Paper Info
Title
DASHR 2.0: integrated database of human small non-coding RNA genes and mature products.
Abstract
Motivation Small non-coding RNAs (sncRNAs, <100 nts) are highly abundant RNAs that regulate diverse and often tissue-specific cellular processes by associating with transcription factor complexes or binding to mRNAs. While thousands of sncRNA genes exist in the human genome, no single resource provides searchable, unified annotation, expression and processing information for full sncRNA transcripts and mature RNA products derived from these larger RNAs. Results Our goal is to establish a complete catalog of annotation, expression, processing, conservation, tissue-specificity and other biological features for all human sncRNA genes and mature products derived from all major RNA classes. DASHR (Database of small human non-coding RNAs) v2.0 database is the first that integrates human sncRNA gene and mature products profiles obtained from multiple RNA-seq protocols. Altogether, 185 tissues/cell types and sncRNA annotations and >800 curated experiments from ENCODE and GEO/SRA across multiple RNA-seq protocols for both GRCh38/hg38 and GRCh37/hg19 assemblies are integrated in DASHR. Moreover, DASHR is the first to contain both known and novel, previously un-annotated sncRNA loci identified by unsupervised segmentation (13 times more loci with 1 678 800 total). Additionally, DASHR v2.0 adds >3 200 000 annotations for non-small RNA genes and other genomic features (long-noncoding RNAs, mRNAs, promoters, repeats). Furthermore, DASHR v2.0 introduces an enhanced user interface, interactive experiment-by-locus table view, sncRNA locus sorting and filtering by biological features. All annotation and expression information directly downloadable and accessible as UCSC genome browser tracks. Availability and implementation DASHR v2.0 is freely available at https://lisanwanglab.org/DASHRv2. Supplementary information Supplementary data are available at Bioinformatics online.
Year
DOI
Venue
2019
10.1093/bioinformatics/bty709
BIOINFORMATICS
Field
DocType
Volume
Data mining,Gene,Computer science,Computational biology,Non-coding RNA
Journal
35
Issue
ISSN
Citations 
6
1367-4803
0
PageRank 
References 
Authors
0.34
12
6
Name
Order
Citations
PageRank
Pavel Kuksa139924.10
Alexandre Amlie-Wolf271.19
Živadin Katanic310.70
Otto Valladares4112.82
Li-San Wang5486.23
Yuk Yee Leung692.70