bioRxivBioinformaticsPreprint
SEA: The Small RNA Expression Atlas
Raza‐Ur RahmaniD, Vikas BansaliD, Maksims FiošinsiD, Anna-Maria LiebhoffiD, Ashish RajputiD, Abdul Sattar, Daniel Sumner MagruderiD, Sumit MadaniD, Ting SuniD, Abhivyakti Gautam, Sven HeinsiD, Timur LiwinskiiD, Jörn BethuneiD, Claudia TrenkwalderiD, Juliane FluckiD, Brit MollenhaueriD, Stefan BonniD
Abstract
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. SEAs user model enables sRNA researchers to compare and re-analyze user-specific and published datasets, highlighting common and distinct sRNA expression patterns.\n\nWe provide evidence for SEAs 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 Parkinsons disease-specific blood biomarker signature using novel data.\n\nWe believe that SEAs 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.\n\nAvailability and ImplementationSEA is implemented in Java, J2EE, spring, Django, html5, css3, JavaScript, Bootstrap, Vue.js, D3, mongodb and neo4j. It is freely available at http://sea.ims.bio/.
- organism: human
- organism: mouse
- organism: rat
- assay: spatial transcriptomics
- assay: bulk RNA-seq
- research focus: database / resource
- research focus: differential expression
- research focus: gene regulatory network inference
- approach: statistical model
- approach: benchmark study
- Citations
- 3
Reproducibility ledger
Nobody has reported trying to reproduce this paper yet. Did you replicate an experiment or run the code? Your report saves the next lab a week.
Sign in to file a reproducibility report.
Related papers
- Oasis2.0: improved online analysis of small RNA-seq data
Raza‐Ur Rahman, Abhivyakti Gautam, … Stefan Bonn·BMC BioinformaticsPeer-reviewed·Feb 14, 2018
- Finding differentially expressed sRNA-Seq regions with srnadiff
Matthias Zytnicki, Ignacio González·PLoS ONEPeer-reviewed·Aug 20, 2021
- QuickRNASeq: Guide For Pipeline Implementation And For Interactive Results Visualization
Wen He, Shanrong Zhao, … Baohong Zhang·Methods in molecular biologyPeer-reviewed·Jan 1, 2018
- Automated analysis of small RNA datasets with RAPID
Sivarajan Karunanithi, Martin C. Simon, Marcel H. Schulz·PeerJPeer-reviewed·Apr 10, 2019
- sncRNAP: Prediction and profiling of full sncRNA repertoires from sRNAseq data
Hesham A. Y. Gibriel, Sharada Baindoor, … Jochen H.M. Prehn·bioRxiv·May 23, 2023
- smallrnaseq: Short Non Coding RNA-Seq Analysis With Python
Damien Farrell·bioRxiv·May 10, 2017