A study on multi-omic oscillations in Escherichia coli metabolic networks

Background Two important challenges in the analysis of molecular biology information are data (multi-omic information) integration and the detection of patterns across large scale molecular networks and sequences. They are are actually coupled beause the integration of omic information may provide b...
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Autor*in:	Bardozzo, Francesco [verfasserIn] Lió, Pietro Tagliaferri, Roberto

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2018

Schlagwörter:	Multi-omics omic regularities Antibiotic response Multi-omic metabolic networks Multi-omic motifs E.

Anmerkung:	© The Author(s) 2018

Übergeordnetes Werk:	Enthalten in: BMC bioinformatics - London : BioMed Central, 2000, 19(2018), Suppl 7 vom: 09. Juli
Übergeordnetes Werk:	volume:19 ; year:2018 ; number:Suppl 7 ; day:09 ; month:07

Links:	Volltext

DOI / URN:	10.1186/s12859-018-2175-5

Katalog-ID:	SPR026920891

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520			\|a Background Two important challenges in the analysis of molecular biology information are data (multi-omic information) integration and the detection of patterns across large scale molecular networks and sequences. They are are actually coupled beause the integration of omic information may provide better means to detect multi-omic patterns that could reveal multi-scale or emerging properties at the phenotype levels. Results Here we address the problem of integrating various types of molecular information (a large collection of gene expression and sequence data, codon usage and protein abundances) to analyse the E.coli metabolic response to treatments at the whole network level. Our algorithm, MORA (Multi-omic relations adjacency) is able to detect patterns which may represent metabolic network motifs at pathway and supra pathway levels which could hint at some functional role. We provide a description and insights on the algorithm by testing it on a large database of responses to antibiotics. Along with the algorithm MORA, a novel model for the analysis of oscillating multi-omics has been proposed. Interestingly, the resulting analysis suggests that some motifs reveal recurring oscillating or position variation patterns on multi-omics metabolic networks. Our framework, implemented in R, provides effective and friendly means to design intervention scenarios on real data. By analysing how multi-omics data build up multi-scale phenotypes, the software allows to compare and test metabolic models, design new pathways or redesign existing metabolic pathways and validate in silico metabolic models using nearby species. Conclusions The integration of multi-omic data reveals that E.coli multi-omic metabolic networks contain position dependent and recurring patterns which could provide clues of long range correlations in the bacterial genome.
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allfields	10.1186/s12859-018-2175-5 doi (DE-627)SPR026920891 (SPR)s12859-018-2175-5-e DE-627 ger DE-627 rakwb eng Bardozzo, Francesco verfasserin aut A study on multi-omic oscillations in Escherichia coli metabolic networks 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2018 Background Two important challenges in the analysis of molecular biology information are data (multi-omic information) integration and the detection of patterns across large scale molecular networks and sequences. They are are actually coupled beause the integration of omic information may provide better means to detect multi-omic patterns that could reveal multi-scale or emerging properties at the phenotype levels. Results Here we address the problem of integrating various types of molecular information (a large collection of gene expression and sequence data, codon usage and protein abundances) to analyse the E.coli metabolic response to treatments at the whole network level. Our algorithm, MORA (Multi-omic relations adjacency) is able to detect patterns which may represent metabolic network motifs at pathway and supra pathway levels which could hint at some functional role. We provide a description and insights on the algorithm by testing it on a large database of responses to antibiotics. Along with the algorithm MORA, a novel model for the analysis of oscillating multi-omics has been proposed. Interestingly, the resulting analysis suggests that some motifs reveal recurring oscillating or position variation patterns on multi-omics metabolic networks. Our framework, implemented in R, provides effective and friendly means to design intervention scenarios on real data. By analysing how multi-omics data build up multi-scale phenotypes, the software allows to compare and test metabolic models, design new pathways or redesign existing metabolic pathways and validate in silico metabolic models using nearby species. Conclusions The integration of multi-omic data reveals that E.coli multi-omic metabolic networks contain position dependent and recurring patterns which could provide clues of long range correlations in the bacterial genome. Multi-omics (dpeaa)DE-He213 omic regularities (dpeaa)DE-He213 Antibiotic response (dpeaa)DE-He213 Multi-omic metabolic networks (dpeaa)DE-He213 Multi-omic motifs (dpeaa)DE-He213 E. (dpeaa)DE-He213 Lió, Pietro aut Tagliaferri, Roberto aut Enthalten in BMC bioinformatics London : BioMed Central, 2000 19(2018), Suppl 7 vom: 09. Juli (DE-627)326644814 (DE-600)2041484-5 1471-2105 nnns volume:19 year:2018 number:Suppl 7 day:09 month:07 https://dx.doi.org/10.1186/s12859-018-2175-5 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 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_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2031 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2061 GBV_ILN_2111 GBV_ILN_2113 GBV_ILN_2190 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_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 19 2018 Suppl 7 09 07
