This blogpost has been created as part of a lab workshop on using git version control and has been a great excuse to share and discuss interesting papers that have influenced our work!

High Throughput Genetics and Cell Biology

Marah Jnied:

“A Network of Cytosolic Factors Targets SRP-Independent Proteins to the Endoplasmic Reticulum” is a paper that highlights the importance and complexity of SRP-independent translocation. In this paper, Ast, Schuldiner, et al., employ a combination of hydropathy-based analysis and microscopy to identify a determinant for Endoplasmic Reticulum targeting, and to reveal a network of cytosolic proteins that facilitate SRP-independent targeting and translocation. This paper inspired an exciting set of experiments for my PhD project concerning the RNA binding protein Ssd1.

Bioinformatics Papers

Flic Anderson:

“Best Practices for Scientific Computing” pulls together ideas for how to produce readable, shareable code that will deliver reliable results worth publishing. It’s informed by the authors’ involvement in the Carpentries Organisation and it’s inspired me to train as a Carpentries Instructor to help teach data and software workshops as well as using the skills day to day in my developer work on riboviz.

Functional Genomics Papers

Edward Wallace:

“Defining the essential function of yeast Hsf1 reveals a compact transcriptional program for maintaining eukaryotic proteostasis” is a neat paper that takes a hard problem and makes it seem easy. Since the 1980s, it was known that Heat shock factor 1 is an essential gene required for transcription of heat shock protein genes. But it wasn’t known why it was essential - which regulatory targets of Hsf1 are actually relied on by the cell? My then-officemate Eric Solís started working on this, and I had little idea what he was so excited about at the start. His work grew into this elegant story that uses genetics, ChIP-seq, RNA-seq, etc, to dissect Hsf1’s function away from everything else going on. This paper is a great model of how to take a “pleiotropic” regulatory factor and find out its specific regulatory functions.

Some of my lovely Chicago colleagues wrote a perspective on this work - “Heat Shock Factor 1: From Fire Chief to Crowd-Control Specialist”.

Eszter Denes:

“Candida glabrata Drug:H+ Antiporter CgQdr2 Confers Imidazole Drug Resistance, Being Activated by Transcription Factor CgPdr1” describes many experiments that can be conducted in order to show that a particular gene causes antifungal resistance in fungi. What is more, the authors also tested which transcription factor controls this gene’s expression levels. As my PhD project is about antifungal adaptation, I find this paper very informative and inspiring.

Translational Control Papers

Rosey Bayne:

“Translation factor mRNA granules direct protein synthetic capacity to regions of polarized growth” is a paper from Mark Ashe’s Lab which shows that, in contrast to P-bodies and stress granules, a subset of RNP granules harbour translated mRNAs under active growth conditions and that these granules are inherited by developing daughter cells, where protein synthesis is most heavily required. I work on Ssd1, an RNA binding protein which translationally represses the cell wall-associated mRNAs it binds, until it is phosphorylated by Cbk1 kinase at or near the bud neck where it is under tight cell-cycle control. This paper suggests that once the Ssd1-associated mRNAs are released from repression they should have access to such translation factories and allow expansion of the bud.

Act now or people will die from COVID-19

The viral pandemic COVID-19 is here, and we must act now or people will die. As that blogpost explains, COVID-19’s fast exponential spread, 2-week latency, and low testing rate provoke complacency. The decisions we make today affect how many people will die in 2 weeks time and beyond, and so we should take action today to flatten the curve of infections.

Inspired by Leslie Vosshall’s twitter thread, How to keep science-ing safely in a pandemic, here’s suggestions on how to be safer and also productive during the epidemic. I wrote this for my lab and am happy to share it.

Be safe - avoid people as much as possible

Social distancing is an effective containment measure. The risk of transmission goes with the square of the number of people: a seminar or bus with 50 people is 100x as risky as a meeting with 5.

Be safe in your life outside the lab

Stock up on prescriptions, consider human food and pet food.

Ideally cancel personal travel and large public events

Rethink your experiments

Downsize/postpone experiments where possible

Order critical reagents now

Develop list of critical lab functions and volunteers to do them

This will help if/when the university is closed to non-essential personnel.

Don’t go to meetings in person

Lab meetings and 1-1s will be online until further notice

1-1s I prefer

Lab meetings probably zoom or skype for business - this is work in progress.

Don’t go to seminars

Ask for a remote option where possible. This is upsetting because of course we want to hear other people’s science. Yet the risk squares with the number of people.

Cancel work travel

Will anyone die if you don’t make that trip? No? Don’t go.

