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Motivation The emergence of drug-resistant bacteria makes the discovery of new antibiotics an urgent issue, but finding new molecules with the desired antibacterial activity is an extremely difficult task. To address this challenge, we established a framework, MDAGS (Molecular Design via Attribute-Guided Search), to optimize and generate potent antibiotic molecules. Results By designing the antibacterial activity latent space and guiding the optimization of functional compounds based on this space, the model MDAGS can generate novel compounds with desirable antibacterial activity without the need for extensive expensive and time-consuming evaluations. Compared with existing antibiotics, candidate antibacterial compounds generated by MDAGS always possessed significantly better antibacterial activity and ensured high similarity. Furthermore, although without explicit constraints on similarity to known antibiotics, these candidate antibacterial compounds all exhibited the highest structural similarity to antibiotics of expected function in the DrugBank database query. Overall, our approach provides a viable solution to the problem of bacterial drug resistance. Availability and implementation Code of the model and datasets can be downloaded from GitHub (https://github.com/LiangYu-Xidian/MDAGS). Supplementary information Supplementary data are available at Bioinformatics online. [ABSTRACT FROM AUTHOR]

Motivation Approaches for the diagnosis and treatment of diseases often adopt the multidrug therapy method because it can increase the efficacy or reduce the toxic side effects of drugs. Using different drugs simultaneously may trigger unexpected pharmacological effects. Therefore, efficient identification of drug interactions is essential for the treatment of complex diseases. Currently proposed calculation methods are often limited by the collection of redundant drug features, a small amount of labeled data and low model generalization capabilities. Meanwhile, there is also a lack of unique methods for multidrug representation learning, which makes it more difficult to take full advantage of the originally scarce data. Results Inspired by graph models and pretraining models, we integrated a large amount of unlabeled drug molecular graph information and target information, then designed a pretraining framework, MGP-DR (Molecular Graph Pretraining for Drug Representation), specifically for drug pair representation learning. The model uses self-supervised learning strategies to mine the contextual information within and between drug molecules to predict drug–drug interactions and drug combinations. The results achieved promising performance across multiple metrics compared with other state-of-the-art methods. Our MGP-DR model can be used to provide a reliable candidate set for the combined use of multiple drugs. Availability and implementation Code of the model, datasets and results can be downloaded from GitHub (https://github.com/LiangYu-Xidian/MGP-DR). Supplementary information Supplementary data are available at Bioinformatics online. [ABSTRACT FROM AUTHOR]

With an in-depth understanding of noncoding ribonucleic acid (RNA), many studies have shown that microRNA (miRNA) plays an important role in human diseases. Because traditional biological experiments are time-consuming and laborious, new calculation methods have recently been developed to predict associations between miRNA and diseases. In this review, we collected various miRNA–disease association prediction models proposed in recent years and used two common data sets to evaluate the performance of the prediction models. First, we systematically summarized the commonly used databases and similarity data for predicting miRNA–disease associations, and then divided the various calculation models into four categories for summary and detailed introduction. In this study, two independent datasets (D 5430 and D 6088) were compiled to systematically evaluate 11 publicly available prediction tools for miRNA–disease associations. The experimental results indicate that the methods based on information dissemination and the method based on scoring function require shorter running time. The method based on matrix transformation often requires a longer running time, but the overall prediction result is better than the previous two methods. We hope that the summary of work related to miRNA and disease will provide comprehensive knowledge for predicting the relationship between miRNA and disease and contribute to advanced computation tools in the future. [ABSTRACT FROM AUTHOR]

Since miRNAs can participate in the posttranscriptional regulation of gene expression, they may provide ideas for the development of new drugs or become new biomarkers for drug targets or disease diagnosis. In this work, we propose an miRNA–disease association prediction method based on meta-paths (MDPBMP). First, an miRNA–disease–gene heterogeneous information network was constructed, and seven symmetrical meta-paths were defined according to different semantics. After constructing the initial feature vector for the node, the vector information carried by all nodes on the meta-path instance is extracted and aggregated to update the feature vector of the starting node. Then, the vector information obtained by the nodes on different meta-paths is aggregated. Finally, miRNA and disease embedding feature vectors are used to calculate their associated scores. Compared with the other methods, MDPBMP obtained the highest AUC value of 0.9214. Among the top 50 predicted miRNAs for lung neoplasms, esophageal neoplasms, colon neoplasms and breast neoplasms, 49, 48, 49 and 50 have been verified. Furthermore, for breast neoplasms, we deleted all the known associations between breast neoplasms and miRNAs from the training set. These results also show that for new diseases without known related miRNA information, our model can predict their potential miRNAs. Code and data are available at https://github.com/LiangYu-Xidian/MDPBMP. [ABSTRACT FROM AUTHOR]

