Research | DEZERAECOX

Terrific TDP-43 Talks in Trieste

Wed, 11 Oct 2023 12:58:17 +0000

Terrific TDP-43 Talks in Trieste

As you may know, I am currently a Lady Edith Wolfson Junior Non-Clinical Research Fellow at the University of Cambridge. My work, supported by the MND Association, seeks to develop new tools that enable us to evaluate models of MND we use in the laboratory for research and therapeutic testing. I have been focused on a protein, one of the fundamental building blocks of every cell in our body, which goes rogue in up to 97% of people living with MND. This protein is called TDP-43. It forms clumps (”aggregates”) in specialised brain cells called motor neurons, and is thought to be intimately linked to the death of these cells which underlies all forms of MND.

Recently, I had the privilege of attending the held at the beautiful Stazione Marittima in Trieste, Italy. As the name implies, this meeting was the first of its kind specifically seeking to bring together people from all around the world whose work is focused on TDP-43; from how it is encoded in our DNA, to how the protein is produced, regulated and functions in healthy cells, to how failures of its function can lead to disease. In contrast to traditional meetings focused on a specific disease or cellular phenomenon, there was a uniquely singular focus on TDP-43 and all aspects of its biology.

The meeting was opened by local host Emanuele Buratti. Dr Buratti treated the audience, spanning students to established giants of the field, to a trip down memory lane. Sharing photos of original results and hand-drawn annotations from his lab books from the late 1990’s, Buratti gave early context to a field that has gone on to span more than two decades. It set the scene for a fantastic program featuring presenters from around the globe who would go on to describe novel experimental approaches, advances in our understanding of how TDP-43 relates to MND, and new therapeutic strategies that are gaining traction toward clinical trials. Recounting it all would take longer than the meeting itself (unfortunately a case of ‘you had to be there’!) but I am delighted to have the opportunity to share a snapshot of some of the outstanding science with you.

An early highlight came in the form of a potential new therapeutic strategy, described by James Shorter in his presentation “ (Dis)solving the problem of aberrant protein states“. Recent work from Shorter’s team has focused on the normal function of TDP-43 in cells which involves the separation into a distinct phase, similar to how oil comes together to form droplets in water. If TDP-43 stays in this separated state for too long then it can turn into elongated aggregates similar to those associated with MND. Shorter’s team have , another fundamental cellular building block, to manipulate the phase formed by TDP-43 and reverse the abnormal phases. In theory, this could prevent or even reverse the characteristic clumps formed by TDP-43 in those living with MND - an exciting proof of principle that offers a promising potential new strategy toward viable therapeutics.

Competing for best pop-culture reference, Michael Ward introduced us to the TDP-43 Multiverse during his talk titled “ Functional genomic strategies to identify regulators of neuronal TDP-43 biology”. Just as big data has taken the world by storm, biology is now generating and exploiting bigger datasets than ever before. Ward’s research encourages us to ‘think bigger’ when it comes to TDP-43. Using computational tools, including different cell types and a range of biological molecules such as DNA, RNA and proteins, in an attempt to unravel the chaos that erupts in neurons when TDP-43 function is lost. The resulting data atlas may let us pinpoint the effects of losing TDP-43 function that are specific to motor neurons, a key mechanism of MND that remains unknown.

Late on the second day, Alyssa Coyne presented “ Large scale analysis of sALS and C9orf72 ALS/FTD iPSNs reveals defects in TDP-43 function repaired by genetic therapies”. In essence, a tour de force analysis of the collection of cell models created from people living with MND, which are an invaluable experimental tool. Alyssa asked a simple question, which is similarly at the heart of my research: How good are these models at capturing what we see in real people? One of the principal functions of TDP-43 is in processing RNA. RNA plays a vital role in our cells by converting the instructions from DNA to make proteins. TDP-43 processes a collection of different RNAs, which form a characteristic signature. Looking at the signature in cell models and comparing them to those of the corresponding donor allowed Coyne’s team to establish that signatures are variable between patients, just like fingerprints, but that cells can faithfully recreate the signature of their matched donor. These signatures together pointed toward defects in how molecules move within cells in MND. When TDP-43 is dysfunctional, it is found to move from the DNA storehouse - the nucleus - into the larger cell body, where it forms clumps. , again offering hope of a new potential target for therapeutic intervention.

Last but not least, the poster session — where many early career researchers showcase their work — is always a firm favourite, and this one did not disappoint. With the backdrop of a stunning sunset, attendees were treated to some stellar discussions from the future of the field. Carlo Scialò shared his progress towards the ultrasensitive detection of TDP-43 in nasal swabs; Oscar Wilkins described a new computational tool, SpliceNouveau, to help design next-generation therapies; and Diana Arseni described her groundbreaking work delivering an atomic-scale picture of the shape TDP-43 adopts when it forms the toxic aggregates found in MND. Such sessions are an invaluable opportunity to make connections, discover new technologies, and see ‘fresh-from-the-bench’ science.

In all, this really was a fantastic event spanning all manner of biochemical, biophysical, and clinical approaches to understanding TDP-43, a molecule at the heart of MND. I learnt more than I thought possible and am so grateful for the opportunity to immerse myself in this welcoming and exciting research community. I have no doubt that the collaborations forged throughout the meeting will drive TDP-43 research forward, and I can’t wait for the 2nd conference on TDP-43 function and dysfunction in disease in a few years’ time!

Can’t get enough TDP-43? Find out more about my research , or check out the complete conference program . For anyone interested in the next iteration of the meeting, stay tuned - I look forward to seeing you there!

This post was developed with editorial contributions from Charlotte Roy from the , who supported my attendance at this meeting. A version of the post first appeared .

Toolbox talk: Lights, Camera, Action!

Sun, 03 Oct 2021 14:28:49 +0000

Toolbox talk: Lights, Camera, Action!

It is more important than ever for scientists to be concise, engaging and easy to understand. Most importantly, we need to diversify our means of communication to meet the audience where they live. In the age of TikToks and Reels, it seems our digital lives increasingly revolve around easily digestible visual media. So: time to go video! I realise, as I am writing this, the irony of that statement… But, as part of a recent communication course, I produced my first scientific videos for a general audience - and I think it should be part of every scientist’s toolbox! If you’re keen to see the finished product, head straight to the . Otherwise, if you’d like to hear more about the tools, tips and tricks I picked up along the way, read on! Who knows, maybe I’ll even make a video version.. 😉

The Art of Science Communication: A little learning, a lot of fun!

Last year I tackled my first official professional development course: . Hosted by the ASBMB, this course covers basic communication skills for scientists looking to increase their public and professional impact. Thanks to a well-paced course, dedicated instructors and a diverse cohort of participants, this course was not only hugely informative but also a lot of fun!

The main goal, achieved over seven weeks, was to craft our own individual message which would introduce a general audience to our primary research topic. From the very start, we were thrust into the deep end as the first week required us to record a short video detailing our research for the cohort. This was something entirely new to me, and at first watching yourself on screen is incredibly daunting! But after the initial icky-ness wears off, these practice videos turned out to be a great resource for identifying areas of opportunity in our speaking practice. After establishing this ‘baseline’, the instructors set to work helping us take the bones of that video and craft the message, increase engagement, remove jargon, and improve production. Together, these iterative exercises left each of us with a developed skill set, covering everything from scientific presentations to a non-expert elevator pitch, and a video product showcasing these skills.

Scripting, recording, editing and posting a video was something I had never done, much less for science communication purposes. So, just in case you (like me) missed that whole YouTube vloggers phase, I thought I would cover here some of the hardware, software and general lessons that I learned along the way to creating my final video product.

Lights: Setting up for success means starting with software

While you certainly don’t need any fancy software to shoot videos (just take a look at everyday people shooting Reels) a handful of (free!) software programs can really make a difference for professional content. A few tools that I found useful were a teleprompter-style recording app, and a video editing suite.

When it comes to talking about science, many academics are used to giving presentations where some multimedia slides guide the content you deliver, and there is a conversational meandering that actually enhances talks like this. When you’re recording, however, maintaining eye contact with the camera is much trickier. This is where a good teleprompter app comes in - it allows you to take these cues in the form of text without breaking your connection to the audience through the screen. There are a bunch of free versions depending on your operating system - I used my iPhone for recording, so I found on the App store. Features to look out for include the ability to change the text size and speed, and the option to export without a watermark.

For video editing, again this choice is OS-dependent. iMovie on macOS seems to be fantastic. On PC, I had reasonable success with , but there are quite a few others gaining traction too. Most will have the same functionality - but, importantly, I would suggest looking for something that has a decent online community and plenty of resources for beginners. As novice filmmakers, it can be hard to know where to start otherwise!

Camera: A little investment goes a long way

As with software, you certainly do NOT need any expensive or fancy equipment. I shot all my videos on my phone, and the quality was sufficient. For a small investment though (~$50 AUD) you can get a great quality boost from two simple tools; a lapel microphone that connects to your recording device, and a tripod.

Lapel microphones help improve the sound quality, particularly as captured by a phone - and there are lots of cheap options available. The has a built-in clip for attaching to clothing, allowing it to be positioned low on the chest out of frame during recording. These are particularly great for people living in places of high background noise (hello friendly neighbourhood leaf-blower that seemed to always be running when I wanted to record!), as they allow you to better isolate speech from contaminating noises during editing.

A tripod might seem like a large ask for a simple scicomm video. But hear me out - it has a few benefits! 1) It allows you to easily and stably position your recording device to free your hands entirely, letting you fall into a natural speaking rhythm with optional props, and 2) you can easily use a teleprompter app. Again, another excuse for all you online shopaholics to bust out your best bargain hunting skills!

The only other equipment I used when recording was already lying around the house - a black sheet as a backdrop, and a floor lamp to help with even illumination of my glasses. For any fellow bespectacled scicommers out there, are some great tips on getting rid of the annoying mirror effect light on glasses can have when recording.

Action: Time to talk the talk!

Now for the best (and most important) bit - the doing! All the great equipment in the world does nothing until you have a great science story to tell.

Before thinking about what you want to talk about, start by reflecting a little on the audience that you want to engage. Audiences composed of fellow scientists outside your field will of course require a very different approach to a collection of non-experts. Likewise, a room full of adults requires a different approach to a room full of children. It’s important to hit the right contextual notes, and this rings especially true in video format where people’s attention is so easily lost scrolling to the next video in line.

Once you know your audience, it’s time to draft your story. Unlike normal scientific presentations, I recommend writing a script here. This lets you make sure you have a reproducible story that can be honed, helping you to fit the time limit and minimise the number of recording attempts. Yes - attempts with an s - there will be many. But that’s ok, it’s just part of the process!

