ROP
Key Features:
- Comprehensive Read Origin Assignment: Identifies sources of mapped and unmapped RNA-seq reads, enabling characterization of diverse RNA species and previously unannotated transcripts.
- Large-Scale Processing Capability: Processes up to one trillion reads and accounts for 99.9% of reads across datasets spanning 2630 individuals and 54 human tissues.
Scientific Applications:
- Transcriptomic Complexity and Host–Microbiome Analysis: Supports discovery of novel transcripts, alternative splicing events, and interactions linking the immune system, microbiome, and disease states.
Methodology:
ROP implements a multi-step RNA-seq analysis pipeline that systematically reanalyzes unmapped and mapped reads using sequential alignment and classification strategies to assign read origins and maximize transcriptome coverage.
Topics
Details
- License:
- GPL-3.0
- Tool Type:
- command-line tool
- Operating Systems:
- Linux, Mac
- Programming Languages:
- Python
- Added:
- 8/15/2018
- Last Updated:
- 11/25/2024
Operations
Publications
Mangul S, Yang HT, Strauli N, Gruhl F, Porath HT, Hsieh K, Chen L, Daley T, Christenson S, Wesolowska-Andersen A, Spreafico R, Rios C, Eng C, Smith AD, Hernandez RD, Ophoff RA, Santana JR, Levanon EY, Woodruff PG, Burchard E, Seibold MA, Shifman S, Eskin E, Zaitlen N. ROP: dumpster diving in RNA-sequencing to find the source of 1 trillion reads across diverse adult human tissues. Genome Biology. 2018;19(1). doi:10.1186/s13059-018-1403-7. PMID:29548336. PMCID:PMC5857127.