Genome, transcriptome and proteome: the rise of omics data and their integration in biomedical sciences

Abstract Advances in the technologies and informatics used to generate and process large biological data sets (omics data) are promoting a critical shift in the study of biomedical sciences. While genomics, transcriptomics and proteinomics, coupled with bioinformatics and biostatistics, are gaining...

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Published inBriefings in bioinformatics Vol. 19; no. 2; pp. 286 - 302
Main Authors Manzoni, Claudia, Kia, Demis A, Vandrovcova, Jana, Hardy, John, Wood, Nicholas W, Lewis, Patrick A, Ferrari, Raffaele
Format Journal Article
LanguageEnglish
Published England Oxford University Press 01.03.2018
Oxford Publishing Limited (England)
Subjects
analytical methods
Animals
Bioinformatics
Biological activity
Biomedical Research
biostatistics
data collection
Data integration
epigenetics
Gene expression
genome
Genome, Human
Genomics
Humans
Integration
Metabolomics
Pharmacogenomics
Precision medicine
Proteome
Proteomics
students
Systems Biology - methods
Transcriptome
transcriptomics
transcriptomics
databases
genomics
omics
proteomics
bioinformatics
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Abstract Abstract Advances in the technologies and informatics used to generate and process large biological data sets (omics data) are promoting a critical shift in the study of biomedical sciences. While genomics, transcriptomics and proteinomics, coupled with bioinformatics and biostatistics, are gaining momentum, they are still, for the most part, assessed individually with distinct approaches generating monothematic rather than integrated knowledge. As other areas of biomedical sciences, including metabolomics, epigenomics and pharmacogenomics, are moving towards the omics scale, we are witnessing the rise of inter-disciplinary data integration strategies to support a better understanding of biological systems and eventually the development of successful precision medicine. This review cuts across the boundaries between genomics, transcriptomics and proteomics, summarizing how omics data are generated, analysed and shared, and provides an overview of the current strengths and weaknesses of this global approach. This work intends to target students and researchers seeking knowledge outside of their field of expertise and fosters a leap from the reductionist to the global-integrative analytical approach in research.
AbstractList Advances in the technologies and informatics used to generate and process large biological data sets (omics data) are promoting a critical shift in the study of biomedical sciences. While genomics, transcriptomics and proteinomics, coupled with bioinformatics and biostatistics, are gaining momentum, they are still, for the most part, assessed individually with distinct approaches generating monothematic rather than integrated knowledge. As other areas of biomedical sciences, including metabolomics, epigenomics and pharmacogenomics, are moving towards the omics scale, we are witnessing the rise of inter-disciplinary data integration strategies to support a better understanding of biological systems and eventually the development of successful precision medicine. This review cuts across the boundaries between genomics, transcriptomics and proteomics, summarizing how omics data are generated, analysed and shared, and provides an overview of the current strengths and weaknesses of this global approach. This work intends to target students and researchers seeking knowledge outside of their field of expertise and fosters a leap from the reductionist to the global-integrative analytical approach in research.
Abstract Advances in the technologies and informatics used to generate and process large biological data sets (omics data) are promoting a critical shift in the study of biomedical sciences. While genomics, transcriptomics and proteinomics, coupled with bioinformatics and biostatistics, are gaining momentum, they are still, for the most part, assessed individually with distinct approaches generating monothematic rather than integrated knowledge. As other areas of biomedical sciences, including metabolomics, epigenomics and pharmacogenomics, are moving towards the omics scale, we are witnessing the rise of inter-disciplinary data integration strategies to support a better understanding of biological systems and eventually the development of successful precision medicine. This review cuts across the boundaries between genomics, transcriptomics and proteomics, summarizing how omics data are generated, analysed and shared, and provides an overview of the current strengths and weaknesses of this global approach. This work intends to target students and researchers seeking knowledge outside of their field of expertise and fosters a leap from the reductionist to the global-integrative analytical approach in research.
