ASaiM-MT: a validated and optimized ASaiM workflow for metatranscriptomics analysis within Galaxy framework [version 2; peer review: 2 approved]

The Earth Microbiome Project (EMP) aided in understanding the role of microbial communities and the influence of collective genetic material (the ‘microbiome’) and microbial diversity patterns across the habitats of our planet. With the evolution of new sequencing technologies, researchers can now investigate the microbiome and map its influence on the environment and human health. Advances in bioinformatics methods for next-generation sequencing (NGS) data analysis have helped researchers to gain an in-depth knowledge about the taxonomic and genetic composition of microbial communities. Metagenomic-based methods have been the most commonly used approaches for microbiome analysis; however, it primarily extracts information about taxonomic composition and genetic potential of the microbiome under study, lacking quantification of the gene products (RNA and proteins). On the other hand, metatranscriptomics, the study of a microbial community’s RNA expression, can reveal the dynamic gene expression of individual microbial populations and the community as a whole, ultimately providing information about the active pathways in the microbiome. In order to address the analysis of NGS data, the ASaiM analysis framework was previously developed and made available via the Galaxy platform. Although developed for both metagenomics and metatranscriptomics, the original publication demonstrated the use of ASaiM only for metagenomics, while thorough testing for metatranscriptomics data was lacking. In the current study, we have focused on validating and optimizing the tools within ASaiM for metatranscriptomics data. As a result, we deliver a robust workflow that will enable researchers to understand dynamic functional response of the microbiome in a wide variety of metatranscriptomics studies. This improved and optimized ASaiM-metatranscriptomics (ASaiM-MT) workflow is publicly available via the ASaiM framework, documented and supported with training material so that users can interrogate and characterize metatranscriptomic data, as part of larger meta-omic studies of microbiomes. Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Subina Mehta [verfasserIn] Marie Crane [verfasserIn] Emma Leith [verfasserIn] Bérénice Batut [verfasserIn] Saskia Hiltemann [verfasserIn] Magnus Ø Arntzen [verfasserIn] Benoit J. Kunath [verfasserIn] Phillip B. Pope [verfasserIn] Francesco Delogu [verfasserIn] Ray Sajulga [verfasserIn] Praveen Kumar [verfasserIn] James E. Johnson [verfasserIn] Timothy J. Griffin [verfasserIn] Pratik D. Jagtap [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2021

Übergeordnetes Werk:	In: F1000Research - F1000 Research Ltd, 2013, 10(2021)
Übergeordnetes Werk:	volume:10 ; year:2021

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.12688/f1000research.28608.2

Katalog-ID:	DOAJ062065432

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allfields	10.12688/f1000research.28608.2 doi (DE-627)DOAJ062065432 (DE-599)DOAJ73e663f556a648a1931080c5ee40ac78 DE-627 ger DE-627 rakwb eng Subina Mehta verfasserin aut ASaiM-MT: a validated and optimized ASaiM workflow for metatranscriptomics analysis within Galaxy framework [version 2; peer review: 2 approved] 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The Earth Microbiome Project (EMP) aided in understanding the role of microbial communities and the influence of collective genetic material (the ‘microbiome’) and microbial diversity patterns across the habitats of our planet. With the evolution of new sequencing technologies, researchers can now investigate the microbiome and map its influence on the environment and human health. Advances in bioinformatics methods for next-generation sequencing (NGS) data analysis have helped researchers to gain an in-depth knowledge about the taxonomic and genetic composition of microbial communities. Metagenomic-based methods have been the most commonly used approaches for microbiome analysis; however, it primarily extracts information about taxonomic composition and genetic potential of the microbiome under study, lacking quantification of the gene products (RNA and proteins). On the other hand, metatranscriptomics, the study of a microbial community’s RNA expression, can reveal the dynamic gene expression of individual microbial populations and the community as a whole, ultimately providing information about the active pathways in the microbiome. In order to address the analysis of NGS data, the ASaiM analysis framework was