TY - JOUR
T1 - Integration of large-scale multi-omic datasets
T2 - A protein-centric view
AU - Rendleman, Justin
AU - Choi, Hyungwon
AU - Vogel, Christine
N1 - Funding Information:
The work was supported by the National Institutes of Health/National Institute for General Medical Sciences grant 1R35GM127089-01 (to C.V.) and Singapore Ministry of Education grant MOE2016-T2-1-001 (to H.C.).
Publisher Copyright:
© 2018 Elsevier Ltd
PY - 2018/10
Y1 - 2018/10
N2 - Innovative mass spectrometry-based proteomics has enabled routine measurements of protein abundance, localization, interactions, and modifications, covering unique aspects of gene expression regulation and function. It is now time to move from isolated analyses of these datasets toward true integration of proteomics with other data types to gain insights from the interactions and interdependencies of biomolecules. When combined with genomic or transcriptomic data, proteomics expands genome annotation to identify variant or missing genes. Dynamic proteomic measurements can move analysis from predominantly concentration-based framework to that of synthesis and degradation of proteins. Proteomic data from thousands of cancer patients can foster identification of novel pathogenic mutations via detection of protein sequence changes that lead to dysregulated pathways in various tumors. Such comprehensive efforts can exploit the synergy arising from large and complex datasets to advance virtually every field of biology.
AB - Innovative mass spectrometry-based proteomics has enabled routine measurements of protein abundance, localization, interactions, and modifications, covering unique aspects of gene expression regulation and function. It is now time to move from isolated analyses of these datasets toward true integration of proteomics with other data types to gain insights from the interactions and interdependencies of biomolecules. When combined with genomic or transcriptomic data, proteomics expands genome annotation to identify variant or missing genes. Dynamic proteomic measurements can move analysis from predominantly concentration-based framework to that of synthesis and degradation of proteins. Proteomic data from thousands of cancer patients can foster identification of novel pathogenic mutations via detection of protein sequence changes that lead to dysregulated pathways in various tumors. Such comprehensive efforts can exploit the synergy arising from large and complex datasets to advance virtually every field of biology.
UR - http://www.scopus.com/inward/record.url?scp=85053775770&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85053775770&partnerID=8YFLogxK
U2 - 10.1016/j.coisb.2018.09.001
DO - 10.1016/j.coisb.2018.09.001
M3 - Review article
AN - SCOPUS:85053775770
SN - 2452-3100
VL - 11
SP - 74
EP - 81
JO - Current Opinion in Systems Biology
JF - Current Opinion in Systems Biology
ER -