Work from home if you can

If you must go to work, for example if something will catch fire or mice will die:

walk/bike to work

avoid public transportation

drive in if you must

Prepare your computers

install and test VPN and remote desktop


UoE remote desktop

put all the data you need somewhere sensible

For us that means UoE datastore, which is backed up to tape.

install any other software you might need

You can be plenty productive during the epidemic

Everyone, in my lab at least, has some written or computational work that will move their project and their career forward. Even the most dedicated experimentalist has data to analyse, papers to read, etc. Perhaps something you have been putting off for months. What a wonderful opportunity to focus on that instead, without guilt! So ask yourself:

What data analysis do I need to do?

What do I need to learn?

What do I need to write?

What should I prioritise?

Help improve this page!

Please make suggestions and comments the github issue for this page.

Update: these deadlines are now passed. Self-funded PhD students are still welcome to contact me following the guidelines below.

We are advertising PhD projects for 2020 entry, on different aspects of how fungi sense and respond to environmental change. They will all provide training in experimental and computational biology, in a world-class research environment. Each project is with a different fantastic collaborator, and the projects have different emphases.

RNA, RNA interference, and growth

These projects on Cryptococcus neoformans are based in Edinburgh, in collaboration with Dr. Elizabeth Bayne.

How fungi respond to antifungal drugs (antimicrobial resistance)

This project is primarily based in the laboratory of Dr. Delma Childers at the University of Aberdeen.

Environmental sensing at the cell surface

This project is a collaboration with Dr. Joanne Thompson and Dr. Jelena Baranovic, at the University of Edinburgh. It’s the most biochemical project we have listed, focused on function and structure cell surface receptor proteins, and would be primarily based in the Baranovic laboratory.

Alternative proteins made by alternative translation

This project follows up on recent exciting work in the lab that you can read on biorXiv. It would start a new collaboration with Prof. Achim Schnaufer, at Edinburgh. The project would be particularly suitable for a self-funded / Darwin Trust / Carnegie Trust student.

How to apply

To apply, first email Edward Wallace for more information! Describe your interest in a specific project in specific terms. The ideal email would say why you are interested in the subject matter (i.e. some combination of fungi, pathogens, stress or environmental sensing, RNA, and sequencing/synthetic biology/computational biology methods), and also briefly describe your experimental and computational skills.

Application deadlines for admission and funding are due in December 2019 and January 2020, for October 2020 entry, please read the individual links for each project. Different funding opportunities are available for UK, EU and overseas students, for example through the BBSRC/EASTBIO doctoral training program or the Darwin Trust of Edinburgh. Ask us about the science first, and if the science is a good fit then we can talk about funding.

More context on PhD applications and the UK system

For more on how the PhD and post-PhD system works here, see the guide to the UK academic system by Geoff Barton.

Edward wrote an article about the life events that collided with opening the Wallace lab, here Scientist and Parent: The bereaved parent.

It was kindly published by eLife in their wonderful Scientist and Parent Collection, describing what it’s like to combine science and family life. We hope that this article, like others in the collection, might help scientists who are going through their own losses, or looking to support their colleagues having a tough time.

Archive post - We are advertising PhD projects for 2019 entry, on different aspects of how fungi dynamically reorganize their RNA and protein to adapt to environmental change. They will all provide training in experimental and computational biology. They differ in their emphasis on gene expression in response to environmental signals, on RNA-protein interactions, (newly posted) on massively parallel interrogation of RNA regulatory sequences, or on fungal pathogenesis. Each with a different and fantastic 2nd supervisor.

Projects for students from anywhere in the world, including the UK

These projects are based in Edinburgh:

Projects for UK/EU students

This project is collaborative with Dr. Vicent Pelechano at the at the Karolinska Institute in Stockholm, and would involve substantial time in Stockholm. It’s part of the Precision Medicine Doctoral Training Program, funded bythe Medical Research Council, University of Edinburgh and University of Glasgow. Please note that both fees and stipend would be covered for EU as well as UK students.

The next project is collaborative with Prof. Alistair Brown at the MRC Centre for Medical Mycology at the University of Aberdeen, as part of the BBSRC/EASTBIO doctoral training program, and would be primarily based in Aberdeen:

How to apply

To apply, first email Edward Wallace for more information! Describe your interest in the project in specific terms. The ideal email would say why you are interested in the subject matter (i.e. some combination of fungi, pathogens, stress adaptation, RNA, and sequencing/synthetic biology/computational biology methods), and also briefly describe your experimental and computational skills.

Application deadlines for funding are due in December 2018 (or 7 January 2019 for the precision medicine project), for October 2019 entry, see individual links for each project.

Further opportunities

We would be very happy to get enquiries, on these or related projects, from students with their own funding or who are applying to independent funding from foundations or governments.

