PMID- 33374478
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20240330
IS  - 2409-9279 (Electronic)
IS  - 2409-9279 (Linking)
VI  - 4
IP  - 1
DP  - 2020 Dec 24
TI  - miRNA Targets: From Prediction Tools to Experimental Validation.
LID - 10.3390/mps4010001 [doi]
LID - 1
AB  - MicroRNAs (miRNAs) are post-transcriptional regulators of gene expression in both 
      animals and plants. By pairing to microRNA responsive elements (mREs) on target 
      mRNAs, miRNAs play gene-regulatory roles, producing remarkable changes in several 
      physiological and pathological processes. Thus, the identification of miRNA-mRNA 
      target interactions is fundamental for discovering the regulatory network 
      governed by miRNAs. The best way to achieve this goal is usually by computational 
      prediction followed by experimental validation of these miRNA-mRNA interactions. 
      This review summarizes the key strategies for miRNA target identification. 
      Several tools for computational analysis exist, each with different approaches to 
      predict miRNA targets, and their number is constantly increasing. The major 
      algorithms available for this aim, including Machine Learning methods, are 
      discussed, to provide practical tips for familiarizing with their assumptions and 
      understanding how to interpret the results. Then, all the experimental procedures 
      for verifying the authenticity of the identified miRNA-mRNA target pairs are 
      described, including High-Throughput technologies, in order to find the best 
      approach for miRNA validation. For each strategy, strengths and weaknesses are 
      discussed, to enable users to evaluate and select the right approach for their 
      interests.
FAU - Riolo, Giulia
AU  - Riolo G
AUID- ORCID: 0000-0003-2888-5755
AD  - Department of Medical, Surgical and Neurological Sciences, University of Siena, 
      53100 Siena, Italy.
FAU - Cantara, Silvia
AU  - Cantara S
AUID- ORCID: 0000-0002-5741-295X
AD  - Department of Medical, Surgical and Neurological Sciences, University of Siena, 
      53100 Siena, Italy.
FAU - Marzocchi, Carlotta
AU  - Marzocchi C
AUID- ORCID: 0000-0002-8573-4913
AD  - Department of Medical, Surgical and Neurological Sciences, University of Siena, 
      53100 Siena, Italy.
FAU - Ricci, Claudia
AU  - Ricci C
AUID- ORCID: 0000-0002-2431-0308
AD  - Department of Medical, Surgical and Neurological Sciences, University of Siena, 
      53100 Siena, Italy.
LA  - eng
PT  - Journal Article
PT  - Review
DEP - 20201224
PL  - Switzerland
TA  - Methods Protoc
JT  - Methods and protocols
JID - 101720073
PMC - PMC7839038
OTO - NOTNLM
OT  - experimental validation
OT  - high-throughput technologies
OT  - machine learning
OT  - miRNA target
OT  - prediction tools
OT  - predictive strategies
OT  - validation criteria
COIS- The authors declare no conflict of interest.
EDAT- 2020/12/31 06:00
MHDA- 2020/12/31 06:01
PMCR- 2020/12/24
CRDT- 2020/12/30 01:01
PHST- 2020/11/19 00:00 [received]
PHST- 2020/12/17 00:00 [revised]
PHST- 2020/12/22 00:00 [accepted]
PHST- 2020/12/30 01:01 [entrez]
PHST- 2020/12/31 06:00 [pubmed]
PHST- 2020/12/31 06:01 [medline]
PHST- 2020/12/24 00:00 [pmc-release]
AID - mps4010001 [pii]
AID - mps-04-00001 [pii]
AID - 10.3390/mps4010001 [doi]
PST - epublish
SO  - Methods Protoc. 2020 Dec 24;4(1):1. doi: 10.3390/mps4010001.