Advanced Search
Display options
Filter resources
Text Availability
Article type
Publication date
Species
Language
Sex
Age
Showing 1 to 10 of 10 entries
Sorted by: Best Match Show Resources per page
Fragment recruitment on metabolic pathways: comparative metabolic profiling of metagenomes and metatranscriptomes.

Bioinformatics (Oxford, England)

Desai DK, Schunck H, Löser JW, Laroche J.
PMID: 23303511
Bioinformatics. 2013 Mar 15;29(6):790-1. doi: 10.1093/bioinformatics/bts721. Epub 2013 Jan 09.

MOTIVATION: The sheer scale of the metagenomic and metatranscriptomic datasets that are now available warrants the development of automated protocols for organizing, annotating and comparing the samples in terms of their metabolic profiles. We describe a user-friendly java program...

Cite
Desai DK, Schunck H, Löser JW, et al. Fragment recruitment on metabolic pathways: comparative metabolic profiling of metagenomes and metatranscriptomes. Bioinformatics. 2013;29(6):790-1doi: 10.1093/bioinformatics/bts721.
Desai, D. K., Schunck, H., Löser, J. W., & Laroche, J. (2013). Fragment recruitment on metabolic pathways: comparative metabolic profiling of metagenomes and metatranscriptomes. Bioinformatics (Oxford, England), 29(6), 790-1. https://doi.org/10.1093/bioinformatics/bts721
Desai, Dhwani K, et al. "Fragment recruitment on metabolic pathways: comparative metabolic profiling of metagenomes and metatranscriptomes." Bioinformatics (Oxford, England) vol. 29,6 (2013): 790-1. doi: https://doi.org/10.1093/bioinformatics/bts721
Desai DK, Schunck H, Löser JW, Laroche J. Fragment recruitment on metabolic pathways: comparative metabolic profiling of metagenomes and metatranscriptomes. Bioinformatics. 2013 Mar 15;29(6):790-1. doi: 10.1093/bioinformatics/bts721. Epub 2013 Jan 09. PMID: 23303511.
Copy Download .nbib Format: NLM
Cellular determinants of metabolite concentration ranges.

PLoS computational biology

Küken A, Eloundou-Mbebi JMO, Basler G, Nikoloski Z.
PMID: 30677015
PLoS Comput Biol. 2019 Jan 24;15(1):e1006687. doi: 10.1371/journal.pcbi.1006687. eCollection 2019 Jan.

Cellular functions are shaped by reaction networks whose dynamics are determined by the concentrations of underlying components. However, cellular mechanisms ensuring that a component's concentration resides in a given range remain elusive. We present network properties which suffice to...

Cite
Küken A, Eloundou-Mbebi JMO, Basler G, et al. Cellular determinants of metabolite concentration ranges. PLoS Comput Biol. 2019;15(1):e1006687doi: 10.1371/journal.pcbi.1006687.
Küken, A., Eloundou-Mbebi, J. M. O., Basler, G., & Nikoloski, Z. (2019). Cellular determinants of metabolite concentration ranges. PLoS computational biology, 15(1), e1006687. https://doi.org/10.1371/journal.pcbi.1006687
Küken, Anika, et al. "Cellular determinants of metabolite concentration ranges." PLoS computational biology vol. 15,1 (2019): e1006687. doi: https://doi.org/10.1371/journal.pcbi.1006687
Küken A, Eloundou-Mbebi JMO, Basler G, Nikoloski Z. Cellular determinants of metabolite concentration ranges. PLoS Comput Biol. 2019 Jan 24;15(1):e1006687. doi: 10.1371/journal.pcbi.1006687. eCollection 2019 Jan. PMID: 30677015; PMCID: PMC6345444.
Copy Download .nbib Format: NLM
Analyzing the metabolome.

Methods in molecular biology (Clifton, N.J.)

Bowling FG, Thomas M.
PMID: 24870129
Methods Mol Biol. 2014;1168:31-45. doi: 10.1007/978-1-4939-0847-9_3.

Metabolites, the chemical entities that are transformed during metabolism, provide a functional readout of cellular biochemistry that offers the best prediction of the phenotype and the nature of a disease. Mass spectrometry now allows thousands of metabolites to be...

