SEA: The Small RNA Expression Atlas
We present the Small RNA Expression Atlas (SEA), a web application that allows for the interactive querying, visualization, and analysis of known and novel small RNAs across ten organisms. It contains sRNA and pathogen expression information for over 4,200 published samples with standardized search...
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Published in | bioRxiv |
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Main Authors | , , , , , , , , , , , , , , , , |
Format | Paper |
Language | English |
Published |
Cold Spring Harbor
Cold Spring Harbor Laboratory Press
02.07.2019
Cold Spring Harbor Laboratory |
Edition | 1.3 |
Subjects | |
Online Access | Get full text |
ISSN | 2692-8205 2692-8205 |
DOI | 10.1101/133199 |
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Summary: | We present the Small RNA Expression Atlas (SEA), a web application that allows for the interactive querying, visualization, and analysis of known and novel small RNAs across ten organisms. It contains sRNA and pathogen expression information for over 4,200 published samples with standardized search terms and ontologies. In addition, SEA allows for the interactive visualization and re-analysis of 879 differential expression and 514 classification comparisons. SEA's user model enables sRNA researchers to compare and re-analyze user-specific and published datasets, highlighting common and distinct sRNA expression patterns. We provide evidence for SEA's fidelity by (i) generating a set of 591 tissue specific miRNAs across 30 tissues, (ii) finding known and novel bacterial and viral infections across diseases, and (iii) determining a Parkinson's disease-specific blood biomarker signature using novel data. We believe that SEA's simple semantic search interface, the flexible interactive reports, and the user model with rich analysis capabilities will enable researchers to better understand the potential function and diagnostic value of sRNAs or pathogens across tissues, diseases, and organisms. Footnotes * To remove wrong ORCIDs and correct spelling of one author name. |
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Bibliography: | SourceType-Working Papers-1 ObjectType-Working Paper/Pre-Print-1 content type line 50 |
ISSN: | 2692-8205 2692-8205 |
DOI: | 10.1101/133199 |