deepBase v3.0: expression atlas and interactive analysis of ncRNAs from thousands of deep-sequencing data

Abstract

Eukaryotic genomes encode thousands of small and large non-coding RNAs (ncRNAs). However, the expression, functions and evolution of these ncRNAs are still largely unknown. In this study, we have updated deepBase to version 3.0 (deepBase v3.0, http://rna.sysu.edu.cn/deepbase3/index.html), an increasingly popular and openly licensed resource that facilitates integrative and interactive display and analysis of the expression, evolution, and functions of various ncRNAs by deeply mining thousands of high-throughput sequencing data from tissue, tumor and exosome samples. We updated deepBase v3.0 to provide the most comprehensive expression atlas of small RNAs and lncRNAs by integrating ∼67 620 data from 80 normal tissues and ∼50 cancer tissues. The extracellular patterns of various ncRNAs were profiled to explore their applications for discovery of noninvasive biomarkers. Moreover, we constructed survival maps of tRNA-derived RNA Fragments (tRFs), miRNAs, snoRNAs and lncRNAs by analyzing >45 000 cancer sample data and corresponding clinical information. We also developed interactive webs to analyze the differential expression and biological functions of various ncRNAs in ∼50 types of cancers. This update is expected to provide a variety of new modules and graphic visualizations to facilitate analyses and explorations of the functions and mechanisms of various types of ncRNAs.

DIANA-miTED: a microRNA tissue expression database

“…SEAWeb contains 4258 datasets from 10 organisms (3360 human datasets). Finally, DeepBase v3.0 ( 20 ) divides data into two classes, namely ‘Cancer data’ (from TCGA and ICGC) and ‘Tissue data’ (500 datasets from SRA/GEO, GTEx, ENCODE). Importantly, miRNA expression values collected in DeepBase refer to precursors or miRNA host genes, falling short on providing abundance estimates of the functional mature miRNA forms in every analysis type it provides.…”

Biogenesis, functions, and clinical implications of circular RNAs in non-small cell lung cancer

“… 67 deepBase v.3.0 expression features of circRNAs in cancer and normal tissues, survival analysis of circRNAs in cancer patients, and evolutional conservation analysis of circRNAs across species http://rna.sysu.edu.cn/deepbase3/ Xie et al. 68 CircInteractome prediction and mapping of binding sites for RBPs and miRNAs on known circRNAs http://circinteractome.nia.nih.gov/ Dudekula et al. 69 CSCD prediction of potential interactions of miRNAs and RBPs with circRNA in various types of cancer, comparison of the expression levels of circRNA-associated RBPs and microRNAs among different cancers, and prediction of potential ORFs in circRNAs http://gb.whu.edu.cn/CSCD Xia et al.…”

Extracellular Vesicles and Alveolar Epithelial-Capillary Barrier Disruption in Acute Respiratory Distress Syndrome: Pathophysiological Role and Therapeutic Potential

“…Apoptotic bodies (50-5000 nm) are the largest EVs and are formed during (Crescitelli et al, 2013;Raposo and Stoorvogel, 2013;Cocucci and Meldolesi, 2015;Yanez-Mo et al, 2015;Borgovan et al, 2019). Extracellular vesicles are carriers of biologically active molecules (nucleic acids, proteins and lipids), whose composition vary based on the parent cell phenotype and biological state (Keerthikumar et al, 2016;Pathan et al, 2019;Xie et al, 2020). Proteins carried by EVs include chemokines, and inflammatory cytokines, integrins, growth factors, enzymes or even cytoskeletal components (Gutierrez-Vazquez et al, 2013;Keerthikumar et al, 2016;Maas et al, 2017;Mardpour et al, 2019).…”

“…Proteins carried by EVs include chemokines, and inflammatory cytokines, integrins, growth factors, enzymes or even cytoskeletal components ( Gutierrez-Vazquez et al, 2013 ; Keerthikumar et al, 2016 ; Maas et al, 2017 ; Mardpour et al, 2019 ). Nucleic acid cargo in EVs comprise mitochondrial and genomic DNA, small non-coding RNA species (such as microRNA or tRNA, small nucleolar RNA, and small nuclear RNA) and long non-coding RNA species ( Kalra et al, 2012 ; Keerthikumar et al, 2016 ; Xie et al, 2020 ). EVs are also an important source of lipids, including sphingomyelin, ceramides, phosphatidylserine (PS), cholesterol or saturated fatty acids ( Gutierrez-Vazquez et al, 2013 ; Chatterjee et al, 2020b ).…”