Wednesday, 21 January 2015

How to apply for a PhD in the Edwards Lab

Choosing the right lab and project in which to do a PhD is one of the most important decisions in the life of a scientist. It is in the best interests of all concerned to make sure that there is a good fit. To this end, there is now a lab PhD application form for all interested applicants. (Click the link or image to download.)

The purpose of the form is two-fold:

  1. To assess the skills, experience, and interests of applicants.
  2. To assess key CV points for those intending to apply for UNSW Scholarships.

The lab does not currently have any funding for students but I am happy to received applications from funded students and students wishing to apply for UNSW scholarships or other schemes. Please check the UNSW key dates page for application deadlines etc. - students may want to delay their application and strengthen their experience/CV in the meantime.

Informal enquiries are welcome but generic “Dear Sir/Professor” emails will be ignored. Please read this blog post, “How (not) to apply for a PhD”, before applying.

Note: Applicants with insufficient bioinformatics experience will not be considered (see below). It is simply too much of a risk (for both student and supervisor) to take such a student on, as not everyone takes to purely computational work. You must also demonstrate good communication in English, which includes all email communication.

Available projects

There are no specific projects on offer and PhD research topics will ultimately be a collaborative decision based on the skills and interests of the student as well as the current status of various research in the lab. Available projects range from algorithm and bioinformatics resource development to primarily data analysis projects. Examples include, but are not limited to:

  • Functional yeast genomics using long-read PacBio sequencing. We are particularly keen to get a student to work on aspects of our ARC Linkage grant, investigating the evolution of a novel biochemical pathway in yeast.
  • PacBio de novo whole genome sequencing and assembly of the cane toad.
  • Development of network approaches to understanding SLiM-mediated protein-protein interactions. (Strong maths required.)
  • Predictions of molecular mimicry from host-pathogen interaction data.
  • Exploring the role of SLiM mutations in cancer and other human diseases.
  • Development of a database of SLiM predictions.
  • Benchmarking, optimising and extending SLiM discovery tools.

There is no single perfect applicant profile: please provide a frank and honest appraisal of your interests, skills and future goals in your application.

Submitting your application

Completed applications forms should be emailed with a CV and degree transcript to richard.edwards@unsw.edu.au. Please name each file with your family name, initials and document type, e.g.

EdwardsRJ.Application.docx
EdwardsRJ.CV.pdf
EdwardsRJ.Transcript.pdf
PDFs are preferred but MS Word *.docx files are also OK. Remember: attention to detail is very important in bioinformatics. Boxes in the application form may be resized but please keep answers succinct; you will be judged on the quality of your writing.

Note: Applicants will also need to submit a formal application through the UNSW Graduate Research School. This is not recommended until an agreement has been made to sponsor your application. Please also note that any agreement to sponsor your application is not agreement to take you on as a student. The final decision regarding supervision will not be made until after all of the applications have been received and processed by UNSW.

Masters and undergraduate project applicants

Applicants for undergraduate projects (Honours/SVRS) or Masters programs should use the same form but indicate the program that they are applying for.

Bioinformatics Experience Requirements

All projects are 100% computational. To be considered as an international PhD applicant, you must have completed at least one 100% computational project as part of your undergrad or masters, or have equivalent computational experience (e.g. work placement as a programmer or data analyst). Taught courses are not sufficient at this level unless you can also provide some evidence of skill at scripting/programming, such as an extensive body of work at Rosalind. Unfortunately, the risks are simply too high otherwise.

Friday, 9 January 2015

Full text available for SLiM prediction review

For those who cannot access Methods in Molecular Biology, the final submitted draft of our recent paper is now available here.

The original publication is available at www.springerlink.com:

Edwards RJ & Palopoli N (2015): Computational Prediction of Short Linear Motifs from Protein Sequences. Methods Mol Biol. 1268:89-141.

Wednesday, 7 January 2015

Monday, 5 January 2015

Computational Prediction of Short Linear Motifs from Protein Sequences

Edwards RJ & Palopoli N (2015): Computational Prediction of Short Linear Motifs from Protein Sequences. Methods Mol Biol. 1268:89-141.

Abstract

Short Linear Motifs (SLiMs) are functional protein microdomains that typically mediate interactions between a short linear region in one protein and a globular domain in another. SLiMs usually occur in structurally disordered regions and mediate low affinity interactions. Most SLiMs are 3-15 amino acids in length and have 2-5 defined positions, making them highly likely to occur by chance and extremely difficult to identify. Nevertheless, our knowledge of SLiMs and capacity to predict them from protein sequence data using computational methods has advanced dramatically over the past decade. By considering the biological, structural, and evolutionary context of SLiM occurrences, it is possible to differentiate functional instances from chance matches in many cases and to identify new regions of proteins that have the features consistent with a SLiM-mediated interaction. Their simplicity also makes SLiMs evolutionarily labile and prone to independent origins on different sequence backgrounds through convergent evolution, which can be exploited for predicting novel SLiMs in proteins that share a function or interaction partner.

In this review, we explore our current knowledge of SLiMs and how it can be applied to the task of predicting them computationally from protein sequences. Rather than focusing on specific SLiM prediction tools, we provide an overview of the methods available and concentrate on principles that should continue to be paramount even in the light of future developments. We consider the relative merits of using regular expressions or profiles for SLiM discovery and discuss the main considerations for both predicting new instances of known SLiMs, and de novo prediction of novel SLiMs. In particular, we highlight the importance of correctly modelling evolutionary relationships and the probability of false positive predictions.

PMID: 25555723

Update: Full text (PDF) available here.