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RNA-GPS predicts high-resolution RNA subcellular localization and highlights the role of splicing.

ABSTRACT: Subcellular localization is essential to RNA biogenesis, processing, and function across the gene expression life cycle. However, the specific nucleotide sequence motifs that direct RNA localization are incompletely understood. Fortunately, new sequencing technologies have provided transcriptome-wide atlases of RNA localization, creating an opportunity to leverage computational modeling. Here we present RNA-GPS, a new machine learning model that uses nucleotide-level features to predict RNA localization across eight different subcellular locations-the first to provide such a wide range of predictions. RNA-GPS's design enables high-throughput sequence ablation and feature importance analyses to probe the sequence motifs that drive localization prediction. We find localization informative motifs to be concentrated on 3'-UTRs and scattered along the coding sequence, and motifs related to splicing to be important drivers of predicted localization, even for cytotopic distinctions for membraneless bodies within the nucleus or for organelles within the cytoplasm. Overall, our results suggest transcript splicing is one of many elements influencing RNA subcellular localization.

SUBMITTER: Wu KE

PROVIDER: S-EPMC7297119 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:This data was generated by ENCODE. If you have questions about the data, contact the submitting laboratory directly (Piero Carninci mailto:carninci@riken.jp). If you have questions about the Genome Browser track associated with this data, contact ENCODE (mailto:genome@soe.ucsc.edu). This track shows 5' cap analysis gene expression (CAGE) tags and clusters in RNA extracts (http://hgwdev.cse.ucsc.edu/cgi-bin/hgEncodeVocab?type=rnaExtract) from different sub-cellular localizations (http://hgwdev.cse.ucsc.edu/cgi-bin/hgEncodeVocab?type=localization) in multiple cell lines (http://hgwdev.cse.ucsc.edu/cgi-bin/hgEncodeVocab?type=cellType). A CAGE cluster is a region of overlapping tags with an assigned value that represents the expression level. The data in this track were produced as part of the ENCODE Transcriptome Project. Release 2 has three new downloads only files per experiment (Clusters, TSS Gencode 7 and TSS HMM) and four new cell lines (A459, AG04450, BJ and SK-N-SH_RA). Release 1 on hg19 contained the original data on hg18 (http://hgwdev.cse.ucsc.edu/cgi-bin/hgTrackUi?db=hg18&g=wgEncodeRikenCage) that was remapped and indicated in this release as Generation 0 since that data had no replicates. If there is both old and new generation data available for a particular experiment, only the new generation data is displayed and the older data is available for download. The new data for this track was done with a different process and has standard replicate numbers. The replicate labeling in the genome browser view is a counter indicating the total number of replicates submitted. The producing lab has replicate numbers that correspond to their internal bio-replicate numbering. Where these two numbering systems conflict, both are listed in the long label of the specific track. For data usage terms and conditions, please refer to http://www.genome.gov/27528022 and http://www.genome.gov/Pages/Research/ENCODE/ENCODEDataReleasePolicyFinal2008.pdf Cells were grown according to the approved ENCODE cell culture protocols (http://hgwdev.cse.ucsc.edu/ENCODE/protocols/cell). RNA molecules longer than 200 nt were isolated from each subcellular compartment and then were fractionated into polyA+ and polyA- fractions as described in these protocols (http://hgwdev.cse.ucsc.edu/ENCODE/protocols/general/rnaExtracts.txt). The CAGE tags were sequenced from the 5' ends of cap-trapped cDNAs produced using RIKEN CAGE technology (Kodzius et al. 2006; Valen et al. 2009). To create the tag, a linker was attached to the 5' end of polyA+ or polyA- reverse-transcribed cDNAs which were selected by cap trapping (Carninci et al. 1996). The first 27 bp of the cDNA were cleaved using class II restriction enzymes. A linker was then attached to the 3' end of the cDNA. After PCR amplification, the tags were sequenced (36 bp single reads) using Illumina's Genome analyzer. Tags were mapped to the human genome (hg19) using the program delve (T. Lassmann manuscript in preparation). Delve is a new probabilistic aligner focused on giving the best possible alignment of reads to a genome rather than focusing on speed. It calculates the mapping accuracy (probability of each alignment being true or not) for each alignment. There is no set limit on the number of errors allowed and therefore the mapping rate is commonly 100%. However, for analysis it is recommended to discard alignments with low mapping qualities. Exceptions to the above protocol are the polyA- RNA samples from K562 cytosol, K562 nucleus, and prostate whole cell which were sequenced using ABI SOLiD (http://www.appliedbiosystems.com/absite/us/en/home/applications-technologies/solid-next-generation-sequencing.html) technology. These reads were mapped using Bowtie using default parameters. Clusters were defined as regions of overlapping CAGE reads. The expression level was computed as the number of reads making up the cluster, divided by the total number of reads sequenced, times 1 million.

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RNA-GPS predicts high-resolution RNA subcellular localization and highlights the role of splicing.

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