spelling	10.1186/s12859-018-2175-5 doi (DE-627)SPR026920891 (SPR)s12859-018-2175-5-e DE-627 ger DE-627 rakwb eng Bardozzo, Francesco verfasserin aut A study on multi-omic oscillations in Escherichia coli metabolic networks 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2018 Background Two important challenges in the analysis of molecular biology information are data (multi-omic information) integration and the detection of patterns across large scale molecular networks and sequences. They are are actually coupled beause the integration of omic information may provide better means to detect multi-omic patterns that could reveal multi-scale or emerging properties at the phenotype levels. Results Here we address the problem of integrating various types of molecular information (a large collection of gene expression and sequence data, codon usage and protein abundances) to analyse the E.coli metabolic response to treatments at the whole network level. Our algorithm, MORA (Multi-omic relations adjacency) is able to detect patterns which may represent metabolic network motifs at pathway and supra pathway levels which could hint at some functional role. We provide a description and insights on the algorithm by testing it on a large database of responses to antibiotics. Along with the algorithm MORA, a novel model for the analysis of oscillating multi-omics has been proposed. Interestingly, the resulting analysis suggests that some motifs reveal recurring oscillating or position variation patterns on multi-omics metabolic networks. Our framework, implemented in R, provides effective and friendly means to design intervention scenarios on real data. By analysing how multi-omics data build up multi-scale phenotypes, the software allows to compare and test metabolic models, design new pathways or redesign existing metabolic pathways and validate in silico metabolic models using nearby species. Conclusions The integration of multi-omic data reveals that E.coli multi-omic metabolic networks contain position dependent and recurring patterns which could provide clues of long range correlations in the bacterial genome. Multi-omics (dpeaa)DE-He213 omic regularities (dpeaa)DE-He213 Antibiotic response (dpeaa)DE-He213 Multi-omic metabolic networks (dpeaa)DE-He213 Multi-omic motifs (dpeaa)DE-He213 E. (dpeaa)DE-He213 Lió, Pietro aut Tagliaferri, Roberto aut Enthalten in BMC bioinformatics London : BioMed Central, 2000 19(2018), Suppl 7 vom: 09. Juli (DE-627)326644814 (DE-600)2041484-5 1471-2105 nnns volume:19 year:2018 number:Suppl 7 day:09 month:07 https://dx.doi.org/10.1186/s12859-018-2175-5 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 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_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2031 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2061 GBV_ILN_2111 GBV_ILN_2113 GBV_ILN_2190 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_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 19 2018 Suppl 7 09 07
allfields_unstemmed	10.1186/s12859-018-2175-5 doi (DE-627)SPR026920891 (SPR)s12859-018-2175-5-e DE-627 ger DE-627 rakwb eng Bardozzo, Francesco verfasserin aut A study on multi-omic oscillations in Escherichia coli metabolic networks 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2018 Background Two important challenges in the analysis of molecular biology information are data (multi-omic information) integration and the detection of patterns across large scale molecular networks and sequences. They are are actually coupled beause the integration of omic information may provide better means to detect multi-omic patterns that could reveal multi-scale or emerging properties at the phenotype levels. Results Here we address the problem of integrating various types of molecular information (a large collection of gene expression and sequence data, codon usage and protein abundances) to analyse the E.coli metabolic response to treatments at the whole network level. Our algorithm, MORA (Multi-omic relations adjacency) is able to detect patterns which may represent metabolic network motifs at pathway and supra pathway levels which could hint at some functional role. We provide a description and insights on the algorithm by testing it on a large database of responses to antibiotics. Along with the algorithm MORA, a novel model for the analysis of oscillating multi-omics has been proposed. Interestingly, the resulting analysis suggests that some motifs reveal recurring oscillating or position variation