previously developed and made available via the Galaxy platform. Although developed for both metagenomics and metatranscriptomics, the original publication demonstrated the use of ASaiM only for metagenomics, while thorough testing for metatranscriptomics data was lacking. In the current study, we have focused on validating and optimizing the tools within ASaiM for metatranscriptomics data. As a result, we deliver a robust workflow that will enable researchers to understand dynamic functional response of the microbiome in a wide variety of metatranscriptomics studies. This improved and optimized ASaiM-metatranscriptomics (ASaiM-MT) workflow is publicly available via the ASaiM framework, documented and supported with training material so that users can interrogate and characterize metatranscriptomic data, as part of larger meta-omic studies of microbiomes. Medicine R Science Q Marie Crane verfasserin aut Emma Leith verfasserin aut Bérénice Batut verfasserin aut Saskia Hiltemann verfasserin aut Magnus Ø Arntzen verfasserin aut Benoit J. Kunath verfasserin aut Phillip B. Pope verfasserin aut Francesco Delogu verfasserin aut Ray Sajulga verfasserin aut Praveen Kumar verfasserin aut James E. Johnson verfasserin aut Timothy J. Griffin verfasserin aut Pratik D. Jagtap verfasserin aut In F1000Research F1000 Research Ltd, 2013 10(2021) (DE-627)735133581 (DE-600)2699932-8 20461402 nnns volume:10 year:2021 https://doi.org/10.12688/f1000research.28608.2 kostenfrei https://doaj.org/article/73e663f556a648a1931080c5ee40ac78 kostenfrei https://f1000research.com/articles/10-103/v2 kostenfrei https://doaj.org/toc/2046-1402 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 10 2021
spelling	10.12688/f1000research.28608.2 doi (DE-627)DOAJ062065432 (DE-599)DOAJ73e663f556a648a1931080c5ee40ac78 DE-627 ger DE-627 rakwb eng Subina Mehta verfasserin aut ASaiM-MT: a validated and optimized ASaiM workflow for metatranscriptomics analysis within Galaxy framework [version 2; peer review: 2 approved] 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The Earth Microbiome Project (EMP) aided in understanding the role of microbial communities and the influence of collective genetic material (the ‘microbiome’) and microbial diversity patterns across the habitats of our planet. With the evolution of new sequencing technologies, researchers can now investigate the microbiome and map its influence on the environment and human health. Advances in bioinformatics methods for next-generation sequencing (NGS) data analysis have helped researchers to gain an in-depth knowledge about the taxonomic and genetic composition of microbial communities. Metagenomic-based methods have been the most commonly used approaches for microbiome analysis; however, it primarily extracts information about taxonomic composition and genetic potential of the microbiome under study, lacking quantification of the gene products (RNA and proteins). On the other hand, metatranscriptomics, the study of a microbial community’s RNA expression, can reveal the dynamic gene expression of individual microbial populations and the community as a whole, ultimately providing information about the active pathways in the microbiome. In order to address the analysis of NGS data, the ASaiM analysis framework was previously developed and made available via the Galaxy platform. Although developed for both metagenomics and metatranscriptomics, the original publication demonstrated the use of ASaiM only for metagenomics, while thorough testing for metatranscriptomics data was lacking. In the current study, we have focused on validating and optimizing the tools within ASaiM for metatranscriptomics data. As a result, we deliver a robust workflow that will enable researchers to understand dynamic functional response of the microbiome in a wide variety of metatranscriptomics studies. This improved and optimized ASaiM-metatranscriptomics (ASaiM-MT) workflow is publicly available via the ASaiM framework, documented and supported with training material so that users can interrogate and characterize metatranscriptomic data, as part of larger meta-omic studies of microbiomes. Medicine R Science Q Marie Crane verfasserin aut Emma Leith verfasserin aut Bérénice Batut verfasserin aut Saskia Hiltemann verfasserin aut Magnus Ø Arntzen verfasserin aut Benoit J. Kunath verfasserin aut Phillip B. Pope verfasserin aut Francesco Delogu verfasserin aut Ray Sajulga verfasserin aut Praveen Kumar verfasserin aut James E. Johnson verfasserin aut Timothy J. Griffin verfasserin aut Pratik D. Jagtap verfasserin aut In F1000Research F1000 Research Ltd, 2013 10(2021) (DE-627)735133581 (DE-600)2699932-8 20461402 nnns volume:10 year:2021 https://doi.org/10.12688/f1000research.28608.2 kostenfrei https://doaj.org/article/73e663f556a648a1931080c5ee40ac78 kostenfrei https://f1000research.com/articles/10-103/v2 kostenfrei https://doaj.org/toc/2046-1402 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 10 2021
