DASHR – Database of small human noncoding RNAs

187 deep sequencing data sets | 42 tissues and cell types | 48,075 sncRNA loci

  DASHR: human small RNA database
The DASHR database provides the most comprehensive information to date on human small non-coding RNA (sncRNA) genes, precursor and mature sncRNA annotations, sequence, expression levels and RNA processing information across 42 normal tissues and cell types in human. The content of the database derives from integrating annotation data with curation, annotation, and computational analysis of 187 small-RNA (smRNA-seq) deep sequencing datasets with over 2.5 billion reads from over 30 independent studies. DASHR contains information on over 48,000 precursor and mature sncRNA annotations in the human genome, of which 82% are expressed in one or more of the curated tissues and cell types.

sncRNA search

DASHR allows search by the full or partial name of sncRNA gene, locate sncRNA loci overlapping with the given genomic interval coordinates, or query sncRNA records in the database by RNA sequence.

sncRNA expression browser

The DASHR expression browser displays information about sncRNA gene expression and mature sncRNA products expression in normal conditions (tissues/cell types). It aims to answer questions such as
  • What is the expression pattern for hsa-mir-200a miRNA gene and its mature products (hsa-mir-200a-5p or hsa-mir-200a-3p) in normal tissues?
  • What are the sncRNA genes and mature sncRNA products expressed in human brain?
  • What annotated sncRNAs are in the specific genomic location and how are they expressed in specific tissue/cell type or across all tissues and cell types?

Data visualization/download

Genome-wide raw signal tracks, sncRNA contigs (called peaks), and annotation tracks for small non-coding RNAs can be viewed via UCSC Genome Browser or downloaded from the DASHR download page.

References

Yuk Yee Leung, Pavel P. Kuksa, Alexandre Amlie-Wolf, Otto Valladares, Lyle H. Ungar, Sampath Kannan, Brian D. Gregory, and Li-San Wang. DASHR: database of small human noncoding RNAs. Nucl. Acids Res., 2015 (Database Issue) doi:10.1093/nar/gkv1188

Distinctive features of DASHR

To get started, click on the Search tab, or click About for help and some use cases.

Questions? dashr@lisanwanglab.orgWang LabThe Institute for Biomedical Informatics | University of Pennsylvania |

All searches will retrieve the sncRNA annotation, ID conversion table, as well as links to their genomic and expression profiles.

Three types of searches are supported:

Search by sncRNA name/ID

Enter small RNA name: Example small RNA

Search by HGNC symbol, RefSeq ID, UCSC ID, small RNA name, or gene name. Search examples:
  1. primary miRNA search: MIR192, hsa-mir-192, NR_029578, uc010rnr.1
  2. mature miRNA 3p/5p search: hsa-miR-192-3p, hsa-miR-192-5p
  3. snoRNA/snRNA search: U14, HBII-251, SNORD85, NR_003066, uc001bsl.1
  4. scRNA search: HY1, RNY1
  5. tRNA search: tRNA22, chr14.tRNA22-ProAGG, uc021rnx.1
  6. piRNA search: piR-36025
  7. tRF-3p/tRF-5p search: chr14.tRNA22-ProAGG-tRF3


Search by genomic coordinates

Enter genomic coordinates: Example genomic coordinates

Search examples: chr1:1103243-1103332, chr1 1103243 1103332

Note: If multiple loci are returned (common among duplicated RNAs such as tRNAs), we will display all annotated sncRNAs of any classes within those particular genomic coordinates on both strands.


Search by sequence

Enter small RNA sequence: Example sequence

Search examples: CAUCUUACUGGGCAGCAUUGGA, or with wildcard CAU_UUACUGGGCA_CAUUGGA

Note: Any sequence that contains the provided nucleotide sequence will be displayed.



Questions? dashr@lisanwanglab.orgWang LabThe Institute for Biomedical Informatics | University of Pennsylvania |

Small non-coding RNA expression

Browse small non-coding RNA gene expression

Browse mature sncRNA products

View genomic region

Enter genomic coordinates: Example genomic coordinates

UCSC sncRNA track hub (42 tissues and cell types)

The genome-wide map of small RNA loci, raw signal track data, sncRNA contigs (called peaks), and annotation can be accessed via the UCSC Genome Browser. Entire small RNA transcriptome

To browse all the mapped sncRNA sequencing data and annotation effectively on the UCSC genome browser, we have created a sncRNA track hub. This hub enables simultaneous visualization of all processed sequencing data from both strands. Both peak and raw signal tracks are included. Processed data is also downloadable (Download). To access our hub on genome browser click here. DASHR track data is also downloadable from the Table Browser.

Questions? dashr@lisanwanglab.orgWang LabThe Institute for Biomedical Informatics | University of Pennsylvania |

Small-RNA sequencing datasets

The database contains a comprehensive set of curated human small RNA sequencing datasets from publicly available sources (SRA, GEO).

The detailed descriptions of the sequencing datasets used are provided below.



