High-throughput RNA-sequencing (RNA-seq) technologies provide an unprecedented opportunity to explore the individual transcriptome. Unmapped reads are a large and often overlooked output of standard RNA-seq analyses. Here, we present Read Origin Protocol (ROP), a tool for discovering the source of all reads originating from complex RNA molecules. We apply ROP to samples across 2630 individuals from 54 diverse human tissues. Our approach can account for 99.9% of 1 trillion reads of various read length. Additionally, we use ROP to investigate the functional mechanisms underlying connections between the immune system, microbiome, and disease. ROP is freely available at https://github.com/smangul1/rop/wiki .
Description
ROP: dumpster diving in RNA-sequencing to find the source of 1 trillion reads across diverse adult human tissues. - PubMed - NCBI
%0 Journal Article
%1 Mangul:2018:Genome-Biol:29548336
%A Mangul, S
%A Yang, H T
%A Strauli, N
%A Gruhl, F
%A Porath, H T
%A Hsieh, K
%A Chen, L
%A Daley, T
%A Christenson, S
%A Wesolowska-Andersen, A
%A Spreafico, R
%A Rios, C
%A Eng, C
%A Smith, A D
%A Hernandez, R D
%A Ophoff, R A
%A Santana, J R
%A Levanon, E Y
%A Woodruff, P G
%A Burchard, E
%A Seibold, M A
%A Shifman, S
%A Eskin, E
%A Zaitlen, N
%D 2018
%J Genome Biol
%K fulltext mapping rna-seq software
%N 1
%P 36-36
%R 10.1186/s13059-018-1403-7
%T ROP: dumpster diving in RNA-sequencing to find the source of 1 trillion reads across diverse adult human tissues
%U https://www.ncbi.nlm.nih.gov/pubmed/29548336
%V 19
%X High-throughput RNA-sequencing (RNA-seq) technologies provide an unprecedented opportunity to explore the individual transcriptome. Unmapped reads are a large and often overlooked output of standard RNA-seq analyses. Here, we present Read Origin Protocol (ROP), a tool for discovering the source of all reads originating from complex RNA molecules. We apply ROP to samples across 2630 individuals from 54 diverse human tissues. Our approach can account for 99.9% of 1 trillion reads of various read length. Additionally, we use ROP to investigate the functional mechanisms underlying connections between the immune system, microbiome, and disease. ROP is freely available at https://github.com/smangul1/rop/wiki .
@article{Mangul:2018:Genome-Biol:29548336,
abstract = {High-throughput RNA-sequencing (RNA-seq) technologies provide an unprecedented opportunity to explore the individual transcriptome. Unmapped reads are a large and often overlooked output of standard RNA-seq analyses. Here, we present Read Origin Protocol (ROP), a tool for discovering the source of all reads originating from complex RNA molecules. We apply ROP to samples across 2630 individuals from 54 diverse human tissues. Our approach can account for 99.9% of 1 trillion reads of various read length. Additionally, we use ROP to investigate the functional mechanisms underlying connections between the immune system, microbiome, and disease. ROP is freely available at https://github.com/smangul1/rop/wiki .},
added-at = {2018-11-18T10:02:48.000+0100},
author = {Mangul, S and Yang, H T and Strauli, N and Gruhl, F and Porath, H T and Hsieh, K and Chen, L and Daley, T and Christenson, S and Wesolowska-Andersen, A and Spreafico, R and Rios, C and Eng, C and Smith, A D and Hernandez, R D and Ophoff, R A and Santana, J R and Levanon, E Y and Woodruff, P G and Burchard, E and Seibold, M A and Shifman, S and Eskin, E and Zaitlen, N},
biburl = {https://www.bibsonomy.org/bibtex/2fe0b4b00cea7e929f52f65dc10bbcb0f/marcsaric},
description = {ROP: dumpster diving in RNA-sequencing to find the source of 1 trillion reads across diverse adult human tissues. - PubMed - NCBI},
doi = {10.1186/s13059-018-1403-7},
interhash = {ff139118c059a981b7abeb5d8b26cc7e},
intrahash = {fe0b4b00cea7e929f52f65dc10bbcb0f},
journal = {Genome Biol},
keywords = {fulltext mapping rna-seq software},
month = {02},
number = 1,
pages = {36-36},
pmid = {29548336},
timestamp = {2018-11-18T10:02:48.000+0100},
title = {ROP: dumpster diving in RNA-sequencing to find the source of 1 trillion reads across diverse adult human tissues},
url = {https://www.ncbi.nlm.nih.gov/pubmed/29548336},
volume = 19,
year = 2018
}