MicroRNAs (miRNAs) are small noncoding RNAs that regulate gene expression via recognition of cognate sequences and interference of transcriptional, translational or epigenetic processes. Bioinformatics tools developed for miRNA study include those for miRNA prediction and discovery, structure, analysis and target prediction. We manually curated 95 review papers and ∼1000 miRNA bioinformatics tools published since 2003. We classified and ranked them based on citation number or PageRank score, and then performed network analysis and text mining (TM) to study the miRNA tools development trends. Five key trends were observed: (1) miRNA identification and target prediction have been hot spots in the past decade; (2) manual curation and TM are the main methods for collecting miRNA knowledge from literature; (3) most early tools are well maintained and widely used; (4) classic machine learning methods retain their utility; however, novel ones have begun to emerge; (5) disease-associated miRNA tools are emerging. Our analysis yields significant insight into the past development and future directions of miRNA tools.
BackgroundBlood-based gene expression or epigenetic biomarkers of Parkinson’s disease (PD) are highly desirable. However, accuracy and specificity need to be improved, and methods for the integration of gene expression with epigenetic data need to be developed in order to make this feasible.MethodsWhole blood gene expression data and DNA methylation data were downloaded from Gene Expression Omnibus (GEO) database. A linear model was used to identify significantly differentially expressed genes (DEGs) and differentially methylated genes (DMGs) according to specific gene regions 5′—C—phosphate—G—3′ (CpGs) or all gene regions CpGs in PD. Gene set enrichment analysis was then applied to DEGs and DMGs. Subsequently, data integration analysis was performed to identify robust PD-associated blood biomarkers. Finally, the random forest algorithm and a leave-one-out cross validation method were performed to construct classifiers based on gene expression data integrated with methylation data.ResultsEighty-five (85) significantly hypo-methylated and upregulated genes in PD patients compared to healthy controls were identified. The dominant hypo-methylated regions of these genes were significantly different. Some genes had a single dominant hypo-methylated region, while others had multiple dominant hypo-methylated regions. One gene expression classifier and two gene methylation classifiers based on all or dominant methylation-altered region CpGs were constructed. All have a good prediction power for PD.ConclusionsGene expression and methylation data integration analysis identified a blood-based 53-gene signature, which could be applied as a biomarker for PD.Electronic supplementary materialThe online version of this article (10.1186/s13148-019-0621-5) contains supplementary material, which is available to authorized users.
BackgroundmicroRNAs have been reported to play critical roles in cancer and to have potential as diagnostic biomarkers. During miRNA biogenesis, one strand of the miRNA hairpin precursor is preferentially selected as a functionally mature miRNA, while the other strand is typically degraded. Arm switching occurs when the strand preference is changed. This preference can be different and can change dynamically depending upon the species, tissue types, or development stages. Due to recent advances in next-generation sequencing methods, arm switching has been observed in a variety of cancers.MethodsA tumour miRNA-Seq dataset was collected from The Cancer Genome Atlas (TCGA). The support vector machine (SVM) method combined with 5-fold cross validation was applied to select the best combination of arm-switched miRNA tumour markers. Survival analysis was also applied to identify patient survival associated miRNA markers.FindingsWe observed 51 arm-switched miRNAs and of these, 7 were associated with patient survival. Twenty-three 1-combination arm switching miRNAs with excellent diagnostic value were identified. Interestingly, ovarian cancer showed a significant difference in arm switching pattern compared with 32 other cancers.InterpretationThese results suggest that arm switching miRNAs could be used as potential biomarkers for various cancers.FundThis work was partially supported by the (no. 61472158, 61572227), and Faculty of Health Sciences (MYRG2016-00101-FHS).
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