Cite
Bowling FG, Thomas M. Analyzing the metabolome. Methods Mol Biol. 2014;1168:31-45doi: 10.1007/978-1-4939-0847-9_3.
Bowling, F. G., & Thomas, M. (2014). Analyzing the metabolome. Methods in molecular biology (Clifton, N.J.), 116831-45. https://doi.org/10.1007/978-1-4939-0847-9_3
Bowling, Francis G, and Thomas, Mervyn. "Analyzing the metabolome." Methods in molecular biology (Clifton, N.J.) vol. 1168 (2014): 31-45. doi: https://doi.org/10.1007/978-1-4939-0847-9_3
Bowling FG, Thomas M. Analyzing the metabolome. Methods Mol Biol. 2014;1168:31-45. doi: 10.1007/978-1-4939-0847-9_3. PMID: 24870129.
Copy Download .nbib Format: NLM
The Connection and Disconnection Between Microbiome and Metabolome: A Critical Appraisal in Clinical Research.

Biological research for nursing

Misra BB.
PMID: 32013533
Biol Res Nurs. 2020 Oct;22(4):561-576. doi: 10.1177/1099800420903083. Epub 2020 Feb 04.

Big data-driven omics research has led to a steep rise in investigations involving two of the most functional omes, the metabolome and microbiome. The former is touted as the closest to the phenotype, and the latter is implicated in...

Cite
Misra BB. The Connection and Disconnection Between Microbiome and Metabolome: A Critical Appraisal in Clinical Research. Biol Res Nurs. 2020;22(4):561-576doi: 10.1177/1099800420903083.
Misra, B. B. (2020). The Connection and Disconnection Between Microbiome and Metabolome: A Critical Appraisal in Clinical Research. Biological research for nursing, 22(4), 561-576. https://doi.org/10.1177/1099800420903083
Misra, Biswapriya B. "The Connection and Disconnection Between Microbiome and Metabolome: A Critical Appraisal in Clinical Research." Biological research for nursing vol. 22,4 (2020): 561-576. doi: https://doi.org/10.1177/1099800420903083
Misra BB. The Connection and Disconnection Between Microbiome and Metabolome: A Critical Appraisal in Clinical Research. Biol Res Nurs. 2020 Oct;22(4):561-576. doi: 10.1177/1099800420903083. Epub 2020 Feb 04. PMID: 32013533.
Copy Download .nbib Format: NLM
Nonadditive Effects of Genes in Human Metabolomics.

Genetics

Tsepilov YA, Shin SY, Soranzo N, Spector TD, Prehn C, Adamski J, Kastenmüller G, Wang-Sattler R, Strauch K, Gieger C, Aulchenko YS, Ried JS.
PMID: 25977471
Genetics. 2015 Jul;200(3):707-18. doi: 10.1534/genetics.115.175760. Epub 2015 May 14.

Genome-wide association studies (GWAS) are widely applied to analyze the genetic effects on phenotypes. With the availability of high-throughput technologies for metabolite measurements, GWAS successfully identified loci that affect metabolite concentrations and underlying pathways. In most GWAS, the effect...

Cite
Tsepilov YA, Shin SY, Soranzo N, et al. Nonadditive Effects of Genes in Human Metabolomics. Genetics. 2015;200(3):707-18doi: 10.1534/genetics.115.175760.
Tsepilov, Y. A., Shin, S. Y., Soranzo, N., Spector, T. D., Prehn, C., Adamski, J., Kastenmüller, G., Wang-Sattler, R., Strauch, K., Gieger, C., Aulchenko, Y. S., & Ried, J. S. (2015). Nonadditive Effects of Genes in Human Metabolomics. Genetics, 200(3), 707-18. https://doi.org/10.1534/genetics.115.175760
Tsepilov, Yakov A, et al. "Nonadditive Effects of Genes in Human Metabolomics." Genetics vol. 200,3 (2015): 707-18. doi: https://doi.org/10.1534/genetics.115.175760
Tsepilov YA, Shin SY, Soranzo N, Spector TD, Prehn C, Adamski J, Kastenmüller G, Wang-Sattler R, Strauch K, Gieger C, Aulchenko YS, Ried JS. Nonadditive Effects of Genes in Human Metabolomics. Genetics. 2015 Jul;200(3):707-18. doi: 10.1534/genetics.115.175760. Epub 2015 May 14. PMID: 25977471; PMCID: PMC4512538.
Copy Download .nbib Format: NLM
Human White Adipose Tissue Metabolome: Current Perspective.

