Handle URI:
http://hdl.handle.net/10754/601411
Title:
Network-based analysis of proteomic profiles
Authors:
Wong, Limsoon
Abstract:
Mass spectrometry (MS)-based proteomics is a widely used and powerful tool for profiling systems-wide protein expression changes. It can be applied for various purposes, e.g. biomarker discovery in diseases and study of drug responses. Although RNA-based high-throughput methods have been useful in providing glimpses into the underlying molecular processes, the evidences they provide are indirect. Furthermore, RNA and corresponding protein levels have been known to have poor correlation. On the other hand, MS-based proteomics tend to have consistency issues (poor reproducibility and inter-sample agreement) and coverage issues (inability to detect the entire proteome) that need to be urgently addressed. In this talk, I will discuss how these issues can be addressed by proteomic profile analysis techniques that use biological networks (especially protein complexes) as the biological context. In particular, I will describe several techniques that we have been developing for network-based analysis of proteomics profile. And I will present evidence that these techniques are useful in identifying proteomics-profile analysis results that are more consistent, more reproducible, and more biologically coherent, and that these techniques allow expansion of the detected proteome to uncover and/or discover novel proteins.
Conference/Event name:
KAUST Research Conference on Computational and Experimental Interfaces of Big Data and Biotechnology
Issue Date:
26-Jan-2016
Type:
Presentation
Appears in Collections:
KAUST Research Conference on Computational and Experimental Interfaces of Big Data and Biotechnology, January 2016

Full metadata record

DC FieldValue Language
dc.contributor.authorWong, Limsoonen
dc.date.accessioned2016-03-16T12:53:33Zen
dc.date.available2016-03-16T12:53:33Zen
dc.date.issued2016-01-26en
dc.identifier.urihttp://hdl.handle.net/10754/601411en
dc.description.abstractMass spectrometry (MS)-based proteomics is a widely used and powerful tool for profiling systems-wide protein expression changes. It can be applied for various purposes, e.g. biomarker discovery in diseases and study of drug responses. Although RNA-based high-throughput methods have been useful in providing glimpses into the underlying molecular processes, the evidences they provide are indirect. Furthermore, RNA and corresponding protein levels have been known to have poor correlation. On the other hand, MS-based proteomics tend to have consistency issues (poor reproducibility and inter-sample agreement) and coverage issues (inability to detect the entire proteome) that need to be urgently addressed. In this talk, I will discuss how these issues can be addressed by proteomic profile analysis techniques that use biological networks (especially protein complexes) as the biological context. In particular, I will describe several techniques that we have been developing for network-based analysis of proteomics profile. And I will present evidence that these techniques are useful in identifying proteomics-profile analysis results that are more consistent, more reproducible, and more biologically coherent, and that these techniques allow expansion of the detected proteome to uncover and/or discover novel proteins.en
dc.titleNetwork-based analysis of proteomic profilesen
dc.typePresentationen
dc.conference.dateJanuary 25-27, 2016en
dc.conference.nameKAUST Research Conference on Computational and Experimental Interfaces of Big Data and Biotechnologyen
dc.conference.locationKAUST, Thuwal, Saudi Arabiaen
dc.contributor.institutionNational University of Singaporeen
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