Popular Computational Tools Used for miRNA Prediction and Their Future Development Prospects

Area of Science:

• Molecular Biology
• Bioinformatics
• Genomics

Background:

• MicroRNAs (miRNAs) are small noncoding RNAs crucial for gene regulation in plants and animals.
• Early miRNA identification relied on labor-intensive cloning and sequencing methods.
• The early 2000s marked a shift towards computational approaches for miRNA discovery.

Purpose of the Study:

• To review and categorize recent computational methods for microRNA prediction.
• To highlight the evolution from experimental to in silico miRNA identification.
• To discuss future directions in computational miRNA prediction.

Main Methods:

• Homology-based methods: Identifying known miRNAs in new species by comparing sequences with orthologs.
• Next-generation sequencing (NGS) combined with advanced algorithms for novel miRNA prediction.
• Ab initio algorithms designed for miRNA prediction independent of existing genomic data.
• Classification of recent methods into homology-based, target-based, scoring-based, and machine-learning-based approaches.

Main Results:

• Computational methods offer greater coverage and speed compared to traditional experimental techniques.
• NGS and improved algorithms have significantly accelerated the discovery of novel miRNAs.
• Diverse computational strategies, including machine learning, are now employed for miRNA prediction.

Conclusions:

• The field has transitioned from experimental to computational miRNA discovery due to efficiency and cost-effectiveness.
• Continued development of algorithms is crucial for advancing miRNA research.
• Future directions include refining existing methods and exploring novel computational strategies for miRNA identification.