Yubin Xie

IBS: an illustrator for the presentation and visualization of biological sequences

Summary: Biological sequence diagrams are fundamental for visualizing various functional elements in protein or nucleotide sequences that enable a summarization and presentation of existing information as well as means of intuitive new discoveries. Here, we present a software package called illustrator of biological sequences (IBS) that can be used for representing the organization of either protein or nucleotide sequences in a convenient, efficient and precise manner. Multiple options are provided in IBS, and biological sequences can be manipulated, recolored or rescaled in a user-defined mode. Also, the final representational artwork can be directly exported into a publication-quality figure. Availability and implementation: The standalone package of IBS was implemented in JAVA, while the online service was implemented in HTML5 and JavaScript. Both the standalone package and online service are freely available at http://ibs.biocuckoo.org. Contact: renjian.sysu@gmail.com or xueyu@hust.edu.cn Supplementary information: Supplementary data are available at Bioinformatics online.

GPS-SUMO: a tool for the prediction of sumoylation sites and SUMO-interaction motifs

Small ubiquitin-like modifiers (SUMOs) regulate a variety of cellular processes through two distinct mechanisms, including covalent sumoylation and non-covalent SUMO interaction. The complexity of SUMO regulations has greatly hampered the large-scale identification of SUMO substrates or interaction partners on a proteome-wide level. In this work, we developed a new tool called GPS-SUMO for the prediction of both sumoylation sites and SUMO-interaction motifs (SIMs) in proteins. To obtain an accurate performance, a new generation group-based prediction system (GPS) algorithm integrated with Particle Swarm Optimization approach was applied. By critical evaluation and comparison, GPS-SUMO was demonstrated to be substantially superior against other existing tools and methods. With the help of GPS-SUMO, it is now possible to further investigate the relationship between sumoylation and SUMO interaction processes. A web service of GPS-SUMO was implemented in PHP + JavaScript and freely available at http://sumosp.biocuckoo.org.

GPS-Lipid: a robust tool for the prediction of multiple lipid modification sites.

As one of the most common post-translational modifications in eukaryotic cells, lipid modification is an important mechanism for the regulation of variety aspects of protein function. Over the last decades, three classes of lipid modifications have been increasingly studied. The co-regulation of these different lipid modifications is beginning to be noticed. However, due to the lack of integrated bioinformatics resources, the studies of co-regulatory mechanisms are still very limited. In this work, we developed a tool called GPS-Lipid for the prediction of four classes of lipid modifications by integrating the Particle Swarm Optimization with an aging leader and challengers (ALC-PSO) algorithm. GPS-Lipid was proven to be evidently superior to other similar tools. To facilitate the research of lipid modification, we hosted a publicly available web server at http://lipid.biocuckoo.org with not only the implementation of GPS-Lipid, but also an integrative database and visualization tool. We performed a systematic analysis of the co-regulatory mechanism between different lipid modifications with GPS-Lipid. The results demonstrated that the proximal dual-lipid modifications among palmitoylation, myristoylation and prenylation are key mechanism for regulating various protein functions. In conclusion, GPS-lipid is expected to serve as useful resource for the research on lipid modifications, especially on their co-regulation.

m6AVar: a database of functional variants involved in m6A modification

Abstract Identifying disease-causing variants among a large number of single nucleotide variants (SNVs) is still a major challenge. Recently, N6-methyladenosine (m6A) has become a research hotspot because of its critical roles in many fundamental biological processes and a variety of diseases. Therefore, it is important to evaluate the effect of variants on m6A modification, in order to gain a better understanding of them. Here, we report m6AVar (http://m6avar.renlab.org), a comprehensive database of m6A-associated variants that potentially influence m6A modification, which will help to interpret variants by m6A function. The m6A-associated variants were derived from three different m6A sources including miCLIP/PA-m6A-seq experiments (high confidence), MeRIP-Seq experiments (medium confidence) and transcriptome-wide predictions (low confidence). Currently, m6AVar contains 16 132 high, 71 321 medium and 326 915 low confidence level m6A-associated variants. We also integrated the RBP-binding regions, miRNA-targets and splicing sites associated with variants to help users investigate the effect of m6A-associated variants on post-transcriptional regulation. Because it integrates the data from genome-wide association studies (GWAS) and ClinVar, m6AVar is also a useful resource for investigating the relationship between the m6A-associated variants and disease. Overall, m6AVar will serve as a useful resource for annotating variants and identifying disease-causing variants.

