ILGBMSH: an interpretable classification model for the shRNA target prediction with ensemble learning algorithm.

Short hairpin RNA (shRNA)-mediated gene silencing is an important technology to achieve RNA interference, in which the design of potent and reliable shRNA molecules plays a crucial role. However, efficient shRNA target selection through biological technology is expensive and time consuming. Hence, it is crucial to develop a more precise and efficient computational method to design potent and reliable shRNA molecules. In this work, we present an interpretable classification model for the shRNA target prediction using the Light Gradient Boosting Machine algorithm called ILGBMSH. Rather than utilizing only the shRNA sequence feature, we extracted 554 biological and deep learning features, which were not considered in previous shRNA prediction research. We evaluated the performance of our model compared with the state-of-the-art shRNA target prediction models. Besides, we investigated the feature explanation from the model's parameters and interpretable method called Shapley Additive Explanations, which provided us with biological insights from the model. We used independent shRNA experiment data from other resources to prove the predictive ability and robustness of our model. Finally, we used our model to design the miR30-shRNA sequences and conducted a gene knockdown experiment. The experimental result was perfectly in correspondence with our expectation with a Pearson's coefficient correlation of 0.985. In summary, the ILGBMSH model can achieve state-of-the-art shRNA prediction performance and give biological insights from the machine learning model parameters.

shRNAPred (version 1.0): An open source and standalone software for short hairpin RNA (shRNA) prediction.

UNLABELLED: The small hairpin RNAs (shRNA) are useful in many ways like identification of trait specific molecular markers, gene silencing and characterization of a species. In public domain, hardly there exists any standalone software for shRNA prediction. Hence, a software shRNAPred (1.0) is proposed here to offer a user-friendly Command-line User Interface (CUI) to predict 'shRNA-like' regions from a large set of nucleotide sequences. The software is developed using PERL Version 5.12.5 taking into account the parameters such as stem and loop length combinations, specific loop sequence, GC content, melting temperature, position specific nucleotides, low complexity filter, etc. Each of the parameters is assigned with a specific score and based on which the software ranks the predicted shRNAs. The high scored shRNAs obtained from the software are depicted as potential shRNAs and provided to the user in the form of a text file. The proposed software also allows the user to customize certain parameters while predicting specific shRNAs of his interest. The shRNAPred (1.0) is open access software available for academic users. It can be downloaded freely along with user manual, example dataset and output for easy understanding and implementation. AVAILABILITY: The database is available for free at http://bioinformatics.iasri.res.in/EDA/downloads/shRNAPred_v1.0.exe.