MOTIVATION: High-throughput sequencing technologies [next-generation sequencing (NGS)] are increasingly used to address diverse biological questions. Despite the rich information in NGS data, particularly with the growing datasets from repositories like the Genome Sequence Archive (GSA) at NGDC, programmatic access to public sequencing data and metadata remains limited. RESULTS: We developed iSeq to enable quick and straightforward retrieval of metadata and NGS data from multiple databases via the command-line interface. iSeq supports simultaneous retrieval from GSA, SRA, ENA, and DDBJ databases. It handles over 25 different accession formats, supports Aspera downloads, parallel downloads, multi-threaded processes, FASTQ file merging, and integrity verification, simplifying data acquisition and enhancing the capacity for reanalyzing NGS data. AVAILABILITY AND IMPLEMENTATION: iSeq is freely available on Bioconda (https://anaconda.org/bioconda/iseq) and GitHub (https://github.com/BioOmics/iSeq).

Neurodegenerative diseases (NDs) usually connect with aggregation and molecular interactions of pathological proteins. The integration of accumulative data from clinical and biomedical research will allow for the excavation of pathological proteins and related interactors. It is also important to systematically study their interacting proteins in order to find more related proteins and potential therapeutic targets. Understanding binding regions in protein interactions will help functional proteomics and provide an alternative method for predicting novel interactions. This study integrated data from biomedical research to achieve systematic mining and analysis of pathogenic proteins and their interaction network. A workflow has been built as a solution for the collective information of proteins involved in NDs, related protein-protein interactions (PPIs) and interactive visualizations. It also included protein isoforms and mapped them in a disease-related PPI network to illuminate the impact of alternative splicing on protein binding. The interacting proteins enriched by diseases and biological processes (BPs) revealed possible regulatory modules. A high-resolution network with structural affinity information was generated. Finally, Neurodegenerative Disease Atlas (NDAtlas) was constructed with an interactive and intuitive view of protein docking with 3D molecular graphics beyond the traditional 2D network. NDAtlas is available at http://bis.zju.edu.cn/ndatlas.

MOTIVATION: Quantifying the similarity of human diseases provides guiding insights to the discovery of micro-scope mechanisms from a macro scale. Previous work demonstrated that better performance can be gained by integrating multiview data sources or applying machine learning techniques. However, designing an efficient framework to extract and incorporate information from different biological data using deep learning models remains unexplored. RESULTS: We present CoGO, a Contrastive learning framework to predict disease similarity based on Gene network and Ontology structure, which incorporates the gene interaction network and gene ontology (GO) domain knowledge using graph deep learning models. First, graph deep learning models are applied to encode the features of genes and GO terms from separate graph structure data. Next, gene and GO features are projected to a common embedding space via a nonlinear projection. Then cross-view contrastive loss is applied to maximize the agreement of corresponding gene-GO associations and lead to meaningful gene representation. Finally, CoGO infers the similarity between diseases by the cosine similarity of disease representation vectors derived from related gene embedding. In our experiments, CoGO outperforms the most competitive baseline method on both AUROC and AUPRC, especially improves 19.57% in AUPRC (0.7733). The prediction results are significantly comparable with other disease similarity studies and thus highly credible. Furthermore, we conduct a detailed case study of top similar disease pairs which is demonstrated by other studies. Empirical results show that CoGO achieves powerful performance in disease similarity problem. AVAILABILITY AND IMPLEMENTATION: https://github.com/yhchen1123/CoGO.

MOTIVATION: Protein-protein interaction (PPI), as a relative property, is determined by two binding proteins, which brings a great challenge to design an expert model with an unbiased learning architecture and a superior generalization performance. Additionally, few efforts have been made to allow PPI predictors to discriminate between relative properties and intrinsic properties. RESULTS: We present a sequence-based approach, DeepTrio, for PPI prediction using mask multiple parallel convolutional neural networks. Experimental evaluations show that DeepTrio achieves a better performance over several state-of-the-art methods in terms of various quality metrics. Besides, DeepTrio is extended to provide additional insights into the contribution of each input neuron to the prediction results. AVAILABILITY AND IMPLEMENTATION: We provide an online application at http://bis.zju.edu.cn/deeptrio. The DeepTrio models and training data are deposited at https://github.com/huxiaoti/deeptrio.git. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