patterns on multi-omics metabolic networks. Our framework, implemented in R, provides effective and friendly means to design intervention scenarios on real data. By analysing how multi-omics data build up multi-scale phenotypes, the software allows to compare and test metabolic models, design new pathways or redesign existing metabolic pathways and validate in silico metabolic models using nearby species. Conclusions The integration of multi-omic data reveals that E.coli multi-omic metabolic networks contain position dependent and recurring patterns which could provide clues of long range correlations in the bacterial genome. Multi-omics (dpeaa)DE-He213 omic regularities (dpeaa)DE-He213 Antibiotic response (dpeaa)DE-He213 Multi-omic metabolic networks (dpeaa)DE-He213 Multi-omic motifs (dpeaa)DE-He213 E. (dpeaa)DE-He213 Lió, Pietro aut Tagliaferri, Roberto aut Enthalten in BMC bioinformatics London : BioMed Central, 2000 19(2018), Suppl 7 vom: 09. Juli (DE-627)326644814 (DE-600)2041484-5 1471-2105 nnns volume:19 year:2018 number:Suppl 7 day:09 month:07 https://dx.doi.org/10.1186/s12859-018-2175-5 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 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_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2031 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2061 GBV_ILN_2111 GBV_ILN_2113 GBV_ILN_2190 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_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 19 2018 Suppl 7 09 07
allfieldsGer	10.1186/s12859-018-2175-5 doi (DE-627)SPR026920891 (SPR)s12859-018-2175-5-e DE-627 ger DE-627 rakwb eng Bardozzo, Francesco verfasserin aut A study on multi-omic oscillations in Escherichia coli metabolic networks 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2018 Background Two important challenges in the analysis of molecular biology information are data (multi-omic information) integration and the detection of patterns across large scale molecular networks and sequences. They are are actually coupled beause the integration of omic information may provide better means to detect multi-omic patterns that could reveal multi-scale or emerging properties at the phenotype levels. Results Here we address the problem of integrating various types of molecular information (a large collection of gene expression and sequence data, codon usage and protein abundances) to analyse the E.coli metabolic response to treatments at the whole network level. Our algorithm, MORA (Multi-omic relations adjacency) is able to detect patterns which may represent metabolic network motifs at pathway and supra pathway levels which could hint at some functional role. We provide a description and insights on the algorithm by testing it on a large database of responses to antibiotics. Along with the algorithm MORA, a novel model for the analysis of oscillating multi-omics has been proposed. Interestingly, the resulting analysis suggests that some motifs reveal recurring oscillating or position variation patterns on multi-omics metabolic networks. Our framework, implemented in R, provides effective and friendly means to design intervention scenarios on real data. By analysing how multi-omics data build up multi-scale phenotypes, the software allows to compare and test metabolic models, design new pathways or redesign existing metabolic pathways and validate in silico metabolic models using nearby species. Conclusions The integration of multi-omic data reveals that E.coli multi-omic metabolic networks contain position dependent and recurring patterns which could provide clues of long range correlations in the bacterial genome. Multi-omics (dpeaa)DE-He213 omic regularities (dpeaa)DE-He213 Antibiotic response (dpeaa)DE-He213 Multi-omic metabolic networks (dpeaa)DE-He213 Multi-omic motifs (dpeaa)DE-He213 E. (dpeaa)DE-He213 Lió, Pietro aut Tagliaferri, Roberto aut Enthalten in BMC bioinformatics London : BioMed Central, 2000 19(2018), Suppl 7 vom: 09. Juli (DE-627)326644814 (DE-600)2041484-5 1471-2105 nnns volume:19 year:2018 number:Suppl 7 day:09 month:07 https://dx.doi.org/10.1186/s12859-018-2175-5 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 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_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2031 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2061 GBV_ILN_2111 GBV_ILN_2113 GBV_ILN_2190 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_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 19 2018 Suppl 7 09 07
