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MicroRNAs (miRNAs) are endogenous non-coding small RNAs (of about 22 nucleotides), which play an important role in the post-transcriptional regulation of gene expression via either mRNA cleavage or translation inhibition. Several machine learning-based approaches have been developed to identify novel miRNAs from next generation sequencing (NGS) data. Typically, precursor/genomic sequences are required as references for most methods. However, the non-availability of genomic sequences is often a limitation in miRNA discovery in non-model plants.

The cell-specific information of transcriptional regulation on microRNAs (miRNAs) is crucial to the precise understanding of gene regulations in various physiological and pathological processes existed in different tissues and cell types. The database, mirTrans, provides comprehensive information about cell-specific transcription of miRNAs including the transcriptional start sites (TSSs) of miRNAs, transcription factor (TF) to miRNA regulations and miRNA promoter sequences.

MicroRNAs form an essential class of post-transcriptional gene regulator of eukaryotic species, and play critical parts in development and disease and stress responses. MicroRNAs may originate from various genomic loci, have structural characteristics, and appear in canonical or modified forms, making them subtle to detect and analyze. We present miRvial, a robust computational method and companion software package that supports parameter adjustment and visual inspection of candidate microRNAs.

The discovery of microRNAs (miRNAs) remains an important problem, particularly given the growth of high-throughput sequencing, cell sorting and single cell biology. While a large number of miRNAs have already been annotated, there may well be large numbers of miRNAs that are expressed in very particular cell types and remain elusive. Sequencing allows us to quickly and accurately identify the expression of known miRNAs from small RNA-Seq data. The biogenesis of miRNAs leads to very specific characteristics observed in their sequences.

Although many machine learning techniques have been proposed for distinguishing miRNA hairpins from other stem-loop sequences, most of the current methods use supervised learning, which requires a very good set of positive and negative examples. Those methods have important practical limitations when they have to be applied to a real prediction task. First, there is the challenge of dealing with a scarce number of positive (well-known) pre-miRNA examples.

MicroRNA (miRNA) sponges with multiple tandem miRNA binding sequences can sequester miRNAs from their endogenous target mRNAs. Therefore, miRNA sponge acting as a decoy is extremely important for long-term loss-of-function studies both in vivo and in silico. Recently, a growing number of in silico methods have been used as an effective technique to generate hypotheses for in vivo methods for studying the biological functions and regulatory mechanisms of miRNA sponges.