Advances in the technologies and informatics used to generate and process large biological data sets (omics data) are promoting a critical shift in the study of biomedical sciences. While genomics, transcriptomics and proteinomics, coupled with bioinformatics and biostatistics, are gaining momentum, they are still, for the most part, assessed individually with distinct approaches generating monothematic rather than integrated knowledge. As other areas of biomedical sciences, including metabolomics, epigenomics and pharmacogenomics, are moving towards the omics scale, we are witnessing the rise of inter-disciplinary data integration strategies to support a better understanding of biological systems and eventually the development of successful precision medicine. This review cuts across the boundaries between genomics, transcriptomics and proteomics, summarizing how omics data are generated, analysed and shared, and provides an overview of the current strengths and weaknesses of this global approach. This work intends to target students and researchers seeking knowledge outside of their field of expertise and fosters a leap from the reductionist to the global-integrative analytical approach in research.Advances in the technologies and informatics used to generate and process large biological data sets (omics data) are promoting a critical shift in the study of biomedical sciences. While genomics, transcriptomics and proteinomics, coupled with bioinformatics and biostatistics, are gaining momentum, they are still, for the most part, assessed individually with distinct approaches generating monothematic rather than integrated knowledge. As other areas of biomedical sciences, including metabolomics, epigenomics and pharmacogenomics, are moving towards the omics scale, we are witnessing the rise of inter-disciplinary data integration strategies to support a better understanding of biological systems and eventually the development of successful precision medicine. This review cuts across the boundaries between genomics, transcriptomics and proteomics, summarizing how omics data are generated, analysed and shared, and provides an overview of the current strengths and weaknesses of this global approach. This work intends to target students and researchers seeking knowledge outside of their field of expertise and fosters a leap from the reductionist to the global-integrative analytical approach in research.
Author Vandrovcova, Jana
Wood, Nicholas W
Lewis, Patrick A
Ferrari, Raffaele
Kia, Demis A
Manzoni, Claudia
Hardy, John
AuthorAffiliation 2 Department Molecular Neuroscience, UCL Institute of Neurology, London, United Kingdom
1 School of Pharmacy, University of Reading, Whiteknights, Reading, United Kingdom
AuthorAffiliation_xml – name: 1 School of Pharmacy, University of Reading, Whiteknights, Reading, United Kingdom
– name: 2 Department Molecular Neuroscience, UCL Institute of Neurology, London, United Kingdom
Author_xml – sequence: 1
  givenname: Claudia
  surname: Manzoni
  fullname: Manzoni, Claudia
  email: c.manzoni@reading.ac.uk
  organization: School of Pharmacy, University of Reading, Whiteknights, Reading, United Kingdom
– sequence: 2
  givenname: Demis A
  surname: Kia
  fullname: Kia, Demis A
  organization: Department Molecular Neuroscience, UCL Institute of Neurology, London, United Kingdom
– sequence: 3
  givenname: Jana
  surname: Vandrovcova
  fullname: Vandrovcova, Jana
  organization: Department Molecular Neuroscience, UCL Institute of Neurology, London, United Kingdom
– sequence: 4
  givenname: John
  surname: Hardy
  fullname: Hardy, John
  organization: Department Molecular Neuroscience, UCL Institute of Neurology, London, United Kingdom
– sequence: 5
  givenname: Nicholas W
  surname: Wood
  fullname: Wood, Nicholas W
  organization: Department Molecular Neuroscience, UCL Institute of Neurology, London, United Kingdom
– sequence: 6
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  surname: Lewis
  fullname: Lewis, Patrick A
  organization: School of Pharmacy, University of Reading, Whiteknights, Reading, United Kingdom
– sequence: 7
  givenname: Raffaele
  surname: Ferrari
  fullname: Ferrari, Raffaele
  organization: Department Molecular Neuroscience, UCL Institute of Neurology, London, United Kingdom
BackLink https://www.ncbi.nlm.nih.gov/pubmed/27881428$$D View this record in MEDLINE/PubMed
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Issue 2
Keywords transcriptomics
databases
genomics
omics
proteomics
bioinformatics
Language English
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Claudia Manzoni and Demis A. Kia authors contributed equally to this work.
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Snippet Abstract Advances in the technologies and informatics used to generate and process large biological data sets (omics data) are promoting a critical shift in...
Advances in the technologies and informatics used to generate and process large biological data sets (omics data) are promoting a critical shift in the study...
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StartPage 286
SubjectTerms analytical methods
Animals
Bioinformatics
Biological activity
Biomedical Research
biostatistics
data collection
Data integration
epigenetics
Gene expression
genome
Genome, Human
Genomics
Humans
Integration
Metabolomics
Pharmacogenomics
Precision medicine
Proteome
Proteomics
students
Systems Biology - methods
Transcriptome
transcriptomics
Title Genome, transcriptome and proteome: the rise of omics data and their integration in biomedical sciences
URI https://www.ncbi.nlm.nih.gov/pubmed/27881428
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https://www.proquest.com/docview/1843969318
https://www.proquest.com/docview/2253249755
https://pubmed.ncbi.nlm.nih.gov/PMC6018996
Volume 19
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