AccessionTissue IDDescriptionTissue / Cell typeStudy IDIllumina platform Pubmed IDLab / InstituteIllumina adapterTrimmed readsMapped reads
SRR772426adipose1AdiposeTissueGSE45159 Illumina HiSeq 2000PMID:23562819Aldons J. Lusis's Lab of University of California Los AngelesTruseq5,230,7975,104,713
SRR772427adipose1AdiposeTissueGSE45159 Illumina HiSeq 2000PMID:23562819Aldons J. Lusis's Lab of University of California Los AngelesTruseq5,284,9165,183,884
SRR772460adipose1AdiposeTissueGSE45159 Illumina HiSeq 2000PMID:23562819Aldons J. Lusis's Lab of University of California Los AngelesTruseq5,220,0295,121,084
SRR772461adipose1AdiposeTissueGSE45159 Illumina HiSeq 2000PMID:23562819Aldons J. Lusis's Lab of University of California Los AngelesTruseq5,320,3115,248,499
SRR772492adipose1AdiposeTissueGSE45159 Illumina HiSeq 2000PMID:23562819Aldons J. Lusis's Lab of University of California Los AngelesTruseq3,833,6693,780,191
SRR772493adipose1AdiposeTissueGSE45159 Illumina HiSeq 2000PMID:23562819Aldons J. Lusis's Lab of University of California Los AngelesTruseq3,877,0023,837,655
SRR772494adipose1AdiposeTissueGSE45159 Illumina HiSeq 2000PMID:23562819Aldons J. Lusis's Lab of University of California Los AngelesTruseq3,637,9253,576,507
SRR772495adipose1AdiposeTissueGSE45159 Illumina HiSeq 2000PMID:23562819Aldons J. Lusis's Lab of University of California Los AngelesTruseq4,348,6014,291,793
SRR772508adipose1AdiposeTissueGSE45159 Illumina HiSeq 2000PMID:23562819Aldons J. Lusis's Lab of University of California Los AngelesTruseq3,054,1643,004,226
SRR772509adipose1AdiposeTissueGSE45159 Illumina HiSeq 2000PMID:23562819Aldons J. Lusis's Lab of University of California Los AngelesTruseq4,884,3874,615,997
SRR772512adipose1AdiposeTissueGSE45159 Illumina HiSeq 2000PMID:23562819Aldons J. Lusis's Lab of University of California Los AngelesTruseq9,812,8069,367,088
SRR772526adipose1AdiposeTissueGSE45159 Illumina HiSeq 2000PMID:23562819Aldons J. Lusis's Lab of University of California Los AngelesTruseq3,922,5603,850,686
SRR772527adipose1AdiposeTissueGSE45159 Illumina HiSeq 2000PMID:23562819Aldons J. Lusis's Lab of University of California Los AngelesTruseq3,973,6353,917,414
SRR772621adipose1AdiposeTissueGSE45159 Illumina HiSeq 2000PMID:23562819Aldons J. Lusis's Lab of University of California Los AngelesTruseq1,592,5971,552,496
SRR772622adipose1AdiposeTissueGSE45159 Illumina HiSeq 2000PMID:23562819Aldons J. Lusis's Lab of University of California Los AngelesTruseq1,889,6581,854,251
SRR772625adipose1AdiposeTissueGSE45159 Illumina HiSeq 2000PMID:23562819Aldons J. Lusis's Lab of University of California Los AngelesTruseq8,123,0697,750,258
SRR015363bcellgerminalcenter1Germinal center B cellPrimary cells (FACS sorted by human lymphoid organ)GSE22898Illumina Genome AnalyzerPMID:20733160Sandeep Dave lab of Duke University 1.01,430,3341,170,669
SRR015359bcellgerminalcenter1Germinal center B cellPrimary cells (FACS sorted by human lymphoid organ)GSE22898Illumina Genome AnalyzerPMID:20733160Sandeep Dave lab of Duke University 1.02,789,9972,274,797
SRR015361bcellmemory1Memory B cellPrimary cells (FACS sorted by human lymphoid organ)GSE22898Illumina Genome AnalyzerPMID:20733160Sandeep Dave lab of Duke University 1.03,077,2892,256,078
SRR015365bcellmemory1Memory B cellPrimary cells (FACS sorted by human lymphoid organ)GSE22898Illumina Genome AnalyzerPMID:20733160Sandeep Dave lab of Duke University 1.0709,810447,166
SRR015358bcellnaive1Naive B CellPrimary cells (FACS sorted by human lymphoid organ)GSE22898Illumina Genome AnalyzerPMID:20733160Sandeep Dave lab of Duke University 1.03,314,0022,615,118
SRR015362bcellnaive1Naive B CellPrimary cells (FACS sorted by human lymphoid organ)GSE22898Illumina Genome AnalyzerPMID:20733160Sandeep Dave lab of Duke University 1.0420,266254,895
SRR015360bcellplasma1Plasma B cellPrimary cells (FACS sorted by human lymphoid organ)GSE22898Illumina Genome AnalyzerPMID:20733160Sandeep Dave lab of Duke University 1.02,428,0371,953,513
SRR015364bcellplasma1Plasma B cellPrimary cells (FACS sorted by human lymphoid organ)GSE22898Illumina Genome AnalyzerPMID:20733160Sandeep Dave lab of Duke University 1.0972,809855,056
SRR333668bladder1BladderTransitional cell typeGSE31616Illumina Genome Analyzer IIPMID:21799901Jiahao Chen of Beijing Genomics Institute1.015,298,54314,762,668
SRR333672bladder1BladderTransitional cell typeGSE31616Illumina Genome Analyzer IIPMID:21799901Jiahao Chen of Beijing Genomics Institute1.514,833,58414,552,996
SRR040573brainog1Orbital gyrusTissueSRA012516Illumina Genome AnalyzerPMID:20507594Claes Wahlested's lab of The Scripps Research Institute1.01,361,1521,349,235
SRR040799brainog1Orbital gyrusTissueSRA012516Illumina Genome AnalyzerPMID:20507594Claes Wahlested's lab of The Scripps Research Institute1.01,314,1411,241,062
GSM1060654 brainpfc1Dorsolateral prefrontal cortexTissueGSE43335Illumina Genome Analyzer IIPMID:24149843Li-San Wang's lab of University of Pennsylvania1.512,329,64710,071,469
GSM1060655brainpfc1Dorsolateral prefrontal cortexTissueGSE43335Illumina Genome Analyzer IIPMID:24149843Li-San Wang's lab of University of Pennsylvania1.513,852,83911,659,074
GSM1060656brainpfc1Dorsolateral prefrontal cortexTissueGSE43335Illumina Genome Analyzer IIPMID:24149843Li-San Wang's lab of University of Pennsylvania1.518,640,88014,684,190
GSM1060657brainpfc1Dorsolateral prefrontal cortexTissueGSE43335Illumina Genome Analyzer IIPMID:24149843Li-San Wang's lab of University of Pennsylvania1.57,105,9876,076,592
SRR1103937brainpfc2Prefrontal cortex TissueGSE48552Illumina HiSeq 2000PMID:24014289Bart De Strooper Lab's of VIB Center for the Biology of DiseaseTruseq8,106,0607,818,377
SRR1103938brainpfc2Prefrontal cortex TissueGSE48552Illumina HiSeq 2000PMID:24014289Bart De Strooper Lab's of VIB Center for the Biology of DiseaseTruseq3,511,6353,413,538
