Utilize este identificador para referenciar este registo: https://hdl.handle.net/1822/44986

TítuloEstimation of biomass composition from genomic and transcriptomic information
Autor(es)Santos, Sophia
Rocha, Isabel
Data27-Nov-2016
EditoraDe Gruyter Open
RevistaJournal of Integrative Bioinformatics
CitaçãoSantos, Sophia; Rocha, Isabel, Estimation of biomass composition from genomic and transcriptomic information. Journal of Integrative Bioinformatics, 13(2), 285, 2016
Resumo(s)Given the great potential impact of the growing number of complete genome-scale metabolic network reconstructions of microorganisms, bioinformatics tools are needed to simplify and accelerate the course of knowledge in this field. One essential component of a genome-scale metabolic model is its biomass equation, whose maximization is one of the most common objective functions used in Flux Balance Analysis formulations. Some components of biomass, such as amino acids and nucleotides, can be estimated from genome information, providing reliable data without the need of performing lab experiments. In this work a java tool is proposed that estimates microbial biomass composition in amino acids and nucleotides, from genome and transcriptomic information, using as input files sequences in FASTA format and files with transcriptomic data in the csv format. This application allows to obtain the results rapidly and is also a user-friendly tool for users with any or little background in informatics (http://darwin.di.uminho.pt/biomass/). The results obtained using this tool are fairly close to experimental data, showing that the estimation of amino acid and nucleotide compositions from genome information and from transcriptomic data is a good alternative when no experimental data is available.
TipoArtigo
URIhttps://hdl.handle.net/1822/44986
DOI10.2390/biecoll-jib-2016-285
ISSN1613-4516
e-ISSN1613-4516
Arbitragem científicayes
AcessoAcesso aberto
Aparece nas coleções:CEB - Publicações em Revistas/Séries Internacionais / Publications in International Journals/Series

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