miRMaster 2.0: multi-species non-coding RNA sequencing analyses at scale
Author(s) -
Tobias Fehlmann,
Fabian Kern,
Omar Laham,
Christina Backes,
Jeffrey Solomon,
Pascal Hirsch,
Carsten Volz,
Rolf Müller,
Andreas Keller
Publication year - 2021
Publication title -
nucleic acids research
Language(s) - Uncategorized
Resource type - Journals
SCImago Journal Rank - 9.008
H-Index - 537
eISSN - 1362-4954
pISSN - 0305-1048
DOI - 10.1093/nar/gkab268
Subject(s) - mirbase , biology , ensembl , rna , computational biology , small rna , identifier , annotation , rna seq , genetics , computer science , gene , gene expression , genomics , genome , transcriptome , programming language
Analyzing all features of small non-coding RNA sequencing data can be demanding and challenging. To facilitate this process, we developed miRMaster. After the analysis of over 125 000 human samples and 1.5 trillion human small RNA reads over 4 years, we present miRMaster 2 with a wide range of updates and new features. We extended our reference data sets so that miRMaster 2 now supports the analysis of eight species (e.g. human, mouse, chicken, dog, cow) and 10 non-coding RNA classes (e.g. microRNAs, piRNAs, tRNAs, rRNAs, circRNAs). We also incorporated new downstream analysis modules such as batch effect analysis or sample embeddings using UMAP, and updated annotation data bases included by default (miRBase, Ensembl, GtRNAdb). To accommodate the increasing popularity of single cell small-RNA sequencing data, we incorporated a module for unique molecular identifier (UMI) processing. Further, the output tables and graphics have been improved based on user feedback and new output formats that emerged in the community are now supported (e.g. miRGFF3). Finally, we integrated differential expression analysis with the miRNA enrichment analysis tool miEAA. miRMaster is freely available at https://www.ccb.uni-saarland.de/mirmaster2.
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