SRR1103939brainpfc2Prefrontal cortex TissueGSE48552Illumina HiSeq 2000PMID:24014289Bart De Strooper Lab's of VIB Center for the Biology of DiseaseTruseq6,092,5525,920,617
SRR1103940brainpfc2Prefrontal cortex TissueGSE48552Illumina HiSeq 2000PMID:24014289Bart De Strooper Lab's of VIB Center for the Biology of DiseaseTruseq4,357,6644,221,627
SRR1103941brainpfc2Prefrontal cortex TissueGSE48552Illumina HiSeq 2000PMID:24014289Bart De Strooper Lab's of VIB Center for the Biology of DiseaseTruseq3,826,5053,713,300
SRR1103942brainpfc2Prefrontal cortex TissueGSE48552Illumina HiSeq 2000PMID:24014289Bart De Strooper Lab's of VIB Center for the Biology of DiseaseTruseq3,932,4893,836,011
SRR828708braintgm1Temporal neocortex gray matterTissueGSE46131Illumina Genome Analyzer IIPMID:23403535Peter Nelson's lab of University of KentuckyTruseq1,649,8851,434,290
SRR828709braintgm1Temporal neocortex gray matterTissueGSE46131Illumina Genome Analyzer IIPMID:23403535Peter Nelson's lab of University of KentuckyTruseq7,771,9466,597,075
SRR518948breast1BreastTissueGSE39162Illumina Genome Analyzer IIPMID:21199797Carlos Rovira' s lab of Lund University 1.05,477,4304,992,563
SRR518951breast1BreastTissueGSE39162Illumina Genome Analyzer IIPMID:21199797Carlos Rovira' s lab of Lund University 1.01,526,1611,447,377
SRR518954breast1BreastTissueGSE39162Illumina Genome Analyzer IIPMID:21199797Carlos Rovira' s lab of Lund University 1.03,817,6093,490,184
SRR518957breast1BreastTissueGSE39162Illumina Genome Analyzer IIPMID:21199797Carlos Rovira' s lab of Lund University 1.03,859,7073,772,050
SRR518960breast1BreastTissueGSE39162Illumina Genome Analyzer IIPMID:21199797Carlos Rovira' s lab of Lund University 1.01,632,5901,568,427
SRR1532971cd4plustcell1CD4+ cellPrimary cells (Primary cells isolated from peripheral blood)GSE59944Illumina HiSeq 2000PMID:23592263Bryan R. Cullen's lab of Duke UniversityTruseq19,566,60718,576,781
SRR649562colon2ColonTissueGSE43550Illumina HiSeq 2000PMID:25521855Yun Zheng of Kunming University of Science and TechnologyTruseq34,100,83333,191,441
SRR649564colon2ColonTissueGSE43550Illumina HiSeq 2000PMID:25521855Yun Zheng of Kunming University of Science and TechnologyTruseq20,260,27619,764,430
SRR837827colonascendens1Ascending colonTissueGSE46622Illumina Genome Analyzer IIxPMID:23874421Michal-Ruth Schweiger lab of Max Planck Institute1.04,650,2204,096,717
SRR837836colonascendens1Ascending colonTissueGSE46622Illumina Genome Analyzer IIxPMID:23874421Michal-Ruth Schweiger lab of Max Planck Institute1.09,535,8196,185,191
SRR837839colonascendens1Ascending colonTissueGSE46622Illumina Genome Analyzer IIxPMID:23874421Michal-Ruth Schweiger lab of Max Planck Institute1.023,317,57221,049,965
SRR837824coloncoecum1CecumTissueGSE46622Illumina Genome Analyzer IIxPMID:23874421Michal-Ruth Schweiger lab of Max Planck Institute1.04,170,5894,011,305
SRR837842coloncoecum1CecumTissueGSE46622Illumina Genome Analyzer IIxPMID:23874421Michal-Ruth Schweiger lab of Max Planck Institute1.025,405,78123,590,218
SRR837830colonrectum1Rectum/SigmaTissueGSE46622Illumina Genome Analyzer IIxPMID:23874421Michal-Ruth Schweiger lab of Max Planck Institute1.023,238,34421,824,346
ERR038425heart1Heart TissueERP000773Illumina Genome Analyzer IIPMID:22454130Janet Kelso's lab of Max Plank Institute1.09,703,6399,289,262
ERR038426heart1Heart TissueERP000773Illumina Genome Analyzer IIPMID:22454130Janet Kelso's lab of Max Plank Institute1.09,118,3358,604,641
ERR038427heart1Heart TissueERP000773Illumina Genome Analyzer IIPMID:22454130Janet Kelso's lab of Max Plank Institute1.0243,827228,509
ERR038428heart1Heart TissueERP000773Illumina Genome Analyzer IIPMID:22454130Janet Kelso's lab of Max Plank Institute1.0155,191146,598
ERR038429heart1Heart TissueERP000773Illumina Genome Analyzer IIPMID:22454130Janet Kelso's lab of Max Plank Institute1.0189,098170,005
SRR040444heart2Heart TissueSRA012516Illumina Genome AnalyzerPMID:20507594Claes Wahlested's lab of The Scripps Research Institute1.01,747,4071,735,569
SRR040575heart2Heart TissueSRA012516Illumina Genome AnalyzerPMID:20507594Claes Wahlested's lab of The Scripps Research Institute1.02,675,0462,651,021
ERR038420kidney1KidneyTissueERP000773Illumina Genome Analyzer IIPMID:22454130Janet Kelso's lab of Max Plank Institute1.07,128,9056,656,376
ERR038421kidney1KidneyTissueERP000773Illumina Genome Analyzer IIPMID:22454130Janet Kelso's lab of Max Plank Institute1.0284,842271,132
ERR038422kidney1KidneyTissueERP000773Illumina Genome Analyzer IIPMID:22454130Janet Kelso's lab of Max Plank Institute1.0172,055162,402
ERR038423kidney1KidneyTissueERP000773Illumina Genome Analyzer IIPMID:22454130Janet Kelso's lab of Max Plank Institute1.0439,392393,596
ERR038424kidney1KidneyTissueERP000773Illumina Genome Analyzer IIPMID:22454130Janet Kelso's lab of Max Plank Institute1.0558,048494,058
SRX029295kidney2KidneyTissueGSE24457Illumina Genome Analyzer IINABeijing Genomics Institue1.012,273,25911,352,990
SRX029297kidney2KidneyTissueGSE24457Illumina Genome Analyzer IINABeijing Genomics Institue1.012,283,49111,771,406
SRX029299kidney2KidneyTissueGSE24457Illumina Genome Analyzer IINABeijing Genomics Institue1.014,112,27913,173,816
SRX029301kidney2KidneyTissueGSE24457Illumina Genome Analyzer IINABeijing Genomics Institue1.014,861,84114,244,117
SRX029303kidney2KidneyTissueGSE24457Illumina Genome Analyzer IINABeijing Genomics Institue1.012,521,67811,971,733
SRX029305kidney2KidneyTissueGSE24457Illumina Genome Analyzer IINABeijing Genomics Institue1.516,242,97715,866,880
ERR038410liver1LiverTissueERP000773Illumina Genome Analyzer IIPMID:22454130Janet Kelso's lab of Max Plank Institute1.04,533,0954,171,171
