cell-based-assays-sineup-non-coding-rnas-that-can-specifically

Cell Based Assays of SINEUP Non-coding RNAs That Can Specifically Enhance mRNA Translation

Targeted-protein enhancement is of importance not only for the study of biological processes but also for therapeutic and biotechnological applications. ...

RIKEN Center for Integrative Medical Sciences

TransSINE Technologies Co. Ltd.

It is known that more than 70% of mammalian genomes are transcribed, yet the vast majority of transcripts do not code for proteins. Are these noncoding transcripts merely transcriptional noise, or do they serve a biological purpose? Recent developments in genomic analysis technologies, especially sequencing methods, have allowed researchers to create a large atlas of transcriptomes, study subcellular localization, and investigate potential interactions with proteins for a growing number of transcripts. Here, we review the current methodologies available for discovering and investigating functions of long noncoding RNAs (lncRNAs), which require a wide variety of applications to study their potential biological roles. This article is part of a Special Issue entitled: Clues to long noncoding RNA taxonomy1, edited by Dr. Tetsuro Hirose and Dr. Shinichi Nakagawa. 

Discovery and functional analysis of lncRNAs: Methodologies to investigate an uncharacterized transcriptome.

Differential gene expression (DGE) analysis is a technique to identify statistically significant differences in RNA abundance for genes or arbitrary features between different biological states. The result of a DGE test is typically further analyzed using statistical software, spreadsheets or custom ad hoc algorithms. We identified a need for a web-based system to share DGE statistical test results, and locate and identify genes in DGE statistical test results with a very low barrier of entry.

DEIVA: a web application for interactive visual analysis of differential gene expression profiles.

The Functional Annotation of the Mammalian Genome 5 (FANTOM5) project conducted transcriptome analysis of various mammalian cell types and provided a comprehensive resource to understand transcriptome and transcriptional regulation in individual cellular states encoded in the genome.FANTOM5 used cap analysis of gene expression (CAGE) with single-molecule sequencing to map transcription start sites (TSS) and measured their expression in a diverse range of samples. The main results from FANTOM5 were published as a promoter-level mammalian expression atlas and an atlas of active enhancers across human cell types. The FANTOM5 dataset is composed of raw experimental data and the results of bioinformatics analyses. In this chapter, we give a detailed description of the content of the FANTOM5 dataset and elaborate on different computing applications developed to publish the data and enable reproducibility and discovery of new findings. We present use cases in which the FANTOM5 dataset has been reused, leading to new findings.

Article	Year
Discovery and functional analysis of lncRNAs: Methodologies to investigate an uncharacterized transcriptome. Biochimica et biophysica acta\| PubMed ID: 26477492	2016
DEIVA: a web application for interactive visual analysis of differential gene expression profiles. BMC genomics\| PubMed ID: 28061742	2017
The FANTOM5 Computation Ecosystem: Genomic Information Hub for Promoters and Active Enhancers. Methods in molecular biology (Clifton, N.J.)\| PubMed ID: 28451981	2017

Piero Carninci

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