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SingmiR: a single-cell miRNA alignment and analysis tool.
Engel, Annika; Rishik, Shusruto; Hirsch, Pascal; Keller, Verena; Fehlmann, Tobias; Kern, Fabian; Keller, Andreas.
Affiliation
  • Engel A; Chair for Clinical Bioinformatics, Saarland University, 66123 Saarbrücken, Germany.
  • Rishik S; Chair for Clinical Bioinformatics, Saarland University, 66123 Saarbrücken, Germany.
  • Hirsch P; Chair for Clinical Bioinformatics, Saarland University, 66123 Saarbrücken, Germany.
  • Keller V; Chair for Clinical Bioinformatics, Saarland University, 66123 Saarbrücken, Germany.
  • Fehlmann T; Chair for Clinical Bioinformatics, Saarland University, 66123 Saarbrücken, Germany.
  • Kern F; Chair for Clinical Bioinformatics, Saarland University, 66123 Saarbrücken, Germany.
  • Keller A; Department of Clinical Bioinformatics (CLIB), Helmholtz Institute for Pharmaceutical Research Saarland (HIPS), Helmholtz Centre for Infection Research, 66123 Saarbrücken, Germany.
Nucleic Acids Res ; 52(W1): W374-W380, 2024 Jul 05.
Article in En | MEDLINE | ID: mdl-38572750
ABSTRACT
Single-cell RNA sequencing (RNA-seq) has revolutionized our understanding of cell biology, developmental and pathophysiological molecular processes, paving the way toward novel diagnostic and therapeutic approaches. However, most of the gene regulatory processes on the single-cell level are still unknown, including post-transcriptional control conferred by microRNAs (miRNAs). Like the established single-cell gene expression analysis, advanced computational expertise is required to comprehensively process newly emerging single-cell miRNA-seq datasets. A web server providing a workflow tailored for single-cell miRNA-seq data with a self-explanatory interface is currently not available. Here, we present SingmiR, enabling the rapid (pre-)processing and quantification of human miRNAs from noncoding single-cell samples. It performs read trimming for different library preparation protocols, generates automated quality control reports and provides feature-normalized count files. Numerous standard and advanced analyses such as dimension reduction, clustered feature heatmaps, sample correlation heatmaps and differential expression statistics are implemented. We aim to speed up the prototyping pipeline for biologists developing single-cell miRNA-seq protocols on small to medium-sized datasets. SingmiR is freely available to all users without the need for a login at https//www.ccb.uni-saarland.de/singmir.
Subject(s)

Full text: 1 Database: MEDLINE Main subject: Software / Sequence Analysis, RNA / MicroRNAs / Single-Cell Analysis Limits: Humans Language: En Journal: Nucleic Acids Res Year: 2024 Type: Article Affiliation country: Germany

Full text: 1 Database: MEDLINE Main subject: Software / Sequence Analysis, RNA / MicroRNAs / Single-Cell Analysis Limits: Humans Language: En Journal: Nucleic Acids Res Year: 2024 Type: Article Affiliation country: Germany