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Machine learning based miRNA-target prediction algorithms often fail to obtain a balanced prediction accuracy in terms of both sensitivity and specificity due to lack of the gold standard of negative examples, miRNA-targeting site context specific relevant features and efficient feature selection process. Moreover, all the sequence, structure and machine learning based algorithms are unable to distribute the true positive predictions preferentially at the top of the ranked list; hence the algorithms become unreliable to the biologists.

MicroRNAs (miRNAs) play key regulatory roles in various biological processes and diseases. A comprehensive analysis of large scale small RNA sequencing data (smRNA-seq) will be very helpful to explore tissue or disease specific miRNA markers and uncover miRNA variants. Here, we systematically analyzed 410 human smRNA-seq datasets, which samples are from 24 tissue/disease/cell lines. We tested the mapping strategies and found that it was necessary to make multiple-round mappings with different mismatch parameters.

The RNAimmuno database was created to provide easy access to information regarding the nonspecific effects generated in cells by RNA interference triggers and microRNA regulators. Various RNAi and microRNA reagents, which differ in length and structure, often cause non-sequence-specific immune responses, in addition to triggering the intended sequence-specific effects. The activation of the cellular sensors of foreign RNA or DNA may lead to the induction of type I interferon and proinflammatory cytokine release.

MicroRNAs were found to play an important role in cancers and several literatures exist to describe the relationship between microRNA and cancer, but the expression pattern was still faintly. There is a need for a comprehensive collection and summary of the interactions under experimental support.