Parallel Greedy Approach for Phylogenetic Tree Construction in the Context of Marine Species

Notice

This is an unedited manuscript accepted for publication and provided as an Article in Press for early access at the author’s request. The article will undergo copyediting, typesetting, and galley proof review before final publication. Please be aware that errors may be identified during production that could affect the content. All legal disclaimers of the journal apply.

Year : 2026 | Volume : 04 | 01 | Page :
    By

    Sathi Lakshmi Teja Sri,

  • Manas Kumar Yogi,

  1. Undergraduate Student, Department of Computer Science and Engineering, Pragati Engineering College (A), Surampalem, Andhra Pradesh, India
  2. Assistant Professor, Department of Computer Science and Engineering, Pragati Engineering College (A), Surampalem, Andhra Pradesh, India

Abstract

The rebuilding of phylogenetic trees for marine species shows major computing problems because of the massive genomic data the huge biodiversity inherent in ocean ecosystems. Traditional phylogenetic methods are accurate but becomes more expensive when they are processing with thousands of marine taxa parallelly. This article shows a critical analysis of parallel greedy algorithms as an adaptable solution for large-scale marine phylogenetics. It examines the main principles of greedy heuristics applied in the construction of tree based on distance, specifically on Neighbor-Joining and its variations. The paper demonstrates that the implementation of the parallelization techniques; data parallelism, task parallelism, and the hybrid models may be utilized to overcome the time complexity factors that restrict the use of the traditional implementation of the usual methods in a quadratic manner. The analysis integrates current advances in shared computing architectures, Graphical Processing Units (GPU) acceleration, algorithmic optimizations that shows the working of marine metagenomic datasets that has tens of thousands of operational taxonomic units. Improvements in performance are evaluated in various fields of marine research, such as microbial community studies and vertebrate evolutionary studies, and examples of nearly linear speedup of parallel greedy approaches on high-performance computing clusters has been documented. In addition to this, We discuss the synthesis of these methods with advanced technologies such as cloud computing and software containerization to enable accessible marine biodiversity studies. The results shows that parallel greedy methods decreases phylogenetic rebuilding time complexity from days to hours for datasets containing 10,000+ marine species, showcasing live analysis of natural samples and providing large scale evolutionary studies crucial for conservation and climate impact assessment.

Keywords: Phylogenetic tree reconstruction, Marine genomics, Parallel greedy algorithms, High-performance computing (HPC), Metagenomic datasets, Scalable phylogenetic pipelines

How to cite this article:
Sathi Lakshmi Teja Sri, Manas Kumar Yogi. Parallel Greedy Approach for Phylogenetic Tree Construction in the Context of Marine Species. International Journal of Algorithms Design and Analysis Review. 2026; 04(01):-.
How to cite this URL:
Sathi Lakshmi Teja Sri, Manas Kumar Yogi. Parallel Greedy Approach for Phylogenetic Tree Construction in the Context of Marine Species. International Journal of Algorithms Design and Analysis Review. 2026; 04(01):-. Available from: https://journals.stmjournals.com/ijadar/article=2026/view=241166


