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Pregled bibliografske jedinice broj: 929372

Big Data, Evolution, and Metagenomes: Predicting Disease from Gut Microbiota Codon Usage Profiles


Fabijanić, Maja; Vlahoviček, Kristian
Big Data, Evolution, and Metagenomes: Predicting Disease from Gut Microbiota Codon Usage Profiles // DATA MINING TECHNIQUES FOR THE LIFE SCIENCES / Carugo, O ; Eisenhaber, F (ur.).
USA: Springer New York, 2016. str. 509-531 doi:10.1007/978-1-4939-3572-7_26


CROSBI ID: 929372 Za ispravke kontaktirajte CROSBI podršku putem web obrasca

Naslov
Big Data, Evolution, and Metagenomes: Predicting Disease from Gut Microbiota Codon Usage Profiles

Autori
Fabijanić, Maja ; Vlahoviček, Kristian

Vrsta, podvrsta i kategorija rada
Poglavlja u knjigama, ostalo

Knjiga
DATA MINING TECHNIQUES FOR THE LIFE SCIENCES

Urednik/ci
Carugo, O ; Eisenhaber, F

Izdavač
Springer New York

Grad
USA

Godina
2016

Raspon stranica
509-531

ISBN
9781493935727

ISSN
1064-3745

Ključne riječi
Big data ; Evolution ; Metagenomes ; Codon Usage Profiles

Sažetak
Metagenomics projects use next-generation sequencing to unravel genetic potential in microbial communities from a wealth of environmental niches, including those associated with human body and relevant to human health. In order to understand large datasets collected in metagenomics surveys and interpret them in context of how a community metabolism as a whole adapts and interacts with the environment, it is necessary to extend beyond the conventional approaches of decomposing metagenomes into microbial species’ constituents and performing analysis on separate components. By applying concepts of translational optimization through codon usage adaptation on entire metagenomic datasets, we demonstrate that a bias in codon usage present throughout the entire microbial community can be used as a powerful analytical tool to predict for community lifestyle-specific metabolism. Here we demonstrate this approach combined with machine learning, to classify human gut microbiome samples according to the pathological condition diagnosed in the human host

Izvorni jezik
Engleski

Znanstvena područja
Biologija



POVEZANOST RADA


Projekti:
KK.01.1.1.01.0010
KK.01.1.1.01.0009 - Napredne metode i tehnologije u znanosti o podatcima i kooperativnim sustavima (EK )
HRZZ-IP-2014-09-6400 - Istraživanje razvoja, diferencijacije i evolucije životinja kroz genomiku bazalnih metazoa (BAMGEN) (Vlahoviček, Kristian, HRZZ - 2014-09) ( POIROT)

Ustanove:
Prirodoslovno-matematički fakultet, Zagreb

Profili:

Avatar Url Kristian Vlahoviček (autor)

Avatar Url Maja Kuzman (autor)

doi

Citiraj ovu publikaciju

Fabijanić, Maja; Vlahoviček, Kristian
Big Data, Evolution, and Metagenomes: Predicting Disease from Gut Microbiota Codon Usage Profiles // DATA MINING TECHNIQUES FOR THE LIFE SCIENCES / Carugo, O ; Eisenhaber, F (ur.).
USA: Springer New York, 2016. str. 509-531 doi:10.1007/978-1-4939-3572-7_26
Fabijanić, M. & Vlahoviček, K. (2016) Big Data, Evolution, and Metagenomes: Predicting Disease from Gut Microbiota Codon Usage Profiles. U: Carugo, O. & Eisenhaber, F. (ur.) DATA MINING TECHNIQUES FOR THE LIFE SCIENCES. USA, Springer New York, str. 509-531 doi:10.1007/978-1-4939-3572-7_26.
@inbook{inbook, year = {2016}, pages = {509-531}, DOI = {10.1007/978-1-4939-3572-7\_26}, keywords = {Big data, Evolution, Metagenomes, Codon Usage Profiles}, doi = {10.1007/978-1-4939-3572-7\_26}, isbn = {9781493935727}, issn = {1064-3745}, title = {Big Data, Evolution, and Metagenomes: Predicting Disease from Gut Microbiota Codon Usage Profiles}, keyword = {Big data, Evolution, Metagenomes, Codon Usage Profiles}, publisher = {Springer New York}, publisherplace = {USA} }
@inbook{inbook, year = {2016}, pages = {509-531}, DOI = {10.1007/978-1-4939-3572-7\_26}, keywords = {Big data, Evolution, Metagenomes, Codon Usage Profiles}, doi = {10.1007/978-1-4939-3572-7\_26}, isbn = {9781493935727}, issn = {1064-3745}, title = {Big Data, Evolution, and Metagenomes: Predicting Disease from Gut Microbiota Codon Usage Profiles}, keyword = {Big data, Evolution, Metagenomes, Codon Usage Profiles}, publisher = {Springer New York}, publisherplace = {USA} }

Časopis indeksira:


  • Web of Science Core Collection (WoSCC)
    • Book Citation Index - Science (BKCI-S)
  • Scopus


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