Title |
ROP: dumpster diving in RNA-sequencing to find the source of 1 trillion reads across diverse adult human tissues
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Published in |
Genome Biology, February 2018
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DOI | 10.1186/s13059-018-1403-7 |
Pubmed ID | |
Authors |
Serghei Mangul, Harry Taegyun Yang, Nicolas Strauli, Franziska Gruhl, Hagit T. Porath, Kevin Hsieh, Linus Chen, Timothy Daley, Stephanie Christenson, Agata Wesolowska-Andersen, Roberto Spreafico, Cydney Rios, Celeste Eng, Andrew D. Smith, Ryan D. Hernandez, Roel A. Ophoff, Jose Rodriguez Santana, Erez Y. Levanon, Prescott G. Woodruff, Esteban Burchard, Max A. Seibold, Sagiv Shifman, Eleazar Eskin, Noah Zaitlen |
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 . |
X Demographics
Geographical breakdown
Country | Count | As % |
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United States | 26 | 39% |
United Kingdom | 5 | 7% |
Germany | 3 | 4% |
Spain | 2 | 3% |
Israel | 1 | 1% |
Switzerland | 1 | 1% |
Denmark | 1 | 1% |
Ecuador | 1 | 1% |
Austria | 1 | 1% |
Other | 5 | 7% |
Unknown | 21 | 31% |
Demographic breakdown
Type | Count | As % |
---|---|---|
Scientists | 45 | 67% |
Members of the public | 22 | 33% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
---|---|---|
Unknown | 90 | 100% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Student > Bachelor | 15 | 17% |
Student > Ph. D. Student | 13 | 14% |
Researcher | 12 | 13% |
Student > Master | 9 | 10% |
Student > Doctoral Student | 8 | 9% |
Other | 13 | 14% |
Unknown | 20 | 22% |
Readers by discipline | Count | As % |
---|---|---|
Biochemistry, Genetics and Molecular Biology | 26 | 29% |
Agricultural and Biological Sciences | 20 | 22% |
Medicine and Dentistry | 9 | 10% |
Engineering | 4 | 4% |
Computer Science | 4 | 4% |
Other | 7 | 8% |
Unknown | 20 | 22% |