One of the major challenges in bioinformatics is the development of efficient computational algorithms for biological sequence motif discovery. In the post-genomic era, the ability to predict the behavior, the function, or the structure of biological entities or motifs such as genes and proteins, as well as interactions among them, play a fundamental role in the discovery of information to help explain biological mechanisms. This necessitated the development of computational methods for identifying these entities. Consequently, a large number of motif finding algorithms have been implemented and applied to various organisms over the past decade. This paper presents a comparative analysis of the latest developments in motif finding algorithms and proposed an algorithm for motif discovery based on a combinatorial approach of pattern driven and statistical based approach. The proposed algorithm, Suffix Tree Gene Enrichment Motif Searching (STGEMS) as reported in [30], proved effective in identifying motifs from organisms with peculiarity in their genomic structure such as the AT-rich sequence of the malaria parasite, P. falciparum. The empirical time analysis of seven motif discovery algorithms was evaluated using four sets of genes from the intraerythrocytic development cycle of P. falciparum. The result shows that algorithms based on a combinatorial approach are more desirable.
| Published in | Computational Biology and Bioinformatics (Volume 4, Issue 1) |
| DOI | 10.11648/j.cbb.20160401.11 |
| Page(s) | 1-9 |
| Creative Commons |
This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited. |
| Copyright |
Copyright © The Author(s), 2024. Published by Science Publishing Group |
Motifs, Suffix Tree, Time Complexity, P. falciparum
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APA Style
Angela Makolo. (2015). A Comparative Analysis of Motif Discovery Algorithms. Computational Biology and Bioinformatics, 4(1), 1-9. https://doi.org/10.11648/j.cbb.20160401.11
ACS Style
Angela Makolo. A Comparative Analysis of Motif Discovery Algorithms. Comput. Biol. Bioinform. 2015, 4(1), 1-9. doi: 10.11648/j.cbb.20160401.11
AMA Style
Angela Makolo. A Comparative Analysis of Motif Discovery Algorithms. Comput Biol Bioinform. 2015;4(1):1-9. doi: 10.11648/j.cbb.20160401.11
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Download@article{10.11648/j.cbb.20160401.11,
author = {Angela Makolo},
title = {A Comparative Analysis of Motif Discovery Algorithms},
journal = {Computational Biology and Bioinformatics},
volume = {4},
number = {1},
pages = {1-9},
doi = {10.11648/j.cbb.20160401.11},
url = {https://doi.org/10.11648/j.cbb.20160401.11},
eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.cbb.20160401.11},
abstract = {One of the major challenges in bioinformatics is the development of efficient computational algorithms for biological sequence motif discovery. In the post-genomic era, the ability to predict the behavior, the function, or the structure of biological entities or motifs such as genes and proteins, as well as interactions among them, play a fundamental role in the discovery of information to help explain biological mechanisms. This necessitated the development of computational methods for identifying these entities. Consequently, a large number of motif finding algorithms have been implemented and applied to various organisms over the past decade. This paper presents a comparative analysis of the latest developments in motif finding algorithms and proposed an algorithm for motif discovery based on a combinatorial approach of pattern driven and statistical based approach. The proposed algorithm, Suffix Tree Gene Enrichment Motif Searching (STGEMS) as reported in [30], proved effective in identifying motifs from organisms with peculiarity in their genomic structure such as the AT-rich sequence of the malaria parasite, P. falciparum. The empirical time analysis of seven motif discovery algorithms was evaluated using four sets of genes from the intraerythrocytic development cycle of P. falciparum. The result shows that algorithms based on a combinatorial approach are more desirable.},
year = {2015}
}
TY - JOUR T1 - A Comparative Analysis of Motif Discovery Algorithms AU - Angela Makolo Y1 - 2015/11/19 PY - 2015 N1 - https://doi.org/10.11648/j.cbb.20160401.11 DO - 10.11648/j.cbb.20160401.11 T2 - Computational Biology and Bioinformatics JF - Computational Biology and Bioinformatics JO - Computational Biology and Bioinformatics SP - 1 EP - 9 PB - Science Publishing Group SN - 2330-8281 UR - https://doi.org/10.11648/j.cbb.20160401.11 AB - One of the major challenges in bioinformatics is the development of efficient computational algorithms for biological sequence motif discovery. In the post-genomic era, the ability to predict the behavior, the function, or the structure of biological entities or motifs such as genes and proteins, as well as interactions among them, play a fundamental role in the discovery of information to help explain biological mechanisms. This necessitated the development of computational methods for identifying these entities. Consequently, a large number of motif finding algorithms have been implemented and applied to various organisms over the past decade. This paper presents a comparative analysis of the latest developments in motif finding algorithms and proposed an algorithm for motif discovery based on a combinatorial approach of pattern driven and statistical based approach. The proposed algorithm, Suffix Tree Gene Enrichment Motif Searching (STGEMS) as reported in [30], proved effective in identifying motifs from organisms with peculiarity in their genomic structure such as the AT-rich sequence of the malaria parasite, P. falciparum. The empirical time analysis of seven motif discovery algorithms was evaluated using four sets of genes from the intraerythrocytic development cycle of P. falciparum. The result shows that algorithms based on a combinatorial approach are more desirable. VL - 4 IS - 1 ER -
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