Iñaki Amatria-Barral
Juan Touriño
ABSTRACT
Long non-coding RNA sequences (lncRNAs) have completely changed how scientists approach genetics. While some believe that many lncRNAs are results of spurious transcriptions, recent evidence suggests that there exist thousands of them and that they have functions and regulate key biological processes. For the experimental characterization of lncRNAs, many tools that try to predict their interactions with other RNAs have been developed. Some of the fastest and more accurate tools, however, require a slow database construction step prior to the identification of interaction partners for each lncRNA. This paper presents a novel and efficient parallel database construction procedure. Benchmarking results on a 16-node multicore cluster show that our parallel algorithm can build databases up to 318 times faster than other tools in the market using just 256 CPU cores. All the code developed in this work is available to download at GitHub under the MIT License (https://github.com/UDC-GAC/pRIblast).
- Iñaki Amatria-Barral, Jorge González-Domínguez, and Juan Touriño. 2023. pRI-blast: a highly efficient parallel application for comprehensive lncRNA-RNA interaction prediction. Future Generation Computer Systems 138 (2023), 270--279.Google Scholar
Digital Library
- Ivan Antonov, Andrey Marakhonov, Maria Zamkova, and Yulia Medvedeva. 2018. ASSA: fast identification of statistically significant interactions between long RNAs. Journal of Bioinformatics and Computational Biology 16, 1 (2018), 1840001.Google ScholarCross Ref
- Ivan Antonov, Evgeny Mazurov, Mark Borodovsky, and Yulia Medvedeva. 2019. Prediction of lncRNAs and their interactions with nucleic acids: benchmarking bioinformatics tools. Briefings in Bioinformatics 20, 2 (2019), 551--564.Google ScholarCross Ref
- Xiaolin Dong, Xiaoxue He, Aoran Guan, Weikang Huang, Hongping Jia, Yun Huang, Sijin Chen, Zhibo Zhang, Jianpeng Gao, and Hui Wang. 2019. Long non-coding RNA Hotair promotes gastric cancer progression via miR-217-GPC5 axis. Life Sciences 217 (2019), 271--282.Google ScholarCross Ref
- Maximilianos Elkouris, Georgia Kouroupi, Alexios Vourvoukelis, Nikolaos Papagiannakis, Valeria Kaltezioti, Rebecca Matsas, Leonidas Stefanis, Maria Xilouri, and Panagiotis K Politis. 2019. Long non-coding RNAs associated with neurodegeneration-linked genes are reduced in Parkinson's disease patients. Frontiers in Cellular Neuroscience 13 (2019), 58.Google ScholarCross Ref
- Tsukasa Fukunaga and Michiaki Hamada. 2017. RIblast: an ultrafast RNA-RNA interaction prediction system based on a seed-and-extension approach. Bioinformatics 33, 17 (2017), 2666--2674.Google ScholarCross Ref
- Tetsuro Hirose, Yuichiro Mishima, and Yukihide Tomari. 2014. Elements and machinery of non-coding RNAs: toward their taxonomy. EMBO Reports 15, 5 (2014), 489--507.Google ScholarCross Ref
- Kevin L Howe, Premanand Achuthan, James Allen, Jamie Allen, Jorge Alvarez-Jarreta, M Ridwan Amode, Irina M Armean, Andrey G Azov, Ruth Bennett, Jyothish Bhai, et al. 2021. Ensembl 2021. Nucleic Acids Research 49, D1 (2021), D884--D891.Google ScholarCross Ref
- Zhongxin Jin, Shiwei Gao, Wanyun Ma, Xinning Lyu, Xiaolei Cao, and Yuxin Yao. 2020. Identification and functional prediction of salt stress-related long noncoding RNAs in grapevine roots. Environmental and Experimental Botany 179 (2020), 104215.Google ScholarCross Ref
- Daniel Lai and Irmtraud M Meyer. 2016. A comprehensive comparison of general RNA-RNA interaction prediction methods. Nucleic Acids Research 44, 7 (2016), e61.Google ScholarCross Ref
- Hangchuan Shi, Yin Sun, Miao He, Xiong Yang, Michiaki Hamada, Tsukasa Fukunaga, Xiaoping Zhang, and Chawnshang Chang. 2020. Targeting the TR4 nuclear receptor-mediated lncTASR/AXL signaling with tretinoin increases the sunitinib sensitivity to better suppress the RCC progression. Oncogene 39, 3 (2020), 530--545.Google ScholarCross Ref
- Maria Lina Tornesello, Raffaella Faraonio, Luigi Buonaguro, Clorinda Annunziata, Noemy Starita, Andrea Cerasuolo, Francesca Pezzuto, Anna Lucia Tornesello, and Franco Maria Buonaguro. 2020. The role of microRNAs, long non-coding RNAs, and circular RNAs in cervical cancer. Frontiers in Oncology 10 (2020), 150.Google ScholarCross Ref
- Sinan Uğur Umu and Paul P Gardner. 2017. A comprehensive benchmark of RNA-RNA interaction prediction tools for all domains of life. Bioinformatics 33, 7 (2017), 988--996.Google ScholarCross Ref
Index Terms
- Parallel construction of RNA databases for extensive lncRNA-RNA interaction prediction
Recommendations
RNA string kernels for RNAi off-target evaluation
RNA interference (RNAi) is a posttranscriptional gene silencing mechanism used to study gene functions, knock down viral genes, and treat diseases therapeutically. However, an 'off-target effect' deteriorates its specificity and applicability. Complete ...
Computational prediction of RNA editing sites
Motivation: Some organisms edit their messenger RNA resulting in differences between the genomic sequence for a gene and the corresponding messenger RNA sequence. This difference complicates experimental and computational attempts to find and study ...
Comments