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Global sequence features based translation initiation site prediction in human genomic sequences
Gene prediction has been increasingly important in genome annotation due to advancements in sequencing technology. Genome annotation further helps in determining the structure and function of these genes. Translation initiation site prediction (TIS) in human genomic sequences is one of the fundament...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7490824/ https://www.ncbi.nlm.nih.gov/pubmed/32964155 http://dx.doi.org/10.1016/j.heliyon.2020.e04825 |
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author | Goel, Neelam Singh, Shailendra Aseri, Trilok Chand |
author_facet | Goel, Neelam Singh, Shailendra Aseri, Trilok Chand |
author_sort | Goel, Neelam |
collection | PubMed |
description | Gene prediction has been increasingly important in genome annotation due to advancements in sequencing technology. Genome annotation further helps in determining the structure and function of these genes. Translation initiation site prediction (TIS) in human genomic sequences is one of the fundamental and essential steps in gene prediction. Thus, accurate prediction of TIS in these sequences is highly desirable. Although many computational methods were developed for this problem, none of them focused on finding these sites in human genomic sequences. In this paper, a new TIS prediction method is proposed by incorporating global sequence based features. Support vector machine is used to assess the prediction power of these features. The proposed method achieved accuracy of above 90% when tested for genomic as well as cDNA sequences. The experimental results indicate that the method works well for both genomic and cDNA sequences. The method can be integrated into gene prediction system in future. |
format | Online Article Text |
id | pubmed-7490824 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Elsevier |
record_format | MEDLINE/PubMed |
spelling | pubmed-74908242020-09-21 Global sequence features based translation initiation site prediction in human genomic sequences Goel, Neelam Singh, Shailendra Aseri, Trilok Chand Heliyon Research Article Gene prediction has been increasingly important in genome annotation due to advancements in sequencing technology. Genome annotation further helps in determining the structure and function of these genes. Translation initiation site prediction (TIS) in human genomic sequences is one of the fundamental and essential steps in gene prediction. Thus, accurate prediction of TIS in these sequences is highly desirable. Although many computational methods were developed for this problem, none of them focused on finding these sites in human genomic sequences. In this paper, a new TIS prediction method is proposed by incorporating global sequence based features. Support vector machine is used to assess the prediction power of these features. The proposed method achieved accuracy of above 90% when tested for genomic as well as cDNA sequences. The experimental results indicate that the method works well for both genomic and cDNA sequences. The method can be integrated into gene prediction system in future. Elsevier 2020-09-14 /pmc/articles/PMC7490824/ /pubmed/32964155 http://dx.doi.org/10.1016/j.heliyon.2020.e04825 Text en © 2020 Published by Elsevier Ltd. http://creativecommons.org/licenses/by-nc-nd/4.0/ This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). |
spellingShingle | Research Article Goel, Neelam Singh, Shailendra Aseri, Trilok Chand Global sequence features based translation initiation site prediction in human genomic sequences |
title | Global sequence features based translation initiation site prediction in human genomic sequences |
title_full | Global sequence features based translation initiation site prediction in human genomic sequences |
title_fullStr | Global sequence features based translation initiation site prediction in human genomic sequences |
title_full_unstemmed | Global sequence features based translation initiation site prediction in human genomic sequences |
title_short | Global sequence features based translation initiation site prediction in human genomic sequences |
title_sort | global sequence features based translation initiation site prediction in human genomic sequences |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7490824/ https://www.ncbi.nlm.nih.gov/pubmed/32964155 http://dx.doi.org/10.1016/j.heliyon.2020.e04825 |
work_keys_str_mv | AT goelneelam globalsequencefeaturesbasedtranslationinitiationsitepredictioninhumangenomicsequences AT singhshailendra globalsequencefeaturesbasedtranslationinitiationsitepredictioninhumangenomicsequences AT aseritrilokchand globalsequencefeaturesbasedtranslationinitiationsitepredictioninhumangenomicsequences |