The coronavirus disease 2019 (COVID-19) pandemic, caused by the coronavirus severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has created an unprecedented threat to public health. The pandemic has been sweeping the globe, impacting more than 200 countries, with more outbreaks still lurking on the horizon. At the time of the writing, no approved drugs or vaccines are available to treat COVID-19 patients, prompting an urgent need to decipher mechanisms underlying the pathogenesis and develop curative treatments. To fight COVID-19, researchers around the world have provided specific tools and molecular information for SARS-CoV-2. These pieces of information can be integrated to aid computational investigations and facilitate clinical research. This paper reviews current knowledge, the current status of drug development and various resources for key steps toward effective treatment of COVID-19, including the phylogenetic characteristics, genomic conservation and interaction data. The final goal of this paper is to provide information that may be utilized in bioinformatics approaches and aid target prioritization and drug repurposing. Several SARS-CoV-2-related tools/databases were reviewed, and a web-portal named OverCOVID (http://bis.zju.edu.cn/overcovid/) is constructed to provide a detailed interpretation of SARS-CoV-2 basics and share a collection of resources that may contribute to therapeutic advances. These information could improve researchers' understanding of SARS-CoV-2 and help to accelerate the development of new antiviral treatments.

DNA repeats are abundant in eukaryotic genomes and have been proved to play a vital role in genome evolution and regulation. A large number of approaches have been proposed to identify various repeats in the genome. Some de novo repeat identification tools can efficiently generate sequence repetitive scores based on k-mer counting for repeat detection. However, we noticed that these tools can still be improved in terms of repetitive score calculation, sensitivity to segmental duplications and detection specificity. Therefore, here, we present a new computational approach named Repeat Locator (RepLoc), which is based on weighted k-mer coverage to quantify the genome sequence repetitiveness and locate the repetitive sequences. According to the repetitiveness map of the human genome generated by RepLoc, we found that there may be relationships between sequence repetitiveness and genome structures. A comprehensive benchmark shows that RepLoc is a more efficient k-mer counting based tool for de novo repeat detection. The RepLoc software is freely available at http://bis.zju.edu.cn/reploc.

MOTIVATION: The studies have indicated that not only microRNAs (miRNAs) or long non-coding RNAs (lncRNAs) play important roles in biological activities, but also their interactions affect the biological process. A growing number of studies focus on the miRNA-lncRNA interactions, while few of them are proposed for plant. The prediction of interactions is significant for understanding the mechanism of interaction between miRNA and lncRNA in plant. RESULTS: This article proposes a new method for fulfilling plant miRNA-lncRNA interaction prediction (PmliPred). The deep learning model and shallow machine learning model are trained using raw sequence and manually extracted features, respectively. Then they are hybridized based on fuzzy decision for prediction. PmliPred shows better performance and generalization ability compared with the existing methods. Several new miRNA-lncRNA interactions in Solanum lycopersicum are successfully identified using quantitative real time-polymerase chain reaction from the candidates predicted by PmliPred, which further verifies its effectiveness. AVAILABILITY AND IMPLEMENTATION: The source code of PmliPred is freely available at http://bis.zju.edu.cn/PmliPred/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Riboswitch, a part of regulatory mRNA (50-250nt in length), has two main classes: aptamer and expression platform. One of the main challenges raised during the classification of riboswitch is imbalanced data. That is a circumstance in which the records of a sequences of one group are very small compared to the others. Such circumstances lead classifier to ignore minority group and emphasize on majority ones, which results in a skewed classification. We considered sixteen riboswitch families, to be in accord with recent riboswitch classification work, that contain imbalanced sequences. The sequences were split into training and test set using a newly developed pipeline. From 5460 k-mers (k value 1 to 6) produced, 156 features were calculated based on CfsSubsetEval and BestFirst function found in WEKA 3.8. Statistically tested result was significantly difference between balanced and imbalanced sequences (p < 0.05). Besides, each algorithm also showed a significant difference in sensitivity, specificity, accuracy, and macro F-score when used in both groups (p < 0.05). Several k-mers clustered from heat map were discovered to have biological functions and motifs at the different positions like interior loops, terminal loops and helices. They were validated to have a biological function and some are riboswitch motifs. The analysis has discovered the importance of solving the challenges of majority bias analysis and overfitting. Presented results were generalized evaluation of both balanced and imbalanced models, which implies their ability of classifying, to classify novel riboswitches. The Python source code is available at https://github.com/Seasonsling/riboswitch.