Drug combination is a sensible strategy for disease treatment because it improves the treatment efficacy and reduces concomitant side effects. Due to the large number of possible combinations among candidate compounds, exhaustive screening is prohibitive. Currently, a large number of studies have focused on predicting potential drug combinations. However, these methods are not entirely satisfactory in terms of performance and scalability. In this paper, we proposed a Network Embedding frameWork in MultIplex Network (NEWMIN) to predict synthetic drug combinations. Based on a multiplex drug similarity network, we offered alternative methods to integrate useful information from different aspects and to decide the quantitative importance of each network. For drug combination prediction, we found seven novel drug combinations that have been validated by external sources among the top-ranked predictions of our model. To verify the feasibility of NEWMIN, we compared NEWMIN with other five methods, for which it showed better performance than other methods in terms of the area under the precision-recall curve and receiver operating characteristic curve. [ABSTRACT FROM AUTHOR]

Motivation The heterologous expression of recombinant protein requires host cells, such as Escherichiacoli , and the solubility of protein greatly affects the protein yield. A novel and highly accurate solubility predictor that concurrently improves the production yield and minimizes production cost, and that forecasts protein solubility in an E.coli expression system before the actual experimental work is highly sought. Results In this article, EPSOL, a novel deep learning architecture for the prediction of protein solubility in an E.coli expression system, which automatically obtains comprehensive protein feature representations using multidimensional embedding, is presented. EPSOL outperformed all existing sequence-based solubility predictors and achieved 0.79 in accuracy and 0.58 in Matthew's correlation coefficient. The higher performance of EPSOL permits large-scale screening for sequence variants with enhanced manufacturability and predicts the solubility of new recombinant proteins in an E.coli expression system with greater reliability. Availability and implementation EPSOL's best model and results can be downloaded from GitHub (https://github.com/LiangYu-Xidian/EPSOL). Supplementary information Supplementary data are available at Bioinformatics online. [ABSTRACT FROM AUTHOR]

Accurate prediction of drug-target interactions (DTIs) through biological data can reduce the time and economic cost of drug development. The prediction method of DTIs based on a similarity network is attracting increasing attention. Currently, many studies have focused on predicting DTIs. However, such approaches do not consider the features of drugs and targets in multiple networks or how to extract and merge them. In this study, we proposed a Network EmbeDding framework in mulTiPlex networks (NEDTP) to predict DTIs. NEDTP builds a similarity network of nodes based on 15 heterogeneous information networks. Next, we applied a random walk to extract the topology information of each node in the network and learn it as a low-dimensional vector. Finally, the Gradient Boosting Decision Tree model was constructed to complete the classification task. NEDTP achieved accurate results in DTI prediction, showing clear advantages over several state-of-the-art algorithms. The prediction of new DTIs was also verified from multiple perspectives. In addition, this study also proposes a reasonable model for the widespread negative sampling problem of DTI prediction, contributing new ideas to future research. Code and data are available at https://github.com/LiangYu-Xidian/NEDTP. [ABSTRACT FROM AUTHOR]

Protein–nucleic acid interactions play essential roles in many biological processes, such as transcription, replication and translation. In protein–nucleic acid interfaces, hotspot residues contribute the majority of binding affinity toward molecular recognition. Hotspot residues are commonly regarded as potential binding sites for compound molecules in drug design projects. The dynamic property is a considerable factor that affects the binding of ligands. Computational approaches have been developed to expedite the prediction of hotspot residues on protein–nucleic acid interfaces. However, existing approaches overlook hotspot dynamics, despite their essential role in protein function. Here, we report a web server named Hotspots In silico Scanning on Nucleic Acid and Protein Interface (HISNAPI) to analyze hotspot residue dynamics by integrating molecular dynamics simulation and one-step free energy perturbation. HISNAPI is capable of not only predicting the hotspot residues in protein–nucleic acid interfaces but also providing insights into their intensity and correlation of dynamic motion. Protein dynamics have been recognized as a vital factor that has an effect on the interaction specificity and affinity of the binding partners. We applied HISNAPI to the case of SARS-CoV-2 RNA-dependent RNA polymerase, a vital target of the antiviral drug for the treatment of coronavirus disease 2019. We identified the hotspot residues and characterized their dynamic behaviors, which might provide insight into the target site for antiviral drug design. The web server is freely available via a user-friendly web interface at http://chemyang.ccnu.edu.cn/ccb/server/HISNAPI/ and http://agroda.gzu.edu.cn:9999/ccb/server/HISNAPI/. [ABSTRACT FROM AUTHOR]

