1. Integrative approaches for analysis of mRNA and microRNA high-throughput data
- Author
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Stephanie Kreis and Petr V. Nazarov
- Subjects
CNN, convolutional neural network ,RNASeq, high-throughput RNA sequencing ,In silico ,Biophysics ,circRNA, circular RNA ,CLASH, cross-linking, ligation and sequencing of hybrids ,Computational biology ,Review Article ,Biology ,computer.software_genre ,Biochemistry ,TDMD, target RNA-directed miRNA degradation ,Transcriptome ,lncRNA, long non-coding RNA ,03 medical and health sciences ,0302 clinical medicine ,Structural Biology ,Target prediction ,microRNA ,miRNA, microRNA ,Genetics ,Transcriptomics ,Throughput (business) ,Gene ,TF, transcription factors ,030304 developmental biology ,GO, gene ontology ,0303 health sciences ,Messenger RNA ,PCA, principal component analysis ,Matrix factorization ,RNA ,CDS, coding sequence ,CCA, canonical correlation analysis ,ICA, independent component analysis ,mRNA, messenger RNA ,Computer Science Applications ,NGS, next-generation sequencing ,NMF, non-negative matrix factorization ,030220 oncology & carcinogenesis ,CLIP, cross-linking immunoprecipitation ,Data integration ,computer ,TP248.13-248.65 ,Biotechnology - Abstract
Highlights • Review on tools and databases linking miRNA and its mRNA targetome. • Databases show little overlap in miRNA targetome predictions suggesting strong contextual effects. • Deconvolution and deep learning approaches are promising new approaches to improve miRNA targetome predictions., Advanced sequencing technologies such as RNASeq provide the means for production of massive amounts of data, including transcriptome-wide expression levels of coding RNAs (mRNAs) and non-coding RNAs such as miRNAs, lncRNAs, piRNAs and many other RNA species. In silico analysis of datasets, representing only one RNA species is well established and a variety of tools and pipelines are available. However, attaining a more systematic view of how different players come together to regulate the expression of a gene or a group of genes requires a more intricate approach to data analysis. To fully understand complex transcriptional networks, datasets representing different RNA species need to be integrated. In this review, we will focus on miRNAs as key post-transcriptional regulators summarizing current computational approaches for miRNA:target gene prediction as well as new data-driven methods to tackle the problem of comprehensively and accurately dissecting miRNome-targetome interactions.
- Published
- 2021