Your browser doesn't support javascript.
loading
In Silico Analysis of Micro-RNA Sequencing Data.
Aparicio-Puerta, Ernesto; Fromm, Bastian; Hackenberg, Michael; Halushka, Marc K.
Affiliation
  • Aparicio-Puerta E; Department of Genetics, University of Granada, Granada, Spain.
  • Fromm B; Department of Molecular Biosciences, Stockholm University, Stockholm, Sweden.
  • Hackenberg M; Department of Genetics, University of Granada, Granada, Spain.
  • Halushka MK; Department of Pathology, Johns Hopkins University, Baltimore, MD, USA. mhalush1@jhmi.edu.
Methods Mol Biol ; 2284: 231-251, 2021.
Article in En | MEDLINE | ID: mdl-33835446
High-throughput sequencing for micro-RNAs (miRNAs) to obtain expression estimates is a central method of molecular biology. Surprisingly, there are a number of different approaches to converting sequencing output into micro-RNA counts. Each has their own strengths and biases that impact on the final data that can be obtained from a sequencing run. This chapter serves to make the reader aware of the trade-offs one must consider in analyzing small RNA sequencing data. It then compares two methods, miRge2.0 and the sRNAbench and the steps utilized to output data from their tools.
Subject(s)
Key words

Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Sequence Analysis, RNA / Computational Biology / MicroRNAs Limits: Animals / Humans Language: En Journal: Methods Mol Biol Journal subject: BIOLOGIA MOLECULAR Year: 2021 Document type: Article Affiliation country: Spain Country of publication: United States

Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Sequence Analysis, RNA / Computational Biology / MicroRNAs Limits: Animals / Humans Language: En Journal: Methods Mol Biol Journal subject: BIOLOGIA MOLECULAR Year: 2021 Document type: Article Affiliation country: Spain Country of publication: United States