Effective drug discovery contributes to the treatment of numerous diseases but is limited by high costs and long cycles. The Quantitative Structure–Activity Relationship (QSAR) method was introduced to evaluate the activity of a large number of compounds virtually, reducing the time and labor costs required for chemical synthesis and experimental determination. Hence, this method increases the efficiency of drug discovery. To meet the needs of researchers to utilize this technology, numerous QSAR-related web servers, such as Web-4D-QSAR and DPubChem, have been developed in recent years. However, none of the servers mentioned above can perform a complete QSAR modeling and supply activity prediction functions. We introduce Cloud 3D-QSAR by integrating the functions of molecular structure generation, alignment, molecular interaction field (MIF) computing and results analysis to provide a one-stop solution. We rigidly validated this server, and the activity prediction correlation was R 2 = 0.934 in 834 test molecules. The sensitivity, specificity and accuracy were 86.9%, 94.5% and 91.5%, respectively, with AUC = 0.981, AUCPR = 0.971. The Cloud 3D-QSAR server may facilitate the development of good QSAR models in drug discovery. Our server is free and now available at http://chemyang.ccnu.edu.cn/ccb/server/cloud3dQSAR/ and http://agroda.gzu.edu.cn:9999/ccb/server/cloud3dQSAR/. [ABSTRACT FROM AUTHOR]

Modifications of protein, RNA and DNA play an important role in many biological processes and are related to some diseases. Therefore, accurate identification and comprehensive understanding of protein, RNA and DNA modification sites can promote research on disease treatment and prevention. With the development of sequencing technology, the number of known sequences has continued to increase. In the past decade, many computational tools that can be used to predict protein, RNA and DNA modification sites have been developed. In this review, we comprehensively summarized the modification site predictors for three different biological sequences and the association with diseases. The relevant web server is accessible at http://lab.malab.cn/∼acy/PTM%5fdata/ some sample data on protein, RNA and DNA modification can be downloaded from that website. [ABSTRACT FROM AUTHOR]

A long-term sublimation model to explain how Phaethon could provide the Geminid stream is proposed. We find that it would take ∼6 Myr or more for Phaethon to lose all of its internal ice (if ever there was) in its present orbit. Thus, if the asteroid moved from the region of a 5:2 or 8:3 mean motion resonance with Jupiter to its present orbit less than 1 Myr ago, it may have retained much of its primordial ice. The dust mantle on the sublimating body should have a thickness of at least 15 m but the mantle could have been less than 1 m thick 1000 yr ago. We find that the total gas production rate could have been as large as |$10^{27}\rm \,s^{-1}$| then, and the gas flow could have been capable of lifting dust particles of up to a few centimetres in size. Therefore, gas production during the past millennium could have been sufficient to blow away enough dust particles to explain the entire Geminid stream. For present-day Phaethon, the gas production is comparatively weak. But strong transient gas release with a rate of |${\sim }4.5\times 10^{19}\rm \,m^{-2}\,s^{-1}$| is expected for its south polar region when Phaethon moves from 0° to 2° mean anomaly near perihelion. Consequently, dust particles with radii of <∼260 μm can be blown away to form a dust tail. In addition, we find that the large surface temperature variation of >600 K near perihelion can generate sufficiently large thermal stress to cause fracture of rocks or boulders and provide an efficient mechanism to produce dust particles on the surface. The time-scale for this process should be several times longer than the seasonal thermal cycle, thereby dominating the cycle of appearance of the dust tail. [ABSTRACT FROM AUTHOR]

We investigate the necessary methodology to optimally measure the baryon acoustic oscillation (BAO) signal from voids, based on galaxy redshift catalogues. To this end, we study the dependence of the BAO signal on the population of voids classified by their sizes. We find for the first time the characteristic features of the correlation function of voids including the first robust detection ofBAOs in mock galaxy catalogues. These showan anti-correlation around the scale corresponding to the smallest size of voids in the sample (the void exclusion effect), and dips at both sides of the BAO peak, which can be used to determine the significance of the BAO signal without any priori model. Furthermore, our analysis demonstrates that there is a scaledependent bias for different populations of voids depending on the radius, with the peculiar property that the void population with the largest BAO significance corresponds to tracers with approximately zero bias on the largest scales. We further investigate the methodology on an additional set of 1000 realistic mock galaxy catalogues reproducing the SDSS-III/BOSS CMASS DR11 data, to control the impact of sky mask and radial selection function. Our solution is based on generating voids from randoms including the same survey geometry and completeness, and a post-processing cleaning procedure in the holes and at the boundaries of the survey. The methodology and optimal selection of void populations validated in this work have been used to perform the first BAO detection from voids in observations, presented in a companion paper. [ABSTRACT FROM AUTHOR]

