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Computational analyses for quantitation of eukaryotic proteomes

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  • Full or part time
    Prof S Hubbard
  • Application Deadline
    Applications accepted all year round
  • Self-Funded PhD Students Only
    Self-Funded PhD Students Only

Project Description

Our lab is interested in proteins, how they are expressed, translated and regulated and how these processes can adapt during different biological processes, such as during development or under stress. In order to understand how global protein levels are regulated we rely on mass spectrometry driven proteomics techniques, which aim to quantify proteomes based on the signal derived from peptides observed in the mass spectrometer. A variety of techniques are used, including both label-mediated and label-free techniques. This project seeks to benchmark, refine and improve bioinformatics tools need to generate reliable quantitative information from proteomics data, as well as analyse the results to understand different aspects of biological function.

We will seek, initially, to build on one tools designed for selection of “quantotypic” peptides, in order to improve label-free approaches and generate more robust data for exemplar systems such as yeast. By exploiting data sets in-house, as well as external repositories, we can improve our machine learning tools and include peptide detectability (Eyers et al 2011) and peptide missed cleavage (Lawless and Hubbard, 2012) as factors for machine learning classifiers.

By comparison with existing transcriptome data sets we will then aim to understand how yeast cells regulate translation from mRNA to protein, analysing and modelling our data in conjunction with external data sets to rationalise why some genes see a massive increase in the ratio of mRNA:protein (> 250,000) and others are much more modest (< 100).

Finally, we aim to extend these analyses to metazoans, exploiting the data available i from our BBSRC sLoLa project, to observe differences in the quantitative proteomes over several time points in embryonic development of the fruit fly.

This project will hence provide a broad-based training and exposure to many aspects of post-transcriptional control of gene expression using post-genomics data.

Funding Notes

This project has a Band 1 fee. Details of our different fee bands can be found on our website. For information on how to apply for this project, please visit the Faculty of Biology, Medicine and Health Doctoral Academy website. Informal enquiries may be made directly to the primary supervisor.


1: Brownridge P, Lawless C, Payapilly AB, Lanthaler K, Holman SW, Harman VM, Grant CM, Beynon RJ, Hubbard SJ. Quantitative analysis of chaperone network throughput in budding yeast. Proteomics. 2013 Apr;13(8):1276-91. doi: 10.1002/pmic.201200412. Epub 2013 Mar 15. PubMed PMID: 23420633; PubMed Central PMCID: PMC3791555.

2: Lawless C, Hubbard SJ. Prediction of missed proteolytic cleavages for the selection of surrogate peptides for quantitative proteomics. OMICS. 2012 Sep;16(9):449-56. doi: 10.1089/omi.2011.0156. Epub 2012 Jul 17. PubMed PMID: 22804685; PubMed Central PMCID: PMC3437044.

3: Eyers CE, Lawless C, Wedge DC, Lau KW, Gaskell SJ, Hubbard SJ. CONSeQuence: prediction of reference peptides for absolute quantitative proteomics using consensus machine learning approaches. Mol Cell Proteomics. 2011 Nov;10(11):M110.003384. doi: 10.1074/mcp.M110.003384. Epub 2011 Aug 3. PubMed PMID: 21813416; PubMed Central PMCID: PMC3226394.

4: Blakeley P, Overton IM, Hubbard SJ. Addressing statistical biases in nucleotide-derived protein databases for proteogenomic search strategies. J Proteome Res. 2012 Nov 2;11(11):5221-34. doi: 10.1021/pr300411q. Epub 2012 Oct 15. PubMed PMID: 23025403; PubMed Central PMCID: PMC3703792.

5: Gonzalez-Galarza FF, Lawless C, Hubbard SJ, Fan J, Bessant C, Hermjakob H, Jones AR. A critical appraisal of techniques, software packages, and standards for quantitative proteomic analysis. OMICS. 2012 Sep;16(9):431-42. doi: 10.1089/omi.2012.0022. Epub 2012 Jul 17. PubMed PMID: 22804616; PubMed Central PMCID: PMC3437040.

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