Computational proteomics using network-based strategies

This thesis examines the productive application of networks towards proteomics, with a specific biological focus on liver cancer. Contempory proteomics (shot- gun) is plagued by coverage and consistency issues. These can be resolved via network-based approaches. The application of 3 classes of netwo...

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Main Author: Goh, Wen
Other Authors: Sergot, Marek
Published: Imperial College London 2013
Subjects:
004
Online Access:http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.656384
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spelling ndltd-bl.uk-oai-ethos.bl.uk-6563842015-12-03T03:45:54ZComputational proteomics using network-based strategiesGoh, WenSergot, Marek2013This thesis examines the productive application of networks towards proteomics, with a specific biological focus on liver cancer. Contempory proteomics (shot- gun) is plagued by coverage and consistency issues. These can be resolved via network-based approaches. The application of 3 classes of network-based approaches are examined: A traditional cluster based approach termed Proteomics Expansion Pipeline), a generalization of PEP termed Maxlink and a feature-based approach termed Proteomics Signature Profiling. PEP is an improvement on prevailing cluster-based approaches. It uses a state- of-the-art cluster identification algorithm as well as network-cleaning approaches to identify the critical network regions indicated by the liver cancer data set. The top PARP1 associated-cluster was identified and independently validated. Maxlink allows identification of undetected proteins based on the number of links to identified differential proteins. It is more sensitive than PEP due to more relaxed requirements. Here, the novel roles of ARRB1/2 and ACTB are identified and discussed in the context of liver cancer. Both PEP and Maxlink are unable to deal with consistency issues, PSP is the first method able to deal with both, and is termed feature-based since the network- based clusters it uses are predicted independently of the data. It is also capable of using real complexes or predicted pathway subnets. By combining pathways and complexes, a novel basis of liver cancer progression implicating nucleotide pool imbalance aggravated by mutations of key DNA repair complexes was identified. Finally, comparative evaluations suggested that pure network-based methods are vastly outperformed by feature-based network methods utilizing real complexes. This is indicative that the quality of current networks are insufficient to provide strong biological rigor for data analysis, and should be carefully evaluated before further validations.004Imperial College Londonhttp://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.656384http://hdl.handle.net/10044/1/24107Electronic Thesis or Dissertation
collection NDLTD
sources NDLTD
topic 004
spellingShingle 004
Goh, Wen
Computational proteomics using network-based strategies
description This thesis examines the productive application of networks towards proteomics, with a specific biological focus on liver cancer. Contempory proteomics (shot- gun) is plagued by coverage and consistency issues. These can be resolved via network-based approaches. The application of 3 classes of network-based approaches are examined: A traditional cluster based approach termed Proteomics Expansion Pipeline), a generalization of PEP termed Maxlink and a feature-based approach termed Proteomics Signature Profiling. PEP is an improvement on prevailing cluster-based approaches. It uses a state- of-the-art cluster identification algorithm as well as network-cleaning approaches to identify the critical network regions indicated by the liver cancer data set. The top PARP1 associated-cluster was identified and independently validated. Maxlink allows identification of undetected proteins based on the number of links to identified differential proteins. It is more sensitive than PEP due to more relaxed requirements. Here, the novel roles of ARRB1/2 and ACTB are identified and discussed in the context of liver cancer. Both PEP and Maxlink are unable to deal with consistency issues, PSP is the first method able to deal with both, and is termed feature-based since the network- based clusters it uses are predicted independently of the data. It is also capable of using real complexes or predicted pathway subnets. By combining pathways and complexes, a novel basis of liver cancer progression implicating nucleotide pool imbalance aggravated by mutations of key DNA repair complexes was identified. Finally, comparative evaluations suggested that pure network-based methods are vastly outperformed by feature-based network methods utilizing real complexes. This is indicative that the quality of current networks are insufficient to provide strong biological rigor for data analysis, and should be carefully evaluated before further validations.
author2 Sergot, Marek
author_facet Sergot, Marek
Goh, Wen
author Goh, Wen
author_sort Goh, Wen
title Computational proteomics using network-based strategies
title_short Computational proteomics using network-based strategies
title_full Computational proteomics using network-based strategies
title_fullStr Computational proteomics using network-based strategies
title_full_unstemmed Computational proteomics using network-based strategies
title_sort computational proteomics using network-based strategies
publisher Imperial College London
publishDate 2013
url http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.656384
work_keys_str_mv AT gohwen computationalproteomicsusingnetworkbasedstrategies
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