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MicroRNA categorization using sequence motifs and k-mers.
Malik Yousef1, Waleed Khalifa2, İlhan Erkin Acar3
1Community Information Systems, Zefat Academic College, Zefat, 13206, Israel. malik.yousef@gmail.com.
BMC Bioinformatics
|March 16, 2017
Summary
Machine learning models can differentiate pre-microRNAs (miRNAs) from different species using sequence motifs and k-mers. Accuracy increases with greater evolutionary distance between species, aiding in identifying potential contaminants.
Area of Science:
- Bioinformatics
- Computational Biology
- Genomics
Background:
- Post-transcriptional gene dysregulation is implicated in diseases like cancer.
- MicroRNAs (miRNAs) are key regulators of translation efficiency.
- Computational detection of pre-miRNAs is crucial for understanding gene regulation.
Purpose of the Study:
- To develop a computational method for differentiating pre-miRNAs from various species.
- To distinguish genuine pre-miRNAs from potential contaminants like xeno-miRNAs.
Main Methods:
- Utilized machine learning models trained on sequence motifs and k-mers.
- Employed cross-species training and testing datasets (one species as positive, another as negative).
Main Results:
- Achieved higher accuracy in distinguishing pre-miRNAs between distantly related species.
- Observed lower accuracy when differentiating between closely related species.
Conclusions:
- The ability to differentiate pre-miRNAs among species increases with evolutionary distance.
- Fast evolutionary changes in miRNAs support the observed discrimination capabilities, even between related species.
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