Some things to think about when honing your story:

Be critical of how engaging your storywill be for your audience - we all love our science emplicitly, but don’t forget to let your audience know why they should love your science too. What is the important problem or question? What makes your work unique?
Be cautious of spewing facts, figures or findings - instead, take the audience on a journey with you, just like a good book they can’t put down. Have you woven a narrative? What are the challenges this research faces, how have you overcome these?
Be creative in your description of complex topics- ditch the fancy words in favour of metaphors. A good metaphor can take some time to work through, but don’t underestimate their power in helping your audience grasp complicated concepts. Jargon is a huge no-no, even for scientists outside your field.

Ok, you have a story written out. Now, it’s time to set your scene. For example, I used a plain black backdrop, with some soft glasses-friendly lighting, and the tripod, phone and microphone about 1 m away. Then it’s time to record! This is the part where you really just have to feel it. Feel the right pace (conversational, not rushing, with nice pauses), feel your body in space (remember you are a person, not a robot, and if you’re anything like me you talk with your hands, like, a lot), and feel the excitement of your story (even on the fifth or sixth time you read it through). To start, I would suggest reading through the whole story on camera from start to finish - ignoring any missteps in favour of practising the whole story. From there, record a few more tries until you are happy. Depending on the video format (and if you plan to have cut sections along the way or a single-take read) you can even piece together sections from different versions that you like the most - this is where editing comes in!

Movie magic: remix your way to the red carpet.

Once you have recorded a few versions, this is where the movie magic happens. Head over to your editor, where you will be able to add transitions (fade-in, fade-out) music, images, sound effects, remove background noise, crop and combine different videos to get a final product you are proud of. In some cases, you might even want to bring in some - a little out of my league so far but something I am keen to try!

I decided to add a lead-in image from (a fantastic collection of images that are free to use with attribution) and in one case I recorded a PowerPoint slideshow that also had a black background, timed to match my finished video. I then overlayed the PowerPoint recording onto my video content, and viola, animated video! There are lots of neat ways to engage your audience beyond a talking head on the screen, so be sure to be creative and take full advantage of this aspect of the medium.

With the complete version, the last step is sharing it with the world. This is actually quite easy to do via youtube - simply sign up for an account (if you don’t have one already) then head to the “YouTube Studio” section where you will find the “Content” tab.

Uploading a video is as simple as selecting the “Create” icon, and following the prompts to upload your masterpiece. There are a few options, like whether or not your video should be indexed with the searchable youtube content, that you can toggle to suit your needs. Don’t forget a catchy title if you want your content to be found!

And that’s a wrap!

That’s it, you are officially a scicomm superstar! While producing video content can seem a daunting task, once you get past the weirdness of seeing yourself recorded it really is a great way to share your science. I am super proud of the content I produced during the course - with a few simple tools and a couple of hours work, for the first time I feel confident to share my passion for science in a whole new context. So, without further ado, here are the videos I created!

Party tunes: A new phase in the hunt for happy brain cells

Turning brain cells into origami masters

That’s all for now. Hopefully, I’ve inspired you to capture your story on camera and share it with the world! Have you made your own scicomm video? Do you have a favourite editing software? Head over to the , or let me know on !

Image credits: via unsplash

Investigating the Investigators: 2020 Edition

Tue, 16 Mar 2021 19:00:00 +0000

Investigating the Investigators: 2020 Edition

Each year the Australian National Health and Medical Research Council award a set of Fellowships to outstanding scientists. As an Early-Career Researcher, you might say I have a vested interest in understanding the picture these outcomes paint for my ability to pursue a research career in Australia. I’ve written extensively about how the outcomes reflect trends of the scheme as a whole (you can find a detailed description of the , and a ). But this still begs the question: just what does a successful Investigator look like in my field and at my career stage in 2020? And perhaps even more importantly for you, what did a successful application look like amongst your peers? So, this year I’ve decided to put the data in your hands!

Introducing the portal

Meet : a collection of interactive datasets underlying the NHMRC outcomes across four key metrics - Research area, Geography, Seniority and Gender. You can now explore how the Fellowship scheme has evolved with time, how the most successful research hubs are distributed, and what characteristics are typical of successful applications within each funding tier. The landing page includes everything you need to know to get started, as well as some resources for the extra curious among you.

A few words on why…

These Fellowships are the life-blood of many Australian researchers, and are always hotly-contested tickets for emerging and established researchers alike. Astoundingly, given the absolute reliance on data when it comes to everyday science, very little is done with the outcome data beyond cursory summaries of gender and career stage equity. Researchers deserve to know what the overarching trends are and how the scheme has evolved. Now, the interactive and wholistic nature of the datasets means that you no longer have to take my word for it (or anyone else’s for that matter!). In addition, my hope is that the portal will be a step toward maximizing access to actionable insights for researchers looking to develop their future funding strategy.

Final thoughts

Researchers are already busy preparing applications for the next round of Investigator Grants which close in just a few short weeks, and this year the stakes are perhaps higher than ever before. As , external funding sources will be paramount. For ECRs especially, the next few years look bleak as we are at without dedicated support.

With outcome data directly in the hands of researchers, everyone has the opportunity to make data-driven decisions about where their energies are best spent next application season. Most importantly, these data offer us as a community the opportunity to evaluate how well the scheme outcomes are aligned with our priorities for supporting innovative and emerging researchers as the future of Australian science.

So, without any further ado…. If you haven’t already, head on over to . Happy exploring!

Are you thinking of applying in the next round of Investigator Grants? Did any of the datasets surprise you, or were they helpful in your decision of whether or not to apply? Head over to the , or let me know on !

Image credits: inspecting gears with magnifying glass | @ pluyer via

Year in review: 2020 Edition

Sun, 14 Mar 2021 10:40:50 +0000

Year in review: 2020 Edition

2020 was… A year that felt like a decade, but with the productivity of a boozy long weekend. In case you haven’t been on planet earth for the entire year (hello friend, teach me your secrets), it was of course the year in which a coronavirus (specifically SARS-Cov2) emerged and rapidly took over the globe.

The ensuing pandemonium was slow to build, and even slower to resolve. In fact, most countries around the world are still living with some form of social restrictions aimed at controlling transmission and preventing severe infection. We’ve since entered a recession, predicted by many to be the largest since the global financial crisis. Most importantly, many people lost their lives to the virus, and many more still have a severe and long-lasting illness.

I am fortunate enough to live in a country with very low infection rates and virtually no community transmission, and so life has returned to a post-virus normal. But (of course, there is always a but) to achieve this hefty goal, we as a community endured the longest and harshest lockdown conditions seen anywhere around the globe for almost a year. Make no mistake; it was essential and worthwhile. However, it was also eye-opening, isolating, disruptive and challenging. And as well as being a bench biologist who couldn’t get to the bench, I faced the lockdown separated by state borders from my partner, family and friends. To say it was mentally tough would be a colossal understatement, and despite this I am well aware of many who faced worse than me.

So, in acknowledging that no two experiences of 2020 were alike, I decided to stick with my traditional by-the-numbers account of the past year. You’ll find wins few and far between, more than a handful of losses, and a list of goals that looked a little different from start to finish. Finally, I look to the future and set my sights on recovery in 2021.

Wins

A snapshot of the high points

I survived 2020. I think that certainly belongs in the win column, don’t you?
I read three books, and reviewed one.
I published five blog posts totalling 6500 words, which were viewed 1557 times by 833 different visitors from more than 10 countries
I contributed my first to an online publication
I published my first , providing analysis tools for researchers considering applying for NHMRC funding
I worked over 2500 hours, more than 40% of which went towards submitting my first first-author postdoctoral paper for review to a top-tier journal
Five manuscripts I contributed to were officially published
I slept an average of 7 hours and 5 minutes
I overhauled my entire organisation system, from code to experiments to publications
I created and published not but science-communication videos as part of my first official scicomm training worskhop
I organised and hosted 17 Departmental seminars from a diverse group of researchers spanning career stages and countries - and made a successful transition to virtual when the pandemic hit!
I gave 2 invited seminars, and attended 3 virtual conferences

Losses

A not-so-snappy shot of the low points

Another year with no published first-author manuscripts, despite furiously writing for most of the year
My first-author paper was rejected from 4 different journals before even being sent out for peer review
Almost one fifth of the hours I worked were spent on emails and other administrative tasks
I didn’t write a single grant or application
I downloaded 1084 papers, and read less than 10% of them
I started 4 books that I didn’t finish
I didn’t publish blog posts in 7 out of 12 months
My coding projects are still still in a state of disarray, in desperate need of a tidy and some documentation
I only averaged 3817 steps per day
I was, perhaps, 50% productive on any given day

Lessons

The bits I wish I knew at the start of 2020, but had to learn the hard way.

Selfish isn’t a dirty word - for much of 2020 I struggled with the feeling of being a bad friend, bad partner, bad lab mate, bad family member as I weathered the storm of Victoria’s lockdown alone. I was lonely, unhealthy, unmotivated and struggling to keep my head above water and still I felt uncommitted to the many relationships in my life I let languish during this period. But in reality, I didn’t have anything to give - and I needed to be selfish, to rebuild my own mental health, to redefine my sense of self in this weird and wonderful (?) new world we were suddenly inhabiting. And it took me a long time to be ok with this idea of a self-centred approach to wellbeing. After all, you can’t pour from an empty cup.
Working from home and living at work are two entirely separate things - At the start of 2020 there was an abundance of tips about productive workspaces, setting routines, engaging in your normal ready-for-work activities, setting aside time each day to step away from work…. As someone who has had the luxury of a few work-from-home days in the past that yielded astounding levels of productivity, I was initially optimistic about the opportunities a few onths of working from home presented. It was only a few weeks into what would turn out to be a year long work-from-home order, before I realised how sorely mistaken I was. This was not the quiet, focused time I had enjoyed pre-pandemic, but clouded, overcast and gloomy time spent doom-scrolling, feeling unengaged and unenthusiastic about the prospect of another day spent alone in my home-office. It was a few months in that I came across the phrase living at work, and it struck home how insidiously this transition had happened. Despite having a separate work space, work never really left my home space in the way that I could leave the work office, and I came to realise how important the role of our choice is in this regard.
Variety is the spice of life - Ok, I know this one is a little cliché. And yet, if you’d asked me a year ago I would have adamantly told you that I am a person who is fond of organisation, planning and routines. From breakfast meals to shampoo brands to pens to friends - I am a creature of habit. I know what I like, and I surround myself with it. I structure my time. I plan and organise weeks, months, years in advance and I work toward goals with almost singular focus. And yet, this year almost every single one of those things was thrown completely out the window. It was impossible to plan a week in advance (or even a day sometimes). I found myself missing those spontaneous interruptions to my calendar or walks for coffee with random colleagues during the day. I missed the variety of faces on the tram each morning, and instead sought out variety in walking routes to new cafes for lunch as a poor surrogate. Most vividly I missed the usual spontaneity of a week in bench research science. Normally, each day is subtly different - a different experiment, or technique, this meeting versus that meeting, a new piece of code to work on or a new manuscript to write. Instead of this variety, life became a blur of zoom meetings, trudging forward on a single manuscript, and trying desperately to halt my doom-scrolling as we awaited daily case numbers and press conferences. I will never again take for granted the motivation a varied work day brings to my passion for science.

