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Computational characterization and analysis of molecular sequence data of Elizabethkingia meningoseptica

OBJECTIVE: Elizabethkingia meningoseptica is a multidrug resistance strain which primarily causes meningitis in neonates and immunocompromised patients. Being a nosocomial infection causing agent, less information is available in literature, specifically, about its genomic makeup and associated feat...

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Autores principales: Girdhar, Neha, Kumari, Nilima, Krishnamachari, A.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8994065/
https://www.ncbi.nlm.nih.gov/pubmed/35397563
http://dx.doi.org/10.1186/s13104-022-06011-5
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author Girdhar, Neha
Kumari, Nilima
Krishnamachari, A.
author_facet Girdhar, Neha
Kumari, Nilima
Krishnamachari, A.
author_sort Girdhar, Neha
collection PubMed
description OBJECTIVE: Elizabethkingia meningoseptica is a multidrug resistance strain which primarily causes meningitis in neonates and immunocompromised patients. Being a nosocomial infection causing agent, less information is available in literature, specifically, about its genomic makeup and associated features. An attempt is made to study them through bioinformatics tools with respect to compositions, embedded periodicities, open reading frames, origin of replication, phylogeny, orthologous gene clusters analysis and pathways. RESULTS: Complete DNA and protein sequence pertaining to E. meningoseptica were thoroughly analyzed as part of the study. E. meningoseptica G4076 genome showed 7593 ORFs it is GC rich. Fourier based analysis showed the presence of typical three base periodicity at the genome level. Putative origin of replication has been identified. Phylogenetically, E. meningoseptica is relatively closer to E. anophelis compared to other Elizabethkingia species. A total of 2606 COGs were shared by all five Elizabethkingia species. Out of 3391 annotated proteins, we could identify 18 unique ones involved in metabolic pathway of E. meningoseptica and this can be an initiation point for drug designing and development. Our study is novel in the aspect in characterizing and analyzing the whole genome data of E. meningoseptica. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13104-022-06011-5.
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spelling pubmed-89940652022-04-10 Computational characterization and analysis of molecular sequence data of Elizabethkingia meningoseptica Girdhar, Neha Kumari, Nilima Krishnamachari, A. BMC Res Notes Research Note OBJECTIVE: Elizabethkingia meningoseptica is a multidrug resistance strain which primarily causes meningitis in neonates and immunocompromised patients. Being a nosocomial infection causing agent, less information is available in literature, specifically, about its genomic makeup and associated features. An attempt is made to study them through bioinformatics tools with respect to compositions, embedded periodicities, open reading frames, origin of replication, phylogeny, orthologous gene clusters analysis and pathways. RESULTS: Complete DNA and protein sequence pertaining to E. meningoseptica were thoroughly analyzed as part of the study. E. meningoseptica G4076 genome showed 7593 ORFs it is GC rich. Fourier based analysis showed the presence of typical three base periodicity at the genome level. Putative origin of replication has been identified. Phylogenetically, E. meningoseptica is relatively closer to E. anophelis compared to other Elizabethkingia species. A total of 2606 COGs were shared by all five Elizabethkingia species. Out of 3391 annotated proteins, we could identify 18 unique ones involved in metabolic pathway of E. meningoseptica and this can be an initiation point for drug designing and development. Our study is novel in the aspect in characterizing and analyzing the whole genome data of E. meningoseptica. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13104-022-06011-5. BioMed Central 2022-04-09 /pmc/articles/PMC8994065/ /pubmed/35397563 http://dx.doi.org/10.1186/s13104-022-06011-5 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Research Note
Girdhar, Neha
Kumari, Nilima
Krishnamachari, A.
Computational characterization and analysis of molecular sequence data of Elizabethkingia meningoseptica
title Computational characterization and analysis of molecular sequence data of Elizabethkingia meningoseptica
title_full Computational characterization and analysis of molecular sequence data of Elizabethkingia meningoseptica
title_fullStr Computational characterization and analysis of molecular sequence data of Elizabethkingia meningoseptica
title_full_unstemmed Computational characterization and analysis of molecular sequence data of Elizabethkingia meningoseptica
title_short Computational characterization and analysis of molecular sequence data of Elizabethkingia meningoseptica
title_sort computational characterization and analysis of molecular sequence data of elizabethkingia meningoseptica
topic Research Note
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8994065/
https://www.ncbi.nlm.nih.gov/pubmed/35397563
http://dx.doi.org/10.1186/s13104-022-06011-5
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