The rapid development of genomic high-throughput sequencing has identified a large number of DNA regulatory elements with abundant epigenetics markers, which promotes the rapid accumulation of functional genomic region data. The comprehensively understanding and research of human functional genomic regions is still a relatively urgent work at present. However, the existing analysis tools lack extensive annotation and enrichment analytical abilities for these regions. Here, we designed a novel software, Genomic Region sets Enrichment Analysis Platform (GREAP), which provides comprehensive region annotation and enrichment analysis capabilities. Currently, GREAP supports 85 370 genomic region reference sets, which cover 634 681 107 regions across 11 different data types, including super enhancers, transcription factors, accessible chromatins, etc. GREAP provides widespread annotation and enrichment analysis of genomic regions. To reflect the significance of enrichment analysis, we used the hypergeometric test and also provided a Locus Overlap Analysis. In summary, GREAP is a powerful platform that provides many types of genomic region sets for users and supports genomic region annotations and enrichment analyses. In addition, we developed a customizable genome browser containing >400 000 000 customizable tracks for visualization. The platform is freely available at http://www.liclab.net/Greap/view/index.

Drug sensitivity has always been at the core of individualized cancer chemotherapy. However, we have been overwhelmed by large-scale pharmacogenomic data in the era of next-generation sequencing technology, which makes it increasingly challenging for researchers, especially those without bioinformatic experience, to perform data integration, exploration and analysis. To bridge this gap, we developed RNAactDrug, a comprehensive database of RNAs associated with drug sensitivity from multi-omics data, which allows users to explore drug sensitivity and RNA molecule associations directly. It provides association data between drug sensitivity and RNA molecules including mRNAs, long non-coding RNAs (lncRNAs) and microRNAs (miRNAs) at four molecular levels (expression, copy number variation, mutation and methylation) from integrated analysis of three large-scale pharmacogenomic databases (GDSC, CellMiner and CCLE). RNAactDrug currently stores more than 4 924 200 associations of RNA molecules and drug sensitivity at four molecular levels covering more than 19 770 mRNAs, 11 119 lncRNAs, 438 miRNAs and 4155 drugs. A user-friendly interface enriched with various browsing sections augmented with advance search facility for querying the database is offered for users retrieving. RNAactDrug provides a comprehensive resource for RNA molecules acting in drug sensitivity, and it could be used to prioritize drug sensitivity-related RNA molecules, further promoting the identification of clinically actionable biomarkers in drug sensitivity and drug development more cost-efficiently by making this knowledge accessible to both basic researchers and clinical practitioners. Database URL: http://bio-bigdata.hrbmu.edu.cn/RNAactDrug.

Numerous studies have shown that copy number variation (CNV) in lncRNA regions play critical roles in the initiation and progression of cancer. However, our knowledge about their functionalities is still limited. Here, we firstly provided a computational method to identify lncRNAs with copy number variation (lncRNAs-CNV) and their driving transcriptional perturbed subpathways by integrating multidimensional omics data of cancer. The high reliability and accuracy of our method have been demonstrated. Then, the method was applied to 14 cancer types, and a comprehensive characterization and analysis was performed. LncRNAs-CNV had high specificity in cancers, and those with high CNV level may perturb broad biological functions. Some core subpathways and cancer hallmarks widely perturbed by lncRNAs-CNV were revealed. Moreover, subpathways highlighted the functional diversity of lncRNAs-CNV in various cancers. Survival analysis indicated that functional lncRNAs-CNV could be candidate prognostic biomarkers for clinical applications, such as ST7-AS1, CDKN2B-AS1 and EGFR-AS1. In addition, cascade responses and a functional crosstalk model among lncRNAs-CNV, impacted genes, driving subpathways and cancer hallmarks were proposed for understanding the driving mechanism of lncRNAs-CNV. Finally, we developed a user-friendly web interface-LncCASE (http://bio-bigdata.hrbmu.edu.cn/LncCASE/) for exploring lncRNAs-CNV and their driving subpathways in various cancer types. Our study identified and systematically characterized lncRNAs-CNV and their driving subpathways and presented valuable resources for investigating the functionalities of non-coding variations and the mechanisms of tumorigenesis.

MOTIVATION: The accurate prediction of disease status is a central challenge in clinical cancer research. Microarray-based gene biomarkers have been identified to predict outcome and outperform traditional clinical parameters. However, the robustness of the individual gene biomarkers is questioned because of their little reproducibility between different cohorts of patients. Substantial progress in treatment requires advances in methods to identify robust biomarkers. Several methods incorporating pathway information have been proposed to identify robust pathway markers and build classifiers at the level of functional categories rather than of individual genes. However, current methods consider the pathways as simple gene sets but ignore the pathway topological information, which is essential to infer a more robust pathway activity. RESULTS: Here, we propose a directed random walk (DRW)-based method to infer the pathway activity. DRW evaluates the topological importance of each gene by capturing the structure information embedded in the directed pathway network. The strategy of weighting genes by their topological importance greatly improved the reproducibility of pathway activities. Experiments on 18 cancer datasets showed that the proposed method yielded a more accurate and robust overall performance compared with several existing gene-based and pathway-based classification methods. The resulting risk-active pathways are more reliable in guiding therapeutic selection and the development of pathway-specific therapeutic strategies. AVAILABILITY: DRW is freely available at http://210.46.85.180:8080/DRWPClass/