Using machine learning to identify the functional consequences of post-translational modifications in the rice proteome

For a rapidly growing global population, providing a long-term stable, source of nutritious food is a major unsolved global challenge. It is certain that successful solutions will involve applying next-generation technologies to improve breeding of crops, and in particular rice. Rice provides the daily calorie needs for the majority of the world’s poor. There is a vast pool of natural genetic resources available for rice with different species and wild varieties grown all over the world. There has been a recent effort to sequence the genomes of over 3000 different varieties of rice, which gives us an unprecedented volume of data to mine for useful traits. Our research teams are working to improve understanding of the function of rice genes, and more specifically the proteins encoded by those genes. Proteins undergo post-translational modifications (PTMs) that have evolved in all forms of life as a mechanism for rapidly adapting protein function in response to many different kinds of stimuli. PTMs are known to be involved with cell signalling, enabling protein-protein interactions, protein turnover, and in the context of rice, are intrinsically involved in key traits, such as response to stresses (drought, flooding, pathogens).

In this project, you will be working on computational analysis of data about rice proteins and PTMs (i.e. using bioinformatics), to understand the signals or motifs that signal for the cellular machinery to add or remove PTMs, and thus rapidly switch function. Our teams using cutting edge “omics” techniques, such as next-generation sequencing and proteomics (mass spectrometry), and you will be analysing these data types (as well as those publicly available) using data science techniques. You will be trained in coding, data visualisation and machine learning, with a view to discovery of the functional consequences of PTMs that are intrinsically associated with desirable traits in rice. We will then test those predictions in rice, with a long term goal of improving rice breeding efforts. You will be embedded within a highly active and collaborative research environment, with potential for involvement with, and travel to, major international research programmes to improve rice.

The supervisory team comprises:

• Andy Jones (Director, Computational Biology Facility, https://www.liverpool.ac.uk/computational-biology-facility/; Twitter: @andy___jones) – https://www.liverpool.ac.uk/integrative-biology/staff/andrew-jones/
• Ari Sadanandom (https://www.dur.ac.uk/biosciences/about/schoolstaff/allstaff/?id=9384)
• Claire Eyers (Director, Centre for Proteome Research, https://www.liverpool.ac.uk/cpr/; Twitter: @ClaireEEyers) – https://www.liverpool.ac.uk/integrative-biology/staff/claire-eyers/


HOW TO APPLY
Applications should be made by emailing [Email Address Removed] with a CV (including contact details of at least two academic (or other relevant) referees), and a covering letter – clearly stating your first choice project, and optionally 2nd and 3rd ranked projects, as well as including whatever additional information you feel is pertinent to your application; you may wish to indicate, for example, why you are particularly interested in the selected project(s) and at the selected University. Applications not meeting these criteria will be rejected.
In addition to the CV and covering letter, please email a completed copy of the Additional Details Form (Word document) to [Email Address Removed]. A blank copy of this form can be found at: https://www.nld-dtp.org.uk/how-apply.

Informal enquiries may be made to [Email Address Removed]