PhenoMeNal: processing and analysis of metabolomics data in the
cloud

J 2019

PhenoMeNal: processing and analysis of metabolomics data in the cloud

PETERS, K, J BRADBURY, S BERGMANN, M CAPUCCINI, M CASCANTE et. al.

Basic information

Original name

PhenoMeNal: processing and analysis of metabolomics data in the cloud

Authors

PETERS, K, J BRADBURY, S BERGMANN, M CAPUCCINI, M CASCANTE, Atauri P DE, TMD EBBELS, C FOGUET, R GLEN, A GONZALEZ-BELTRAN, UL GUNTHER, E HANDAKAS, T HANKEMEIER, K HAUG, S HERMAN, Petr HOLUB, M IZZO, D JACOB, D JOHNSON, F JOURDAN, N KALE, I KARAMAN, B KHALILI, PE KHONSARI, K KULTIMA, S LAMPA, A LARSSON, C LUDWIG, P MORENO, S NEUMANN, JA NOVELLA, O Donovan C, JTM PEARCE, A PELUSO, ME PIRAS, L PIREDDU, MAC REED, P ROCCA-SERRA, P ROGER, A ROSATO, R RUEEDI, C RUTTKIES, N SADAWI, RM SALEK, SA SANSONE, V SELIVANOV, O SPJUTH, D SCHOBER, EA THEVENOT, M TOMASONI, M VAN RIJSWIJK, M VAN VLIET, MR VIANT, RJM WEBER, G ZANETTI and C STEINBECK

Edition

GIGASCIENCE, OXFORD, OXFORD UNIV PRESS, 2019, 2047-217X

Other information

Language

English

Type of outcome

Článek v odborném periodiku

Field of Study

10201 Computer sciences, information science, bioinformatics

Country of publisher

United States of America

Confidentiality degree

není předmětem státního či obchodního tajemství

References:

URL

Impact factor

Impact factor: 5.993

DOI

http://dx.doi.org/10.1093/gigascience/giy149

UT WoS

000462551600002

Keywords in English

metabolomics; data analysis; e-infrastructures; NMR; mass spectrometry; computational workflows; galaxy; cloud computing; standardization; statistics

Změněno: 29/4/2020 22:17, doc. RNDr. Petr Holub, Ph.D.

Abstract

V originále

Background Metabolomics is the comprehensive study of a multitude of small molecules to gain insight into an organism's metabolism. The research field is dynamic and expanding with applications across biomedical, biotechnological, and many other applied biological domains. Its computationally intensive nature has driven requirements for open data formats, data repositories, and data analysis tools. However, the rapid progress has resulted in a mosaic of independent, and sometimes incompatible, analysis methods that are difficult to connect into a useful and complete data analysis solution. Findings PhenoMeNal (Phenome and Metabolome aNalysis) is an advanced and complete solution to set up Infrastructure-as-a-Service (IaaS) that brings workflow-oriented, interoperable metabolomics data analysis platforms into the cloud. PhenoMeNal seamlessly integrates a wide array of existing open-source tools that are tested and packaged as Docker containers through the project's continuous integration process and deployed based on a kubernetes orchestration framework. It also provides a number of standardized, automated, and published analysis workflows in the user interfaces Galaxy, Jupyter, Luigi, and Pachyderm. Conclusions PhenoMeNal constitutes a keystone solution in cloud e-infrastructures available for metabolomics. PhenoMeNal is a unique and complete solution for setting up cloud e-infrastructures through easy-to-use web interfaces that can be scaled to any custom public and private cloud environment. By harmonizing and automating software installation and configuration and through ready-to-use scientific workflow user interfaces, PhenoMeNal has succeeded in providing scientists with workflow-driven, reproducible, and shareable metabolomics data analysis platforms that are interfaced through standard data formats, representative datasets, versioned, and have been tested for reproducibility and interoperability. The elastic implementation of PhenoMeNal further allows easy adaptation of the infrastructure to other application areas and omics research domains.