The heat stress response is an important adaptation, enabling plants to survive challenging environmental conditions. Our previous work demonstrated that Arabidopsis thaliana Phosphoinositide-Specific Phospholipase C Isoform 9 (AtPLC9) plays an important role in thermotolerance. During prolonged heat treatment, mutants of AtPLC3 showed decreased heat resistance. We observed no obvious phenotypic differences between plc3 mutants and wild type (WT) seedlings under normal growth conditions, but after heat shock, the plc3 seedlings displayed a decline in thermotolerance compared with WT, and also showed a 40–50% decrease in survival rate and chlorophyll contents. Expression of AtPLC3 in plc3 mutants rescued the heat-sensitive phenotype; the AtPLC3-overexpressing lines also exhibited much higher heat resistance than WT and vector-only controls. The double mutants of plc3 and plc9 displayed increased sensitivity to heat stress, compared with either single mutant. In transgenic lines containing a AtPLC3:GUS promoter fusion, GUS staining showed that AtPLC3 expresses in all tissues, except anthers and young root tips. Using the Ca2+-sensitive fluorescent probe Fluo-3/AM and aequorin reconstitution, we showed that plc3 mutants show a reduction in the heat-induced Ca2+ increase. The expression of HSP genes (HSP18.2, HSP25.3, HSP70-1 and HSP83) was down-regulated in plc3 mutants and up-regulated in AtPLC3-overexpressing lines after heat shock. These results indicated that AtPLC3 also plays a role in thermotolerance in Arabidopsis, and that AtPLC3 and AtPLC9 function additionally to each other. [ABSTRACT FROM AUTHOR]

The dextranase added in current commercial dextranase-containing mouthwashes is largely from fungi. However, fungal dextranase has shown much higher optimum temperature than bacterial dextranase and relatively low activity when used in human oral cavities. Bacterial dextranase has been considered to be more effective and suitable for dental caries prevention. In this study, a dextranase (Dex410) from marine Arthrobacter sp. was purified and characterized. Dex410 is a 64-kDa endoglycosidase. The specific activity of Dex410 was 11.9 U/mg at optimum pH 5.5 and 45 °C. The main end-product of Dex410 was isomaltotriose, isomaltoteraose, and isomaltopentaose by hydrolyzing dextran T2000. In vitro studies showed that Dex410 effectively inhibited the Streptococcus mutans biofilm growth in coverage, biomass, and water-soluble glucan (WSG) by more than 80, 90, and 95 %, respectively. The animal experiment revealed that for short-term use (1.5 months), both Dex410 and the commercial mouthwash Biotene (Laclede Professional Products, Gardena, CA, USA) had a significant inhibitory effect on caries ( p = 0.0008 and 0.0001, respectively), while for long-term use (3 months), only Dex410 showed significant inhibitory effect on dental caries ( p = 0.005). The dextranase Dex410 from a marine-derived Arthrobacter sp. strain possessed the enzyme properties suitable to human oral environment and applicable to oral hygiene products. [ABSTRACT FROM AUTHOR]

The article focuses on a study which investigated the silencing of molt-regulating transcription factor gene CiHR3. The study examined the impact of the double-stranded RNA (dsRNA) CiHR3 gene silencing on the growth and development of sugarcane stem borer Chilo infuscatellus. Study findings showed that feeding-based RNA interference can be used to manage Chilo infuscatellus.

The article focuses on a research conducted in order to identify the characterization and expression of the cytochrome P450 gene, CYP6AE25 isolated from the Asian corn borer, Ostrinia furnacalis. It describes the cDNA sequence of CYP6AE25 that comprises of 2315 base pairs and a 1569 nucleotides open reading frame using real-time polymerase chain reaction. The study revealed the tissue specific expression level of CYP6AE25 in midgut, malpighian tube and fat body.

