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.

The N 6 -methyladenosine (m 6 A) modification influences various mRNA metabolic events and tumorigenesis, however, its functions in nonsense-mediated mRNA decay (NMD) and whether NMD detects induced carcinogenesis pathways remain undefined. Here, we showed that the m 6 A methyltransferase METTL3 sustained its oncogenic role by modulating NMD of splicing factors and alternative splicing isoform switches in glioblastoma (GBM). Methylated RNA immunoprecipitation-seq (MeRIP-seq) analyses showed that m 6 A modification peaks were enriched at metabolic pathwayrelated transcripts in glioma stem cells (GSC) compared with neural progenitor cells. In addition, the clinical aggressiveness of malignant gliomas was associated with elevated expression of METTL3. Furthermore, silencing METTL3 or overexpressing dominant-negative mutant METTL3 suppressed the growth and self-renewal of GSCs. Integrated transcriptome and MeRIP-seq analyses revealed that downregulating the expression of METTL3 decreased m 6 A modification levels of serineand arginine-rich splicing factors (SRSF), which led to YTHDC1-dependent NMD of SRSF transcripts and decreased SRSF protein expression. Reduced expression of SRSFs led to larger changes in alternative splicing isoform switches. Importantly, the phenotypes mediated by METTL3 deficiency could be rescued by downregulating BCL-X or NCOR2 isoforms. Overall, these results establish a novel function of m 6 A in modulating NMD and uncover the mechanism by which METTL3 promotes GBM tumor growth and progression.Significance: These findings establish the oncogenic role of m 6 A writer METTL3 in glioblastoma stem cells.

No abstract

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.

Long noncoding RNAs (lncRNA) have emerged as promising biomarkers in cancer diagnosis, treatment, and prognosis. Recent studies suggest that a large number of coding gene expression microarray probes could be reannotated as lncRNAs. Microarray, once the most cutting-edge highthroughput gene expression technology, has been used for thousands of cancer studies and has brought invaluable resources for studying the functions of lncRNA in cancer development. However, a comprehensive lncRNA resource based on microarray data is still lacking. Here, we present lnCAR (lncRNAs from cancer arrays), a comprehensive open resource for providing expression profiles and prognostic landscape of lncRNAs derived from reannotation of public microarray data. Currently, lnCAR contains 52,300 samples for differential expression analysis and 12,883 samples for survival analysis from 10 cancer types. lnCAR allows users to interactively explore any annotated or novel lncRNAs. We believe lnCAR will serve as a valuable resource for the community focused on lncRNA research in cancer. Significance: lnCAR, a new interactive tool of reannotated public cancer-related microarray data, provides expression profiles and prognostic landscapes of lncRNAs across thousands of samples and multiple cancer types.

BackgroundLarge-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.FindingsIn 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.ConclusionsWe 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].