ERR038411liver1LiverTissueERP000773Illumina Genome Analyzer IIPMID:22454130Janet Kelso's lab of Max Plank Institute1.0313,179301,337
ERR038412liver1LiverTissueERP000773Illumina Genome Analyzer IIPMID:22454130Janet Kelso's lab of Max Plank Institute1.0414,417376,685
ERR038413liver1LiverTissueERP000773Illumina Genome Analyzer IIPMID:22454130Janet Kelso's lab of Max Plank Institute1.0518,968458,965
ERR038414liver1LiverTissueERP000773Illumina Genome Analyzer IIPMID:22454130Janet Kelso's lab of Max Plank Institute1.03,570,9633,307,969
SRR040571liver2LiverTissueSRA012516Illumina Genome AnalyzerPMID:20507594Claes Wahlested's lab of The Scripps Research Institute1.01,477,5471,454,194
SRR040690liver2LiverTissueSRA012516Illumina Genome AnalyzerPMID:20507594Claes Wahlested's lab of The Scripps Research Institute1.01,257,9621,234,933
SRR039611liver3LiverTissueGSE21279Illumina Genome AnalyzerPMID:21316602Xuetao Cao's lab of National Key Laboratory of Medical Immunology1.08,945,6458,767,463
SRR039612liver3LiverTissueGSE21279Illumina Genome AnalyzerPMID:21316602Xuetao Cao's lab of National Key Laboratory of Medical Immunology1.012,495,93312,254,020
SRR039613liver3LiverTissueGSE21279Illumina Genome AnalyzerPMID:21316602Xuetao Cao's lab of National Key Laboratory of Medical Immunology1.011,428,87711,155,246
SRR372618lung1LungTissueGSE33858Illumina Genome Analyzer IIxNASimon D. Spivack's lab of Albert Einstein College of Medicine1.532,008,65731,703,877
SRR372620lung1LungTissueGSE33858Illumina Genome Analyzer IIxNASimon D. Spivack's lab of Albert Einstein College of Medicine1.529,944,51729,762,590
SRR372632lung1LungTissueGSE33858Illumina Genome Analyzer IIxNASimon D. Spivack's lab of Albert Einstein College of Medicine1.56,227,8776,183,229
SRR372636lung1LungTissueGSE33858Illumina Genome Analyzer IIxNASimon D. Spivack's lab of Albert Einstein College of Medicine1.526,036,40225,233,534
SRR372640lung1LungTissueGSE33858Illumina Genome Analyzer IIxNASimon D. Spivack's lab of Albert Einstein College of Medicine1.532,671,83832,441,777
SRR040446lung2LungTissueSRA012516Illumina Genome AnalyzerPMID:20507594Claes Wahlested's lab of The Scripps Research Institute1.02,099,5182,071,907
SRR040691lung2LungTissueSRA012516Illumina Genome AnalyzerPMID:20507594Claes Wahlested's lab of The Scripps Research Institute1.02,435,5482,327,856
SRR1532974monocytemacrophage1Monocyte-derived macrophagePrimary cells (Primary cells isolated from peripheral blood)GSE59944Illumina HiSeq 2000PMID:23592263Bryan R. Cullen's lab of Duke UniversityTruseq9,663,0828,880,964
SRR040572muscle1MuscleTissueSRA012516Illumina Genome AnalyzerPMID:20507594Claes Wahlested's lab of The Scripps Research Institute1.03,417,1733,400,328
SRR040798muscle1MuscleTissueSRA012516Illumina Genome AnalyzerPMID:20507594Claes Wahlested's lab of The Scripps Research Institute1.03,224,2073,212,992
SRR040574pancreas1PancreasTissueSRA012516Illumina Genome AnalyzerPMID:20507594Claes Wahlested's lab of The Scripps Research Institute1.0501,621489,500
SRR040800pancreas1PancreasTissueSRA012516Illumina Genome AnalyzerPMID:20507594Claes Wahlested's lab of The Scripps Research Institute1.01,241,0671,169,492
SRR871671pancreaticbetacell1Pancreatic beta-cellsPrimary cells (FACS-enriched pancreatic beta-cells)GSE47720Illumina Genome Analyzer IIxPMID:23372846Mark McCarthy's lab of Wellcome Trust Centre for Human Genetics1.53,395,4043,326,168
SRR873401pancreaticbetacell1Pancreatic beta-cellsPrimary cells (FACS-enriched pancreatic beta-cells)GSE47720Illumina Genome Analyzer IIxPMID:23372846Mark McCarthy's lab of Wellcome Trust Centre for Human GeneticsTruseq40,129,75238,287,813
SRR873410pancreaticbetacell1Pancreatic beta-cellsPrimary cells (FACS-enriched pancreatic beta-cells)GSE47720Illumina Genome Analyzer IIxPMID:23372846Mark McCarthy's lab of Wellcome Trust Centre for Human GeneticsTruseq32,010,09730,126,448
SRR871609pancreaticislet1Pancreatic isletsPrimary cells (Purified pancreatic islets)GSE47720Illumina Genome Analyzer IIxPMID:23372846Mark McCarthy's lab of Wellcome Trust Centre for Human Genetics1.516,861,78016,619,586
SRR873381pancreaticislet1Pancreatic isletsPrimary cells (Purified pancreatic islets)GSE47720Illumina Genome Analyzer IIxPMID:23372846Mark McCarthy's lab of Wellcome Trust Centre for Human GeneticsTruseq74,713,80973,633,894
SRR039190peripheralbmc1Peripheral blood mononuclear cells (PBMC)Primary cells (Peripheral blood mononuclear cells (PBMC))GSE19812Illumina Genome AnalyzerPMID:20459673Alok Bhattacharya's lab of Jawaharlal Nehru University1.03,827,9243,462,995
SRR493380peripheralbmc2Peripheral blood mononuclear cells (PBMC)Primary cells (Peripheral blood mononuclear cells (PBMC))GSE37710Illumina HiSeq 2000PMID:23430754Xiaolin wang's lab of Zhongshan Hospital, Fudan University1.59,766,3759,596,388
SRR1042915plasma1Plasma TissueGSE52981Illumina MiSeqNADavid Arthur Simpson's lab of Queen's University BelfastTruseq11,887,54911,709,091
SRR1042916plasma1Plasma TissueGSE52981Illumina MiSeqNADavid Arthur Simpson's lab of Queen's University BelfastTruseq6,456,9786,215,771
SRR1042917plasma1Plasma TissueGSE52981Illumina MiSeqNADavid Arthur Simpson's lab of Queen's University BelfastTruseq4,031,9053,380,788
SRR1054203serum1SerumTissueGSE53439Illumina Genome Analyzer IIPMID:24088671Kevin G Becker's lab of NIATruseq313,621273,817
SRR1054204serum1SerumTissueGSE53439Illumina Genome Analyzer IIPMID:24088671Kevin G Becker's lab of NIATruseq232,966200,902
SRR1054205serum1SerumTissueGSE53439Illumina Genome Analyzer IIPMID:24088671Kevin G Becker's lab of NIATruseq323,519274,619
SRR1054206serum1SerumTissueGSE53439Illumina Genome Analyzer IIPMID:24088671Kevin G Becker's lab of NIATruseq373,999332,295
SRR1054207serum1SerumTissueGSE53439Illumina Genome Analyzer IIPMID:24088671Kevin G Becker's lab of NIATruseq573,319512,179