allfields_unstemmed	10.12688/f1000research.28608.2 doi (DE-627)DOAJ062065432 (DE-599)DOAJ73e663f556a648a1931080c5ee40ac78 DE-627 ger DE-627 rakwb eng Subina Mehta verfasserin aut ASaiM-MT: a validated and optimized ASaiM workflow for metatranscriptomics analysis within Galaxy framework [version 2; peer review: 2 approved] 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The Earth Microbiome Project (EMP) aided in understanding the role of microbial communities and the influence of collective genetic material (the ‘microbiome’) and microbial diversity patterns across the habitats of our planet. With the evolution of new sequencing technologies, researchers can now investigate the microbiome and map its influence on the environment and human health. Advances in bioinformatics methods for next-generation sequencing (NGS) data analysis have helped researchers to gain an in-depth knowledge about the taxonomic and genetic composition of microbial communities. Metagenomic-based methods have been the most commonly used approaches for microbiome analysis; however, it primarily extracts information about taxonomic composition and genetic potential of the microbiome under study, lacking quantification of the gene products (RNA and proteins). On the other hand, metatranscriptomics, the study of a microbial community’s RNA expression, can reveal the dynamic gene expression of individual microbial populations and the community as a whole, ultimately providing information about the active pathways in the microbiome. In order to address the analysis of NGS data, the ASaiM analysis framework was previously developed and made available via the Galaxy platform. Although developed for both metagenomics and metatranscriptomics, the original publication demonstrated the use of ASaiM only for metagenomics, while thorough testing for metatranscriptomics data was lacking. In the current study, we have focused on validating and optimizing the tools within ASaiM for metatranscriptomics data. As a result, we deliver a robust workflow that will enable researchers to understand dynamic functional response of the microbiome in a wide variety of metatranscriptomics studies. This improved and optimized ASaiM-metatranscriptomics (ASaiM-MT) workflow is publicly available via the ASaiM framework, documented and supported with training material so that users can interrogate and characterize metatranscriptomic data, as part of larger meta-omic studies of microbiomes. Medicine R Science Q Marie Crane verfasserin aut Emma Leith verfasserin aut Bérénice Batut verfasserin aut Saskia Hiltemann verfasserin aut Magnus Ø Arntzen verfasserin aut Benoit J. Kunath verfasserin aut Phillip B. Pope verfasserin aut Francesco Delogu verfasserin aut Ray Sajulga verfasserin aut Praveen Kumar verfasserin aut James E. Johnson verfasserin aut Timothy J. Griffin verfasserin aut Pratik D. Jagtap verfasserin aut In F1000Research F1000 Research Ltd, 2013 10(2021) (DE-627)735133581 (DE-600)2699932-8 20461402 nnns volume:10 year:2021 https://doi.org/10.12688/f1000research.28608.2 kostenfrei https://doaj.org/article/73e663f556a648a1931080c5ee40ac78 kostenfrei https://f1000research.com/articles/10-103/v2 kostenfrei https://doaj.org/toc/2046-1402 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 10 2021
allfieldsGer	10.12688/f1000research.28608.2 doi (DE-627)DOAJ062065432 (DE-599)DOAJ73e663f556a648a1931080c5ee40ac78 DE-627 ger DE-627 rakwb eng Subina Mehta verfasserin aut ASaiM-MT: a validated and optimized ASaiM workflow for metatranscriptomics analysis within Galaxy framework [version 2; peer review: 2 approved] 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The Earth Microbiome Project (EMP) aided in understanding the role of microbial communities and the influence of collective genetic material (the ‘microbiome’) and microbial diversity patterns across the habitats of our planet. With the evolution of new sequencing technologies, researchers can now investigate the microbiome and map its influence on the environment and human health. Advances in bioinformatics methods for next-generation sequencing (NGS) data analysis have helped researchers to gain an in-depth knowledge about the taxonomic and genetic composition of microbial communities. Metagenomic-based methods have