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.cnSupplementary information: Supplementary data are available at Bioinformatics online.

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.

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.

BACKGROUND: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. METHODS: In total, 979 patients with NPC who received intensity-modulated radiotherapy (IMRT) were retrospectively reviewed. Recursive partitioning analysis was conducted based on tumor (T) classification, lymph node (N) classification, and EBV DNA measurement to derive objectively the proposed stage groupings. The validity of the proposed stage groupings was confirmed in a prospective cohort of 550 consecutive patients who also received with IMRT. RESULTS: The pretreatment plasma EBV DNA level was identified as a significant, negative prognostic factor for progression-free survival and overall survival in univariate analysis (all P < .001) and multivariate analysis (all P < .05). Recursive partitioning analysis of the primary cohort to incorporate EBV DNA generated the following proposed stage groupings: stage RI (T1N0), RIIA (T2-T3N0 or T1-T3N1, EBV DNA ≤2000 copies/mL), stage RIIB (T2-T3N0 or T1-T3N1, EBV DNA >2000 copies/mL; T1-T3N2, EBV DNA ≤2000 copies/mL), stage RIII (T1-T3N2, EBV DNA >2000 copies/mL; T4N0-N2), and stage RIVA (any T and N3). In the validation cohort, the 5-year progression-free survival rate was 100%, 87.9%, 76.7%, 68.7%, and 50.4% for proposed stage RI, RIIA, RIIB, RIII, and RIV NPC, respectively (P < .001). Compared with the eighth edition TNM stage groupings, the proposed stage groupings incorporating EBV DNA provided better hazard consistency, hazard discrimination, outcome prediction, and sample size balance. CONCLUSIONS: The proposed stage groupings have better prognostic performance than the eighth edition of the TNM staging system. EBV DNA titers should be included in the TNM staging system to assess patients who have EBVrelated NPC. Cancer 2019;125:79-89.

Distinguishing the few disease-related variants from a massive number of passenger variants is a major challenge. Variants affecting RNA modifications that play critical roles in many aspects of RNA metabolism have recently been linked to many human diseases, such as cancers. Evaluating the effect of genetic variants on RNA modifications will provide a new perspective for understanding the pathogenic mechanism of human diseases. Previously, we developed a database called ‘m6AVar’ to host variants associated with m6A, one of the most prevalent RNA modifications in eukaryotes. To host all RNA modification (RM)-associated variants, here we present an updated version of m6AVar renamed RMVar (http://rmvar.renlab.org). In this update, RMVar contains 1 678 126 RM-associated variants for 9 kinds of RNA modifications, namely m6A, m6Am, m1A, pseudouridine, m5C, m5U, 2′-O-Me, A-to-I and m7G, at three confidence levels. Moreover, RBP binding regions, miRNA targets, splicing events and circRNAs were integrated to assist investigations of the effects of RM-associated variants on posttranscriptional regulation. In addition, disease-related information was integrated from ClinVar and other genome-wide association studies (GWAS) to investigate the relationship between RM-associated variants and diseases. We expect that RMVar may boost further functional studies on genetic variants affecting RNA modifications.