SRR1054208serum1SerumTissueGSE53439Illumina Genome Analyzer IIPMID:24088671Kevin G Becker's lab of NIATruseq292,390278,971
SRR1054209serum1SerumTissueGSE53439Illumina Genome Analyzer IIPMID:24088671Kevin G Becker's lab of NIATruseq403,502364,634
SRR1054210serum1SerumTissueGSE53439Illumina Genome Analyzer IIPMID:24088671Kevin G Becker's lab of NIATruseq257,715235,328
SRR1054212serum1SerumTissueGSE53439Illumina Genome Analyzer IIPMID:24088671Kevin G Becker's lab of NIATruseq224,869214,528
SRR1054213serum1SerumTissueGSE53439Illumina Genome Analyzer IIPMID:24088671Kevin G Becker's lab of NIATruseq297,246285,533
SRR1054214serum1SerumTissueGSE53439Illumina Genome Analyzer IIPMID:24088671Kevin G Becker's lab of NIATruseq231,696211,130
SRR1054215serum1SerumTissueGSE53439Illumina Genome Analyzer IIPMID:24088671Kevin G Becker's lab of NIATruseq359,537346,838
SRR1054217serum1SerumTissueGSE53439Illumina Genome Analyzer IIPMID:24088671Kevin G Becker's lab of NIATruseq621,958607,893
SRR1054218serum1SerumTissueGSE53439Illumina Genome Analyzer IIPMID:24088671Kevin G Becker's lab of NIATruseq318,038307,078
SRR1054219serum1SerumTissueGSE53439Illumina Genome Analyzer IIPMID:24088671Kevin G Becker's lab of NIATruseq283,301274,004
SRR1054220serum1SerumTissueGSE53439Illumina Genome Analyzer IIPMID:24088671Kevin G Becker's lab of NIATruseq205,632194,573
SRR1054221serum1SerumTissueGSE53439Illumina Genome Analyzer IIPMID:24088671Kevin G Becker's lab of NIATruseq442,202429,690
SRR1054222serum1SerumTissueGSE53439Illumina Genome Analyzer IIPMID:24088671Kevin G Becker's lab of NIATruseq387,416365,758
SRR1054223serum1SerumTissueGSE53439Illumina Genome Analyzer IIPMID:24088671Kevin G Becker's lab of NIATruseq531,318507,077
SRR1054224serum1SerumTissueGSE53439Illumina Genome Analyzer IIPMID:24088671Kevin G Becker's lab of NIATruseq596,545583,899
SRR1054225serum1SerumTissueGSE53439Illumina Genome Analyzer IIPMID:24088671Kevin G Becker's lab of NIATruseq521,142506,194
SRR1054226serum1SerumTissueGSE53439Illumina Genome Analyzer IIPMID:24088671Kevin G Becker's lab of NIATruseq252,173239,019
SRR396642serum2SerumTissueGSE34891Illumina Genome Analyzer IIxNANanjing Univerisity 1.013,839,48710,577,856
SRR330904skin1Skin TissueGSE31037Illumina Genome Analyzer IIxPMID:21807764Weixiong Zhang's lab of Washington University School of Medicine St Louis1.59,796,1819,450,735
SRR330905skin1Skin TissueGSE31037Illumina Genome Analyzer IIxPMID:21807764Weixiong Zhang's lab of Washington University School of Medicine St Louis1.55,035,6804,786,528
SRR330906skin1Skin TissueGSE31037Illumina Genome Analyzer IIxPMID:21807764Weixiong Zhang's lab of Washington University School of Medicine St Louis1.59,144,4198,907,932
SRR330907skin1Skin TissueGSE31037Illumina Genome Analyzer IIxPMID:21807764Weixiong Zhang's lab of Washington University School of Medicine St Louis1.59,135,2568,808,214
SRR330908skin1Skin TissueGSE31037Illumina Genome Analyzer IIxPMID:21807764Weixiong Zhang's lab of Washington University School of Medicine St Louis1.510,215,3039,807,738
SRR330909skin1Skin TissueGSE31037Illumina Genome Analyzer IIxPMID:21807764Weixiong Zhang's lab of Washington University School of Medicine St Louis1.51,287,6561,243,956
SRR330910skin1Skin TissueGSE31037Illumina Genome Analyzer IIxPMID:21807764Weixiong Zhang's lab of Washington University School of Medicine St Louis1.53,280,8033,139,939
SRR330911skin1Skin TissueGSE31037Illumina Genome Analyzer IIxPMID:21807764Weixiong Zhang's lab of Washington University School of Medicine St Louis1.57,881,0037,592,715
SRR330912skin1Skin TissueGSE31037Illumina Genome Analyzer IIxPMID:21807764Weixiong Zhang's lab of Washington University School of Medicine St Louis1.56,876,1286,631,547
SRR330913skin1Skin TissueGSE31037Illumina Genome Analyzer IIxPMID:21807764Weixiong Zhang's lab of Washington University School of Medicine St Louis1.510,934,77810,624,705
SRR330914skin1Skin TissueGSE31037Illumina Genome Analyzer IIxPMID:21807764Weixiong Zhang's lab of Washington University School of Medicine St Louis1.57,981,3197,733,105
SRR330915skin1Skin TissueGSE31037Illumina Genome Analyzer IIxPMID:21807764Weixiong Zhang's lab of Washington University School of Medicine St Louis1.515,795,44215,365,119
SRR330916skin1Skin TissueGSE31037Illumina Genome Analyzer IIxPMID:21807764Weixiong Zhang's lab of Washington University School of Medicine St Louis1.513,779,96513,331,257
SRR330917skin1Skin TissueGSE31037Illumina Genome Analyzer IIxPMID:21807764Weixiong Zhang's lab of Washington University School of Medicine St Louis1.59,498,0369,269,609
SRR330918skin1Skin TissueGSE31037Illumina Genome Analyzer IIxPMID:21807764Weixiong Zhang's lab of Washington University School of Medicine St Louis1.5401,811374,452
SRR330919skin1Skin TissueGSE31037Illumina Genome Analyzer IIxPMID:21807764Weixiong Zhang's lab of Washington University School of Medicine St Louis1.513,491,35712,840,177
SRR330920skin1Skin TissueGSE31037Illumina Genome Analyzer IIxPMID:21807764Weixiong Zhang's lab of Washington University School of Medicine St Louis1.59,172,2588,960,386
SRR330921skin1Skin TissueGSE31037Illumina Genome Analyzer IIxPMID:21807764Weixiong Zhang's lab of Washington University School of Medicine St Louis1.515,398,02914,959,763
SRR330922skin1Skin TissueGSE31037Illumina Genome Analyzer IIxPMID:21807764Weixiong Zhang's lab of Washington University School of Medicine St Louis1.514,130,08413,776,112
SRR330923skin1Skin TissueGSE31037Illumina Genome Analyzer IIxPMID:21807764Weixiong Zhang's lab of Washington University School of Medicine St Louis1.515,170,76214,874,792
SRR1057529skin2Skin TissueGSE53600Illumina HiSeq 2000PMID:24627810Thomas Andl's lab of Vanderbilt University Medical CenterStandard32,618,52531,484,039
