Zobrazeno 1 - 7
of 7
pro vyhledávání: '"João C. Sequeira"'
Publikováno v:
Computational and Structural Biotechnology Journal, Vol 20, Iss , Pp 1798-1810 (2022)
Omics and meta-omics technologies are powerful approaches to explore microorganisms’ functions, but the sheer size and complexity of omics datasets often turn the analysis into a challenging task. Software developed for omics and meta-omics analyse
Externí odkaz:
https://doaj.org/article/3b6bfca9cb424adfa95aba3b1a017572
Autor:
Valdo R. Martins, Carlos J. B. Freitas, A. Rita Castro, Rita M. Silva, Eduardo J. Gudiña, João C. Sequeira, Andreia F. Salvador, M. Alcina Pereira, Ana J. Cavaleiro
Publikováno v:
Frontiers in Microbiology, Vol 12 (2021)
Biosorbent materials are effective in the removal of spilled oil from water, but their effect on hydrocarbonoclastic bacteria is not known. Here, we show that corksorb, a cork-based biosorbent, enhances growth and alkane degradation by Rhodococcus op
Externí odkaz:
https://doaj.org/article/e698759966c4464091a85349c7f85415
Autor:
Ana J. Cavaleiro, Ana P. Guedes, Sérgio A. Silva, Ana L. Arantes, João C. Sequeira, Andreia F. Salvador, Diana Z. Sousa, Alfons J. M. Stams, M. Madalena Alves
Publikováno v:
Microorganisms, Vol 8, Iss 9, p 1375 (2020)
Long-chain fatty acids (LCFA) are common contaminants in municipal and industrial wastewater that can be converted anaerobically to methane. A low hydrogen partial pressure is required for LCFA degradation by anaerobic bacteria, requiring the establi
Externí odkaz:
https://doaj.org/article/e551796d11bd4f519101d2b53efacd58
Autor:
Alexandre Oliveira, Emanuel Cunha, Fernando Cruz, João Capela, João C. Sequeira, Marta Sampaio, Cláudia Sampaio, Oscar Dias
Publikováno v:
Journal of integrative bioinformatics. 19(3)
Genome-scale metabolic models (GEMs) are essential tools for in silico phenotype prediction and strain optimisation. The most straightforward GEMs reconstruction approach uses published models as templates to generate the initial draft, requiring fur
Autor:
Oscar Dias, João Capela, Emanuel Cunha, Marta Sampaio, Alexandre Oliveira, João C. Sequeira, Fernando Cruz
Publikováno v:
Practical Applications of Computational Biology & Bioinformatics, 15th International Conference (PACBB 2021) ISBN: 9783030862572
PACBB
PACBB
Genome-Scale metabolic models (GEMs) are a relevant tool in systems biology for in silico strain optimisation and drug discovery. An easier way to reconstruct a model is to use available GEMs as templates to create the initial draft, which can be cur
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::d7b085ed01f6104f6513b94b02b0722c
https://doi.org/10.1007/978-3-030-86258-9_14
https://doi.org/10.1007/978-3-030-86258-9_14
Autor:
João C. Sequeira, M. Madalena Alves, Ana Luísa Arantes, Andreia Filipa Ferreira Salvador, Diana Z. Sousa, Alfons J. M. Stams, Sérgio Silva, Ana Júlia Cavaleiro, Ana P. Guedes
Publikováno v:
Repositório Científico de Acesso Aberto de Portugal
Repositório Científico de Acesso Aberto de Portugal (RCAAP)
instacron:RCAAP
Microorganisms 8 (2020) 9
Microorganisms, 8(9)
Microorganisms
Volume 8
Issue 9
Microorganisms, Vol 8, Iss 1375, p 1375 (2020)
Repositório Científico de Acesso Aberto de Portugal (RCAAP)
instacron:RCAAP
Microorganisms 8 (2020) 9
Microorganisms, 8(9)
Microorganisms
Volume 8
Issue 9
Microorganisms, Vol 8, Iss 1375, p 1375 (2020)
Long-chain fatty acids (LCFA) are common contaminants in municipal and industrial wastewater that can be converted anaerobically to methane. A low hydrogen partial pressure is required for LCFA degradation by anaerobic bacteria, requiring the establi
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::1000ed85434d1684a6a98a9934d6c9bd
Publikováno v:
Practical Applications of Computational Biology and Bioinformatics, 12th International Conference ISBN: 9783319987019
PACBB
PACBB
Metagenomics (MG) and Metatranscriptomics (MT) approaches open new perspectives on the interpretation of biological systems composed by complex microbial communities. Dealing with large sequencing datasets, to extract the desired information and inte
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::574d0334f55a61dc4373a71fc7fb6c8a
https://doi.org/10.1007/978-3-319-98702-6_22
https://doi.org/10.1007/978-3-319-98702-6_22