Obesity (Silver Spring, Md.)

Kučera J, Spáčil Z, Friedecký D, Novák J, Pekař M, Bienertová-Vašků J.
PMID: 30369078
Obesity (Silver Spring). 2018 Dec;26(12):1870-1878. doi: 10.1002/oby.22336. Epub 2018 Oct 28.

OBJECTIVE: Interest in metabolites produced by adipose tissue has increased substantially in the past several decades. Previously regarded as an inert energy storage depot, adipose tissue is now viewed as a complex metabolically active organ with considerable impact on...

Cite
Kučera J, Spáčil Z, Friedecký D, et al. Human White Adipose Tissue Metabolome: Current Perspective. Obesity (Silver Spring). 2018;26(12):1870-1878doi: 10.1002/oby.22336.
Kučera, J., Spáčil, Z., Friedecký, D., Novák, J., Pekař, M., & Bienertová-Vašků, J. (2018). Human White Adipose Tissue Metabolome: Current Perspective. Obesity (Silver Spring, Md.), 26(12), 1870-1878. https://doi.org/10.1002/oby.22336
Kučera, Jan, et al. "Human White Adipose Tissue Metabolome: Current Perspective." Obesity (Silver Spring, Md.) vol. 26,12 (2018): 1870-1878. doi: https://doi.org/10.1002/oby.22336
Kučera J, Spáčil Z, Friedecký D, Novák J, Pekař M, Bienertová-Vašků J. Human White Adipose Tissue Metabolome: Current Perspective. Obesity (Silver Spring). 2018 Dec;26(12):1870-1878. doi: 10.1002/oby.22336. Epub 2018 Oct 28. PMID: 30369078.
Copy Download .nbib Format: NLM
Informatics for Metabolomics.

Advances in experimental medicine and biology

Kusonmano K, Vongsangnak W, Chumnanpuen P.
PMID: 27807745
Adv Exp Med Biol. 2016;939:91-115. doi: 10.1007/978-981-10-1503-8_5.

Metabolome profiling of biological systems has the powerful ability to provide the biological understanding of their metabolic functional states responding to the environmental factors or other perturbations. Tons of accumulative metabolomics data have thus been established since pre-metabolomics era....

Cite
Kusonmano K, Vongsangnak W, Chumnanpuen P. Informatics for Metabolomics. Adv Exp Med Biol. 2016;939:91-115doi: 10.1007/978-981-10-1503-8_5.
Kusonmano, K., Vongsangnak, W., & Chumnanpuen, P. (2016). Informatics for Metabolomics. Advances in experimental medicine and biology, 93991-115. https://doi.org/10.1007/978-981-10-1503-8_5
Kusonmano, Kanthida, et al. "Informatics for Metabolomics." Advances in experimental medicine and biology vol. 939 (2016): 91-115. doi: https://doi.org/10.1007/978-981-10-1503-8_5
Kusonmano K, Vongsangnak W, Chumnanpuen P. Informatics for Metabolomics. Adv Exp Med Biol. 2016;939:91-115. doi: 10.1007/978-981-10-1503-8_5. PMID: 27807745.
Copy Download .nbib Format: NLM
Metabolomics: small molecules that matter more.

Molecular omics

Muthubharathi BC, Gowripriya T, Balamurugan K.
PMID: 33598670
Mol Omics. 2021 Apr 01;17(2):210-229. doi: 10.1039/d0mo00176g. Epub 2021 Feb 18.