SRR042289spermatozoa1SpermatozoaCell typeGSE21191Illumina Genome Analyzer IIPMID:21989093Stephen A Krawetz's lab of Wayne State University1.06,515,7573,306,357
SRR042290spermatozoa1SpermatozoaCell typeGSE21191Illumina Genome Analyzer IIPMID:21989093Stephen A Krawetz's lab of Wayne State University1.02,286,3511,080,698
SRR042291spermatozoa1SpermatozoaCell typeGSE21191Illumina Genome Analyzer IIPMID:21989093Stephen A Krawetz's lab of Wayne State University1.02,942,6481,512,989
SRR1264562sperm1SpermCell typeGSE49624Illumina Genome Analyzer IIPMID:24835570Ernesto Guccione's lab of A*STAR 1.53,788,5523,242,955
SRR1264563sperm1SpermCell typeGSE49624Illumina Genome Analyzer IIPMID:24835570Ernesto Guccione's lab of A*STAR 1.56,798,8775,799,496
SRR1264564sperm1SpermCell typeGSE49624Illumina Genome Analyzer IIPMID:24835570Ernesto Guccione's lab of A*STAR 1.52,247,1531,882,594
SRR1264565sperm1SpermCell typeGSE49624Illumina Genome Analyzer IIPMID:24835570Ernesto Guccione's lab of A*STAR 1.53,243,5242,703,437
SRR1264566sperm1SpermCell typeGSE49624Illumina Genome Analyzer IIPMID:24835570Ernesto Guccione's lab of A*STAR 1.53,129,0332,607,739
SRR333678testiculargerm1Testicular germCell type: tesciulargermGSE31616Illumina Genome Analyzer IIPMID:21799901Jiahao Chen of BGI1.023,148,23921,839,423
SRR333680testiculargerm1Testicular germCell type: tesciulargermGSE31616Illumina Genome Analyzer IIPMID:21799901Jiahao Chen of BGI1.024,318,45523,900,016
SRR333682testiculargerm1Testicular germCell type: tesciulargermGSE31616Illumina Genome Analyzer IIPMID:21799901Jiahao Chen of BGI1.019,191,05118,385,130
SRR333684testiculargerm1Testicular germCell type: tesciulargermGSE31616Illumina Genome Analyzer IIPMID:21799901Jiahao Chen of BGI1.012,841,34212,316,848
SRR333686testiculargerm1Testicular germCell type: tesciulargermGSE31616Illumina Genome Analyzer IIPMID:21799901Jiahao Chen of BGI1.019,671,81618,887,777
SRR333688testiculargerm1Testicular germCell type: tesciulargermGSE31616Illumina Genome Analyzer IIPMID:21799901Jiahao Chen of BGI1.012,765,79712,062,017
SRX273451wholeblood1Whole bloodTissueGSE46579Illumina HiSeq 2000PMID:23895045Andreas Keller's lab of Saarland UniversityTruseq10,189,86610,150,059
SRX273452wholeblood1Whole bloodTissueGSE46579Illumina HiSeq 2000PMID:23895045Andreas Keller's lab of Saarland UniversityTruseq9,873,9779,838,999
SRX273453wholeblood1Whole bloodTissueGSE46579Illumina HiSeq 2000PMID:23895045Andreas Keller's lab of Saarland UniversityTruseq10,005,1719,961,643
SRX273454wholeblood1Whole bloodTissueGSE46579Illumina HiSeq 2000PMID:23895045Andreas Keller's lab of Saarland UniversityTruseq11,699,38511,657,099
SRX273455wholeblood1Whole bloodTissueGSE46579Illumina HiSeq 2000PMID:23895045Andreas Keller's lab of Saarland UniversityTruseq12,309,08712,263,137
SRX273456wholeblood1Whole bloodTissueGSE46579Illumina HiSeq 2000PMID:23895045Andreas Keller's lab of Saarland UniversityTruseq9,909,1579,874,180
SRX273457wholeblood1Whole bloodTissueGSE46579Illumina HiSeq 2000PMID:23895045Andreas Keller's lab of Saarland UniversityTruseq18,380,15418,295,139
SRX273458wholeblood1Whole bloodTissueGSE46579Illumina HiSeq 2000PMID:23895045Andreas Keller's lab of Saarland UniversityTruseq21,742,99221,656,659
SRX273459wholeblood1Whole bloodTissueGSE46579Illumina HiSeq 2000PMID:23895045Andreas Keller's lab of Saarland UniversityTruseq15,822,60515,740,246
SRX273460wholeblood1Whole bloodTissueGSE46579Illumina HiSeq 2000PMID:23895045Andreas Keller's lab of Saarland UniversityTruseq14,953,01314,889,508
SRX273461wholeblood1Whole bloodTissueGSE46579Illumina HiSeq 2000PMID:23895045Andreas Keller's lab of Saarland UniversityTruseq16,012,10115,928,838
SRX273462wholeblood1Whole bloodTissueGSE46579Illumina HiSeq 2000PMID:23895045Andreas Keller's lab of Saarland UniversityTruseq6,584,6676,546,177
SRX273463wholeblood1Whole bloodTissueGSE46579Illumina HiSeq 2000PMID:23895045Andreas Keller's lab of Saarland UniversityTruseq13,732,54213,522,634
SRX273464wholeblood1Whole bloodTissueGSE46579Illumina HiSeq 2000PMID:23895045Andreas Keller's lab of Saarland UniversityTruseq16,142,63815,972,721
SRX273465wholeblood1Whole bloodTissueGSE46579Illumina HiSeq 2000PMID:23895045Andreas Keller's lab of Saarland UniversityTruseq16,705,12416,527,618
SRX273466wholeblood1Whole bloodTissueGSE46579Illumina HiSeq 2000PMID:23895045Andreas Keller's lab of Saarland UniversityTruseq11,034,75410,913,254
SRX273467wholeblood1Whole bloodTissueGSE46579Illumina HiSeq 2000PMID:23895045Andreas Keller's lab of Saarland UniversityTruseq16,404,38716,247,280
SRX273468wholeblood1Whole bloodTissueGSE46579Illumina HiSeq 2000PMID:23895045Andreas Keller's lab of Saarland UniversityTruseq12,516,38212,405,627
SRX273469wholeblood1Whole bloodTissueGSE46579Illumina HiSeq 2000PMID:23895045Andreas Keller's lab of Saarland UniversityTruseq20,015,46019,762,274
SRX273470wholeblood1Whole bloodTissueGSE46579Illumina HiSeq 2000PMID:23895045Andreas Keller's lab of Saarland UniversityTruseq23,558,89923,285,103
SRX273471wholeblood1Whole bloodTissueGSE46579Illumina HiSeq 2000PMID:23895045Andreas Keller's lab of Saarland UniversityTruseq19,740,78619,492,901
SRX273472wholeblood1Whole bloodTissueGSE46579Illumina HiSeq 2000PMID:23895045Andreas Keller's lab of Saarland UniversityTruseq14,336,27414,158,815
SRR1028929wholeislet1Whole isletCell typeGSE52314Illumina Genome Analyzer IIxPMID:24374217Jonathan Schug's lab of University of Pennsylvania1.01,292,6321,075,951
SRR1028930wholeislet1Whole isletCell typeGSE52314Illumina Genome Analyzer IIxPMID:24374217Jonathan Schug's lab of University of Pennsylvania1.02,320,5181,722,922
SRR1028931wholeislet1Whole isletCell typeGSE52314Illumina Genome Analyzer IIxPMID:24374217Jonathan Schug's lab of University of Pennsylvania1.01,401,0371,252,121

