.png){kind=icon}
.png){kind=icon}
Return
Detecting actively translated open reading frames in ribosome profiling data
.png){kind=icon}
OA
AI
DOI：10.1038/NMETH.3688.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
RNA-sequencing protocols can quantify gene expression regulation from transcription to protein synthesis. Ribosome profiling (Ribo-seq) maps the positions of translating ribosomes over the entire transcriptome. We have developed RiboTaper (available at https://ohlerlab.mdc-berlin.de/software/), a rigorous statistical approach that identifies translated regions on the basis of the characteristic three-nucleotide periodicity of Ribo-seq data. We used RiboTaper with deep Ribo-seq data from HEK293 cells to derive an extensive map of translation that covered open reading frame (ORF) annotations for more than 11,000 protein-coding genes. We also found distinct ribosomal signatures for several hundred upstream ORFs and ORFs in annotated noncoding genes (ncORFs). Mass spectrometry data confirmed that RiboTaper achieved excellent coverage of the cellular proteome. Although dozens of novel peptide products were validated in this manner, few of the currently annotated long noncoding RNAs appeared to encode stable polypeptides. RiboTaper is a powerful method for comprehensive de novo identification of actively used ORORFs from Ribo-seq data.
Keywords:
LONG NONCODING RNAS
HUMAN GENOME
IN-VIVO
SMALL ORFS
IDENTIFICATION
QUANTIFICATION
LANDSCAPE
ULTRAFAST
SEQUENCES
ALIGNMENT