miRToolsGallery is a database of miRNA tools. It provides the following services: (a) Search，(b) Filter and (c) Rank the tools. Our database aim to make it easy for researchers to find the right tools or data source for their own specific study in miRNA field. And it’s also very convenient for writing a tools review paper. Now we have collect above 1000 tools. miRToolsGallery will update when every new 100 tools add in. The first public online was in 1st Oct, 2016, and latest update time is 22nd April, 2018(v1.2).
MicroRNAs (miRNAs) play crucial roles in post-transcriptional regulations and various cellular processes. The identification of disease-related miRNAs provides great insights into the underlying pathogenesis of diseases at a system level. However, most existing computational approaches are biased towards known miRNA-disease associations, which is inappropriate for those new diseases or miRNAs without any known association information.
Web repositories for almost all 'omics' types have been generated-detailing the repertoire of representatives across different tissues or cell types. A logical next step is the combination of these valuable sources. With IMOTA (interactive multi omics tissue atlas), we developed a database that includes 23 725 relations between miRNAs and 23 tissues, 310 932 relations between mRNAs and the same tissues as well as 63 043 relations between proteins and the 23 tissues in Homo sapiens. IMOTA also contains data on tissue-specific interactions, e.g.
With this study, we provide a comprehensive reference dataset of detailed miRNA expression profiles from seven typesof human peripheral blood cells(NK cells, B lymphocytes, cytotoxic T lymphocytes, T helper cells, monocytes, neutrophils and erythrocytes), serum, exosomes and whole blood. The peripheral blood cells from buffy coats were typed and sorted using FACS/MACS. The overall dataset was generated from 450 small RNA libraries using high-throughput sequencing.
As a promising field of individualized therapy, non-coding RNA pharmacogenomics promotes the understanding of different individual responses to certain drugs and acts as a reasonable reference for clinical treatment. However, relevant information is scattered across the published literature, which is inconvenient for researchers to explore non-coding RNAs that are involved in drug resistance. To address this, we systemically identified validated and predicted drug resistance-associated microRNAs and long non-coding RNAs through manual curation and computational analysis.
Identification of disease-associated miRNAs (disease miRNAs) is critical for understanding disease etiology and pathogenesis. Since miRNAs exert their functions by regulating the expression of their target mRNAs, several methods based on the target genes were proposed to predict disease miRNA candidates. They achieved only limited success as they all suffered from the high false-positive rate of target prediction results.
MicroRNAs (miRNAs) are key regulators of cell-fate decisions in development and disease with a vast array of target interactions that can be investigated using computational approaches. For this study, we developed metaMIR, a combinatorial approach to identify miRNAs that co-regulate identified subsets of genes from a user-supplied list. We based metaMIR predictions on an improved dataset of human miRNA-target interactions, compiled using a machine-learning-based meta-analysis of established algorithms.
