In Silico Analysis of Micro-RNA Sequencing Data

Ernesto Aparicio-Puerta; Bastian Fromm; Michael Hackenberg; Marc K Halushka

doi:10.1007/978-1-0716-1307-8_13

In Silico Analysis of Micro-RNA Sequencing Data

Methods Mol Biol. 2021:2284:231-251. doi: 10.1007/978-1-0716-1307-8_13.

Authors

Ernesto Aparicio-Puerta¹, Bastian Fromm², Michael Hackenberg¹, Marc K Halushka³

Affiliations

¹ Department of Genetics, University of Granada, Granada, Spain.
² Department of Molecular Biosciences, Stockholm University, Stockholm, Sweden.
³ Department of Pathology, Johns Hopkins University, Baltimore, MD, USA. mhalush1@jhmi.edu.

PMID: 33835446
DOI: 10.1007/978-1-0716-1307-8_13

Abstract

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.

Keywords: Alignment; Bowtie; Micro-RNA; MirGeneDB; Small RNA sequencing; isomiR; miRBase.

MeSH terms

Animals
Computational Biology / methods*
Computer Simulation
Datasets as Topic
Gene Expression Profiling
High-Throughput Nucleotide Sequencing / methods
Humans
MicroRNAs / analysis
MicroRNAs / genetics*
Polymorphism, Single Nucleotide
RNA Isoforms / analysis
RNA Isoforms / genetics
Sequence Analysis, RNA / methods*
Software

Substances

MicroRNAs
RNA Isoforms