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Updated: Jan 23, 2026

Three Differential Expression Analysis Methods for RNA Sequencing: limma, EdgeR, DESeq2
Published on: September 18, 2021
DEUS: an R package for accurate small RNA profiling based on differential expression of unique sequences
Tim Jeske1,2, Peter Huypens3,4, Laura Stirm4,5
1Institute of Bioinformatics and Systems Biology, Helmholtz Zentrum München, Neuherberg 85764, Germany.
Summary:
Despite their fundamental role in various biological processes, the analysis of small RNA sequencing data remains a challenging task. Major obstacles arise when short RNA sequences map to multiple locations in the genome, align to regions that are not annotated or underwent post-transcriptional changes which hamper accurate mapping. In order to tackle these issues, we present a novel profiling strategy that circumvents the need for read mapping to a reference genome by utilizing the actual read sequences to determine expression intensities. After differential expression analysis of individual sequence counts, significant sequences are annotated against user defined feature databases and clustered by sequence similarity. This strategy enables a more comprehensive and concise representation of small RNA populations without any data loss or data distortion.
Availability And Implementation:
Code and documentation of our R package at http://ibis.helmholtz-muenchen.de/deus/.
Supplementary Information:
Supplementary data are available at Bioinformatics online.
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