Advances in RNA sequencing technologies and computational methodologies have provided a huge impetus to noncoding RNA (ncRNA) study. Once regarded as inconsequential results of transcriptional promiscuity, ncRNAs were later found to exert great roles in various aspects of biological functions. They are emerging as key players in gene regulatory networks by interacting with other biomolecules (DNA, RNA or protein). Here, we provide an overview of ncRNA repertoire and highlight recent discoveries of their versatile interactions. To better investigate the ncRNA-mediated regulation, it is necessary to make full use of innovative sequencing techniques and computational tools. We further describe a comprehensive workflow for in silico ncRNA analysis, providing up-to-date platforms, databases and tools dedicated to ncRNA identification and functional annotation.

Deoxyribonuclease I (DNase I)-hypersensitive site sequencing (DNase-seq) has been widely used to determine chromatin accessibility and its underlying regulatory lexicon. However, exploring DNase-seq data requires sophisticated downstream bioinformatics analyses. In this study, we first review computational methods for all of the major steps in DNase-seq data analysis, including experimental design, quality control, read alignment, peak calling, annotation of cis-regulatory elements, genomic footprinting and visualization. The challenges associated with each step are highlighted. Next, we provide a practical guideline and a computational pipeline for DNase-seq data analysis by integrating some of these tools. We also discuss the competing techniques and the potential applications of this pipeline for the analysis of analogous experimental data. Finally, we discuss the integration of DNase-seq with other functional genomics techniques.

Insights into the circular RNA (circRNA) exploration have revealed that they are abundant in eukaryotic transcriptomes. Diverse genomic regions can generate different types of RNA circles, implying their diversity. Covalently closed loop structures elevate the stability of this new type of noncoding RNA. High-throughput sequencing analyses suggest that circRNAs exhibit tissue- and developmental-specific expression, indicating that they may play crucial roles in multiple cellular processes. Strikingly, several circRNAs could function as microRNA sponges and regulate gene transcription, highlighting a new class of important regulators. Here, we review the recent advances in knowledge of endogenous circRNA biogenesis, properties and functions. We further discuss the current findings about circRNAs in human diseases. In plants, the roles of circRNAs remain a mystery. Online resources and bioinformatics identification of circRNAs are essential for the analysis of circRNA biology, although different strategies yield divergent results. The understanding of circRNA functions remains limited; however, circRNAs are enriching the RNA world, acting as an emerging key player.

Motivation: Gap filling for the reconstruction of metabolic networks is to restore the connectivity of metabolites via finding high-confidence reactions that could be missed in target organism. Current methods for gap filling either fall into the network topology or have limited capability in finding missing reactions that are indirectly related to dead-end metabolites but of biological importance to the target model. Results: We present an automated dead-end filling (DEF) approach, which is derived from the wisdom of endosymbiosis theory, to fill gaps by finding the most efficient dead-end utilization paths in a constructed quasi-endosymbiosis model. The recalls of reactions and dead ends of DEF reach around 73% and 86%, respectively. This method is capable of finding indirectly dead-end-related reactions with biological importance for the target organism and is applicable to any given metabolic model. In the E. coli iJR904 model, for instance, about 42% of the dead-end metabolites were fixed by our proposed method. Availabilty and Implementaion: DEF is publicly available at http://bis.zju.edu.cn/DEF/. Contact: [email protected] Supplimentary Information: Supplementary data are available at Bioinformatics online.

SUMMARY: Circular RNAs (circRNAs), a novel class of endogenous RNAs, are widespread in eukaryotic cells. Emerging roles in diverse biological processes suggest that circRNA is a promising key player in RNA world. Most circRNAs are generated through back-splicing of pre-mRNAs, forming a covalently closed loop structure with no 5' caps or 3' polyadenylated tails. In addition, most circRNAs were not associated with translating ribosomes, therefore, circRNAs were deemed to be noncoding. However, the latest research findings revealed that some circRNAs could generate proteins in vivo, which expands the landscape of transcriptome and proteome. To gain insights into the new area of circRNA translation, we introduce an integrated tool capable of detecting circRNAs with protein-coding potential from high-throughput sequencing data. AVAILABILITY AND IMPLEMENTATION: CircPro is available at http://bis.zju.edu.cn/CircPro. CONTACT: [email protected]. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

RNA structure plays a crucial role in gene maturation, regulation and function. Determining the form and frequency of RNA folds is essential for a better understanding of how RNA exerts its functions. Low-throughput studies have focused on RNA primary sequences and expression levels, but with an emphasis on relatively small numbers of transcripts. However, with the recent advent of high-throughput technologies, it is realistic to begin analyzing RNA secondary structures on a genome-wide scale. Here, we review genome-wide RNA secondary structure profiles as well as advances in computational structure predictions. We further discuss the novel characteristics of RNA secondary structure across messenger RNAs. Probing RNA secondary structure by high-throughput sequencing will enable us to build atlases of RNA secondary structures, an important step in helping us to understand the versatility of RNA functions in diverse cellular processes.

