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Jeske, T. ; Huypens, P. ; Stirm, L. ; Höckele, S. ; Wurmser, C.M.* ; Böhm, A. ; Weigert, C. ; Staiger, H. ; Klein, C.* ; Beckers, J. ; Hastreiter, M.

DEUS: An R package for accurate small RNA profiling based on differential expression of unique sequences.

Bioinformatics 35, 4834-4836 (2019)
Verlagsversion Postprint Forschungsdaten DOI PMC
Open Access Gold
A 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.
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Publikationstyp Artikel: Journalartikel
Dokumenttyp Wissenschaftlicher Artikel
Schlagwörter Cd-hit
Sprache englisch
Veröffentlichungsjahr 2019
HGF-Berichtsjahr 2019
e-ISSN 1367-4811
Zeitschrift Bioinformatics
Quellenangaben Band: 35, Heft: 22, Seiten: 4834-4836 Artikelnummer: , Supplement: ,
Verlag Oxford University Press
Verlagsort Oxford
Begutachtungsstatus Peer reviewed
Institut(e) Institute of Computational Biology (ICB)
Institute of Bioinformatics and Systems Biology (IBIS)
Institute of Experimental Genetics (IEG)
Institute of Diabetes Research and Metabolic Diseases (IDM)
POF Topic(s) 30205 - Bioengineering and Digital Health
30505 - New Technologies for Biomedical Discoveries
30201 - Metabolic Health
90000 - German Center for Diabetes Research
Forschungsfeld(er) Enabling and Novel Technologies
Genetics and Epidemiology
Helmholtz Diabetes Center
PSP-Element(e) G-503891-001
G-503700-001
G-500600-004
G-502400-001
G-501900-065
DOI 10.1093/bioinformatics/btz495
WOS ID WOS:000501728500052
Scopus ID 85074963245
PubMed ID 31228198
Erfassungsdatum 2019-06-24
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