Application and Algorithm: Maximal Motif Discovery for Biological Data in a Sliding Window

Since the discovery of motifs in molecular sequences for real genomic data, research into this phenomenon has attracted increased attention. Motifs are relatively short sequences that are biologically significant. This paper utilises the bioinformatics application of the algorithm outlined in [5], t...

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Detalles Bibliográficos
Autores principales: Alshammary, Miznah H., Iliopoulos, Costas S., Mohamed, Manal, Vayani, Fatima
Formato: Online Artículo Texto
Lenguaje:English
Publicado: 2020
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7256372/
http://dx.doi.org/10.1007/978-3-030-49190-1_19
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author Alshammary, Miznah H.
Iliopoulos, Costas S.
Mohamed, Manal
Vayani, Fatima
author_facet Alshammary, Miznah H.
Iliopoulos, Costas S.
Mohamed, Manal
Vayani, Fatima
author_sort Alshammary, Miznah H.
collection PubMed
description Since the discovery of motifs in molecular sequences for real genomic data, research into this phenomenon has attracted increased attention. Motifs are relatively short sequences that are biologically significant. This paper utilises the bioinformatics application of the algorithm outlined in [5], testing it using real genomic data from large sequences. It intends to implement bioinformatics application for real genomic data, in order to discover interesting regions for all maximal motifs, in a sliding window of length ℓ, on a sequence x of length n.
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spelling pubmed-72563722020-05-29 Application and Algorithm: Maximal Motif Discovery for Biological Data in a Sliding Window Alshammary, Miznah H. Iliopoulos, Costas S. Mohamed, Manal Vayani, Fatima Artificial Intelligence Applications and Innovations. AIAI 2020 IFIP WG 12.5 International Workshops Article Since the discovery of motifs in molecular sequences for real genomic data, research into this phenomenon has attracted increased attention. Motifs are relatively short sequences that are biologically significant. This paper utilises the bioinformatics application of the algorithm outlined in [5], testing it using real genomic data from large sequences. It intends to implement bioinformatics application for real genomic data, in order to discover interesting regions for all maximal motifs, in a sliding window of length ℓ, on a sequence x of length n. 2020-05-04 /pmc/articles/PMC7256372/ http://dx.doi.org/10.1007/978-3-030-49190-1_19 Text en © IFIP International Federation for Information Processing 2020 This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic.
spellingShingle Article
Alshammary, Miznah H.
Iliopoulos, Costas S.
Mohamed, Manal
Vayani, Fatima
Application and Algorithm: Maximal Motif Discovery for Biological Data in a Sliding Window
title Application and Algorithm: Maximal Motif Discovery for Biological Data in a Sliding Window
title_full Application and Algorithm: Maximal Motif Discovery for Biological Data in a Sliding Window
title_fullStr Application and Algorithm: Maximal Motif Discovery for Biological Data in a Sliding Window
title_full_unstemmed Application and Algorithm: Maximal Motif Discovery for Biological Data in a Sliding Window
title_short Application and Algorithm: Maximal Motif Discovery for Biological Data in a Sliding Window
title_sort application and algorithm: maximal motif discovery for biological data in a sliding window
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7256372/
http://dx.doi.org/10.1007/978-3-030-49190-1_19
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