Goals

As with much about 2020, my goals unfortunately went promptly out the window around June. Even still, I finally submitted the first of three first-author postdoctoral research papers, and successfully created a growing community of Happy Hacker scientists who are learning to code. I started semi-regular yoga and implemented a new morning routine focused on engaging mindfully with the day ahead. While my writing here lagged severely, those posts I managed to write and publish are some of those I am most proud of. None of these things went according to plan, and it certainly wasn’t a glamorous insta-worthy road to ticking boxes. And yet, here we are, moving forward ever so slightly. So where to from here?

Papers are paramount. Déjà vu aside, with drafts still floating around in various states of disarray the first half of 2021 demands some serious focus on wrapping up the next two manuscripts. Using structured writing time of at least twenty hours a week, my goal is to have submitted the remaining two manuscripts before starting my next postdoctoral position mid-year. While last year writing sessions were often sidetracked by more ‘pressing’ needs, this year I will plan each session in advance, using calendar entries for accountability and a master task list for assigning goals and tracking progress.
Revamp the reading routine. Last year I hinted at a timely review of how I consume scientific literature. I’ve often pushed reading to the end of my to-do list and settled for skimming daily email alerts or saving an article or two from Twitter with some handwavy expectation of actually reading them. Instead, I want to not only find time for an engaged reading practice but also give myself space to really think about what I am reading, parlaying this into more (and better!) scientific insights. I will implement two reading sessions per week, drawing pre-selected articles from my ever-growing pile of exciting new material. This will be tracked using a database, which simultaneously enables detailed cross-connection between individual resources and developing insights and ideas within overarching project themes.
Commit to coding cleanup. One of the few positives to come from a year of working from home, coupled with a freshly-submitted manuscript combing computational analyses of wet-lab experiments, was a rapid expansion and solidification of my amateur coding skills. I am often pleasantly surprised when I realise just how far these skills have developed, and it makes me even more excited to pass them on during Hacky Hours each week. And yet, two packages I first wrote more than four years ago still languish, lacking love and in desperate need of refactoring. This is the year I commit to cleaning up these packages. I will spend an hour each week tackling small chucks, which will be tracked using GitHub issues and measured in milestones.
Invest in intentional. Sometimes a week can pass by and I barely realise due to a blur of endless triaging. These type of weeks tend to feel like an old-school cartoon where my wheels are spinning yet the car is going nowhere fast. My goal is to understand (and correct) my balance between the urgent but unimportant tasks that often fill my day (hellloooo email, I’m looking at you…), and the essential but non-urgent tasks that will be of most tangible benefit long term. Beyond task management, I will start each morning by writing down three intentions for the day, using these to anchor how my time is spent and as a record for reflection. I will also frame these intentions within the time management matrix, to ensure I am investing in long term progress by hitting small milestones each and every day.

2020 was without a doubt challenging and confronting and, just between you and I, 2021 was not looking to be off to a great start. After handing over my role in Melbourne and leaping out of my comfort zone, I landed in limbo - awaiting a revised start date for my international Fellowship, unemployed and uncertain of my direction for the first time in over a decade. And yet, here I am daring the universe by setting goals for the year ahead. From here, 2021 is about regrouping, recovering and recalibrating, readying for reentry to the world post-COVID. And I couldn’t be happier to have you along for the ride.

Image credits: @brookelark via

Learning to code: it's as easy and as complex as that.

Sun, 05 Jul 2020 15:12:56 +0000

Learning to code: it’s as easy and as complex as that.

Many life scientists have used their time under pandemic-induced lockdown to learn their first programming language. Yay! Coding has many many advantages (reproducibility, more complex analyses, reusability, time-saving) that I have espoused all too many times before.

Many people start with a tutorial or two, then jump straight in and analyse a dataset. They get a plot, maybe a few statistics and voila! They are now scientists who can code.

Kinda.

See, the thing is there is more (much more) to implementing programmatic workflows in a biological context. Unfortunately, these are the skills that intro-to-programming courses often fail to teach you (or sometimes even mention) - but without them, adding scripts to your workflow can often make your work less reproducible, not more. For the sake of the argument, lets pick one.

Version control.

Imagine a bowl of spaghetti (lots of lines of code), which you admire briefly before throwing at a canvas (your interpreter). You create a beautiful work of art (your plot), and stand back to admire your handiwork. Maybe you show it to a few friends, and they are super impressed. (Awesome!). And then they ask - how did you do it? Not just the throwing motion, or the rough distance between the bowl and the canvas. They want to know the precise location of every single strand of spaghetti in the bowl before you threw it at the canvas. They want to know the recipe you used for the sauce, down to the precise number of grams of oregano.

Now, of course, we aren’t actually talking about a bowl of spaghetti - we are talking about your code. So - of course - you can simply show them the script! Huzzah! The precise instructions that enabled you to make that very specific plot. But here’s the kicker. The code is not static. In fact, chances are that you poured over it for hours (maybe days) adjusting, tweaking, changing, testing and rerunning - all to get the glorious, awe-inspiring plot at the end.

By this stage, you’ve probably made over 100 spaghetti-splattered-spectacles (plots) and your house is starting to smell like an Italian Pizza joint. If I asked you to produce the exact recipe, down to the very position of each strand of spaghetti, for plot number 47 - could you do it?

My guess is probably no.

Conducting experiments is at the heart of science. However, without the essential extracurricular activities like maintaining backups, organising your results files and optimising project management systems, you wouldn’t last very long in academic research. The same is true of coding. Learning to write a functioning script is just the tip of the iceberg when it comes to implementing reproducible computing workflows in biology.

So, with this in mind, what are those all important skills you ask? Well, luckily, far more experienced scholars than I have written on this topic and provided guides (like and ) for those starting out in the computational space. These are highly applicable to biologists learning to code, and I encourage you to check them out before you stray too far down the spaghetti-on-the-wall path (like I did). As with any system, it is easiest to implement new routines before you have become to set in the old ones! At the very least, here are a few things to consider:

, including Github/GitLab/BitBucket
, including efficient folder layouts
and docker containers
practices
, including repositories and citing software you use

For more, make sure you check out the resources below.

Oh, and one last thing.

Unfortunately, this type of organisational work is not typically measured as a key performance outcome for biologists. Your number of git repositories or test coverage for an analysis suite is unlikely to come up in an award nomination or promotion application. And yet, it is entirely crucial as we move toward bigger and more complicated data and analyses - and so I encourage you to take the time to learn anyway. Maybe one day it will be recognised as essential work by the powers that be - but until then, at the very least, it remains essential for anyone wanting to do good reproducible science. And who doesn’t want that?

Final thoughts

So you want to learn to code? Yes - do it, 100%. I honestly couldn’t recommend or encourage it more. But please do so mindfully. If you want to have the best possible chance of integrating this wonderful tool into your scientific ecosystem in the long-term, you have to lay solid foundations and develop sustainable practice/methods.

Have you used any of these techniques as a budding bench-to-bytes biologist? Find me on or head over to the to tell me more!

Resources:

Image credits: Tomwsulcer via & olamishchenko via

Toolbox Talk: VSCode and Conda

Mon, 13 Apr 2020 07:51:36 +0000

There have been many rapid changes to how, and where, we work as the world settles into a pandemic-induced hibernation. After coming to terms with the fact that many labs could be closed for several months, life scientists around the world are increasingly resolving to make the most of their ‘downtime’. For some, this means upskilling in leading data analysis tools and techniques - and of course, I whole-heartedly encourage this initiative!

In my experience, this often takes two forms - those who use a course to learn and tackle generic coding projects, and the learn-by-doing camp who google as they go and tackle their own projects. I was a bit of both: using a few tutorials to understand the basic concepts and the remainder by going to work on real analysis problems. With that in mind, I thought it might be helpful to share a little about my setup and why a combination approach is useful (maybe even important) for self-taught data detectives.

One of the issues I have with generic online python courses is their often-overly simplistic approach to interpreted development environments (IDE’s) and package management. In my case, I had been dabbling in coding for a year or two before I came across the concept of virtual environments, and it took me another year or two to get comfortable in making, using and deploying these environments. Ditto with version control. Don’t get me wrong - I think it is great that scientists are diving headfirst into coding, and jumping into a course with a simple editor helps you hit the ground running. Unfortunately, this only defers the learning curve - if you are serious about integrating these tools into your workflow a grounding in the most basic of programming hygiene habits is essential. Shortcuts were necessary in days past, as installing and running python could be … cough … tricky for beginners. However, these processes have come along in leaps and bounds - meaning that it is now incredibly simple to get going with a basic python install in an editor that is easily expandable to incorporate, even encourage, basic code hygiene.

You may have come across my previous Toolbox talk about - these were the collection of tools I relied on to manage my coding, from writing to running to environments to version control. However, over the last 12 months, I have migrated to Visual Studio Code (VSCode) and Conda. I might be (a little!) biased, but this is a great place to start for anyone new to coding in python!

Conda: easy environment management

Before we dive into an editor, let’s start with creating and managing virtual environments. If you’re still not sure what virtual environments are and why you might need to use them, check out these great tutorials from and . In essence, virtual environments are essential for keeping your base install of python from becoming littered with packages, many of which might have dependencies that break each other.

Conda solves this problem for python and non-python dependencies alike, and it comes bundled with the ability to install various python versions. In fact, Anaconda and Miniconda have become the most popular Python distributions for data science in research laboratories worldwide. For a more in-depth discussion of these features, and whether you should choose the complete (Anaconda) or lite installation (Miniconda) check out the documentation .

Installating MiniConda

For many purposes, Miniconda is ample and is easy to and install by simply following the prompts. Real Python has an extra-detailed walk-through of the installation and starting your first environment in their fantastic , including creating your first environment and installing packages within that environment.

Once you’ve created your first environment using the conda create --name=my_env command, install the ipython and jupyter packages using the conda install ipython jupyter command. These packages will enable VSCode to locate and use this environment as a python kernel (more on that later!)

VSCode: a full-featured, user-friendly editor

Although technically VS Code is not a dedicated IDE, an abundance of out-of-the-box features supplemented with a few extensions gives you an editor that supports debugging, embedded Git, syntax highlighting, intelligent code completion, snippets, and code refactoring, among an abundance of other features. In short, it is incredibly customisable, user-friendly, language-agnostic, and seamlessly integrates with code hygiene tools to give every budding research software scientist the best start in their programming journey. In case I haven’t convinced you yet, here is a quick run-through from zero to coding hero with VSCode.

Basic installation

Download install VSCode for your operating system . Once installed, VSCode will open to the Welcome Screen. This is a great place to get familiar with VSCode functionality,including a number of helpful customisation portals (helloooo colour themes and keybindings!). For first-time users, your first stop should be the interactive playground. This window walks through some of the key features of VSCode and gets you started moving around the editor.