References

  1. Stewart EE, Hartmann FT, Morgenstern Y, Storrs KR, Maiello G, Fleming RW. Mental object rotation based on two-dimensional visual representations. Current Biology. 2022 Nov 7;32(21):R1224-5.
  2. Lin X, Amalraj M, Blanton C, Avila B, Holmes TC, Nitz DA, Xu X. Noncanonical projections to the hippocampal CA3 regulate spatial learning and memory by modulating the feedforward hippocampal trisynaptic pathway. PLoS biology. 2021 Dec 20;19(12):e3001127.
  3. Kapli P, Yang Z, Telford MJ. Phylogenetic tree building in the genomic age. Nature Reviews Genetics. 2020 Jul;21(7):428-44.
  4. Purkayastha P, Pendyala K, Saxena AS, Hakimjavadi H, Chamala S, Dixit P, Baer CF, Lele TP. Reverse plasticity underlies rapid evolution by clonal selection within populations of fibroblasts propagated on a novel soft substrate. Molecular Biology and Evolution. 2021 Aug 1;38(8):3279-93.
  5. Borenstein E, Kupiec M, Feldman MW, Ruppin E. Large-scale reconstruction and phylogenetic analysis of metabolic environments. Proceedings of the National Academy of Sciences. 2008 Sep 23;105(38):14482-7.
  6. Sunagawa S, Acinas SG, Bork P, Bowler C, Eveillard D, Gorsky G, Guidi L, Iudicone D, Karsenti E, Lombard F. Tara Oceans: towards global ocean ecosystems biology. Nature Reviews Microbiology. 2020 Aug;18(8):428-45.
  7. Obradovic A, Chowdhury N, Haake SM, Ager C, Wang V, Vlahos L, Guo XV, Aggen DH, Rathmell WK, Jonasch E, Johnson JE. Single-cell protein activity analysis identifies recurrence-associated renal tumor macrophages. Cell. 2021 May 27;184(11):2988-3005.
  8. Karmin M, Flores R, Saag L, Hudjashov G, Brucato N, Crenna-Darusallam C, Larena M, Endicott PL, Jakobsson M, Lansing JS, Sudoyo H. Episodes of diversification and isolation in Island Southeast Asian and Near Oceanian male lineages. Molecular Biology and Evolution. 2022 Mar 1;39(3):msac045.
  9. Ren Y, Chakraborty T, Doijad S, Falgenhauer L, Falgenhauer J, Goesmann A, Hauschild AC, Schwengers O, Heider D. Prediction of antimicrobial resistance based on whole-genome sequencing and machine learning. Bioinformatics. 2022 Jan 15;38(2):325-34.
  10. Price MN, Dehal PS, Arkin AP. FastTree 2–approximately maximum-likelihood trees for large alignments. PloS one. 2010 Mar 10;5(3):e9490.
  11. Friedman ST, Collyer ML, Price SA, Wainwright PC. Divergent processes drive parallel evolution in marine and freshwater fishes. Systematic biology. 2022 Nov 1;71(6):1319-30.
  12. Nyström-Persson J, Keeble-Gagnère G, Zawad N. Compact and evenly distributed k-mer binning for genomic sequences. Bioinformatics. 2021 Sep 1;37(17):2563-9.
  13. Gao Y, Liu Y, Ma Y, Liu B, Wang Y, Xing Y. abPOA: an SIMD-based C library for fast partial order alignment using adaptive band. Bioinformatics. 2021 Aug 1;37(15):2209-11.
  14. Gao Y, Liu Y, Ma Y, Liu B, Wang Y, Xing Y. abPOA: an SIMD-based C library for fast partial order alignment using adaptive band. 2021 Aug 1;37(15):2209-11.
  15. Czech L, Stamatakis A. Scalable methods for analyzing and visualizing phylogenetic placement of metagenomic samples. PLoS One. 2019 May 28;14(5):e0217050.
  16. Günder M, Ispizua Yamati FR, Kierdorf J, Roscher R, Mahlein AK, Bauckhage C. Agricultural plant cataloging and establishment of a data framework from UAV-based crop images by computer vision. GigaScience. 2022;11:giac054.
  17. Hughes EC, Edwards DP, Thomas GH. The homogenization of avian morphological and phylogenetic diversity under the global extinction crisis. Current Biology. 2022 Sep 12;32(17):3830-7.
  18. Yap TK, Frieder O, Martino RL. Parallel computation in biological sequence analysis. IEEE Transactions on parallel and Distributed Systems. 2002 Aug 6;9(3):283-94.
  19. van Rosmalen L, Riedstra B, Beemster N, Dijkstra C, Hut RA. Differential temperature effects on photoperiodism in female voles: A possible explanation for declines in vole populations. Molecular ecology. 2022 Jun;31(12):3360-73.
  20. Johnston A, Matechou E, Dennis EB. Outstanding challenges and future directions for biodiversity monitoring using citizen science data. Methods in Ecology and Evolution. 2023 Jan;14(1):103-16.
Ahead of Print Subscription Review Article
Volume 04
01
Received 09/03/2026
Accepted 18/03/2026
Published 28/04/2026
Publication Time 50 Days
48

Login


My IP

PlumX Metrics