Metabolomics, an analytical study with high-throughput profiling, helps to understand interactions within a biological system. Small molecules, called metabolites or metabolomes with the size of

Cite
Muthubharathi BC, Gowripriya T, Balamurugan K. Metabolomics: small molecules that matter more. Mol Omics. 2021;17(2):210-229doi: 10.1039/d0mo00176g.
Muthubharathi, B. C., Gowripriya, T., & Balamurugan, K. (2021). Metabolomics: small molecules that matter more. Molecular omics, 17(2), 210-229. https://doi.org/10.1039/d0mo00176g
Muthubharathi, Balasubramanian Chellammal, et al. "Metabolomics: small molecules that matter more." Molecular omics vol. 17,2 (2021): 210-229. doi: https://doi.org/10.1039/d0mo00176g
Muthubharathi BC, Gowripriya T, Balamurugan K. Metabolomics: small molecules that matter more. Mol Omics. 2021 Apr 01;17(2):210-229. doi: 10.1039/d0mo00176g. Epub 2021 Feb 18. PMID: 33598670.
Copy Download .nbib Format: NLM
Metabolomics Analyses to Investigate the Role of Diet and Physical Training.

Methods in molecular biology (Clifton, N.J.)

Herrero P, Rodríguez MÁ, Ras MR, Del Pino A, Arola L, Canela N.
PMID: 31119677
Methods Mol Biol. 2019;1978:403-430. doi: 10.1007/978-1-4939-9236-2_25.

The metabolic and physiologic responses to healthy dietary habits and physical exercise have become an increasingly interesting research area, since equilibrated diet and regular physical activity are commonly recommended for their antioxidant capacity and for the prevention and treatment...

Cite
Herrero P, Rodríguez MÁ, Ras MR, et al. Metabolomics Analyses to Investigate the Role of Diet and Physical Training. Methods Mol Biol. 2019;1978:403-430doi: 10.1007/978-1-4939-9236-2_25.
Herrero, P., Rodríguez, M. �. �., Ras, M. R., Del Pino, A., Arola, L., & Canela, N. (2019). Metabolomics Analyses to Investigate the Role of Diet and Physical Training. Methods in molecular biology (Clifton, N.J.), 1978403-430. https://doi.org/10.1007/978-1-4939-9236-2_25
Herrero, Pol, et al. "Metabolomics Analyses to Investigate the Role of Diet and Physical Training." Methods in molecular biology (Clifton, N.J.) vol. 1978 (2019): 403-430. doi: https://doi.org/10.1007/978-1-4939-9236-2_25
Herrero P, Rodríguez MÁ, Ras MR, Del Pino A, Arola L, Canela N. Metabolomics Analyses to Investigate the Role of Diet and Physical Training. Methods Mol Biol. 2019;1978:403-430. doi: 10.1007/978-1-4939-9236-2_25. PMID: 31119677.
Copy Download .nbib Format: NLM
Causal relationships between genetically determined metabolites and human intelligence: a Mendelian randomization study.

Molecular brain

Yang J, Zhao B, Qian L, Gao F, Fan Y, He X, Ma Q, Yang L, Yan B, Wang W, Ma X.
PMID: 33563321
Mol Brain. 2021 Feb 09;14(1):29. doi: 10.1186/s13041-021-00743-4.

Intelligence predicts important life and health outcomes, but the biological mechanisms underlying differences in intelligence are not yet understood. The use of genetically determined metabotypes (GDMs) to understand the role of genetic and environmental factors, and their interactions, in...

Cite
Yang J, Zhao B, Qian L, et al. Causal relationships between genetically determined metabolites and human intelligence: a Mendelian randomization study. Mol Brain. 2021;14(1):29doi: 10.1186/s13041-021-00743-4.
Yang, J., Zhao, B., Qian, L., Gao, F., Fan, Y., He, X., Ma, Q., Yang, L., Yan, B., Wang, W., & Ma, X. (2021). Causal relationships between genetically determined metabolites and human intelligence: a Mendelian randomization study. Molecular brain, 14(1), 29. https://doi.org/10.1186/s13041-021-00743-4
Yang, Jian, et al. "Causal relationships between genetically determined metabolites and human intelligence: a Mendelian randomization study." Molecular brain vol. 14,1 (2021): 29. doi: https://doi.org/10.1186/s13041-021-00743-4
Yang J, Zhao B, Qian L, Gao F, Fan Y, He X, Ma Q, Yang L, Yan B, Wang W, Ma X. Causal relationships between genetically determined metabolites and human intelligence: a Mendelian randomization study. Mol Brain. 2021 Feb 09;14(1):29. doi: 10.1186/s13041-021-00743-4. PMID: 33563321; PMCID: PMC7871559.
Copy Download .nbib Format: NLM
Showing 1 to 10 of 10 entries