allfieldsSound	10.1186/s12859-018-2175-5 doi (DE-627)SPR026920891 (SPR)s12859-018-2175-5-e DE-627 ger DE-627 rakwb eng Bardozzo, Francesco verfasserin aut A study on multi-omic oscillations in Escherichia coli metabolic networks 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2018 Background Two important challenges in the analysis of molecular biology information are data (multi-omic information) integration and the detection of patterns across large scale molecular networks and sequences. They are are actually coupled beause the integration of omic information may provide better means to detect multi-omic patterns that could reveal multi-scale or emerging properties at the phenotype levels. Results Here we address the problem of integrating various types of molecular information (a large collection of gene expression and sequence data, codon usage and protein abundances) to analyse the E.coli metabolic response to treatments at the whole network level. Our algorithm, MORA (Multi-omic relations adjacency) is able to detect patterns which may represent metabolic network motifs at pathway and supra pathway levels which could hint at some functional role. We provide a description and insights on the algorithm by testing it on a large database of responses to antibiotics. Along with the algorithm MORA, a novel model for the analysis of oscillating multi-omics has been proposed. Interestingly, the resulting analysis suggests that some motifs reveal recurring oscillating or position variation patterns on multi-omics metabolic networks. Our framework, implemented in R, provides effective and friendly means to design intervention scenarios on real data. By analysing how multi-omics data build up multi-scale phenotypes, the software allows to compare and test metabolic models, design new pathways or redesign existing metabolic pathways and validate in silico metabolic models using nearby species. Conclusions The integration of multi-omic data reveals that E.coli multi-omic metabolic networks contain position dependent and recurring patterns which could provide clues of long range correlations in the bacterial genome. Multi-omics (dpeaa)DE-He213 omic regularities (dpeaa)DE-He213 Antibiotic response (dpeaa)DE-He213 Multi-omic metabolic networks (dpeaa)DE-He213 Multi-omic motifs (dpeaa)DE-He213 E. (dpeaa)DE-He213 Lió, Pietro aut Tagliaferri, Roberto aut Enthalten in BMC bioinformatics London : BioMed Central, 2000 19(2018), Suppl 7 vom: 09. Juli (DE-627)326644814 (DE-600)2041484-5 1471-2105 nnns volume:19 year:2018 number:Suppl 7 day:09 month:07 https://dx.doi.org/10.1186/s12859-018-2175-5 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 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_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2031 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2061 GBV_ILN_2111 GBV_ILN_2113 GBV_ILN_2190 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_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 19 2018 Suppl 7 09 07
language	English
source	Enthalten in BMC bioinformatics 19(2018), Suppl 7 vom: 09. Juli volume:19 year:2018 number:Suppl 7 day:09 month:07
sourceStr	Enthalten in BMC bioinformatics 19(2018), Suppl 7 vom: 09. Juli volume:19 year:2018 number:Suppl 7 day:09 month:07
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Multi-omics omic regularities Antibiotic response Multi-omic metabolic networks Multi-omic motifs E.
isfreeaccess_bool	true
container_title	BMC bioinformatics
authorswithroles_txt_mv	Bardozzo, Francesco @@aut@@ Lió, Pietro @@aut@@ Tagliaferri, Roberto @@aut@@
publishDateDaySort_date	2018-07-09T00:00:00Z
hierarchy_top_id	326644814
id	SPR026920891
language_de	englisch
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author	Bardozzo, Francesco
spellingShingle	Bardozzo, Francesco misc Multi-omics misc omic regularities misc Antibiotic response misc Multi-omic metabolic networks misc Multi-omic motifs misc E. A study on multi-omic oscillations in Escherichia coli metabolic networks
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topic_title	A study on multi-omic oscillations in Escherichia coli metabolic networks Multi-omics (dpeaa)DE-He213 omic regularities (dpeaa)DE-He213 Antibiotic response (dpeaa)DE-He213 Multi-omic metabolic networks (dpeaa)DE-He213 Multi-omic motifs (dpeaa)DE-He213 E. (dpeaa)DE-He213
topic	misc Multi-omics misc omic regularities misc Antibiotic response misc Multi-omic metabolic networks misc Multi-omic motifs misc E.
topic_unstemmed	misc Multi-omics misc omic regularities misc Antibiotic response misc Multi-omic metabolic networks misc Multi-omic motifs misc E.
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title	A study on multi-omic oscillations in Escherichia coli metabolic networks
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title_full	A study on multi-omic oscillations in Escherichia coli metabolic networks
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author_browse	Bardozzo, Francesco Lió, Pietro Tagliaferri, Roberto
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title_sort	study on multi-omic oscillations in escherichia coli metabolic networks
title_auth	A study on multi-omic oscillations in Escherichia coli metabolic networks
