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Published on: August 21, 2019
Identification of microRNA precursor with the degenerate K-tuple or Kmer strategy
Bin Liu1, Longyun Fang2, Shanyi Wang2
1School of Computer Science and Technology, Harbin Institute of Technology Shenzhen Graduate School, Shenzhen, Guangdong, China; Key Laboratory of Network Oriented Intelligent Computation, Harbin Institute of Technology Shenzhen Graduate School, Shenzhen, Guangdong, China; Gordon Life Science Institute, Boston, MA 0478, USA.
Abstract:
The microRNA (miRNA), a small non-coding RNA molecule, plays an important role in transcriptional and post-transcriptional regulation of gene expression. Its abnormal expression, however, has been observed in many cancers and other disease states, implying that the miRNA molecules are also deeply involved in these diseases, particularly in carcinogenesis. Therefore, it is important for both basic research and miRNA-based therapy to discriminate the real pre-miRNAs from the false ones (such as hairpin sequences with similar stem-loops). Most existing methods in this regard were based on the strategy in which RNA samples were formulated by a vector formed by their Kmer components. But the length of Kmers must be very short; otherwise, the vector's dimension would be extremely large, leading to the "high-dimension disaster" or overfitting problem. Inspired by the concept of "degenerate energy levels" in quantum mechanics, we introduced the "degenerate Kmer" (deKmer) to represent RNA samples. By doing so, not only we can accommodate long-range coupling effects but also we can avoid the high-dimension problem. Rigorous jackknife tests and cross-species experiments indicated that our approach is very promising. It has not escaped our notice that the deKmer approach can also be applied to many other areas of computational biology. A user-friendly web-server for the new predictor has been established at http://bioinformatics.hitsz.edu.cn/miRNA-deKmer/, by which users can easily get their desired results.
Insights
This study introduces a novel "degenerate Kmer" (deKmer) method to accurately identify microRNA (miRNA) precursors. This approach overcomes limitations of existing Kmer methods, improving cancer research and therapeutic development.
Area of Science:
- Computational Biology
- Molecular Biology
- Bioinformatics
Background:
- MicroRNAs (miRNAs) are crucial regulators of gene expression, with abnormal levels linked to various diseases, including cancer.
- Distinguishing genuine pre-miRNAs from similar hairpin structures is vital for research and therapeutic applications.
- Existing Kmer-based methods face challenges with high dimensionality and overfitting due to short Kmer lengths.
Purpose of the Study:
- To develop a novel computational method for accurately identifying pre-microRNAs (pre-miRNAs).
- To address the limitations of existing Kmer-based approaches in pre-miRNA prediction.
- To provide a user-friendly tool for pre-miRNA identification.
Main Methods:
- Introduction of the "degenerate Kmer" (deKmer) concept, inspired by quantum mechanics, to represent RNA sequences.
- Application of deKmers to overcome the high-dimension problem and accommodate long-range coupling effects.
- Rigorous validation using jackknife tests and cross-species experiments.
Main Results:
- The deKmer approach demonstrated high promise in discriminating real pre-miRNAs from false positives.
- The method effectively avoids the "high-dimension disaster" and overfitting issues.
- The developed web server provides an accessible platform for pre-miRNA prediction.
Conclusions:
- The deKmer method offers a significant advancement in pre-miRNA identification.
- This approach has broad applicability in computational biology beyond miRNA research.
- The user-friendly web server facilitates practical application of the deKmer predictor.
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