The purpose of this study was to investigate invasion- and metastasis-related genes in gastric cancer. To this end, we used the transwell system to select a highly invasive subcell line from minimally invasive parent cells and compared gene expression in paired cell lines with high- and low-invasive potentials. Lysyl oxidase-like 2 (LOXL2) was overexpressed in the highly invasive subcell line. Immunohistochemical analysis revealed that LOXL2 expression was markedly increased in carcinoma relative to normal epithelia, and this overexpression in primary tumor was significantly associated with depth of tumor invasion, lymph node metastasis and poorer overall survival. Moreover, LOXL2 expression was further increased in lymph node metastases compared with primary cancer tissues. RNA interference-mediated knockdown and ectopic expression of LOXL2 showed that LOXL2 promoted tumor cell invasion in vitro and increased gastric carcinoma metastasis in vivo. Subsequent mechanistic studies showed that LOXL2 could activate both the Snail/E-cadherin and Src kinase/Focal adhesion kinase (Src/FAK) pathways. However, secreted LOXL2 induced gastric tumor cell invasion and metastasis exclusively via the Src/FAK pathway. Expression correlation analysis in gastric carcinoma tissues also revealed that LOXL2 promoted invasion via the Src/FAK pathway but not the Snail/E-cadherin pathway. We then evaluated secreted LOXL2 as a target for gastric carcinoma treatment and found that an antibody against LOXL2 significantly inhibited tumor growth and metastasis. Overall, our data revealed that LOXL2 overexpression, a frequent event in gastric carcinoma progression, contributes to tumor cell invasion and metastasis, and LOXL2 may be a therapeutic target for preventing and treating metastases. [ABSTRACT FROM PUBLISHER]

In analysis of longitudinal data, it is often assumed that observation times are predetermined and are the same across study subjects. Such an assumption, however, is often violated in practice. As a result, the observation times may be highly irregular. It is well known that if the sampling scheme is correlated with the outcome values, the usual statistical analysis may yield bias. In this article, we propose joint modeling and analysis of longitudinal data with possibly informative observation times via latent variables. A two-step estimation procedure is developed for parameter estimation. We show that the resulting estimators are consistent and asymptotically normal, and that the asymptotic variance can be consistently estimated using the bootstrap method. Simulation studies and a real data analysis demonstrate that our method performs well with realistic sample sizes and is appropriate for practical use. [ABSTRACT FROM AUTHOR]

2'-O-methylation (Nm) is a post-transcriptional modification of RNA that is catalyzed by 2'-O-methyltransferase and involves replacing the H on the 2′-hydroxyl group with a methyl group. The 2'-O-methylation modification site is detected in a variety of RNA types (miRNA, tRNA, mRNA, etc.), plays an important role in biological processes and is associated with different diseases. There are few functional mechanisms developed at present, and traditional high-throughput experiments are time-consuming and expensive to explore functional mechanisms. For a deeper understanding of relevant biological mechanisms, it is necessary to develop efficient and accurate recognition tools based on machine learning. Based on this, we constructed a predictor called NmRF based on optimal mixed features and random forest classifier to identify 2'-O-methylation modification sites. The predictor can identify modification sites of multiple species at the same time. To obtain a better prediction model, a two-step strategy is adopted; that is, the optimal hybrid feature set is obtained by combining the light gradient boosting algorithm and incremental feature selection strategy. In 10-fold cross-validation, the accuracies of Homo sapiens and Saccharomyces cerevisiae were 89.069 and 93.885%, and the AUC were 0.9498 and 0.9832, respectively. The rigorous 10-fold cross-validation and independent tests confirm that the proposed method is significantly better than existing tools. A user-friendly web server is accessible at http://lab.malab.cn/∼acy/NmRF. [ABSTRACT FROM AUTHOR]

Protein post-translational modifications (PTM) play vital roles in cellular regulation, modulating functions by driving changes in protein structure and dynamics. Exploring comprehensively the influence of PTM on conformational dynamics can facilitate the understanding of the related biological function and molecular mechanism. Currently, a series of excellent computation tools have been designed to analyze the time-dependent structural properties of proteins. However, the protocol aimed to explore conformational dynamics of post-translational modified protein is still a blank. To fill this gap, we present PTMdyna to visually predict the conformational dynamics differences between unmodified and modified proteins, thus indicating the influence of specific PTM. PTMdyna exhibits an AUC of 0.884 tested on 220 protein–protein complex structures. The case of heterochromatin protein 1α complexed with lysine 9-methylated histone H3, which is critical for genomic stability and cell differentiation, was used to demonstrate its applicability. PTMdyna provides a reliable platform to predict the influence of PTM on protein dynamics, making it easier to interpret PTM functionality at the structure level. The web server is freely available at http://ccbportal.com/PTMdyna. [ABSTRACT FROM AUTHOR]