MOTIVATION: Small RNA sequencing and degradome sequencing (also known as parallel analysis of RNA ends) have provided rich information on the microRNA (miRNA) and its cleaved mRNA targets on a genome-wide scale in plants, but no computational tools have been developed to effectively and conveniently deconvolute the miRNA-target interaction (MTI). RESULTS: A freely available package, MTide, was developed by combining modified miRDeep2 and CleaveLand4 with some other useful scripts to explore MTI in a comprehensive way. By searching for targets of a complete miRNAs, we can facilitate large-scale identification of miRNA targets, allowing us to discover regulatory interaction networks. AVAILABILITY AND IMPLEMENTATION: http://bis.zju.edu.cn/MTide.

BACKGROUND: Predication of gene regularity network (GRN) from expression data is a challenging task. There are many methods that have been developed to address this challenge ranging from supervised to unsupervised methods. Most promising methods are based on support vector machine (SVM). There is a need for comprehensive analysis on prediction accuracy of supervised method SVM using different kernels on different biological experimental conditions and network size. RESULTS: We developed a tool (CompareSVM) based on SVM to compare different kernel methods for inference of GRN. Using CompareSVM, we investigated and evaluated different SVM kernel methods on simulated datasets of microarray of different sizes in detail. The results obtained from CompareSVM showed that accuracy of inference method depends upon the nature of experimental condition and size of the network. CONCLUSIONS: For network with nodes (<200) and average (over all sizes of networks), SVM Gaussian kernel outperform on knockout, knockdown, and multifactorial datasets compared to all the other inference methods. For network with large number of nodes (~500), choice of inference method depend upon nature of experimental condition. CompareSVM is available at http://bis.zju.edu.cn/CompareSVM/ .

Most plant microRNAs (miRNAs) perform their repressive regulation through target cleavages. The resulting slicing sites on the target transcripts could be mapped by sequencing of the 3'-cleavage remnants, called degradome sequencing. The high sequence complementarity between miRNAs and their targets has greatly facilitated the development of the target prediction tools for plant miRNAs. The prediction results were then subjected to degradome sequencing data-based validation, through which numerous miRNA-target interactions have been extracted. However, some drawbacks are unavoidable when using this forward approach. Essentially, a known list of plant miRNAs should be obtained in advance of target prediction and validation. This becomes an obstacle to discover novel miRNAs and their targets. Here, after reviewing the current available algorithms for reverse identification of miRNA-target pairs in plants, a case study was performed by using a newly established framework with adjustable parameters. In this workflow, integration of degradome and ARGONAUTE 1-enriched small RNA sequencing data was recommended to do a relatively comprehensive and reliable search. Besides, several computational algorithms such as BLAST, target plots and RNA secondary structure prediction were used. The results demonstrated the prevalent utility of the reversed approach for uncovering miRNA-target interactions in plants.

Current achievements in plant microRNA (miRNA) research area are inspiring. Molecular cloning and functional elucidation have greatly advanced our understanding of this small RNA species. As one of the ultimate goals, many research efforts devoted to draw a comprehensive view of miRNA-mediated gene regulatory networks in plants. Numerous bioinformatics tools competent for network analysis have been available. However, the most important point for network construction is to obtain reliable analytical results based on sufficient experimental data. Here, we introduced a general workflow to retrieve and analyze the desired data sets that serve as the cornerstones for network construction. For the upstream analyses of miRNA genes, the sequence feature of miRNA promoters should be characterized. And, regulatory relationships between transcription factors (TFs) and miRNA genes need to be investigated. For the downstream part, we emphasized that the high-throughput degradome sequencing data were especially useful for genuine miRNA-target pair identification. Functional characterization of the miRNA targets is essential to provide deep biological insights into certain miRNA-mediated pathways. For miRNAs themselves, studies on their organ- or tissue-specific expression patterns and the mechanism of self-regulation were discussed. Besides, exhaustive literature mining is required to further support or improve the established networks. It is desired that the introduced framework for miRNA-mediated network construction is timely and useful and could inspire more research efforts in the miRNA research area.