m6ASNP: a tool for annotating genetic variants by m6A function

Abstract Background Large-scale genome sequencing projects have identified many genetic variants for diverse diseases. A major goal of these projects is to characterize these genetic variants to provide insight into their function and roles in diseases. N6-methyladenosine (m6A) is one of the most abundant RNA modifications in eukaryotes. Recent studies have revealed that aberrant m6A modifications are involved in many diseases. Findings In this study, we present a user-friendly web server called “m6ASNP” that is dedicated to the identification of genetic variants that target m6A modification sites. A random forest model was implemented in m6ASNP to predict whether the methylation status of an m6A site is altered by the variants that surround the site. In m6ASNP, genetic variants in a standard variant call format (VCF) are accepted as the input data, and the output includes an interactive table that contains the genetic variants annotated by m6A function. In addition, statistical diagrams and a genome browser are provided to visualize the characteristics and to annotate the genetic variants. Conclusions We believe that m6ASNP is a very convenient tool that can be used to boost further functional studies investigating genetic variants. The web server “m6ASNP” is implemented in JAVA and PHP and is freely available at [60].

IDEA: a web server for Interactive Differential Expression Analysis with R Packages

Differential expression (DE) analysis is a fundamental task in the downstream analysis of the next-generation sequencing (NGS) data. Up to now, a number of R packages have been developed for detecting differentially expressed genes. Although R language has an interaction-oriented programming design, for many biology researchers, a lack of basic programming skills has greatly hindered the application of these R packages. To address this issue, we developed the Interactive Differential Expression Analyzer (IDEA), a Shiny-based web application integrating the differential expression analysis related R packages into a graphical user interface (GUI), allowing users to run the analysis without writing any new code. A wide variety of charts and tables are generated to facilitate the interpretation of the results. In addition, IDEA also provides a combined analysis framework which helps to reconcile any discrepancy from different computational methods. As a public data analysis server, IDAE is implemented in HTML, CSS and JavaScript, and is freely available at http://idea.renlab.org.

DeepNitro: Prediction of Protein Nitration and Nitrosylation Sites by Deep Learning

Protein nitration and nitrosylation are essential post-translational modifications (PTMs) involved in many fundamental cellular processes. Recent studies have revealed that excessive levels of nitration and nitrosylation in some critical proteins are linked to numerous chronic diseases. Therefore, the identification of substrates that undergo such modifications in a site-specific manner is an important research topic in the community and will provide candidates for targeted therapy. In this study, we aimed to develop a computational tool for predicting nitration and nitrosylation sites in proteins. We first constructed four types of encoding features, including positional amino acid distributions, sequence contextual dependencies, physicochemical properties, and position-specific scoring features, to represent the modified residues. Based on these encoding features, we established a predictor called DeepNitro using deep learning methods for predicting protein nitration and nitrosylation. Using n-fold cross-validation, our evaluation shows great AUC values for DeepNitro, 0.65 for tyrosine nitration, 0.80 for tryptophan nitration, and 0.70 for cysteine nitrosylation, respectively, demonstrating the robustness and reliability of our tool. Also, when tested in the independent dataset, DeepNitro is substantially superior to other similar tools with a 7%−42% improvement in the prediction performance. Taken together, the application of deep learning method and novel encoding schemes, especially the position-specific scoring feature, greatly improves the accuracy of nitration and nitrosylation site prediction and may facilitate the prediction of other PTM sites. DeepNitro is implemented in JAVA and PHP and is freely available for academic research at http://deepnitro.renlab.org.