Lung cancer is the leading cause of cancer-related mortality, with most cases attributed to tobacco smoking, in which nicotine-derived nitrosamine ketone (NNK) is the most potent lung carcinogen. The ADAM17 protease is responsible for the ectodomain shedding of many pro-tumorigenic cytokines, growth factors and receptors, and therefore is an attractive target in cancer. However, the role of ADAM17 in promoting tobacco smoke carcinogen-induced lung carcinogenesis is unknown. The hypomorphic Adam17 ex/ex mice—characterized by reduced global ADAM17 expression—were backcrossed onto the NNK-sensitive pseudo-A/J background. CRISPR-driven and inhibitor-based (GW280264X, and ADAM17 prodomain) ADAM17 targeting was employed in the human lung adenocarcinoma cell lines A549 and NCI-H23. Human lung cancer biopsies were also used for analyses. The Adam17 ex/ex mice displayed marked protection against NNK-induced lung adenocarcinoma. Specifically, the number and size of lung lesions in NNK-treated pseudo-A/J Adam17 ex/ex mice were significantly reduced compared with wild-type littermate controls. This was associated with lower proliferative index throughout the lung epithelium. ADAM17 targeting in A549 and NCI-H23 cells led to reduced proliferative and colony-forming capacities. Notably, among select ADAM17 substrates, ADAM17 deficiency abrogated shedding of the soluble IL-6 receptor (sIL-6R), which coincided with the blockade of sIL-6R-mediated trans-signaling via ERK MAPK cascade. Furthermore, NNK upregulated phosphorylation of p38 MAPK, whose pharmacological inhibition suppressed ADAM17 threonine phosphorylation. Importantly, ADAM17 threonine phosphorylation was significantly upregulated in human lung adenocarcinoma with smoking history compared with their cancer-free controls. Our study identifies the ADAM17/sIL-6R/ERK MAPK axis as a candidate therapeutic strategy against tobacco smoke-associated lung carcinogenesis. [ABSTRACT FROM AUTHOR]

Glandular secretory trichomes (GSTs) can secrete and store a variety of specific metabolites. By increasing GST density, valuable metabolites can be enhanced in terms of productivity. However, the comprehensive and detailed regulatory network of GST initiation still needs further investigation. By screening a complementary DNA library derived from young leaves of Artemisia annua, we identified a MADS-box transcription factor, AaSEPALLATA1 (AaSEP1), that positively regulates GST initiation. Overexpression of AaSEP1 in A. annua substantially increased GST density and artemisinin content. The HOMEODOMAIN PROTEIN 1 (AaHD1)-AaMYB16 regulatory network regulates GST initiation via the jasmonate (JA) signaling pathway. In this study, AaSEP1 enhanced the function of AaHD1 activation on downstream GST initiation gene GLANDULAR TRICHOMESPECIFIC WRKY 2 (AaGSW2) through interaction with AaMYB16. Moreover, AaSEP1 interacted with the JA ZIM-domain 8 (AaJAZ8) and served as an important factor in JA-mediated GST initiation. We also found that AaSEP1 interacted with CONSTITUTIVE PHOTOMORPHOGENIC 1 (AaCOP1), a major repressor of light signaling. In this study, we identified a MADS-box transcription factor that is induced by JA and light signaling and that promotes the initiation of GST in A. annua. [ABSTRACT FROM AUTHOR]

Motivation Multiple sequence alignment (MSA) is a fundamental problem in bioinformatics. The quality of alignment will affect downstream analysis. MAFFT has adopted the Fast Fourier Transform method for searching the homologous segments and using them as anchors to divide the sequences, then making alignment only on segments, which can save time and memory without overly reducing the sequence alignment quality. MAFFT becomes slow when the dataset is large. Results We made a software, WMSA, which uses the divide-and-conquer method to split the sequences into clusters, aligns those clusters into profiles with the center star strategy and then makes a progressive profile–profile alignment. The alignment is conducted by the compiled algorithms of MAFFT, K-Band with multithread parallelism. Our method can balance time, space and quality and performs better than MAFFT in test experiments on highly conserved datasets. Availability and implementation Source code is freely available at https://github.com/malabz/WMSA/ , which is implemented in C/C++ and supported on Linux, and datasets are available at https://github.com/malabz/WMSA-dataset. Supplementary information Supplementary data are available at Bioinformatics online. [ABSTRACT FROM AUTHOR]