Questions? dashr@lisanwanglab.orgWang LabThe Institute for Biomedical Informatics | University of Pennsylvania |

DASHR reports the annotation, expression and evidence for specific RNA processing (cleavage specificity scores/entropy) of sncRNA genes, precursor and mature sncRNA products across different human tissues and cell types. Simply input your sncRNA of interest (in a variety of formats) and click Search. By default, a) summary table; b) expression profiles; c) entropy/specific processing information, and d) structure will be produced as outputs for each locus. Users can also view the outputs via an expression browser.

DASHR implementation

The DASHR database was implemented using MySql database engine, while all the scripts were written in PHP (website), AWK and BASH scripting languages. R (v3.1.0) is used for plotting and visualization. Currently, DASHR is hosted on a server with two CPUs (Intel Xeon E5450@3GHz) and 16GB RAM running CentOS 5.11. The database schema can be viewed here.

Methods - Small-RNA-sequencing and annotation pipeline

Annotation resources

DASHR integrates multiple existing annotation resources for small non-coding RNAs including miRNA annotations from miRBase (v19); snRNA, snoRNA, scRNA and rRNA annotations are from UCSC genome browser; and GENCODE; tRNA annotations tRNAdb; and piRNA annotations from NCBI. The current annotation in DASHR also includes tRNA fragment annotations that are created based on the 50nt sequences upstream (5p) and downstream (3p) of the known tRNA genes. Information from NCBI, HGNC, RefSeq and UCSC was used to build the ID conversion table for each processed and mature sncRNA entry in DASHR.

Data collection and curation of smRNA-seq datasets

We manually curated Illumina smRNA-seq datasets from Gene Expression Omnibus (GEO) and Sequence Read Archive (SRA). These datasets were obtained from non-diseased human tissues and cell types and were generated for studying or profiling small non-coding RNAs. The full list of included samples (smRNA-seq libraries) categorized by tissue and cell type is shown in the Data page.

Categorizing datasets into tissues and cell types

We then manually categorized the smRNA-seq samples into different groups of tissues and cell types, stratified by their experimental sources (i.e. Study ID found in Data page).

Processing smRNA-seq datasets

After curation and categorization, we then used a modified pipeline built upon CoRAL to process the smRNA-seq datasets and generate sncRNA expression levels. The pipeline can be summarized in three parts: 1) processing and quality control; 2) segmentation and quantification; and 3) annotation.

Processing and Quality control (trimming, alignment)

We first identified the correct adapter (Illumina adapter column found in Data page) and trimmed the sequencing reads. We then mapped the trimmed reads to a standardized version of the human reference genome (GRCh37/hg19). The reads were aligned using STAR allowing for multi-mapping. Over 93% of the trimmed reads were mapped to the human genome on average per dataset (Data page details trimming and alignment statistics for each smRNA-seq dataset).

Segmentation and quantification of sncRNA loci

As one of our goals was to identify mature sncRNA products (i.e. peaks with evidence of specific processing patterns, e.g. with low 5p read entropy) and quantify their expression levels in addition to precursor sncRNA gene expression levels, we developed a customized approach to identify peaks with evidence of specific processing for mature sncRNA products in base pair resolution. After identifying the mature sncRNA locations, we then quantified the number of reads falling within these regions as read counts for each sncRNA. To enable comparison across tissues, we took into account the library size information (i.e. number of mapped reads) for each of the sequencing experiments (found in Data page) and reported the read count in "reads per million" (RPM).

Annotation of mature RNA products

We overlapped each peak with evidence of specific processing (identified in the previous step) with DASHR annotations (see Annotation resources). Each peak was assigned to its precursor RNA class and sncRNA gene.

Outputs

Summary table

The summary table contains annotation information, including the sequence, genomic coordinates, and structural information for the selected locus. This table also includes summary statistics for expression information, highlighting the total number of experiments with observed expression, total raw read count and total reads count in reads per million (RPM).

Expression profiles

For any set of the specific tissues/cell types, users can view expression profiles or read coverage profiles representing detailed expression information for any sncRNA locus across the set of tissues/cell types. There are also links to the UCSC genome browser, allowing users to view the genomic locus with mapped sequencing data across all tissues.

The expression profile plot shows the reads in RPM across all tissues in DASHR for a given locus. Users can choose to display the expression profile in a bar-plot or heatmap format, in which the RPM values are shown.

For anyone (or all) chosen specific tissues/cell types, users can view the detailed expression information represented by expression profiles or read coverage profiles.

The read coverage plot available for a single tissue or the entire set of tissues, displays the number of raw reads from the chosen tissue along the genomic coordinates of the locus. The sequence is displayed underneath the plot.

We have also created a genome browser track hub called smRNA Hub, representing raw signal and peak calls (regions of enrichment) that were generated based on a uniform processing pipeline by Wang lab . This hub thus enables the simultaneous visualization of the processed sequencing data from 42 tissues on both strands.

Entropy/specific processing

For each processed sncRNA locus, we also describe the processing specificity for both ends of its mature sncRNA products in each tissue using cleavage specificity scores (calculated using CoRAL. This information is available in the tissue-specific section of the entry page along with the read coverage profile for the sncRNA locus.

The entropy score was computed based on the distributions of the 5p and 3p end positions of all sncRNA reads mapped to a given locus, respectively. This entropy feature was designed to capture the specificity (or degeneracy) of RNA cleaving enzymes specific to the production of different types of sncRNAs. For example, the processing of mature miRNA products from precursor miRNAs tends to produce fragments with a more stable 5p cleavage position (low entropy) and more variable 3p end (higher entropy).

All these plots and expression tables for a locus can be downloaded on the same page.

Browse

Small non-coding RNA expression

The browsing section provides two tables which allow users to identify either small non-coding RNA genes or mature sncRNA products of interest and download their expression profiles. With the two drop-down menus, users can a)choose which specific sncRNA type they want to look at; and b) choose which tissue/cell type to use for sorting the expression values from largest to smallest. Users can also choose to sort by the average RPM values across all tissues. Note: all genomic coordinates/locations can be sorted.

A snapshot of the small non-coding RNA gene expression table, which contains the expression profiles in RPM for sncRNA genes is shown below:

A snapshot of the table containing mature sncRNA product information is shown, including a) length of the mature sncRNA products; b) entropy/specific processing information at both 5p and 3p ends of these products; and c) percentage of reads falling into the same 5p and 3p position of the mature products.

View genomic region

In the text box, users can input a genomic region with chromosome, start and end coordinates, and strand information. The information for this region will be displayed in a new page containing the information described in the "Outputs" section.

UCSC sncRNA track hub (42 tissues and cell types)

To browse all the processed data effectively on the UCSC genome browser, we have created a sncRNA track hub. This hub enables simultaneous visualization of all processed sequencing data from both strands. The processed data is also downloadable (Download). To access our hub on genome browser click here.

Frequently Asked Questions (FAQ)

How can I obtain a list of all the mature miRNA products for a given tissue/cell type?

