Functional characteristics of DNA N6-methyladenine modification based on long-read sequencing in pancreatic cancer.

Abnormalities of DNA modifications are closely related to the pathogenesis and prognosis of pancreatic cancer. The development of third-generation sequencing technology has brought opportunities for the study of new epigenetic modification in cancer. Here, we screened the N6-methyladenine (6mA) and 5-methylcytosine (5mC) modification in pancreatic cancer based on Oxford Nanopore Technologies sequencing.

DiseaseMeth version 3.0: a major expansion and update of the human disease methylation database.

DNA methylation has a growing potential for use as a biomarker because of its involvement in disease. DNA methylation data have also substantially grown in volume during the past 5 years. To facilitate access to these fragmented data, we proposed DiseaseMeth version 3.

Accurate Prediction of Prognosis by Integrating Clinical and Molecular Characteristics in Colon Cancer.

Various factors affect the prognosis of patients with colon cancer. Complicated factors are found to be conducive to accurate assessment of prognosis. In this study, we developed a series of prognostic prediction models for survival time of colon cancer patients after surgery.

M6ADD: a comprehensive database of mA modifications in diseases.

N6-methyladenosine (mA) modification is an important regulatory factor affecting diseases, including multiple cancers and it is a developing direction for targeted disease therapy. Here, we present the M6ADD (mA-diseases database) database, a public data resource containing manually curated data on potential mA-disease associations for which some experimental evidence is available; the related high-throughput sequencing data are also provided and analysed by using different computational methods. To give researchers a tool to query the m6A modification data, the M6ADD was designed as a web-based comprehensive resource focusing on the collection, storage and online analysis of m6A modifications, aimed at exploring the associations between m6A modification and gene disorders and diseases.

CLING: Candidate Cancer-Related lncRNA Prioritization via Integrating Multiple Biological Networks.

Identification and characterization of lncRNAs in cancer with a view to their application in improving diagnosis and therapy remains a major challenge that requires new and innovative approaches. We have developed an integrative framework termed "CLING", aimed to prioritize candidate cancer-related lncRNAs based on their associations with known cancer lncRNAs. CLING focuses on joint optimization and prioritization of all candidates for each cancer type by integrating lncRNA topological properties and multiple lncRNA-centric networks.

SEA version 3.0: a comprehensive extension and update of the Super-Enhancer archive.

Super-enhancers (SEs) are critical for the transcriptional regulation of gene expression. We developed the super-enhancer archive version 3.0 (SEA v.

LncACTdb 2.0: an updated database of experimentally supported ceRNA interactions curated from low- and high-throughput experiments.

We describe LncACTdb 2.0 (http://www.bio-bigdata.

Lnc2Cancer v2.0: updated database of experimentally supported long non-coding RNAs in human cancers.

Lnc2Cancer 2.0 (http://www.bio-bigdata.

Inferences of individual drug responses across diverse cancer types using a novel competing endogenous RNA network.

Differences in individual drug responses are an obstacle to progression in cancer treatment, and predicting responses would help to plan treatment. The accumulation of cancer molecular profiling and drug response data provides opportunities and challenges to identify novel molecular signatures and mechanisms of tumor responsiveness to drugs. This study evaluated drug responses with a competing endogenous RNA (ceRNA) system that depended on competition between diverse RNA species.

MSDD: a manually curated database of experimentally supported associations among miRNAs, SNPs and human diseases.

The MiRNA SNP Disease Database (MSDD, http://www.bio-bigdata.com/msdd/) is a manually curated database that provides comprehensive experimentally supported associations among microRNAs (miRNAs), single nucleotide polymorphisms (SNPs) and human diseases.

Lnc2Meth: a manually curated database of regulatory relationships between long non-coding RNAs and DNA methylation associated with human disease.

Lnc2Meth (http://www.bio-bigdata.com/Lnc2Meth/), an interactive resource to identify regulatory relationships between human long non-coding RNAs (lncRNAs) and DNA methylation, is not only a manually curated collection and annotation of experimentally supported lncRNAs-DNA methylation associations but also a platform that effectively integrates tools for calculating and identifying the differentially methylated lncRNAs and protein-coding genes (PCGs) in diverse human diseases.

LincSNP 2.0: an updated database for linking disease-associated SNPs to human long non-coding RNAs and their TFBSs.

We describe LincSNP 2.0 (http://bioinfo.hrbmu.