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NCBI prokaryotic genome annotation pipeline

Recent technological advances have opened unprecedented opportunities for large-scale sequencing and analysis of populations of pathogenic species in disease outbreaks, as well as for large-scale diversity studies aimed at expanding our knowledge across the whole domain of prokaryotes. To meet the c...

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Autores principales: Tatusova, Tatiana, DiCuccio, Michael, Badretdin, Azat, Chetvernin, Vyacheslav, Nawrocki, Eric P., Zaslavsky, Leonid, Lomsadze, Alexandre, Pruitt, Kim D., Borodovsky, Mark, Ostell, James
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2016
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5001611/
https://www.ncbi.nlm.nih.gov/pubmed/27342282
http://dx.doi.org/10.1093/nar/gkw569
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author Tatusova, Tatiana
DiCuccio, Michael
Badretdin, Azat
Chetvernin, Vyacheslav
Nawrocki, Eric P.
Zaslavsky, Leonid
Lomsadze, Alexandre
Pruitt, Kim D.
Borodovsky, Mark
Ostell, James
author_facet Tatusova, Tatiana
DiCuccio, Michael
Badretdin, Azat
Chetvernin, Vyacheslav
Nawrocki, Eric P.
Zaslavsky, Leonid
Lomsadze, Alexandre
Pruitt, Kim D.
Borodovsky, Mark
Ostell, James
author_sort Tatusova, Tatiana
collection PubMed
description Recent technological advances have opened unprecedented opportunities for large-scale sequencing and analysis of populations of pathogenic species in disease outbreaks, as well as for large-scale diversity studies aimed at expanding our knowledge across the whole domain of prokaryotes. To meet the challenge of timely interpretation of structure, function and meaning of this vast genetic information, a comprehensive approach to automatic genome annotation is critically needed. In collaboration with Georgia Tech, NCBI has developed a new approach to genome annotation that combines alignment based methods with methods of predicting protein-coding and RNA genes and other functional elements directly from sequence. A new gene finding tool, GeneMarkS+, uses the combined evidence of protein and RNA placement by homology as an initial map of annotation to generate and modify ab initio gene predictions across the whole genome. Thus, the new NCBI's Prokaryotic Genome Annotation Pipeline (PGAP) relies more on sequence similarity when confident comparative data are available, while it relies more on statistical predictions in the absence of external evidence. The pipeline provides a framework for generation and analysis of annotation on the full breadth of prokaryotic taxonomy. For additional information on PGAP see https://www.ncbi.nlm.nih.gov/genome/annotation_prok/ and the NCBI Handbook, https://www.ncbi.nlm.nih.gov/books/NBK174280/.
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spelling pubmed-50016112016-12-07 NCBI prokaryotic genome annotation pipeline Tatusova, Tatiana DiCuccio, Michael Badretdin, Azat Chetvernin, Vyacheslav Nawrocki, Eric P. Zaslavsky, Leonid Lomsadze, Alexandre Pruitt, Kim D. Borodovsky, Mark Ostell, James Nucleic Acids Res Computational Biology Recent technological advances have opened unprecedented opportunities for large-scale sequencing and analysis of populations of pathogenic species in disease outbreaks, as well as for large-scale diversity studies aimed at expanding our knowledge across the whole domain of prokaryotes. To meet the challenge of timely interpretation of structure, function and meaning of this vast genetic information, a comprehensive approach to automatic genome annotation is critically needed. In collaboration with Georgia Tech, NCBI has developed a new approach to genome annotation that combines alignment based methods with methods of predicting protein-coding and RNA genes and other functional elements directly from sequence. A new gene finding tool, GeneMarkS+, uses the combined evidence of protein and RNA placement by homology as an initial map of annotation to generate and modify ab initio gene predictions across the whole genome. Thus, the new NCBI's Prokaryotic Genome Annotation Pipeline (PGAP) relies more on sequence similarity when confident comparative data are available, while it relies more on statistical predictions in the absence of external evidence. The pipeline provides a framework for generation and analysis of annotation on the full breadth of prokaryotic taxonomy. For additional information on PGAP see https://www.ncbi.nlm.nih.gov/genome/annotation_prok/ and the NCBI Handbook, https://www.ncbi.nlm.nih.gov/books/NBK174280/. Oxford University Press 2016-08-19 2016-06-24 /pmc/articles/PMC5001611/ /pubmed/27342282 http://dx.doi.org/10.1093/nar/gkw569 Text en Published by Oxford University Press on behalf of Nucleic Acids Research 2016. This work is written by (a) US Government employee(s) and is in the public domain in the US.
spellingShingle Computational Biology
Tatusova, Tatiana
DiCuccio, Michael
Badretdin, Azat
Chetvernin, Vyacheslav
Nawrocki, Eric P.
Zaslavsky, Leonid
Lomsadze, Alexandre
Pruitt, Kim D.
Borodovsky, Mark
Ostell, James
NCBI prokaryotic genome annotation pipeline
title NCBI prokaryotic genome annotation pipeline
title_full NCBI prokaryotic genome annotation pipeline
title_fullStr NCBI prokaryotic genome annotation pipeline
title_full_unstemmed NCBI prokaryotic genome annotation pipeline
title_short NCBI prokaryotic genome annotation pipeline
title_sort ncbi prokaryotic genome annotation pipeline
topic Computational Biology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5001611/
https://www.ncbi.nlm.nih.gov/pubmed/27342282
http://dx.doi.org/10.1093/nar/gkw569
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