Click the "Browse" tab at the top of the page. Choose "Browse mature sncRNA products" under "Small non-coding RNA expression". Click to select which specific mature miRNA product (mir-3p, mir-5p or other mature products, i.e. mir-5p3pno) in the first drop-down. and choose the specific tissue/cell type you want to look at in the second drop-down. Finally, scroll down the page and click "Download table (csv)" to obtain all the mature miRNA products found in the chosen specific tissue/cell type.

How many small non-coding RNA mature products in DASHR have profiled expressions?

Go to the "Data summary" tab, users can view the statistics under the figure "Number of sncRNA mature products with profiled expressions across each tissue/cell type in DASHR".

How can I retrieve annotations across various sncRNA classes simultaneously for any genomic interval?

Go to the "Search" tab, "Search by genomic coordinates" session, and input your target genomic location. DASHR will output a list of matching entries across various sncRNA classes.

How can I obtain all the annotation files for sncRNA genes, precursor and mature sncRNA products?

Go to the "Download" tab and click "sncRNA annotation" under the first row of the table to download.

How can I obtain all the small RNA genes' sequences?

Go to the "Download" tab and click "Sequence table" under the second row of the table to download.

How can I obtain ID conversion table for all the sncRNA genes?

Go to the "Download" tab and click "ID table" under the third row of the table to download.

How can I compare small non-coding RNA gene expression levels in RPM across different tissues/cell types?

Go to "Download" tab and click "sncRNA gene expression (RPM)" under the fourth row of the table to download.

How can I compare the expression profiles in read counts for mature small non-coding RNA products across different tissues/cell types?

Go to the "Download" tab and click "mature sncRNA expression" in the download table.

How can I access the raw deep small-RNA-sequencing datasets?

All the datasets used in DASHR are listed in the table under the "Data summary" tab. Users can also go to the "Download" tab and click on the "Samples list" in the last row of the table to download.

How can I access the processed deep small-RNA-sequencing datasets?

All the processed smRNA-seq datasets in DASHR can be downloaded at the "Download" tab. Under the "Genome-wide smRNA expression tracks', user can specify the tissue and strand information under the drop-down menu. The processed smRNA-seq data in csv format will be downloaded automatically.

Do I need permission to download/use data obtained in DASHR for my own research?

No. All the data are available publicly without restrictions. For the smRNA-seq sequencing datasets, please refer to the table under the "Data summary" tab for users agreements for each of these datasets.

Why are outputs not complete for every mature small non-coding RNA products?

There is an output page for every annotated small non-coding RNA gene in DASHR. Information such as RNA, genomic coordinates, length, sequence, and structure are always available in the summary table. In the "expression information" section, if the output is `0 tissues` then this small non-coding RNA is not expressed in any of the tissues/cell types in DASHR. In this case, the expression profile in the output will display values of 0 for all tissues/cell types.

How often do you update DASHR?

We aim to update DASHR every six months.

Further questions?

Please feel free to email dashr@lisanwanglab.org if you have any further questions.

Acknowledgments

DASHR utilizes the following software packages (credit to their authors):

  1. CoRAL [1,2] - pipeline used for processing smRNA-seq datasets and calculating cleavage specificity of processed loci
  2. STAR [3] - for mapping sequences to genome

References

  1. Yuk Yee Leung, Pavel P. Kuksa, Alexandre Amlie-Wolf, Otto Valladares, Lyle H. Ungar, Sampath Kannan, Brian D. Gregory, and Li-San Wang. DASHR: database of small human noncoding RNAs. Nucl. Acids Res., 2015 (Database Issue) doi:10.1093/nar/gkv1188.
  2. Leung YY, Ryvkin P, Ungar LH, Gregory BD, Wang LS. CoRAL: predicting non-coding RNAs from small RNA-sequencing data. Nucleic Acids Res. 2013 Aug;41(14):e137. doi: 10.1093/nar/gkt426. PMID: 23700308
  3. Ryvkin P, Leung YY, Ungar LH, Gregory BD, Wang LS. Using machine learning and high-throughput RNA sequencing to classify the precursors of small non-coding RNAs. Methods. 2014 May 1;67(1):28-35. doi: 10.1016/j.ymeth.2013.10.002. PMID: 24145223
  4. Dobin A, Davis CA, Schlesinger F, Drenkow J, Zaleski C, Jha S, Batut P, Chaisson M, Gingeras TR. STAR: ultrafast universal RNA-seq aligner. Bioinformatics. 2013 Jan 1;29(1):15-21. doi: 10.1093/bioinformatics/bts635. PMID: 23104886

Funding support

  1. National Institute of General Medical Sciences [R01-GM099962]
  2. National Institute on Aging [U24-AG041689 and U01-AG032984]
  3. Penn Alzheimer's Disease Center [P30-AG10124]
  4. National Science Foundation [MCB-1053846]

Questions? dashr@lisanwanglab.orgWang LabThe Institute for Biomedical Informatics | University of Pennsylvania |

DASHR Release 1.0 (August 25, 2015)

ContentDescriptionDownload
Small RNA annotation
  • Main annotation file for human small non-coding RNA
  • RNA types: miRNA, snRNA, scRNA, snoRNA, piRNA, tRF3, tRF5, tRNA, rRNA
Small RNA sequences
  • Sequences of individual human small non-coding RNAs
  • RNA types: miRNA, snRNA, scRNA, snoRNA, piRNA, tRF3, tRF5, tRNA, rRNA
Small RNA ID conversion table
  • Contains matched RefSeq, HGNC, UCSC, and RNA database-specific IDs
Small RNA Expression (RPM) profiles
  • Expression profiles for individual sncRNA genes across tissues/cell types
  • Expression values in reads per million (RPM)
Small RNA Expression profiles (raw read counts)
  • Expression profiles for individual sncRNA genes across tissues/cell types
  • Expression values are raw read counts
Mature small RNA expression profiles
  • Expression profiles for mature sncRNA products
  • Expression values are read counts per mature sncRNA
Small RNA-seq data description
  • List of curated smRNA-seq datasets from normal human tissues/cell types

Genome-wide smRNA expression tracks

Note: The data values represent raw read counts.

Software

Visit DASHR github page

Questions? dashr@lisanwanglab.orgWang LabThe Institute for Biomedical Informatics | University of Pennsylvania |

What's new
Dec 22, 2015 UCSC Genome Browser now integrates DASHR data tracks! Look for DASHR ncRNA hub in the list of UCSC Genome Browser public hubs or connect to DASHR hub directly. Many thanks to UCSC Genome Browser Team!
Dec 08, 2015 EBI MIRIAM registry now integrates DASHR RNA and expression data records! Special thanks to Nick Juty of EBI!
Nov 25, 2015 News article about DASHR database in RNA-Seq Blog news Read here
Nov 20, 2015 News article about DASHR database in miRNA blog on miRNA Research & Industry news Read here
Oct 25, 2015 DASHR article is accepted for publication in the NAR Database Issue 2016 and is now online! Read here
Sept 21, 2015 DASHR now integrates (SAVoR) for visualizing RNA secondary structure information and RNA structure sequencing read coverage
Aug 25, 2015 DASHR v1.0 released. Release notes