Getting started

The next stop is the Extensions tab, which is your gateway to much of VSCode’s fabulous functionality. You can explore the offerings and pick your favourites (there are extensions for almost anything you could want to do!), but the one must-have for our purposes is the Python extension.

python-extension

After installation, open the command palette using ctrl-shift-p and search for settings. In the settings search box, type “python” then scroll through the settings until you find the Python > Data Science: Send Selection to Interactive Window and make sure this setting is ticked. After this, you’re good to go!

python-setting

Piecing it all together: using your conda environment in VSCode

Now let’s get to work running your first python script in VSCode with your brand new environment! If you don’t have an existing python file to test out, not to worry we can make one!

Open a new file within VSCode, and save with a .py extension.
Inside the file, add the following line:
print(“Hello World”)
Save the file, then place the cursor in the ‘print’ line.

You should see in the bottom left corner of the editor that there is a python version displayed, followed by the name of your current environment. This will likely be the ‘base’ environment to start with. If you click on the environment name, then you should be able to select your new environment from the pop-up list. This will then be used to run the code selection.

To run your code within the built in interactive window, highlight the line and shift+enter

This will send the code to the interactive interpreter, which will open in a new tab within the VSCode window. The kernel may take a little while to start in the first instance, but you should see the Jupyter server details listed in the top right corner - note that it should be the name of your environment. Once it has run, it will look something like this:

hello-world

That’s it! You are all set to run python files for exploratory data analysis in VSCode.

Favourite features

Now we have mastered the basics, you have enough to get going with python in VSCode. A few features that make VSCode my one-stop-shop for all my coding adventures and beyond include:

The python extension itself! It is an evolving masterpiece - huge shout-out to the creators and maintainers - with a multitude of features packed within the interactive window that, frankly, could occupy a post on their own. Highlights include the variable explorer, ability to save the current state of the interactive window directly to a Jupyter notebook, and the ability to render a Jupyter notebook in a standalone tab where you can edit and run code cells without spawning a browser window.

The second important, must-use feature is version control. If you haven’t incorporated version control into your workflow yet, or aren’t familiar with it, you can read more . In short, even if you have never written a single line of code in your life, version control is a must for anyone routinely creating and editing documents of any kind. And the best news is, VSCode makes it only too easy! You can create repositories, then stage and commit changes from right there in the version control tab. More advanced functionality is also available in the extension.

Another handy feature is the ability to launch a command-line terminal within VSCode. This can be done via the “Terminal” menu, or keyboard shortcut ctrl+shift+` . As a bonus, if you have an active conda environment, this command will launch a conda terminal with the conda environment pre-activate on the command line. This makes it easy to install new packages and create new environments right from within the editor.

Last but certainly not least is the dynamic documentation display for functions derived from imported packages. For example, when using a the pandas melt function, typing pd.melt() prompts a popup which contains the function definition and variable explanation. This means no more googling docs to check that you have set all the required arguments!

VSCode also features all the best bits of Atom, like being able to set the colour theme and change the layout of windows, and a built-in debugger window that allows you to investigate the state of variable and monitor breakpoints in your code. The more you use and explore, the more functions you will undoubtedly find that make your life better. However, here are a few more of my favourites for inspiration:

Collaboration: (think googledocs for code)
Text functionality: , ,
Coding cheats: , ,
Prettifying the editor: , ,
Preview functionality: ,

Some final thoughts

I’ve gone through a number of IDE’s since my coding journey began. As a general rule, IDE’s suit my style of work better than command-line activities and the ability to run and interact with code and plots in short succession is essential for this. However, a good programmer needs at least some level of experience with the command line, so try not to avoid it! Luckily, VSCode makes transitioning between the command line and interactive windows so easy that there really is no excuse.

When picking an editor, getting comfortable with a single system setup can make it difficult to leap into something new - especially with the time and effort investment required to get everything customised and functional for how you work best. However, it is worthwhile keeping in mind that where the field moves as a whole is often where the greatest level of innovation will be (extensions, support etc). It is good to keep an eye on what the ’next big thing’ is, but don’t be afraid to wait it out until you are happy with the level of stability and usefulness to make the tradeoff worthwhile.

Last but certainly not least, don’t worry if there were parts of this post that sounded like mumbo-jumbo: everyone is at different stages in their coding journey. One idea I hope I have instilled is that VSCode caters to all skill levels, from the ease of installation and setup to the massive extensibility provided by the extension marketplace. In short, VSCode ticks all of my must-have boxes for an editor and I thoroughly recommend you try it out!

Do you have a favourite IDE for editing python code? Having trouble getting started in VSCode? Or maybe you tried VSCode and found it doesn’t quite suit? Get in touch via the or on - I’d love to hear about your experiences!

Image credits: via unsplash

Lorne Proteins 2020

Sat, 08 Feb 2020 05:19:01 +0000

Lorne Proteins 2020

If you’ve landed here after checking out my poster , welcome. If you stumbled here by accident, the more the merrier. Either way, thanks for stopping by!

I hope you found something interesting or useful, and enjoyed hearing about the work we are doing to develop new tools for measuring proteostasis. If you have any questions, want to know more about what we do, or simply want to get in touch, you can find me on twitter head over to the .

For anyone who wasn’t at the conference, or didn’t get a chance to say hi, I have included a brief run-down (and tried to skip over most of the gory – boring, technical – details!).

What’s all this proteostasis business?

Cells have an extensive quality control network responsible for maintaining their molecular machines, including synthesis, folding, degradation and transport

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. Collectively, this machinery is known as the proteostasis network. Proteostasis imbalance results in protein misfolding and aggregation, the central molecular signature of neurodegenerative diseases such as Alzheimer’s and Parkinson’s.

We lack knowledge of which proteins in the cell become vulnerable to improper folding during proteostasis imbalance. One measure of protein foldedness is the extent to which a protein can be unfolded thermally or with a chemical denaturant such as urea. Until recently, it has been difficult to track foldedness in cells due to sheer complexity of the many ten’s of thousands of proteins and proteoforms needed for our cells to function.

To overcome this, this work specifically aimed to:

Devise a chemical biology and proteomics approach to monitor the foldedness of the proteome.
Determine the influence of pharmacological agents that unbalance proteostasis on proteome foldedness.

What is tetraphenylethene maleimide, other than a tongue twister?!

Tetraphenylethene maleimide (TPE-MI) is a dye (tetraphenylethene maleimide) that becomes fluorescent when it binds to reactive thiol residues

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. Buried, reactive thiols are the most buried residue of all amino acids in globular (properly folded) proteins. Monitoring the exposure of these thiols using TPE-MI gives us a sensitive way to probe foldedness in complex mixtures.

To test this strategy, we used a purified, well-studied protein – β-lactoglobulin. We know both the sequence and 3D structure of this protein, and its behaviour in a range of denaturants is known. β-lactoglobulin has 5 thiol residues, of which two pairs are bonded and the remaining one (Cys121) is buried in the core of the folded protein. As β-lactoglobulin unfolds in increasing concentrations of denaturant, we get a corresponding increase in TPE-MI fluorescence.

But what about the giant protein soup our cells make, I hear you ask…

While TPE-MI can reliably tell us about the unfolding of a single, purified protein, our cells are jam-packed with thousands of copies of thousands of different proteins. How can we possibly know which proteins TPE-MI is binding to?

Enter: proteomics. This revolutionary tool is a workflow based on mass spectrometry which can report back on the composition of complex mixtures – both which proteins are present, and the relative amount of that protein. The basics of this technique are beyond the scope of this post, but if you are looking for more details check out the resources section below

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. I have included a brief explanation of the method we use below, which relies on isotopically labelling cells in culture before lysing, denaturing and labelling with TPE-MI. Proteins are digested (chopped up) into regular, smaller pieces before analysis via mass spectrometry.

Using custom software, we can identify and quantify protein pieces. Unfortunately, we cannot identify the protein pieces specifically labelled with TPE-MI, but instead, we look for the loss of those pieces. We can do this for many many proteins and start to understand the concentration at which specific proteins unfold. We can even start to understand intricacies in unfolding for separate protein domains.

How does this help us understand disease mechanisms?

This new method now provides a way to monitor how the stability of proteins (measured how much denaturant it takes to unfold them) changes under conditions of proteostasis impairment that are present in disease. For example, we are now investigating the changes in proteome foldedness when we inhibit hubs of the proteostasis network such as molecular chaperones. How do the proteins that rely on specific molecular chaperones to be folded change when those proteins are no longer active? What if we stop the cell from degrading old proteins? Or from decorating their proteins with extra modifications that mediate structure and activity like phosphorylation? Defects in this machinery are common in neurodegenerative protein aggregation diseases and we now have the tools to start tackling these questions.

References and handy links

Chiti F, Dobson CM (2017) . Annu Rev Biochem 86: 27–68.
Chen MZ, Moily NS, Bridgford JL, Wood RJ, Radwan M, Smith TA, Song Z, Tang BZ, Tilley L, Xu X, Reid GE, Pouladi MA, Hong Y, Hatters DM (2017) . Nat Commun 8: 1–10.
Lottspeich, F. (2009). . In Proteomics (pp. 3-10). Humana Press.
Graves, P. R., & Haystead, T. A. (2002). . Microbiology and molecular biology reviews, 66(1), 39-63.

That’s all from me for now. Still can’t get enough or want to know more? Simply want to get in touch? Find me on twitter head over to the - love to hear from you! Now, back to the beach 🏄🌴☀️ !

A year in review: 2019 edition

Fri, 31 Jan 2020 12:46:00 +0000

The holiday season is a time of reflection for many, me included. However, this was heightened this year, and somewhat hijacked, by the looming start of a new decade.

While there are plenty of discussions of why 2019 isn’t the end of the decade (like , and ), it did little to stem the tide of ten-year progress reports flooding . These outpourings of academic (and personal) success are often incredible and inspirational, but can also be overwhelming and disheartening for those of us who feel less accomplished.

So as is tradition, I decided to stick with my by-the-numbers account of the past year. You’ll find some wins, a handful of losses, and a few goals that looked a little different from start to finish. Finally, I look to the future and set my sights on kicking brand new goals in 2020.