been the most commonly used approaches for microbiome analysis; however, it primarily extracts information about taxonomic composition and genetic potential of the microbiome under study, lacking quantification of the gene products (RNA and proteins). On the other hand, metatranscriptomics, the study of a microbial community’s RNA expression, can reveal the dynamic gene expression of individual microbial populations and the community as a whole, ultimately providing information about the active pathways in the microbiome. In order to address the analysis of NGS data, the ASaiM analysis framework was previously developed and made available via the Galaxy platform. Although developed for both metagenomics and metatranscriptomics, the original publication demonstrated the use of ASaiM only for metagenomics, while thorough testing for metatranscriptomics data was lacking. In the current study, we have focused on validating and optimizing the tools within ASaiM for metatranscriptomics data. As a result, we deliver a robust workflow that will enable researchers to understand dynamic functional response of the microbiome in a wide variety of metatranscriptomics studies. This improved and optimized ASaiM-metatranscriptomics (ASaiM-MT) workflow is publicly available via the ASaiM framework, documented and supported with training material so that users can interrogate and characterize metatranscriptomic data, as part of larger meta-omic studies of microbiomes. Medicine R Science Q Marie Crane verfasserin aut Emma Leith verfasserin aut Bérénice Batut verfasserin aut Saskia Hiltemann verfasserin aut Magnus Ø Arntzen verfasserin aut Benoit J. Kunath verfasserin aut Phillip B. Pope verfasserin aut Francesco Delogu verfasserin aut Ray Sajulga verfasserin aut Praveen Kumar verfasserin aut James E. Johnson verfasserin aut Timothy J. Griffin verfasserin aut Pratik D. Jagtap verfasserin aut In F1000Research F1000 Research Ltd, 2013 10(2021) (DE-627)735133581 (DE-600)2699932-8 20461402 nnns volume:10 year:2021 https://doi.org/10.12688/f1000research.28608.2 kostenfrei https://doaj.org/article/73e663f556a648a1931080c5ee40ac78 kostenfrei https://f1000research.com/articles/10-103/v2 kostenfrei https://doaj.org/toc/2046-1402 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 10 2021
allfieldsSound	10.12688/f1000research.28608.2 doi (DE-627)DOAJ062065432 (DE-599)DOAJ73e663f556a648a1931080c5ee40ac78 DE-627 ger DE-627 rakwb eng Subina Mehta verfasserin aut ASaiM-MT: a validated and optimized ASaiM workflow for metatranscriptomics analysis within Galaxy framework [version 2; peer review: 2 approved] 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The Earth Microbiome Project (EMP) aided in understanding the role of microbial communities and the influence of collective genetic material (the ‘microbiome’) and microbial diversity patterns across the habitats of our planet. With the evolution of new sequencing technologies, researchers can now investigate the microbiome and map its influence on the environment and human health. Advances in bioinformatics methods for next-generation sequencing (NGS) data analysis have helped researchers to gain an in-depth knowledge about the taxonomic and genetic composition of microbial communities. Metagenomic-based methods have been the most commonly used approaches for microbiome analysis; however, it primarily extracts information about taxonomic composition and genetic potential of the microbiome under study, lacking quantification of the gene products (RNA and proteins). On the other hand, metatranscriptomics, the study of a microbial community’s RNA expression, can reveal the dynamic gene expression of individual microbial populations and the community as a whole, ultimately providing information about the active pathways in the microbiome. In order to address the analysis of NGS data, the ASaiM analysis framework was previously developed and made available via the Galaxy platform. Although developed for both metagenomics and metatranscriptomics, the original publication demonstrated the use of ASaiM only for metagenomics, while thorough testing for metatranscriptomics data was lacking. In the current study, we have focused on validating and optimizing the tools within ASaiM for metatranscriptomics data. As a result, we deliver a robust workflow that will enable researchers to understand dynamic functional response of the microbiome in a wide variety of metatranscriptomics studies. This improved and optimized ASaiM-metatranscriptomics (ASaiM-MT) workflow is publicly available via the ASaiM framework, documented and supported with training