Citovat

@article{1534757,
   author = {Peters, K and Bradbury, J and Bergmann, S and Capuccini, M and Cascante, M and de, Atauri P and Ebbels, TMD and Foguet, C and Glen, R and GonzalezandBeltran, A and Gunther, UL and Handakas, E and Hankemeier, T and Haug, K and Herman, S and Holub, Petr and Izzo, M and Jacob, D and Johnson, D and Jourdan, F and Kale, N and Karaman, I and Khalili, B and Khonsari, PE and Kultima, K and Lampa, S and Larsson, A and Ludwig, C and Moreno, P and Neumann, S and Novella, JA and C, O Donovan and Pearce, JTM and Peluso, A and Piras, ME and Pireddu, L and Reed, MAC and RoccaandSerra, P and Roger, P and Rosato, A and Rueedi, R and Ruttkies, C and Sadawi, N and Salek, RM and Sansone, SA and Selivanov, V and Spjuth, O and Schober, D and Thevenot, EA and Tomasoni, M and van Rijswijk, M and van Vliet, M and Viant, MR and Weber, RJM and Zanetti, G and Steinbeck, C},
   article_location = {OXFORD},
   article_number = {2},
   doi = {http://dx.doi.org/10.1093/gigascience/giy149},
   keywords = {metabolomics; data analysis; e-infrastructures; NMR; mass spectrometry; computational workflows; galaxy; cloud computing; standardization; statistics},
   language = {eng},
   issn = {2047-217X},
   journal = {GIGASCIENCE},
   title = {PhenoMeNal: processing and analysis of metabolomics data in the cloud},
   url = {https://academic.oup.com/gigascience/article/8/2/giy149/5232984},
   volume = {8},
   year = {2019}
}

TY  - JOUR
ID  - 1534757
AU  - Peters, K - Bradbury, J - Bergmann, S - Capuccini, M - Cascante, M - de, Atauri P - Ebbels, TMD - Foguet, C - Glen, R - Gonzalez-Beltran, A - Gunther, UL - Handakas, E - Hankemeier, T - Haug, K - Herman, S - Holub, Petr - Izzo, M - Jacob, D - Johnson, D - Jourdan, F - Kale, N - Karaman, I - Khalili, B - Khonsari, PE - Kultima, K - Lampa, S - Larsson, A - Ludwig, C - Moreno, P - Neumann, S - Novella, JA - C, O Donovan - Pearce, JTM - Peluso, A - Piras, ME - Pireddu, L - Reed, MAC - Rocca-Serra, P - Roger, P - Rosato, A - Rueedi, R - Ruttkies, C - Sadawi, N - Salek, RM - Sansone, SA - Selivanov, V - Spjuth, O - Schober, D - Thevenot, EA - Tomasoni, M - van Rijswijk, M - van Vliet, M - Viant, MR - Weber, RJM - Zanetti, G - Steinbeck, C
PY  - 2019
TI  - PhenoMeNal: processing and analysis of metabolomics data in the cloud
JF  - GIGASCIENCE
VL  - 8
IS  - 2
PB  - OXFORD UNIV PRESS
SN  - 2047217X
KW  - metabolomics
KW  - data analysis
KW  - e-infrastructures
KW  - NMR
KW  - mass spectrometry
KW  - computational workflows
KW  - galaxy
KW  - cloud computing
KW  - standardization
KW  - statistics
UR  - https://academic.oup.com/gigascience/article/8/2/giy149/5232984
N2  - Background Metabolomics is the comprehensive study of a multitude of small molecules to gain insight into an organism's metabolism. The research field is dynamic and expanding with applications across biomedical, biotechnological, and many other applied biological domains. Its computationally intensive nature has driven requirements for open data formats, data repositories, and data analysis tools. However, the rapid progress has resulted in a mosaic of independent, and sometimes incompatible, analysis methods that are difficult to connect into a useful and complete data analysis solution. Findings PhenoMeNal (Phenome and Metabolome aNalysis) is an advanced and complete solution to set up Infrastructure-as-a-Service (IaaS) that brings workflow-oriented, interoperable metabolomics data analysis platforms into the cloud. PhenoMeNal seamlessly integrates a wide array of existing open-source tools that are tested and packaged as Docker containers through the project's continuous integration process and deployed based on a kubernetes orchestration framework. It also provides a number of standardized, automated, and published analysis workflows in the user interfaces Galaxy, Jupyter, Luigi, and Pachyderm. Conclusions PhenoMeNal constitutes a keystone solution in cloud e-infrastructures available for metabolomics. PhenoMeNal is a unique and complete solution for setting up cloud e-infrastructures through easy-to-use web interfaces that can be scaled to any custom public and private cloud environment. By harmonizing and automating software installation and configuration and through ready-to-use scientific workflow user interfaces, PhenoMeNal has succeeded in providing scientists with workflow-driven, reproducible, and shareable metabolomics data analysis platforms that are interfaced through standard data formats, representative datasets, versioned, and have been tested for reproducibility and interoperability. The elastic implementation of PhenoMeNal further allows easy adaptation of the infrastructure to other application areas and omics research domains.
ER  -

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