Wins

A snapshot of the high points

I contributed to 4 manuscripts, of which 3 were posted as preprints
I completed my first independently-funded research project using funds totalling $40 000
I was awarded ~ $10 000 in travel awards
I attended 4 national and 2 international conferences
I presented 5 research talks, 2 posters and chaired an academic career development panel
I gave my first invited research seminar at my alma matta
I was awarded an international post-doctoral fellowship (!)
I performed over 45 (mostly week-long) experiments in just 159 working days, and over 75% of these gave useable results
I wrote or edited over 30 000 lines of code
I was an active member of 4 conference and professional committees, and an ordinary member of 4 professional societies
I directly supervised 3 students and informally mentored at least 4 others
I attended a 5-day leadership course, making 5 new connections that have blossomed into 3 new collaborative relationships
I accounted for each of my working days by posting a reflection to a purpose-built Instagram profile, receiving a total of 3191 likes
I authored 10 blog posts containing a total of 14 642 words, which received 5437 views from 3849 visitors across 36 different countries - more than half of these views were on a single post after I shared it with the Twitterverse!
I slept an average of 7 hours a night, did an average 6260 steps per day, and lost 12 kilograms
I read 1 1/2 more books than last year and completed 1/4 of my first ever attempt at cross stitch
I gave my very first tech talk at a local meetup group
I travelled to 3 countries and explored 5 new cities, and took 2571 photos
I spent 365 days being a partner, sister, colleague and friend - being a happy, (mostly!) healthy human

Losses

A snappier shot of the low points

Only one manuscript made it through the review process to be published
None of my first-author manuscripts are any closer to being submitted
Of the 431 papers I downloaded, I read less than 10% of them in full
I am not proud of my ‘bedside manner’, as I was impatient and frequently frustrated with students
I spent over 50 hours preparing 5 unsuccessful grant or fellowship applications
My coding projects are still in a state of disarray, in desperate need of a tidy and some documentation
I only read 3 1/2 books and put 12 kilograms back on
There were days where I barely slept 4 hours and struggled to make 1000 steps
I only took 11 days of leave, and was fuelled by stress and anxiety far more often than passion and curiosity

Lessons

The bits I wish I knew at the start of 2019, but had to learn the hard way.

You have to crawl before you can walk - academic conferences are great, especially the chance to travel internationally. However, you have to have the time and effort already committed to a project to have something new and interesting to present, and in turn to make the most out of the experience.
Time tracking - the more you know! There are already people using your personal data to sell you shoes and expensive holidays - why not use your personal data for good, not evil? Late in 2019, I started tracking how I spend my workday (keep an eye out for more details coming soon!), helping me refocus on my goals, dedicate productive time to important work and better plan my time for specific activities according to how long it had taken in the past.
The sunk-cost fallacy - Sometimes you have to cut your losses and move on, despite having invested so much time and effort and energy into one particular thing. If it’s not right, or not happening, maybe it never will be. And that’s ok - sometimes you have to empty some space to leave room for the next great thing, whether that’s the next big experiment or project, or the next townhouse or friendship. However…
Perseverance is key - every application makes your next application better. Although, being privileged enough to be able to persevere is a huge source of diversity challenge in academia. Appreciate when you have the option to continue chasing a dream, and not just when that dream becomes reality.
Decisions are hard - and making them can change the course of your life immediately, even in how you feel about the people around you.
Some people will never understand - and that’s on them, not you. Living your best life is unlikely to please everyone, so live a life you’re proud of and whether others understand your path or not is irrelevant.

Goals

This time last year I set some goals. This was a new approach to goal-setting for me, and I’m pleased that it worked (moderately) well! Physical activity was a consistent focus - even if only a short walk in the afternoon, I was conscious of the link between how my brain and body feel. I made substantial progress on building reflective practices and, last but not least, I landed the next job!

Building on this foundation, here are my goals for 2020

1. Papers are paramount. With a looming international move, I have a little over six months to tie-up all my projects and get my first-author manuscripts over the line. My goal is to finalise three publication-ready first-author manuscripts by April. With drafts floating around in various states of disarray, I will use structured writing time for at least eight hours a week to make focused progress. I will use calendar entries for accountability and maintain a master task list for tracking progress. I would also like to reorganise my reading and writing process and systems, but that’s a goal for another time!

2. Hacking hand-over. Part of the leadership course I attended encouraged us to think about ourselves as changemakers. This could result from our science, or more broadly our leadership, but nonetheless we were to imagine what change we wanted to make in the world over the next year. Then we had to plan it. Who would we connect with? What skills/tools did we need? I have always been a passionate advocate for programming for life scientists, and I would love to be the catalyst for this in our institute - to take the plan I sketched on paper (yes - real-life butchers paper!) and put it into action. My goal is to establish a local hacky hour at my institute, bringing together experienced, amateur and coding-curious researchers in a collaborative space to learn from each other. I will gather a small working group of researchers experienced in programming by the end of February, where we will brainstorm the best structure, location and time for meetups. The action items from that meeting will serve as a task list for tracking progress, and I will coordinate at least three hacky hours before handing over the reins when I move.

3. Blog better. Despite averaging one post per month last year, I felt there was a lack of substance with so many conference wrapups and few useful, practical or probative posts. The one obvious exception was a rather popular commentary on the state of NHMRC Investigator Grants. This was a combination of an important issue and a good coding project, and I would love to produce more content like this in the future. My goal is to continue posting at least once per month, of which half will pursue practical or probative themes. This will be easily tracked by my posting record here, so feel free to help me stay accountable!

4. Healthy is happy, and mindfulness matters. As I approach another decade of life (that’s right, a big three-oh this year), I am starting to appreciate that a healthy body and mind is not really something you can achieve. There is no box to tick to say you are done, and there isn’t really a finish line. A commitment to maintaining your physical and psychological health is lifelong, and relapses will happen when you aren’t paying attention. My goal is to recover my healthy habits, focusing on yoga and reflections once a week. My progress will be easily tracked by my health app (thanks technology!), and I will continue to maintain my purpose-built Instagram account with weekly posts. For the safety of me and my lab mates, that account will stay anonymous - but it serves as a neat record for me at least!

2019 was undoubtedly one of the hardest, but most rewarding years yet. Just when I finally felt comfortable in my space and role in Melbourne, I landed an international Fellowship - a huge win that is equal parts exciting and terrifying. As they say, life begins at the edge of your comfort zone. So here’s to an adventurous, unknown 2020 - may it be filled with science, self-exploration and smiles.

Image credits: @brookelark via

BioInfoSummer 2019

Sun, 29 Dec 2019 09:28:44 +0000

With half my home state on fire and the lead up to Christmas, this post has been in the works for a few weeks. But here it is - better late than never! My brief recap of my recent adventure to BioInfoSummer 2019.

Conference details

Title:

Date: December 2nd - 6th 2019

Location: University of Sydney, Australia

Overview:

BioInfoSummer brings together advanced undergraduate and postgraduate students, researchers and professionals from the mathematics, statistics, medical sciences and information technology disciplines. Attendees develop bioinformatics skills, national networks and employability. The central themes of the 2019 conference were Epigenetics/genomics, Single cell omics, Mass spec analytics and BioCAsia/precision medicine.

Overall thoughts and impressions

Of all the conference travel I have done this year, BioInfoSummer was the closest to home and the furthest from a normal conference. The wide applicability of omics techniques meant a broad spectrum of research topics presented by a diverse collection of researchers with a common love for data analysis. In addition, being targeted at students and early-career researchers meant the dynamic of the seminar sessions was quite different - an atmosphere in which students were comfortable asking questions and driving the discussion around world-leading best practices in the field of bioinformatics. This was supported by plentiful and generous travel scholarships boosting the participation of interstate early-career researchers.

Even though my research has been edging into bioinformatics for a little while, this was my first formal foray into the community. While I was a little anxious about being immersed in a new group of people, luckily bioinformaticians are a friendly bunch! This meant that the workshops had a welcoming feel, creating a safe space to actively try out the techniques presented during the morning symposium sessions.

Last but not least, the location and timing of BioInfoSummer meant that they were able to share sessions and speakers with , and were followed directly by . This enriched the sessions and provided even greater diversity and networking opportunities. This truly was a fantastic blend representative of the bioinformatics scene in Australia. I cannot recommend enough for new PhD students or post-doctoral researchers new to bioinformatics in it’s many incarnations.

Lessions learnt

1. Bioinformatic techniques for proteomic analyses lags behind the trail blazed by genomics - this was evident in the distribution of the program, but also attendees. However, I also think that…

2. The distinction between proteomics and genomics is sometimes unhelpful - similar underlying data structures make some methods applicable to both, and leveraging the substantial ground that has been covered in genomics will assist in the rapid progression and development of proteomics methods.

3. Single cells are the future, but not the complete story - innovations in single-cell proteomics and ongoing developments in single-cell RNAseq will continue to push the boundaries of our understanding of biology on the smallest scales. However, one of my favourite talks of the conference was on the value of bulk methods in an era of single-cell capabilities. And I agree - these methods should be applied as appropriate and often one and inform the other.

4. Seeing is believing - two-dimensional imaging mass spec and spatial transcriptomics are the next frontier. With these techniques, we are starting to understand the spatial distribution and cooperation of cells in health and disease.

5. The future is now - the big data buzzword - everyone will have heard the term big data. This is becoming more the norm than the exception, even in biological research. This has meant that method development and bioinformaticians are in high demand, and become recognised as an integral part to any research project. This was exemplified by one of the conference keynotes, in which the speaker discussed the essential skills for a bioinformatician in the next five years; many of these included the ability to wrangle, access and store large amounts of data, as well as being able to leverage this data for biological insight.

6. Unexpected acquaintances - I travelled to Sydney expecting to know no-one. In a classic small-science-world moment, within five minutes of arriving I ran into a post-doc from another research group in my building! While we had met and talked a little previously, the chance to spend a week nerding out over data analysis and coding methods was the perfect way to get to know each other better. In fact, this was one of the best parts of the conference. As well as this, I met a handful of people from my alma mata who grew up in a similar corner of the world as I did.

Take home resources

There were too many great resources to share them all, but if you’re looking for tutorial style activities then the Material for each of the is a great place to start. Other than this, below are a few of the packages, tutorials or databases that I am keen to check out after hearing about them during the conference:

’s featured workspaces demonstrate common genomic analysis pipelines deployed using data and tools available on AnVIL.
is a portal to a series of public experiments describing mouse and human stem cells and how they differentiate to become mature cells, tissues and organs.
is a collection of lightweight and handy tools for Python that help to handle various sorts of proteomics data.
: A Python Library for High-Throughput (Phospho)proteomics Data Analysis
The repository contains a list of open source Python tools for Proteomics analysis. The list is very likely incomplete and we are happy to take pull request with new tools.
The workflow management system is a tool to create reproducible and scalable data analyses. Workflows are described via a human readable, Python based language.
workflow: differential discovery in high-throughput high-dimensional cytometry datasets

These will hopefully give you a flavour of the overall themes covered at the conference, but if you are new to the world of bioinformatics I thoroughly encourage you to get involved next year. The conference is slated to be held next December in Canberra, so keep an eye out for the official announcement!

Overall, BioInfoSummer was a great opportunity to connect and build community with like-minded people. I was so lucky to be supported by an AMSI ChooseMaths travel award - without their support, I could not have travelled to attend the conference and I cannot thank them enough for their support of Women in STEM. If you are new to or interested in the world of bioinformatics I whole-heartedly recommend checking it out next year!

Image credits:

Behind the scenes: Investigating the Investigators

Thu, 31 Oct 2019 01:30:27 +0000

The hottest topic on Australian researchers lips of late has been the recent NHMRC Investigator Grant announcement. I recently set about flexing my data-science muscles to see what the outcomes of the scheme were overall, and what a successful application might look like for the next round. If you haven’t come across it yet, you can read more .