material so that users can interrogate and characterize metatranscriptomic data, as part of larger meta-omic studies of microbiomes. Medicine R Science Q Marie Crane verfasserin aut Emma Leith verfasserin aut Bérénice Batut verfasserin aut Saskia Hiltemann verfasserin aut Magnus Ø Arntzen verfasserin aut Benoit J. Kunath verfasserin aut Phillip B. Pope verfasserin aut Francesco Delogu verfasserin aut Ray Sajulga verfasserin aut Praveen Kumar verfasserin aut James E. Johnson verfasserin aut Timothy J. Griffin verfasserin aut Pratik D. Jagtap verfasserin aut In F1000Research F1000 Research Ltd, 2013 10(2021) (DE-627)735133581 (DE-600)2699932-8 20461402 nnns volume:10 year:2021 https://doi.org/10.12688/f1000research.28608.2 kostenfrei https://doaj.org/article/73e663f556a648a1931080c5ee40ac78 kostenfrei https://f1000research.com/articles/10-103/v2 kostenfrei https://doaj.org/toc/2046-1402 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 10 2021
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author	Subina Mehta
spellingShingle	Subina Mehta misc Medicine misc R misc Science misc Q ASaiM-MT: a validated and optimized ASaiM workflow for metatranscriptomics analysis within Galaxy framework [version 2; peer review: 2 approved]
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title	ASaiM-MT: a validated and optimized ASaiM workflow for metatranscriptomics analysis within Galaxy framework [version 2; peer review: 2 approved]
ctrlnum	(DE-627)DOAJ062065432 (DE-599)DOAJ73e663f556a648a1931080c5ee40ac78
title_full	ASaiM-MT: a validated and optimized ASaiM workflow for metatranscriptomics analysis within Galaxy framework [version 2; peer review: 2 approved]
author_sort	Subina Mehta
journal	F1000Research
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lang_code	eng
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author_browse	Subina Mehta Marie Crane Emma Leith Bérénice Batut Saskia Hiltemann Magnus Ø Arntzen Benoit J. Kunath Phillip B. Pope Francesco Delogu Ray Sajulga Praveen Kumar James E. Johnson Timothy J. Griffin Pratik D. Jagtap
container_volume	10
format_se	Elektronische Aufsätze
author-letter	Subina Mehta
doi_str_mv	10.12688/f1000research.28608.2
author2-role	verfasserin
title_sort	asaim-mt: a validated and optimized asaim workflow for metatranscriptomics analysis within galaxy framework [version 2; peer review: 2 approved]
title_auth	ASaiM-MT: a validated and optimized ASaiM workflow for metatranscriptomics analysis within Galaxy framework [version 2; peer review: 2 approved]
abstract	The Earth Microbiome Project (EMP) aided in understanding the role of microbial communities and the influence of collective genetic material (the ‘microbiome’) and microbial diversity patterns across the habitats of our planet. With the evolution of new sequencing technologies, researchers can now investigate the microbiome and map its influence on the environment and human health. Advances in bioinformatics methods for next-generation sequencing (NGS) data analysis have helped researchers to gain an in-depth knowledge about the taxonomic and genetic composition of microbial communities. Metagenomic-based methods have been the most commonly used approaches for microbiome analysis; however, it primarily extracts information about taxonomic composition and genetic potential of the microbiome under study, lacking quantification of the gene products (RNA and proteins). On the other hand, metatranscriptomics, the study of a microbial community’s RNA expression, can reveal the dynamic gene expression of individual microbial populations and the community as a whole, ultimately providing information about the active pathways in the microbiome. In order to address the analysis of NGS data, the ASaiM analysis framework was previously developed and made available via the Galaxy platform. Although developed for both metagenomics and metatranscriptomics, the original publication demonstrated the use of ASaiM only for metagenomics, while thorough testing for metatranscriptomics data was lacking. In the current study, we have focused on validating and optimizing the tools within ASaiM for metatranscriptomics data. As a result, we deliver a robust workflow that will enable researchers to understand dynamic functional response of the microbiome in a wide variety of metatranscriptomics studies. This improved and optimized ASaiM-metatranscriptomics (ASaiM-MT) workflow is publicly available via the ASaiM framework, documented and supported with training material so that users can interrogate and characterize metatranscriptomic data, as part of larger meta-omic studies of microbiomes.
