The RNA Atlas Expands the Catalog of Human Non-Coding RNAs
Nature Biotechnology(2021)SCI 1区
Center for Medical Genetics | Texas Children’s Cancer Center | Illumina | Australian e-Health Research Centre | Department of Data Analysis and Mathematical Modelling | Interdisciplinary Nanoscience Centre (iNANO) | Biogazelle | Department of Diagnostic Sciences | Department of Respiratory Medicine | VIB-UGent Center for Medical Biotechnology | Cancer Research Institute Ghent (CRIG) | Systems Biology Initiative | Adult Cancer Program | Institute of Bioinformatics and Systems Biology | Department of Oncogenomics
Abstract
Existing compendia of non-coding RNA (ncRNA) are incomplete, in part because they are derived almost exclusively from small and polyadenylated RNAs. Here we present a more comprehensive atlas of the human transcriptome, which includes small and polyA RNA as well as total RNA from 300 human tissues and cell lines. We report thousands of previously uncharacterized RNAs, increasing the number of documented ncRNAs by approximately 8%. To infer functional regulation by known and newly characterized ncRNAs, we exploited pre-mRNA abundance estimates from total RNA sequencing, revealing 316 microRNAs and 3,310 long non-coding RNAs with multiple lines of evidence for roles in regulating protein-coding genes and pathways. Our study both refines and expands the current catalog of human ncRNAs and their regulatory interactions. All data, analyses and results are available for download and interrogation in the R2 web portal, serving as a basis for future exploration of RNA biology and function.
MoreTranslated text
Key words
Gene expression profiling,Genetic interaction,Non-coding RNAs,RNA sequencing,Transcriptomics,Life Sciences,general,Biotechnology,Biomedicine,Agriculture,Biomedical Engineering/Biotechnology,Bioinformatics
PDF
View via Publisher
AI Read Science
AI Summary
AI Summary is the key point extracted automatically understanding the full text of the paper, including the background, methods, results, conclusions, icons and other key content, so that you can get the outline of the paper at a glance.
Example
Background
Key content
Introduction
Methods
Results
Related work
Fund
Key content
- Pretraining has recently greatly promoted the development of natural language processing (NLP)
- We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
- We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
- The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
- Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance
Try using models to generate summary,it takes about 60s
Must-Reading Tree
Example

Generate MRT to find the research sequence of this paper
Related Papers
2010
被引用15898 | 浏览
Featurecounts: an Efficient General Purpose Program for Assigning Sequence Reads to Genomic Features
2013
被引用22993 | 浏览
2009
被引用24633 | 浏览
2015
被引用760 | 浏览
2011
被引用3896 | 浏览
2012
被引用3057 | 浏览
2016
被引用859 | 浏览
2018
被引用246 | 浏览
2018
被引用256 | 浏览
2020
被引用41 | 浏览
Data Disclaimer
The page data are from open Internet sources, cooperative publishers and automatic analysis results through AI technology. We do not make any commitments and guarantees for the validity, accuracy, correctness, reliability, completeness and timeliness of the page data. If you have any questions, please contact us by email: report@aminer.cn
Chat Paper
去 AI 文献库 对话