Below is a behind-the-scenes look at what went into the data analysis for this post, and what I learnt along the way. I have also released all the code and results as a for those who like the real nitty-gritty details or want to extend the analysis.

Raw data

The raw data used in this analysis came from three main sources.

The first was the , which offers spreadsheet summaries for grants released since 2013. I chose to use only the data from 2015 onwards, for a couple of reasons: (1) the structure of the Fellowship system appears to have changed in 2014 to the ECF, CDF, RF layout which remained in place until 2018. This meant that 2013 data was poorly correlated with the more recent datasets. (2) The 2014 dataset did not have as much detail in the gender, age, state breakdowns that could be easily compared to the following years. (3) Five years seemed like a nice time period to work with!

The second source of data was the Field of Research codes used to classify research. You can find the complete list at the . I struggled to find an easily-downloadable version, and instead copied them from the University of Melbourne intranet. With a little post-processing, I had a fully functional list of each level of classification, which I could then use to understand which types of research were popular for funding.

The last source of data was , which I used to gather the number of research publications and average Field-Weighted-Citation-Impact (FWCI) for each awardee in the ten years previous to their year of award. This was somewhat of a manual process, and I used the ‘best match’ profile for each awardee imported into SciVal. Overall, 88% of the awardees were matched accurately (and this could be increased with a little manual curation). I also did a little digging around in the PubMed Central API using a python package (see the resources list below for more information) to batch-query the author names and collect their publication history, to compare with the matches generated by SciVal.

Processing and analysis

After initial cleaning of the raw data, I then equated the new and old schemes by matching the tiers. Although the correlation is imperfect (due to changes to eligibility between the old and new schemes), this resulted in Early Career Fellowships mapping to Emerging Leader 1, Career Development Fellowships mapping to Emerging Leader 2, and Research Fellowships mapping to Leadership Fellowships.

Lessons learnt

Data is messy. This was evident in all of the raw data I collected - naively, I expected simple-to-use spreadsheets from the NHMRC. At the very least, I was anticipating similar formats for the more recent 5 years. What I was greeted with was a complicated series of tables designed for visual interpretation by human eyes and not easy accessibility via computer scraping. The initial data cleanup took more than half the time it took to complete the analysis.

Through this process, it became clear to me that as a general rule people, despite often working in a science-oriented role, do not handle, label or store data well. With the increasingly data-driven world we live in, we would all benefit from improving our data hygiene.

Lastly, while the trends I saw and commented on are indeed interesting, they should be interpreted cautiously. The data that the NHMRC provides is somewhat fragmented (to protect the privacy of successful applicants). More importantly, the data they do provide is focused on successful applications. There are lots of important details about the makeup of the applicant pool that we don’t see and this is important - albeit missing - context for interpreting the trends I highlighted.

Similarly, publication history searches are a tricky one. Pay-walled publication information is a nightmare to access, and although PubMed searches are OK they are limited. To get standard metrics such as H-indices and field-weighted citation impacts requires access to subscription services. Moreover, people’s names are difficult! Even once you have access to the databases, it can be difficult to know whether/how to split the given names into first/last and there is almost no chance of avoiding manual curation if you want a complete dataset.

Tricks and tools of the trade

As this was my first dedicated data-science style project, I quickly ran across a few questions.

When wanting to plot the per-state distribution of applications and successful awardees, the most obvious visualisation was a map. I’d never plotted a map before, and after a quick google search I found myself asking What on earth is a choropleth and where do I find a shapefile? It turned out a chloropleth is what I wanted to make - a thematic map in which areas are shaded or patterned in proportion to the measurement variable being displayed on the map. And to do this, you need a shapefile - a vector data storage format for storing the location, shape, and attributes of geographic features. Luckily there are a few , and eventually, I found the I was looking for. Amazingly, using meant dealing with this type of data relied on many of the skills I already have and before I knew it Voila! - one map of Australia complete with colour-mapped and labelled data.

While handling the somewhat messy task of linking successful awardees to their publication track record, I came across the problem of slight variations between different naming formats. How do you match text that is fuzzy, and how does Levenshtein help? It turns out that Python has a whole range of functions via the that help to compare strings that are referring to the same thing but are written slightly differently. The most simple version of this makes use of the Levenshtein distance, named after Vladimir Levenshtein who originally considered this phenomenon in 1965. This metric measures how far apart two sequences of words are according to the minimum number of edits needed to change one into the other. These edits can be insertions, deletions or substitutions. One detailed later, and I was on my way matching fellowship awardees to scival authors in no time.

Finally, throughout the whole plotting process, I was conscious of my colour palette. I am a firm believer that good dataviz should be both functional and beautiful. With plenty of gender-based comparisons, you might wonder how I could go past the tried and true colour combination. So why I didn’t choose pink and blue for gender studies? It turns out there are a few reasons why red and blue for gender data is an ‘unawesome choice’ rooted in gender stereotypes (pink = girls = weaky, cute and blue = boys =strong, bold). Luckily there are plenty of good colour-combinations that circumvent these .

With these tools in hand, I had everything necessary to analyse and visualise the datasets at my disposal. To see these tricks and tools in action, don’t forget to check out the or head along to some of the resources listed below.

Resources

Wishlist: I haven’t had a chance to implement this functionality yet but these dataviz tools are high on my to-try list to extend the accessibility of this dataset!

Disclaimer

The original analysis was intended to inform my personal decision of whether to apply for an Investigator Grant in the 2019 round. Any action you take as a result of this information is done at your own peril. If you do decide to act on this information, I wish you the best of luck whichever path you may choose. May the odds be ever in your favour.

This being said, I have of course aimed to be as unbiased and informative as possible. This is also my first foray into data-science-for-public-consumption, so if you do notice any overt errors or bugs feel free to raise an issue on or get in touch via and I will check it out as soon as possible.

Image credits: cadop via

Investigating the Investigator Grants 2019

Mon, 30 Sep 2019 01:26:50 +0000

Introduction

The hottest topic on Australian researchers lips this month has been the recent NHMRC Investigator Grant announcement. After all the commotion around the new scheme and the distribution of research dollars, I set about flexing my data-science muscles to see what the outcomes of the scheme were overall, and what a successful application might look like for the next round.

What are Investigator Grants?

If you are a scientific researcher in Australia, chances are you were aware of the NHMRC announcement a few weeks ago of their latest round of funding. For those who are outside health-focused research, this was a highly-anticipated announcement after a complete re-structure of the NHMRC Funding Schemes over the previous year. The reform included replacing the Fellowship system (funds to support individual researchers to establish their own research programme) under the unified banner of Investigator Grants.

Historically, previous incarnations of these Fellowships (Early Career, Career Development and Research Fellowships) have supported the very best and brightest researchers in Australia with varying levels of lottery-luck in their award. Importantly, having been successful at the first rungs on the Fellowship ladder, researchers were then much more likely to gain the next level of support. Therefore, the award of these Fellowships to emerging researchers is a crucial support system that could make or break a career.

In particular, the lists better support for Early and Mid-Career Researchers (EMCRs) as a key goal of their reform:

Early and mid-career researchers are being discouraged from pursuing a research career.
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In response to these issues, NHMRC reviewed the structure of its research funding and has reformed its grant program.

NHMRC, 2019

Why the fuss?

For researchers who spend many (many) weeks (months) working on the application for these schemes, the outcomes are of intense interest. The research funding environment in Australia is extremely competitive, and often the difference between funded and not funded can be little more than pot-luck. Early reports during the peer review process this year suggested that the new scheme had an abundance of issues and was likely to be even more of a lottery than previous years.

After the announcement, scientists flooded Twitter with their thoughts, interpretations and statistical analysis of the overall outcomes. There were many deserving people who were lucky enough to make the grade this time around. However, there were also an abundance of leading researchers who missed out and cursory analyses highlighted an abundance of inequities and unforeseen results of the reform.

_Sheldrick/status/1166908265743015936

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Many of the numbers you will see online come from the key performance data released by the NHMRC (data on gender, age and state awardee rates has been made available since at least 2013). It is my firm belief that, as scientists, we should use science (including data science) to drive our decision making. The trends reveal biases/underlying issues in the grant scheme, and provide rationale/direction for additional reform. In addition, the profile of previously successful applicants can help researchers like myself understand how we fit within the scheme and, according to whether (or not) we fit the profile, inform our decision to apply in the next round (which is not that far away!).

Crunching the numbers

Most of the raw data used in this analysis came from the . I collected some extra data on the Field of Research codes and individual researchers from additional sources including the and .

After initial cleaning of the raw data, I decided to equate the new and old schemes by ‘matching’ the tiers as best as possible. According to a fact sheet provided by the , equivalent schemes are as follows: Early Career Fellowships map to Emerging Leadership level 1, Career Development Fellowships map to Emerging Leadership level 2, and Research Fellowships map to Leadership levels.

Due to differences in eligibility (according to years post-PhD) as shown above, the correlation is not perfect. But it serves the purpose of being able to compare as the NHMRC intended at least.

For the nitty-gritty details of the number crunching and visualisation techniques that went into the makings of this post, be sure to check out the post soon. If you’re simply here for the pretty pictures and insights, read on!

Key insights

Overall trends

At first glance, it appears as though the new investigator scheme has seen an injection of more money into the Fellowships portion of NHMRC funding. However, this funding includes more money per person at the upper tiers without a substantial increase in the number of applications funded. Moreover, the number of successfully funded applications and dollars was skewed toward the Leadership levels with an overall decline in the number of level 1 and 2 (Emerging Leader) applications funded.

The decline in the number of applications funded was matched by a substantial increase in the number of applications submitted, leading to a nosedive in the success rates particularly in the EL2 and L1 brackets. This increase in applications was thought to reflect the changes in eligibility structure meaning everyone had a go and often at levels far below what was appropriate for their research experience. The NHMRC is reported to be hopeful the number of applicants will decrease in the next round (thus artificially inflating the success rate).

Location, location, location!

With the conglomerate of research institutes, personnel and equipment in Melbourne, it has always been tough to beat in funding success. The same was true of this year, with Victoria the overwhelming leader in the number of awarded applications and second-highest success rate. In fact, Victoria received more than 45% of the awarded Fellowships in 2019.

The large number of applications originating in Victoria, coupled with their high success rate, suggests that not only is there a critical mass of outstanding researchers concentrated around the Melbourne biomedical hubs, but that they benefited immensely from the support processes in place in the lead up to submission. Hopefully, other institutes around the country can find value in their strategies for the following rounds.

Gauging the gender gap

Gender has always been a touchy topic when it comes to Fellowship funding in Australia, especially at the later stages of academia. There were concerted efforts made to target gender equality in the new scheme. However, at first glance, there was an enormous bias in the total number and dollars awarded. In fact, this disparity is the worst that the Fellowships scheme has seen in the last five years.