There’s related scope for bioinformatics/computational projects relating to gene expression and translation regulation in fungi, and methods devlopment, possibly working with our collaborators. Now there are 1000 sequenced fungal genomes that need analyzing, so plenty of biology to discover!

More context on PhD applications and the UK system

For more on how the PhD and post-PhD system works here, see the very useful guide to the UK academic system by Geoff Barton.

We are advertising a BBSRC/EASTBIO PhD project on Machine Learning of multi-omics data. This project is joint with Guido Sanguinetti, please share with anyone who might be interested.

To apply, first email Edward Wallace for more information! More application details here. Application deadline was 4th July 2018 - contact us if you’re interested in anything similar!

This funding will only be applicable for UK/EU students.

Model-based machine learning of multi-omics data

What scientific question will you investigate?

How do cells change their gene expression to respond to a changing environment? How do we turn massive “multi-omics” data - measurements of many different kinds of molecular states in cells - to produce an accurate quantitative picture of changing gene expression patterns? This PhD project will develop artificial intelligence and machine learning methods to quantify multi-omics data, and apply them to sequencing datasets to understand how fungal cells dynamically regulate RNA expression and processing.

The project necessarily addresses the key technical problem of normalization. How do you compare counts of molecules per cell between two very different groups of cells? For example, the number of messenger RNA molecules per cell varies hugely in different growth states of the fungi including the pathogen Cryptococcus neoformans. Current methods, that assume that most RNA molecules don’t change in count, cannot accurately detect this variation. This project will develop rigorous methods to compare mRNA counts across growth states using external reference “spike-in” whole cells and RNAs.

How do you compare different molecular states in the same group of cells? For example, we have measurements of RNA in different conditions, and also of a sub-population of RNA that is regulated by a specific protein, in budding yeast Saccharomyces cerevisiae. The project will develop quantitative models of the RNA-protein interactions, and apply them to these measurements to understand how distinct RNAs are regulated as conditions change.

What training will you receive?

You will receive expert training in machine learning, bayesian modeling, bioinformatics/next-generation sequencing, and RNA biology. Your project will develop fundamental data science skills, and you will have the opportunity to take short courses to build other specific skills as needed.

You will have the opportunity to work with experimentalists in the Wallace lab to design new experiments to test the results of your computational work.

You will complete an industrial placement, spending 3 months working with scientists at a company to apply machine learning methods to their sequencing data.

What could you do afterwards?

The completion of this project will build the skills to tackle a range of problems in quantitative biology and beyond. There is huge demand for people who can combine theoretical and practical insights to make sense of big data. You will be particularly well-equipped to tackle analogous quantitative questions in biology, extending beyond the gene expression questions directly addressed towards single-cell sequencing, proteomics/metabolomics, and microbiome research.

What kind of student would fit the project?

We are seeking someone with a strong interest in developing models that bring insight into quantitative biology. This is an interdisciplinary project that brings together ideas from theoretical statistics/machine learning, bioinformatics/programming, and gene expression/RNA biology; it would be sensible to have a strong background in one of these, and demonstrable interest in the other two.


Growth Rate-Dependent Global Amplification of Gene Expression. Niki Athanasiadou, Benjamin Neymotin, Nathan Brandt, Darach Miller, Daniel Tranchina, David Gresham.

  • Spike-in quantification with synthetic RNAs, not with whole cells.

BASiCS: Bayesian Analysis of Single-Cell Sequencing Data. Catalina A. Vallejos, John C. Marioni, Sylvia Richardson.

  • Spike-in for single-cells, again with synthetic RNAs.

Quality control of transcription start site selection by nonsense-mediated-mRNA decay. Christophe Malabat, Frank Feuerbach, Laurence Ma, Cosmin Saveanu, and Alain Jacquier,

  • Whole cell spike-in to compare yeast mutants in a RNA decay pathway.

Kinetic CRAC uncovers a role for Nab3 in determining gene expression profiles during stress. Rob van Nues, Gabriele Schweikert, Erica de Leau, Alina Selega, Andrew Langford, Ryan Franklin, Ira Iosub, Peter Wadsworth, Guido Sanguinetti, Sander Granneman.

  • RNA-protein binding measurements in a stress timecourse, statistical modeling.

Modeling and analysis of RNA-seq data: a review from a statistical perspective. Wei Vivian Li, Jingyi Jessica Li.

  • Review of statistics of RNA-seq.
13 Dec 2017 by Edward

It’s an exciting time, with the award of a Wellcome Trust/Royal Society Sir Henry Dale Fellowship supporting five years of science in a new lab!!

The lab website is up and running. This is a big step for a new lab, a new public face!


The site is fully open source, available in a GitHub repository.

The site was inspired by, and adapted from, that of my postdoc mentor D. Allan Drummond. This in turn was built from a design from Trevor Bedford.