Pan-Cancer Analysis Reveals the Functional Importance of Protein Lysine Modification in Cancer Development

Large-scale tumor genome sequencing projects have revealed a complex landscape of genomic mutations in multiple cancer types. A major goal of these projects is to characterize somatic mutations and discover cancer drivers, thereby providing important clues to uncover diagnostic or therapeutic targets for clinical treatment. However, distinguishing only a few somatic mutations from the majority of passenger mutations is still a major challenge facing the biological community. Fortunately, combining other functional features with mutations to predict cancer driver genes is an effective approach to solve the above problem. Protein lysine modifications are an important functional feature that regulates the development of cancer. Therefore, in this work, we have systematically analyzed somatic mutations on seven protein lysine modifications and identified several important drivers that are responsible for tumorigenesis. From published literature, we first collected more than 100,000 lysine modification sites for analysis. Another 1 million non-synonymous single nucleotide variants (SNVs) were then downloaded from TCGA and mapped to our collected lysine modification sites. To identify driver proteins that significantly altered lysine modifications, we further developed a hierarchical Bayesian model and applied the Markov Chain Monte Carlo (MCMC) method for testing. Strikingly, the coding sequences of 473 proteins were found to carry a higher mutation rate in lysine modification sites compared to other background regions. Hypergeometric tests also revealed that these gene products were enriched in known cancer drivers. Functional analysis suggested that mutations within the lysine modification regions possessed higher evolutionary conservation and deleteriousness. Furthermore, pathway enrichment showed that mutations on lysine modification sites mainly affected cancer related processes, such as cell cycle and RNA transport. Moreover, clinical studies also suggested that the driver proteins were significantly associated with patient survival, implying an opportunity to use lysine modifications as molecular markers in cancer diagnosis or treatment. By searching within protein-protein interaction networks using a random walk with restart (RWR) algorithm, we further identified a series of potential treatment agents and therapeutic targets for cancer related to lysine modifications. Collectively, this study reveals the functional importance of lysine modifications in cancer development and may benefit the discovery of novel mechanisms for cancer treatment.

Proposed modifications and incorporation of plasma Epstein-Barr virus DNA improve the TNM staging system for Epstein-Barr virus-related nasopharyngeal carcinoma: Refined TNM Staging for EBV-Related NPC

The prognosis of patients who have Epstein‐Barr virus (EBV)‐related nasopharyngeal carcinoma (NPC) in which the tumor tissues harbor EBV have a better prognosis than those without EBV‐related NPC. Therefore, the eighth edition of the TNM staging system could be modified for EBV‐related NPC by incorporating the measurement of plasma EBV DNA.

An integrated overview of spatiotemporal organization and regulation in mitosis in terms of the proteins in the functional supercomplexes

Eukaryotic cells may divide via the critical cellular process of cell division/mitosis, resulting in two daughter cells with the same genetic information. A large number of dedicated proteins are involved in this process and spatiotemporally assembled into three distinct super-complex structures/organelles, including the centrosome/spindle pole body, kinetochore/centromere and cleavage furrow/midbody/bud neck, so as to precisely modulate the cell division/mitosis events of chromosome alignment, chromosome segregation and cytokinesis in an orderly fashion. In recent years, many efforts have been made to identify the protein components and architecture of these subcellular organelles, aiming to uncover the organelle assembly pathways, determine the molecular mechanisms underlying the organelle functions, and thereby provide new therapeutic strategies for a variety of diseases. However, the organelles are highly dynamic structures, making it difficult to identify the entire components. Here, we review the current knowledge of the identified protein components governing the organization and functioning of organelles, especially in human and yeast cells, and discuss the multi-localized protein components mediating the communication between organelles during cell division.