If we dig a little deeper, it becomes clear that this skew is due mainly to biases in the upper tier of the scheme. In fact, at the first and second tiers (level 1 and 2), there has been progress toward equality and, in some cases, even over-representation of women for the last five years. However, the successful males outnumber females two to one at the highest tier (level 3).

Two to one. The new scheme, if anything, has made this worse with the proportion of awards for women the lowest it has been since 2015. This points toward a systemic issue with how relative to opportunity is assessed and the lack of support for women entering the upper echelons of academia that is still ingrained in the research culture in Australia. Moreover, the extended 5-year term of these awards means that these differences will permeate University faculty for many years to come.

Titles and track records

As a general benchmark, academic titles say something about a researcher’s seniority. Considering the distribution of titles among successful awardees, there is a clear trend toward more and more senior researchers finding success at lower levels of the Fellowship scheme. For example, Associate Professors have taken a share of the level 1 funding for the last two years and for the first time, there was an Emeritus Professor awarded level 3 funding in 2019.

Similar trends can be seen in the track record of successful awardees, especially their publication history. While this is imperfect due to author name mismatches, overall the median number of publications for level 2 awardees has held steady around 50. In contrast, level 3 saw a sharp increase this year for the first time. The median number of publications for level 1 awardees has also steadily increased, meaning that to be competitive ECRs now need on average twice as many publications as they did 5 years ago.

With the revamp of funding levels and removal of specific ‘years-post-PhD’ ranges for level 1 and 2, this was always a concern. While I have (many, many) issues with using the years-post-PhD award as a ruler to measure relative success, at the bare minimum I do believe this distinction helped stratify junior researchers in the eyes of reviewers and assisted their assessment relative to opportunity. Unfortunately, the lack of boundaries this year left many researchers unsure of the appropriate level of funding they should apply for and moreover allowed many to take advantage of the lower levels in the scheme. This placed a large responsibility on reviewers to fairly evaluate an individuals trajectory against others with up to 10 years longer in research. A big ask!

Trendy topics

With every application, researchers include up to five keyword phrases describing the focus of their proposed research. By looking at the most popular keywords, I wanted to understand the research themes attracting the most funding and potentially consider how this has evolved over the last few years.

2015 keywords

2019 keywords

Interestingly, the top five keywords for the previous five years have typically been some iteration of health, disease, biology and cancer. While these seem very general, reading between the lines reveals a transition from ‘disease’-driven research to ‘health’-centric. Interestingly, epidemiology has emerged as a prominent focus this year. Cancer research has also held a steady proportion of funded applications, suggesting an area of high priority either among reviewers or impressive researchers embedded in this field.

What is unclear from these trends is the type of research being funded in these proposals. This is captured in the NHMRC reporting process as ‘broad research themes’. Traditionally, fundamental (Basic) research has held a large share of the total funding. However, the last five years have seen a steady decline in this proportion and a corresponding increase in the more translational themes.

In particular, clinical medicine and science enjoyed a sizeable bump in the proportion of funding awarded such that it has almost reached parody with basic science. This likely stems from the strong emphasis on ‘research impact’ that permeates all aspects of the new scheme, and at face value is not terribly alarming. However, our translational research must be underpinned by quality fundamental understanding and our ability to fund this type of research from the NHMRC scheme appears to be questionable in the future.

What does this mean for science, and ECRs, in Australia?

Overall, there has been a shift toward more senior and established researchers in all three levels of award. This is not merely a consequence of the new scheme, but has definitely been accentuated by it. This is likely reflective of the desire to invest in researchers who can demonstrate their previous impact has a direct and immediate public benefit.

This is also reflected in the share of the pie gobbled up by each of the four broad research areas, where Clinical and Medical Science has seen a steady increase at the expense of Basic Science.

While I made every effort to take a comprehensive snapshot of the available awardee data, these insights were limited in part by fragmentation of the data. The raw data provided by the NHMRC includes summary totals e.g. by gender or by state, but often the per-applicant information, due to obvious privacy reasons, is not available.

Some of the missing data, such as the years-post-PhD for successful applicants, could, in theory, be provided anonymously and would provide tremendous insight for potential applicants. At this stage, the closest proxy for years post-PhD is the mean age of awardees (although this as a measure is complicated by relative to opportunity). This year the mean age for level 1 awardees increased from 35 in 2018 to 37, agreeing with the overall sentiment of increasing seniority among successful applicants. With the restructuring in 2019, the NHMRC has all but done away with the ECR funding dedicated for those entering the postdoctoral workforce and require us instead to relying on the provision of project funding by senior researchers for many years before being deemed worthy and ‘impactful’.

There have been a number of statements released by associations commenting on this and other perceived failures of the new scheme, and suggesting changes to overcome these issues. For example, the released a host of recommendations aimed at alleviating many of the pain points ranging from the application structure to review processes. However, the short turnaround time (given applications for the next round open tomorrow and close in a little under two months) means that these recommendations are extremely unlikely to be implemented until next year. Moreover, this is a general and of research funding schemes world-wide. At this point, like so many other ECRs in my position, it is now time for me to think about whether my time, energy and effort are best placed in the hands of the NHMRC, other Australian funding schemes or - like so many in my position - whether I should instead focus on opportunities abroad. In the spirit of data-driven decision making, hopefully these insights have helped guide your thoughts as they have mine.

Resources

The original data was sourced from the
For more on the initial guidelines provided during the scheme restructure, check out the
Author track record information, including publication number and field-weighted citation impact, were collected from . If you are considering an application in the upcoming round, it’s a great idea to benchmark yourself against previous successful applicants.
For more info on the specific number crunching and data visualisation techniques used here, don’t forget to keep an eye out for my post.

Are you thinking of applying in the next round of Investigator Grants? Did any of these stats surprise you, or were they helpful in your decision of whether or not to apply in the next round? Head over to the , or let me know on .

Image credits: inspecting gears with magnifying glass | @ pluyer via

Toolbox Talk: Scientific posters

Tue, 30 Jul 2019 10:39:57 +0000

Going to a large, often international, conference jam-packed with superstars in your field is one of the privileges of being a scientist. Conferences are essential for building your network, keeping up-to-date with the progress inside your niche and more broadly in your field, and establishing or maintaining collaborations. If you’d like to know more about my conference experiences, check out . I was lucky enough to attend and present short talks at a few conferences in 2017, and it was a wonderful opportunity to show the scientific community what I have been working on.

However, there are only so many oral presenter slots available at such events, and often these are used by the organising committee to highlight the very best and newest science. But what about the rest of us? It can be really difficult to get the most out of a conference, especially as a student, if you don’t have an opportunity to showcase your work. Enter: poster presentations.

Wait, like in elementary school?

If you are thinking of a giant piece of butcher’s paper, covered with scribbles in multi-coloured permanent markers, you aren’t too far wrong. But things have evolved a little since the school days, and we do have slightly more developed tools for displaying our work these days. Unfortunately, making an engaging scientific poster is often not something we are formally prepared for during our scientific training. We are being moulded into the next generation of scientists, after all - what no one tells you is that artist is just one of the many additional hats that you will wear often as your career progresses.

With this in mind, I thought I would share a little about my process and tools for making an engaging poster. It is by no means a one-stop-shop, as every time I make a new poster I learn a little more about the process, but hopefully, you will find something useful as you prepare for that next science-filled conference adventure.

Tools

There are plenty of purpose-built design software packages out there specifically for making commercial posters, and some even targetted at producing scientific visuals. I have included a few links in the additional resources, but to be honest, I have had mixed success with these - if you find any that I have missed or that you’ve found particularly helpful to let me know!

In the meantime, I have settled on a combination of a freely-available vector graphics program (Inkscape) for compiling individual panels into standalone figures and presentation software (e.g. or ) for the overall poster design. If you haven’t heard of vector graphics programs before, or are unsure why you might want to use one, you can read more . In essence, because of the way they are constructed, vector graphics are easy to manipulate and can be scaled indefinitely without losing their quality as they are not composed of pixels, in contrast to the raster-based file types jpeg and png.

For data-laden figure panels, I will usually stick with python or (if you are interested in this, check out some of my other toolbox posts ). For any work dealing with protein structure, is your friend. The learning curve can be quite steep (I am only just now mastering the basics of opening a structure file, editing the colours and changing the display type from ball and stick to ribbon!) but it is relatively easy to produce a striking visual that will aid in any handwavy descriptions of a protein’s structure. Keep an eye out for my beginner’s basic guide to PyMol in a future Toolbox Talk!

Last, but definitely not least, I use a to create a link to this website. Even if the name is not familiar, I am sure that you would have come across in the wild before - they are a type of matrix barcode that links to a website or application when scanned with a smartphone camera. There are plenty of free online platforms for creating your own QR codes, and many that give you the ability to adjust the shape and colour of the individual elements, however be careful to know the difference between static (meaning you have no flexibility to adjust where the link directs after printing) or dynamic (that contain an automatic redirect which you can adjust after printing) to make sure you get one that suits your particular needs. I have found this especially handy for posters - by encoding your contact details or link to your website, it is easy for conference attendees to get in touch without having to hastily scribble down your email on their crumbled napkin.

Techniques and tips

Even the most exciting and life-changing data can find itself on a poor poster. There are many unwritten (and written) rules for making an engaging and eye-catching scientific poster, and many more experienced people than I have written guidelines and tips, some of which you can find in the resources section below. In any case, I thought I would throw in a few of my trusty tips here:

Layouts should be simple and easy to follow. I find that creating boxes to guide the reader through the poster in a logical fashion can be helpful. Remember a poster is about walking someone through your science and stories that jump backwards and forwards can make it hard to stay engaged. As a general rule, try not to require your audience to do too many optical gymnastics to follow the narrative of your science. When creating a new poster from scratch, I like to sketch out the layout, brief text tidbits and figure ideas by hand in A4 format to help get a handle on what data I need to track down and figures I need to produce. I also find drafting this initial layout in A4 size helps to keep my final poster from getting too overcrowded.
Find SVG versions of logos for the institutes that contributed to the work. It is good practice to acknowledge the financial support any project receives, and posters are no exception. In practice, I typically will have anywhere from 1 - 3 logos on my poster and any additional smaller contributions can be acknowledged in the text if you feel it is necessary. I will always find SVG versions of the logos, such that they can be edited and resized without losing any quality. Generally, you will be able to access these from your University brand team, or a quick google search with an advanced filter to find SVGs works well too.

Never underestimate the power of a good colour scheme. There is nothing worse than a poster that looks more like a rainbow kaleidoscope than a scientific document. Try to steer clear of elaborate colour schemes, and away from anything too light or bright. Also, keep in mind that picking a simple colour scheme to use throughout the entire poster (e.g. one type of sample is the same colour in all panels) helps to provide continuity for your reader. For those who are colour-coordinator challenged, a quick google search for colour palettes will provide all the inspiration you need.

