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ZCURVE_CoV: a new system to recognize protein coding genes in coronavirus genomes, and its applications in analyzing SARS-CoV genomes
A new system to recognize protein coding genes in the coronavirus genomes, specially suitable for the SARS-CoV genomes, has been proposed in this paper. Compared with some existing systems, the new program package has the merits of simplicity, high accuracy, reliability, and quickness. The system ZC...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier Science (USA).
2003
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7134609/ https://www.ncbi.nlm.nih.gov/pubmed/12859968 http://dx.doi.org/10.1016/S0006-291X(03)01192-6 |
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author | Chen, Ling-Ling Ou, Hong-Yu Zhang, Ren Zhang, Chun-Ting |
author_facet | Chen, Ling-Ling Ou, Hong-Yu Zhang, Ren Zhang, Chun-Ting |
author_sort | Chen, Ling-Ling |
collection | PubMed |
description | A new system to recognize protein coding genes in the coronavirus genomes, specially suitable for the SARS-CoV genomes, has been proposed in this paper. Compared with some existing systems, the new program package has the merits of simplicity, high accuracy, reliability, and quickness. The system ZCURVE_CoV has been run for each of the 11 newly sequenced SARS-CoV genomes. Consequently, six genomes not annotated previously have been annotated, and some problems of previous annotations in the remaining five genomes have been pointed out and discussed. In addition to the polyprotein chain ORFs 1a and 1b and the four genes coding for the major structural proteins, spike (S), small envelop (E), membrane (M), and nuleocaspid (N), respectively, ZCURVE_CoV also predicts 5–6 putative proteins in length between 39 and 274 amino acids with unknown functions. Some single nucleotide mutations within these putative coding sequences have been detected and their biological implications are discussed. A web service is provided, by which a user can obtain the annotated result immediately by pasting the SARS-CoV genome sequences into the input window on the web site (http://tubic.tju.edu.cn/sars/). The software ZCURVE_CoV can also be downloaded freely from the web address mentioned above and run in computers under the platforms of Windows or Linux. |
format | Online Article Text |
id | pubmed-7134609 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2003 |
publisher | Elsevier Science (USA). |
record_format | MEDLINE/PubMed |
spelling | pubmed-71346092020-04-07 ZCURVE_CoV: a new system to recognize protein coding genes in coronavirus genomes, and its applications in analyzing SARS-CoV genomes Chen, Ling-Ling Ou, Hong-Yu Zhang, Ren Zhang, Chun-Ting Biochem Biophys Res Commun Article A new system to recognize protein coding genes in the coronavirus genomes, specially suitable for the SARS-CoV genomes, has been proposed in this paper. Compared with some existing systems, the new program package has the merits of simplicity, high accuracy, reliability, and quickness. The system ZCURVE_CoV has been run for each of the 11 newly sequenced SARS-CoV genomes. Consequently, six genomes not annotated previously have been annotated, and some problems of previous annotations in the remaining five genomes have been pointed out and discussed. In addition to the polyprotein chain ORFs 1a and 1b and the four genes coding for the major structural proteins, spike (S), small envelop (E), membrane (M), and nuleocaspid (N), respectively, ZCURVE_CoV also predicts 5–6 putative proteins in length between 39 and 274 amino acids with unknown functions. Some single nucleotide mutations within these putative coding sequences have been detected and their biological implications are discussed. A web service is provided, by which a user can obtain the annotated result immediately by pasting the SARS-CoV genome sequences into the input window on the web site (http://tubic.tju.edu.cn/sars/). The software ZCURVE_CoV can also be downloaded freely from the web address mentioned above and run in computers under the platforms of Windows or Linux. Elsevier Science (USA). 2003-07-25 2003-06-27 /pmc/articles/PMC7134609/ /pubmed/12859968 http://dx.doi.org/10.1016/S0006-291X(03)01192-6 Text en Copyright © 2003 Elsevier Science (USA). All rights reserved. Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active. |
spellingShingle | Article Chen, Ling-Ling Ou, Hong-Yu Zhang, Ren Zhang, Chun-Ting ZCURVE_CoV: a new system to recognize protein coding genes in coronavirus genomes, and its applications in analyzing SARS-CoV genomes |
title | ZCURVE_CoV: a new system to recognize protein coding genes in coronavirus genomes, and its applications in analyzing SARS-CoV genomes |
title_full | ZCURVE_CoV: a new system to recognize protein coding genes in coronavirus genomes, and its applications in analyzing SARS-CoV genomes |
title_fullStr | ZCURVE_CoV: a new system to recognize protein coding genes in coronavirus genomes, and its applications in analyzing SARS-CoV genomes |
title_full_unstemmed | ZCURVE_CoV: a new system to recognize protein coding genes in coronavirus genomes, and its applications in analyzing SARS-CoV genomes |
title_short | ZCURVE_CoV: a new system to recognize protein coding genes in coronavirus genomes, and its applications in analyzing SARS-CoV genomes |
title_sort | zcurve_cov: a new system to recognize protein coding genes in coronavirus genomes, and its applications in analyzing sars-cov genomes |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7134609/ https://www.ncbi.nlm.nih.gov/pubmed/12859968 http://dx.doi.org/10.1016/S0006-291X(03)01192-6 |
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