Accurate prediction of drug-target interactions (DTIs) can reduce the cost and time of drug repositioning and drug discovery. Many current methods integrate information from multiple data sources of drug and target to improve DTIs prediction accuracy. However, these methods do not consider the complex relationship between different data sources. In this study, we propose a novel computational framework, called MccDTI, to predict the potential DTIs by multiview network embedding, which can integrate the heterogenous information of drug and target. MccDTI learns high-quality low-dimensional representations of drug and target by preserving the consistent and complementary information between multiview networks. Then MccDTI adopts matrix completion scheme for DTIs prediction based on drug and target representations. Experimental results on two datasets show that the prediction accuracy of MccDTI outperforms four state-of-the-art methods for DTIs prediction. Moreover, literature verification for DTIs prediction shows that MccDTI can predict the reliable potential DTIs. These results indicate that MccDTI can provide a powerful tool to predict new DTIs and accelerate drug discovery. The code and data are available at: https://github.com/ShangCS/MccDTI. [ABSTRACT FROM AUTHOR]

We present a novel parameter-free cosmological void finder (DIVE, Delaunay TrIangulation Void findEr) based on Delaunay Triangulation (DT), which efficiently computes the empty spheres constrained by a discrete set of tracers.We define the spheres as DT voids, and describe their properties, including a universal density profile together with an intrinsic scatter.We apply this technique on 100 halo catalogues with volumes of 2.5 h-1Gpc side each, with a bias and number density similar to the Baryon Oscillation Spectroscopic Survey CMASS luminous red galaxies, performed with the PATCHY code. Our results show that there are two main species of DT voids, which can be characterized by the radius: they have different responses to halo redshift space distortions, to number density of tracers, and reside in different dark matter environments. Based on dynamical arguments using the tidal field tensor, we demonstrate that large DT voids are hosted in expanding regions, whereas the haloes used to construct them reside in collapsing ones. Our approach is therefore able to efficiently determine the troughs of the density field from galaxy surveys, and can be used to study their clustering. We further study the power spectra of DT voids, and find that the bias of the two populations are different, demonstrating that the small DT voids are essentially tracers of groups of haloes. [ABSTRACT FROM AUTHOR]

Motivation: Position weight matrices (PMWs) are simple models commonly used in motif-finding algorithms to identify short functional elements, such as cis-regulatory motifs, on genes. When few experimentally verified motifs are available, estimation of the PWM may be poor. The resultant PWM may not reliably discriminate a true motif from a false one. While experimentally identifying such motifs remains time-consuming and expensive, low-resolution binding data from techniques such as ChIP-on-chip and ChIP-PET have become available. We propose a novel but simple method to improve a poorly estimated PWM using ChIP data. Methodology: Starting from an existing PWM, a set of ChIP sequences, and a set of background sequences, our method, GAPWM, derives an improved PWM via a genetic algorithm that maximizes the area under the receiver operating characteristic (ROC) curve. GAPWM can easily incorporate prior information such as base conservation. We tested our method on two PMWs (Oct4/Sox2 and p53) using three recently published ChIP data sets (human Oct4, mouse Oct4 and human p53). Results: GAPWM substantially increased the sensitivity/specificity of a poorly estimated PWM and further improved the quality of a good PWM. Furthermore, it still functioned when the starting PWM contained a major error. The ROC performance of GAPWM compared favorably with that of MEME and others. With increasing availability of ChIP data, our method provides an alternative for obtaining high-quality PWMs for genome-wide identification of transcription factor binding sites. Availability: The C source code and all data used in this report are available at http://dir.niehs.nih.gov/dirbb/gapwm Contact: li3@niehs.nih.gov Supplementary information: Supplementary data are available at Bioinformatics online. [ABSTRACT FROM AUTHOR]

A clear systematic delineation of the interactions between phosphorylation sites on substrates and their effector kinases plays a fundamental role in revealing cellular activities, understanding signaling modulation mechanisms and proposing novel hypotheses. The emergence of bioinformatics tools contributes to studying phosphorylation network. Some of them feature the visualization of network, enabling more effective trace of the underlying biological problems in a clear and succinct way. In this review, we aimed to provide a toolbox for exploring phosphorylation network. We first systematically surveyed 19 tools that are available for exploring phosphorylation networks, and subsequently comparatively analyzed and summarized these tools to guide tool selection in terms of functionality, data sources, performance, network visualization and implementation, and finally briefly discussed the application cases of these tools. In different scenarios, the conclusion on the suitability of a tool for a specific user may vary. Nevertheless, easily accessible bioinformatics tools are proved to facilitate biological findings. Hopefully, this work might also assist non-specialists, students, as well as computational scientists who aim at developing novel tools in the field of phosphorylation modification. [ABSTRACT FROM AUTHOR]