abstractGer	The Earth Microbiome Project (EMP) aided in understanding the role of microbial communities and the influence of collective genetic material (the ‘microbiome’) and microbial diversity patterns across the habitats of our planet. With the evolution of new sequencing technologies, researchers can now investigate the microbiome and map its influence on the environment and human health. Advances in bioinformatics methods for next-generation sequencing (NGS) data analysis have helped researchers to gain an in-depth knowledge about the taxonomic and genetic composition of microbial communities. Metagenomic-based methods have been the most commonly used approaches for microbiome analysis; however, it primarily extracts information about taxonomic composition and genetic potential of the microbiome under study, lacking quantification of the gene products (RNA and proteins). On the other hand, metatranscriptomics, the study of a microbial community’s RNA expression, can reveal the dynamic gene expression of individual microbial populations and the community as a whole, ultimately providing information about the active pathways in the microbiome. In order to address the analysis of NGS data, the ASaiM analysis framework was previously developed and made available via the Galaxy platform. Although developed for both metagenomics and metatranscriptomics, the original publication demonstrated the use of ASaiM only for metagenomics, while thorough testing for metatranscriptomics data was lacking. In the current study, we have focused on validating and optimizing the tools within ASaiM for metatranscriptomics data. As a result, we deliver a robust workflow that will enable researchers to understand dynamic functional response of the microbiome in a wide variety of metatranscriptomics studies. This improved and optimized ASaiM-metatranscriptomics (ASaiM-MT) workflow is publicly available via the ASaiM framework, documented and supported with training material so that users can interrogate and characterize metatranscriptomic data, as part of larger meta-omic studies of microbiomes.
abstract_unstemmed	The Earth Microbiome Project (EMP) aided in understanding the role of microbial communities and the influence of collective genetic material (the ‘microbiome’) and microbial diversity patterns across the habitats of our planet. With the evolution of new sequencing technologies, researchers can now investigate the microbiome and map its influence on the environment and human health. Advances in bioinformatics methods for next-generation sequencing (NGS) data analysis have helped researchers to gain an in-depth knowledge about the taxonomic and genetic composition of microbial communities. Metagenomic-based methods have been the most commonly used approaches for microbiome analysis; however, it primarily extracts information about taxonomic composition and genetic potential of the microbiome under study, lacking quantification of the gene products (RNA and proteins). On the other hand, metatranscriptomics, the study of a microbial community’s RNA expression, can reveal the dynamic gene expression of individual microbial populations and the community as a whole, ultimately providing information about the active pathways in the microbiome. In order to address the analysis of NGS data, the ASaiM analysis framework was previously developed and made available via the Galaxy platform. Although developed for both metagenomics and metatranscriptomics, the original publication demonstrated the use of ASaiM only for metagenomics, while thorough testing for metatranscriptomics data was lacking. In the current study, we have focused on validating and optimizing the tools within ASaiM for metatranscriptomics data. As a result, we deliver a robust workflow that will enable researchers to understand dynamic functional response of the microbiome in a wide variety of metatranscriptomics studies. This improved and optimized ASaiM-metatranscriptomics (ASaiM-MT) workflow is publicly available via the ASaiM framework, documented and supported with training material so that users can interrogate and characterize metatranscriptomic data, as part of larger meta-omic studies of microbiomes.
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title_short	ASaiM-MT: a validated and optimized ASaiM workflow for metatranscriptomics analysis within Galaxy framework [version 2; peer review: 2 approved]
url	https://doi.org/10.12688/f1000research.28608.2 https://doaj.org/article/73e663f556a648a1931080c5ee40ac78 https://f1000research.com/articles/10-103/v2 https://doaj.org/toc/2046-1402
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author2	Marie Crane Emma Leith Bérénice Batut Saskia Hiltemann Magnus Ø Arntzen Benoit J. Kunath Phillip B. Pope Francesco Delogu Ray Sajulga Praveen Kumar James E. Johnson Timothy J. Griffin Pratik D. Jagtap
author2Str	Marie Crane Emma Leith Bérénice Batut Saskia Hiltemann Magnus Ø Arntzen Benoit J. Kunath Phillip B. Pope Francesco Delogu Ray Sajulga Praveen Kumar James E. Johnson Timothy J. Griffin Pratik D. Jagtap
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up_date	2024-07-04T00:06:20.234Z
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