skip to main content
10.1145/3555776.3577772acmconferencesArticle/Chapter ViewAbstractPublication PagessacConference Proceedingsconference-collections
poster

Parallel construction of RNA databases for extensive lncRNA-RNA interaction prediction

Published: 07 June 2023 Publication History

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).

References

[1]
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.
[2]
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.
[3]
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.
[4]
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.
[5]
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.
[6]
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.
[7]
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.
[8]
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.
[9]
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.
[10]
Daniel Lai and Irmtraud M Meyer. 2016. A comprehensive comparison of general RNA-RNA interaction prediction methods. Nucleic Acids Research 44, 7 (2016), e61.
[11]
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.
[12]
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.
[13]
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.

Recommendations

Comments

Information & Contributors

Information

Published In

cover image ACM Conferences
SAC '23: Proceedings of the 38th ACM/SIGAPP Symposium on Applied Computing
March 2023
1932 pages
ISBN:9781450395175
DOI:10.1145/3555776
Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the owner/author(s).

Sponsors

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 07 June 2023

Check for updates

Author Tags

  1. bioinformatics
  2. lncRNA-RNA
  3. high-performance computing
  4. parallel computing
  5. MPI
  6. OpenMP

Qualifiers

  • Poster

Funding Sources

  • Ministry of Science and Innovation, Spain
  • Xunta de Galicia and FEDER funds of the European Union

Conference

SAC '23
Sponsor:

Acceptance Rates

Overall Acceptance Rate 1,650 of 6,669 submissions, 25%

Upcoming Conference

SAC '25
The 40th ACM/SIGAPP Symposium on Applied Computing
March 31 - April 4, 2025
Catania , Italy

Contributors

Other Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

  • 0
    Total Citations
  • 37
    Total Downloads
  • Downloads (Last 12 months)5
  • Downloads (Last 6 weeks)0
Reflects downloads up to 16 Feb 2025

Other Metrics

Citations

View Options

Login options

View options

PDF

View or Download as a PDF file.

PDF

eReader

View online with eReader.

eReader

Figures

Tables

Media

Share

Share

Share this Publication link

Share on social media