The 1999 September 20 Chi-Chi earthquake is the largest seismic event which occurred in the island during the twentieth century. Available seismic data relative to this earthquake are of high quality, and surface ruptures identified as features associated to the Chelungpu fault can be clearly observed at the surface and precisely mapped. We calculated the fractal dimension ( D) and b value of Gutenberg–Richter law for 6-month aftershocks of the Chi-Chi earthquake for the fault area, and find that the surface ruptures exhibit self-similar geometry only within specific ruler intervals. The D values of the surface ruptures reflect the fault slip and geometry at depth. More importantly, the small-size aftershocks seem more likely to occur within high D value and high b value areas, whereas small D value and small b value areas have a high potential for medium- and large-size aftershocks. [ABSTRACT FROM AUTHOR]

Objective. Developing a new measurement index is the first step in evaluating pain relief outcomes. Although the percentage difference in pain intensity (%PID) is the most popular indicator, this indicator does not take into account the goal of pain relief. Therefore, the aims of this study were to develop a pain relief index (PRI) for outcome evaluation and to examine the index using demographic characteristics of cancer inpatients with clinically significant pain. Design. Retrospective cohort study. Setting. A national hospital. Subjects. All cancer inpatients. Methods. Pain intensity was assessed using a numerical rating scale, a faces pain scale or the Face, Legs, Activity, Cry, Consolability (FLACC) Behavioral Tool. Using a nursing information system, a pain score database containing data from 2011 through 2013 was analyzed. Results. Cancer patients representing 93,812 hospitalizations were considered in this study. We focused on cancer patients for whom the worst pain intensity (WPI) was ≥ 4 points. PRI values of - 6 2. 0 2% to - 7 2. 5 5% were observed in the WPI ≥ 7 and 4 ≤ WPI ≤ 6 groups. Significant (P<0.05) effects on PRI values were observed among patients who were>65 years old, those who were admitted to the medicine or gynecology and those who had a hospital stay > 30 days. Conclusion. This hospital-based study demonstrated that the PRI is an effective and valid measure for evaluating outcome data using an electronic nursing information system. We will further define the meaningful range of percentage difference in PRI from various perspectives. [ABSTRACT FROM AUTHOR]

Motivation: Most de novo motif identification methods optimize the motif model first and then separately test the statistical significance of the motif score. In the first stage, a motif abundance parameter needs to be specified or modeled. In the second stage, a Z-score or P-value is used as the test statistic. Error rates under multiple comparisons are not fully considered. Methodology: We propose a simple but novel approach, fdrMotif, that selects as many binding sites as possible while controlling a user-specified false discovery rate (FDR). Unlike existing iterative methods, fdrMotif combines model optimization [e.g. position weight matrix (PWM)] and significance testing at each step. By monitoring the proportion of binding sites selected in many sets of background sequences, fdrMotif controls the FDR in the original data. The model is then updated using an expectation (E)- and maximization (M)-like procedure. We propose a new normalization procedure in the E-step for updating the model. This process is repeated until either the model converges or the number of iterations exceeds a maximum. Results: Simulation studies suggest that our normalization procedure assigns larger weights to the binding sites than do two other commonly used normalization procedures. Furthermore, fdrMotif requires only a user-specified FDR and an initial PWM. When tested on 542 high confidence experimental p53 binding loci, fdrMotif identified 569 p53 binding sites in 505 (93.2%) sequences. In comparison, MEME identified more binding sites but in fewer ChIP sequences than fdrMotif. When tested on 500 sets of simulated ‘ChIP’ sequences with embedded known p53 binding sites, fdrMotif, compared to MEME, has higher sensitivity with similar positive predictive value. Furthermore, fdrMotif is robust to noise: it selected nearly identical binding sites in data adulterated with 50% added background sequences and the unadulterated data. We suggest that fdrMotif represents an improvement over MEME. Availability: C code can be found at: http://www.niehs.nih.gov/research/resources/software/fdrMotif/ Contact: li3@niehs.nih.gov Supplementary information: Supplementary data are available at http://www.niehs.nih.gov/research/resources/software/fdrMotif/ [ABSTRACT FROM AUTHOR]