abstract	Background Two important challenges in the analysis of molecular biology information are data (multi-omic information) integration and the detection of patterns across large scale molecular networks and sequences. They are are actually coupled beause the integration of omic information may provide better means to detect multi-omic patterns that could reveal multi-scale or emerging properties at the phenotype levels. Results Here we address the problem of integrating various types of molecular information (a large collection of gene expression and sequence data, codon usage and protein abundances) to analyse the E.coli metabolic response to treatments at the whole network level. Our algorithm, MORA (Multi-omic relations adjacency) is able to detect patterns which may represent metabolic network motifs at pathway and supra pathway levels which could hint at some functional role. We provide a description and insights on the algorithm by testing it on a large database of responses to antibiotics. Along with the algorithm MORA, a novel model for the analysis of oscillating multi-omics has been proposed. Interestingly, the resulting analysis suggests that some motifs reveal recurring oscillating or position variation patterns on multi-omics metabolic networks. Our framework, implemented in R, provides effective and friendly means to design intervention scenarios on real data. By analysing how multi-omics data build up multi-scale phenotypes, the software allows to compare and test metabolic models, design new pathways or redesign existing metabolic pathways and validate in silico metabolic models using nearby species. Conclusions The integration of multi-omic data reveals that E.coli multi-omic metabolic networks contain position dependent and recurring patterns which could provide clues of long range correlations in the bacterial genome. © The Author(s) 2018
abstractGer	Background Two important challenges in the analysis of molecular biology information are data (multi-omic information) integration and the detection of patterns across large scale molecular networks and sequences. They are are actually coupled beause the integration of omic information may provide better means to detect multi-omic patterns that could reveal multi-scale or emerging properties at the phenotype levels. Results Here we address the problem of integrating various types of molecular information (a large collection of gene expression and sequence data, codon usage and protein abundances) to analyse the E.coli metabolic response to treatments at the whole network level. Our algorithm, MORA (Multi-omic relations adjacency) is able to detect patterns which may represent metabolic network motifs at pathway and supra pathway levels which could hint at some functional role. We provide a description and insights on the algorithm by testing it on a large database of responses to antibiotics. Along with the algorithm MORA, a novel model for the analysis of oscillating multi-omics has been proposed. Interestingly, the resulting analysis suggests that some motifs reveal recurring oscillating or position variation patterns on multi-omics metabolic networks. Our framework, implemented in R, provides effective and friendly means to design intervention scenarios on real data. By analysing how multi-omics data build up multi-scale phenotypes, the software allows to compare and test metabolic models, design new pathways or redesign existing metabolic pathways and validate in silico metabolic models using nearby species. Conclusions The integration of multi-omic data reveals that E.coli multi-omic metabolic networks contain position dependent and recurring patterns which could provide clues of long range correlations in the bacterial genome. © The Author(s) 2018
abstract_unstemmed	Background Two important challenges in the analysis of molecular biology information are data (multi-omic information) integration and the detection of patterns across large scale molecular networks and sequences. They are are actually coupled beause the integration of omic information may provide better means to detect multi-omic patterns that could reveal multi-scale or emerging properties at the phenotype levels. Results Here we address the problem of integrating various types of molecular information (a large collection of gene expression and sequence data, codon usage and protein abundances) to analyse the E.coli metabolic response to treatments at the whole network level. Our algorithm, MORA (Multi-omic relations adjacency) is able to detect patterns which may represent metabolic network motifs at pathway and supra pathway levels which could hint at some functional role. We provide a description and insights on the algorithm by testing it on a large database of responses to antibiotics. Along with the algorithm MORA, a novel model for the analysis of oscillating multi-omics has been proposed. Interestingly, the resulting analysis suggests that some motifs reveal recurring oscillating or position variation patterns on multi-omics metabolic networks. Our framework, implemented in R, provides effective and friendly means to design intervention scenarios on real data. By analysing how multi-omics data build up multi-scale phenotypes, the software allows to compare and test metabolic models, design new pathways or redesign existing metabolic pathways and validate in silico metabolic models using nearby species. Conclusions The integration of multi-omic data reveals that E.coli multi-omic metabolic networks contain position dependent and recurring patterns which could provide clues of long range correlations in the bacterial genome. © The Author(s) 2018
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title_short	A study on multi-omic oscillations in Escherichia coli metabolic networks
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A study on multi-omic oscillations in Escherichia coli metabolic networks

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