SUMMARY: MyBioNet is a web-based application for biological network analysis, which provides user-friendly web interfaces to visualize, edit and merge biological networks. In addition, MyBioNet integrated KEGG metabolic network data from 1366 organisms and allows users to search and navigate interesting networks. AVAILABILITY AND IMPLEMENTATION: All KEGG metabolic network data are organized and stored in the MySQL database. MyBioNet is implemented in Flex/Actionscript and PHP languages and deployed on an Apache web server. MyBioNet is accessible through all the Flash-embedded browsers at http://bis.zju.edu.cn/mybionet/. CONTACT: [email protected].

BACKGROUND: Protein-protein interactions play a fundamental role in elucidating the molecular mechanisms of biomolecular function, signal transductions and metabolic pathways of living organisms. Although high-throughput technologies such as yeast two-hybrid system and affinity purification followed by mass spectrometry are widely used in model organisms, the progress of protein-protein interactions detection in plants is rather slow. With this motivation, our work presents a computational approach to predict protein-protein interactions in Oryza sativa. RESULTS: To better understand the interactions of proteins in Oryza sativa, we have developed PRIN, a Predicted Rice Interactome Network. Protein-protein interaction data of PRIN are based on the interologs of six model organisms where large-scale protein-protein interaction experiments have been applied: yeast (Saccharomyces cerevisiae), worm (Caenorhabditis elegans), fruit fly (Drosophila melanogaster), human (Homo sapiens), Escherichia coli K12 and Arabidopsis thaliana. With certain quality controls, altogether we obtained 76,585 non-redundant rice protein interaction pairs among 5,049 rice proteins. Further analysis showed that the topology properties of predicted rice protein interaction network are more similar to yeast than to the other 5 organisms. This may not be surprising as the interologs based on yeast contribute nearly 74% of total interactions. In addition, GO annotation, subcellular localization information and gene expression data are also mapped to our network for validation. Finally, a user-friendly web interface was developed to offer convenient database search and network visualization. CONCLUSIONS: PRIN is the first well annotated protein interaction database for the important model plant Oryza sativa. It has greatly extended the current available protein-protein interaction data of rice with a computational approach, which will certainly provide further insights into rice functional genomics and systems biology. PRIN is available online at http://bis.zju.edu.cn/prin/.

High-throughput sequencing (HTS) has opened up a new era for small RNA (sRNA) exploration. Using HTS data for a global survey of sRNAs in 26 angiosperms, elevated GC contents were detected in the monocots, whereas the 5(')-terminal compositions were quite uniform among the angiosperms. Chromosome-wide distribution patterns of sRNAs were investigated by using scrolling-window analysis. We performed de novo natural antisense transcript (NAT) prediction, and found that the overlapping regions of trans-NATs, but not cis-NATs, were hotspots for sRNA generation. One cis-NAT generates phased natural antisense short interfering RNAs (nat-siRNAs) specifically from flowers in Arabidopsis, while one in rice produces phased nat-siRNAs from grains, suggesting their organ-specific regulatory roles.

Single carrier frequency division multiple access (SC-FDMA) is adopted as uplink multiple access scheme in 3G long-term evolution (LTE). Combining SC-FDMA with MIMO (multiple-input multiple-output) can increase capacity and throughput but detection technique is required at the base station (BS). Interference cancellation (IC) based on cyclic redundancy check (CRC) is a conventional detection algorithm. In this paper, we divide IC into useful IC and useless IC. Simulation results indicate that CRC-based IC algorithm performs too many useless ICs, which increases the complexity. Thresholded IC algorithm is proposed to reduce the complexity. In the proposed algorithm, IC is performed only when the probability that the IC is useful exceeds a threshold. We select 0.5 as the threshold. With the threshold, the proposed algorithm reduces the complexity significantly and keeps the performance loss negligible.

UNLABELLED: BNArray is a systemized tool developed in R. It facilitates the construction of gene regulatory networks from DNA microarray data by using Bayesian network. Significant sub-modules of regulatory networks with high confidence are reconstructed by using our extended sub-network mining algorithm of directed graphs. BNArray can handle microarray datasets with missing data. To evaluate the statistical features of generated Bayesian networks, re-sampling procedures are utilized to yield collections of candidate 1st-order network sets for mining dense coherent sub-networks. AVAILABILITY: The R package and the supplementary documentation are available at http://www.cls.zju.edu.cn/binfo/BNArray/.