Identification, Prediction and Data Analysis of Noncoding RNAs: A Review

Area of Science:

• Molecular Biology
• Bioinformatics
• Genomics

Background:

• Noncoding RNAs (ncRNAs) are vital in cellular processes and implicated in cancer.
• Dysregulation of ncRNAs contributes to human tumorigenesis.
• Accurate identification and prediction of ncRNAs are essential for medical applications and drug design.

Purpose of the Study:

• To review experimental and computational methods for identifying and predicting ncRNA structure.
• To briefly explain the roles of ncRNAs in cellular processes and drug design.

Main Methods:

• Survey of ncRNAs and their biological/medicinal roles.
• Introduction to laboratory techniques for ncRNA identification.
• Overview of state-of-the-art computational models, algorithms, tools, and databases.

Main Results:

• Integration of experimental and computational approaches enhances ncRNA identification.
• Comparison of high-accuracy databases, algorithms, and tools for ncRNA prediction.
• Validation of computational methods for predicting ncRNA structure and function.

Conclusions:

• ncRNA prediction is a dynamic field with challenges requiring accurate algorithms and tools.
• Computational costs (runtime, memory usage) are critical considerations for prediction methods.
• Suggestions are provided for improving computational methods for ncRNA gene and structural prediction.