were also validated by reverse‐transcription quantitative polymerase chain reaction (RT‐qPCR). Moreover, these genes were further validated in a recombinant inbred line (RIL) population, in which the glucose‐6‐phosphate‐1‐epimerase encoding gene mediated yield and correlated traits to phosphorus availability. Together, our results provide a framework for understanding the metabolic processes underlying Pi‐deficient responses and give multiple insights into improving the efficiency of Pi use in maize.

The new advances in various experimental techniques that provide complementary information about the spatial conformations of chromosomes have inspired researchers to develop computational methods to fully exploit the merits of individual data sources and combine them to improve the modeling of chromosome structure. Here we propose GEM-FISH, a method for reconstructing the 3D models of chromosomes through systematically integrating both Hi-C and FISH data with the prior biophysical knowledge of a polymer model. Comprehensive tests on a set of chromosomes, for which both Hi-C and FISH data are available, demonstrate that GEM-FISH can outperform previous chromosome structure modeling methods and accurately capture the higher order spatial features of chromosome conformations. Moreover, our reconstructed 3D models of chromosomes revealed interesting patterns of spatial distributions of super-enhancers which can provide useful insights into understanding the functional roles of these super-enhancers in gene regulation.

This paper designs an accurate and low-cost phishing detection sensor by exploring deep learning techniques. Phishing is a very common social engineering technique. The attackers try to deceive online users by mimicking a uniform resource locator (URL) and a webpage. Traditionally, phishing detection is largely based on manual reports from users. Machine learning techniques have recently been introduced for phishing detection. With the recent rapid development of deep learning techniques, many deep-learning-based recognition methods have also been explored to improve classification performance. This paper proposes a light-weight deep learning algorithm to detect the malicious URLs and enable a real-time and energy-saving phishing detection sensor. Experimental tests and comparisons have been conducted to verify the efficacy of the proposed method. According to the experiments, the true detection rate has been improved. This paper has also verified that the proposed method can run in an energy-saving embedded single board computer in real-time.

Neural tube defects (NTDs) are serious congenital malformations. In this study, we aimed to identify more specific and sensitive maternal serum biomarkers for noninvasive NTD screenings. We collected serum from 37 pregnant women carrying fetuses with NTDs and 38 pregnant women carrying normal fetuses. Isobaric tags for relative and absolute quantitation were conducted for differential proteomic analysis, and an enzyme-linked immunosorbent assay was used to validate the results. We then used a support vector machine (SVM) classifier to establish a disease prediction model for NTD diagnosis. We identified 113 differentially expressed proteins; of these, 23 were either upor downregulated 1.5-fold or more, including five complement proteins (C1QA, C1S, C1R, C9, and C3); C3 and C9 were downregulated significantly in NTD groups. The accuracy rate of the SVM model of the complement factors (including C1QA, C1S, and C3) was 62.5%, with 60% sensitivity and 67% specificity, while the accuracy rate of the SVM model of alpha-fetoprotein (AFP, an established biomarker for NTDs) was 62.5%, with 75% sensitivity and 50% specificity. Combination of the complement factor and AFP data resulted in the SVM model accuracy of 75%, and receiver operating characteristic curve analysis showed 75% sensitivity and 75% specificity. These data suggest that a disease prediction model based on combined complement factor and AFP data could serve as a more accurate method of noninvasive prenatal NTD diagnosis.

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N6-methyladenosine (m6A) is the most pervasive modification in eukaryotic mRNAs. Numerous biological processes are regulated by this critical post-transcriptional mark, such as gene expression, RNA stability, RNA structure and translation. Recently, various experimental techniques and computational methods have been developed to characterize the transcriptome-wide landscapes of m6A modification for understanding its underlying mechanisms and functions in mRNA regulation. However, the experimental techniques are generally costly and time-consuming, while the existing computational models are usually designed only for m6A site prediction in a single-species and have significant limitations in accuracy, interpretability and generalizability. Here, we propose a highly interpretable computational framework, called MASS, based on a multi-task curriculum learning strategy to capture m6A features across multiple species simultaneously. Extensive computational experiments demonstrate the superior performances of MASS when compared to the state-of-the-art prediction methods. Furthermore, the contextual sequence features of m6A captured by MASS can be explained by the known critical binding motifs of the related RNA-binding proteins, which also help elucidate the similarity and difference among m6A features across species. In addition, based on the predicted m6A profiles, we further delineate the relationships between m6A and various properties of gene regulation, including gene expression, RNA stability, translation, RNA structure and histone modification. In summary, MASS may serve as a useful tool for characterizing m6A modification and studying its regulatory code. The source code of MASS can be downloaded from https://github.com/mlcb-thu/MASS.

Background Long noncoding RNAs (lncRNAs) play important roles in essential biological processes. However, our understanding of lncRNAs as competing endogenous RNAs (ceRNAs) and their responses to nitrogen stress is still limited. Results Here, we surveyed the lncRNAs and miRNAs in maize inbred line P178 leaves and roots at the seedling stage under high-nitrogen (HN) and low-nitrogen (LN) conditions using lncRNA-Seq and small RNA-Seq. A total of 894 differentially expressed lncRNAs and 38 different miRNAs were identified. Co-expression analysis found that two lncRNAs and four lncRNA-targets could competitively combine with ZmmiR159 and ZmmiR164, respectively. To dissect the genetic regulatory by which lncRNAs might enable adaptation to limited nitrogen availability, an association mapping panel containing a high-density single–nucleotide polymorphism (SNP) array (56,110 SNPs) combined with variable LN tolerant-related phenotypes obtained from hydroponics was used for a genome-wide association study (GWAS). By combining GWAS and RNA-Seq, 170 differently expressed lncRNAs within the range of significant markers were screened. Moreover, 40 consistently LN-responsive genes including those involved in glutamine biosynthesis and nitrogen acquisition in root were identified. Transient expression assays in Nicotiana benthamiana demonstrated that LNC_002923 could inhabit ZmmiR159-guided cleavage of Zm00001d015521. Conclusions These lncRNAs containing trait-associated significant SNPs could consider to be related to root development and nutrient utilization. Taken together, the results of our study can provide new insights into the potential regulatory roles of lncRNAs in response to LN stress, and give valuable information for further screening of candidates as well as the improvement of maize resistance to LN stress.