Remember the power of size, shape and colour for drawing attention. With the limited time and large amount of information available at poster sessions, conveying the most important points from your work is crucial. Using accent colours, bold fonts or larger sizes can help to draw your reader’s eye to the key aspects of your work, and hopefully prompt them to ask more about the details!
Consistency is key. A poster, aside from displaying scientific results and information, is at the end of the day a piece of art. As with any beautiful artwork, the little things matter! Use the handy aligning and specific dimension setting functions in powerpoint to make sure everything is balanced and consistent. Check that the text sizes, fonts, line widths etc are identical throughout your poster, especially for things like headers and panel outlines. There will always be one or two typos that sneak past you, but it’s also a good idea do several final read-throughs to check for typos - once your masterpiece is printed, there is no turning back!
Print smaller versions for proofing. Printing an A4 version of your poster is a great way to check for readability and those last-minute alignment or spelling errors. As a general rule, anything that is not readable in this format will not become more-so simply by being printed bigger - remember you want to be able to engage your visitors in conversation, not have them glueing their nose to your poster trying to make out the details!
PDF is your friend. Particularly if you include transparent artwork or special characters, it is good practice to export your poster from powerpoint to PDF before submitting for printing. There are also a few tricks with powerpoint to ensure the quality is maximal, which can be found in the ‘Options’ menu of the dialogue box during export.
Be prepared to print early. Some institutions have their own print services, and these can be a cost-effective way to have your poster printed. I have a lot of experience with Officeworks and have generally been thrilled with their service, although I would recommend visiting a store in person to submit and collect your poster. They will generally give you a test print which is handy to make sure your creation survived the file format change. Regardless of which route you choose, plan to have your poster finalised and printed several days before departure - this way you will have a few days up your sleeve to handle the inevitable curve-balls!

Useful resources

from Animate Your Science has some straightforward tips for layout and content. I will admit to disobeying a few of their “rules” but generally great advice!
Toolbox talk on and the

Did I miss your favourite tool or technique? Find me on or head over to the to tell me more!

Image credits: codytdavis, greyritualstudio, alecuffia and arterbury via

#LorneProteins2019 & #FASEB2019

Mon, 11 Feb 2019 06:07:09 +0000

If you’ve landed here after checking out my poster or the , welcome. Thanks for stopping by!

Just can’t get enough of the pretty pretty science? You can find details about the work I presented on my poster here. For anyone who wasn’t at these conferences, or didn’t get a chance to say hi, I have included a brief run-down (and tried to skip over most of the gory - boring, technical - details!).

Now - onto the science!

Setting the scene.

Cells have an extensive quality control network responsible for maintaining their molecular machines, including synthesis, folding, degradation and transport

\[1\]

To overcome this, this work specifically aimed to:

Devise a chemical biology and proteomics approach to monitor the foldedness of the proteome.
Determine the influence of pharmacological agents that unbalance proteostasis on proteome foldedness.

What is tetraphenylethene maelimide, other than a tongue twister?!

Tetraphenylethene maelimide (TPE-MI) is a dye (tetraphenylethene maelimide) that becomes fluorescent when it binds to reactive thiol residues

\[2\]

To test this strategy, we used a purified, well-studied protein - β-lactoglobulin. We know both the sequence and 3D structure of this protein, and its behaviour in a range of denaturants is known. β-lactoglobulin has 5 thiol residues, of which two pairs are bonded and the remaining one (Cys121) is buried in the core of the folded protein. As β-lactoglobulin unfolds in increasing concentrations of denaturant, we get a corresponding increase in TPE-MI fluorescence.

But what about the giant protein soup our cells make, I hear you ask…

Enter: . This revolutionary tool is a workflow based on mass spectrometry which can report back on the composition of complex mixtures - both which proteins are present, and the relative amount of that protein. The basics of this technique are beyond the scope of this post, but if you are looking for more details check out the resources section below

\[3-5\]

Using custom software, we can identify and quantify protein pieces. Unfortunately, we cannot identify the protein pieces specifically labelled with TPE-MI, but instead we look for the loss of those pieces. We can do this for many many proteins and start to understand the concentration at which specific proteins unfold. We can even start to understand intricacies in unfolding for seperate protein domains, such as in the example below where different reactive thiols give information on specific domains of Hsp70.

How does this help us understand disease mechanisms?

References and handy links

Chiti F, Dobson CM (2017) Annu Rev Biochem 86: 27–68.
Chen MZ, Moily NS, Bridgford JL, Wood RJ, Radwan M, Smith TA, Song Z, Tang BZ, Tilley L, Xu X, Reid GE, Pouladi MA, Hong Y, Hatters DM (2017) Nat Commun 8: 1–10.
Lottspeich, F. (2009). In Proteomics (pp. 3-10). Humana Press.
Graves, P. R., & Haystead, T. A. (2002). Microbiology and molecular biology reviews, 66(1), 39-63.

That’s all from me for now. Like the pretty pictures? Still can’t get enough of the science? Simply want to get in touch? Find me on twitter or head over the to page - I’d love to hear from you! Now, back to the beach 🏄🌴☀️ or the snow-covered mountains 🏔️⛷️!

NoBell Prize 2018

Fri, 30 Nov 2018 11:01:39 +0000

The phrase “Nobel Prize” usually evokes a sense of prestige and gravitas. There is no doubt that these prizes are bestowed upon individuals who have made outstanding contributions to the human race. However, sometimes what exactly the prize-winners invention or contribution was can be a little beyond the grasp of your average citizen.

Enter the “NoBell Prize”.

Hosted each year by the Melbourne Neuroscience Institute, this fantastic event is a funny scicomm-athon open to the general public, where brain and mind researchers attempt to explain their research without jargon in three minutes. Should they utter a sceric of technical jargon, the judging panel ring a bell and the researcher must satisfactorily explain the concept in lay terms before moving on. At the end of the night, the number of bell rings is tallied up and the researcher with the least bells is crowned victorious with a ‘prestigous’ faux-gold cup. This year pitted 5 fantastic researchers against the bell:

Dr David Farmer - Research Fellow, Autonomic Neuroscience Laboratory, The Florey Institute
Dr Holly Chinnery - Corneal and Ocular Immunology Unit Lab Director
A/Prof Carsten Murawski- Principal Investigator, Brain Mind and Markets Laboratory
Prof Erica Fletcher - Visual Neuroscience Laboratory Head
Dr Scott Kolbe - Advanced Neuroimaging Laboratory Head

While at face value this may seem like a frivolous take on a serious and distinguished event, in the age of fake news and dismissed scientific evidence the importance of events like these cannot be understated. It was articulated best by the host of the evening, Dr. Shane Huntington, when he espoused the responsibility we have as researchers not only to conduct good science, but to demand good science, to advocate good science, and to communicate good science.

In fact, as Dr. Huntington believes, the current fracturing of truth and lack of belief in evidence-based policy is a reflection of a failure by us as an academic community to effectively engage and communicate with the public. Events like these are essential for both public science literacy and practice in effective communication for researchers.

Even with the serious (and important) aspects, all around the NoBell Prize was a fantastic evening with lots of laughs and an equal helping of learning - even for me! If you have the chance, I highly recommend checking out the NoBell Prize in 2019. Maybe you could even go up against the bells yourself!

Toolbox talk: Trello

Thu, 30 Aug 2018 12:34:06 +0000

As an early-career researcher, I am still figuring out the best way to get stuff done. Every day it seems like my to-do list gets longer and longer, for every item I tick off two more take its place and I have no doubt it will only get worse. I am all about finding the best, most efficient and connected technologies to help alleviate some unnecessary legwork. After my first , I have decided to write more on the programs, apps and integrations that I use every day starting with one of the newest additions: Trello!

The basics: What the Tr-hell-o is all the fuss about??

Trello is fundamentally a project management application with a simple but sophisticated hierarchical interface consisting of boards, lists and cards. Trello thrives in settings suited to organisation. In the context of Trello, kanban is most often laid out as three stages (lists) - to-do, doing, done - and each element (or card) of a project (board) is represented within a list according to its state of completion. Elements progress through each list, giving a simple, visual overview of the project’s overall state.

This style works well when you work alone on individual projects - but it is also something you could easily accomplish with a chalkboard and some post-it notes. Where Trello really comes into its own is the ability to share and collaborate, such that individual tasks can be assigned to different people, comments can be added to cards specific for that task, and everyone can know at a glance the current big picture.

Trello is free (for the basic version, which is all I have needed thus far!), and accessible via the web interface, PC and iOS apps. It also integrates with all manner of other productivity apps, including GoogleDrive, GitHub, Dropbox, Slack, Evernote, OneDrive… Together, these features enrich the functionality of boards, providing a kind of dashboard from which to tackle projects and giving unparalleled flexibility for teams with diverse computing and file storage preferences.

Trello in action: managing your research projects

First things first: keeping track of experiments. There are many many electronic lab notebooks currently on the market, and I have tried more than my fair share. If you are not stuck in your ways (like I am) I highly recommend shopping around and picking one that you can integrate into your work as soon as possible. Unfortunately, I am a control freak and I am yet to find one that fits seamlessly with my workflow, and so I have resorted to building my own system using Trello!

The basic elements include lists for Ideas, Planned experiments, Lab work in progress, Analysis in progress, Ready to writeup, and Complete. Each new experiment or idea gets a card, for which I quickly note the purpose, reagents to be used, techniques and instrument details, and any pertinent references in the description. I then link this card (using the GoogleDrive powerup) to the folder containing the full experimental plan, results, analysis and write-up. The card then gets moved across the columns (lists) as each stage of the experiment is completed. For similar types of experiments, I have a checklist that allows me to easily track specific small elements of the experiment (e.g. plating cells, collecting results etc). If I complete a particular action for the experiment, I will also note in the comments any details specifically about that action. These comments are date and time stamped, helping to keep track of my activity on a day-to-day basis.

Another awesome Trello feature is the ability to label cards with a customisable category, which appears as a coloured bar on the front of the card. I use this feature here to label experiments with which one of my (many) projects it is specifically associated with.

This brings me to my second board - my project board. This is a board with which I track all of the ongoing projects I am involved in. There are lists for my individual projects (think: first author work), students projects I am involved with, collaborators projects, and ideas to keep in mind. The cards on each of these lists refer to a specific project, which can then also link to another Trello board or card which contains information, ideas and important references specifically for that project. That’s right baby, it’s Trello-ception!

Along similar lines, I have a board for manuscripts which tracks each manuscript I am involved in (cards) as they progress from the first draft to submission to revision to (if I’m lucky!) acceptance. In this case, labels are assigned to indicate my author position, and whether my contribution is required to move the article to the next stage. Individual cards link to additional Trello boards for each manuscript (normally only those for which I am the first author) where I can store more detailed information and ideas, tasks etc in a kanban layout.

By starring the top-level boards for Projects, Manuscripts and my Virtual Lab Book (starring is an inbuilt Trello function that allows you to favourite boards to be displayed at the top of your home page) I can easily get an overview of current progress, then by clicking a specific project or manuscript card I am automatically linked to all the more specific information I might need when working on that project, article or experiment.

The bigger picture: managing your science

When I first started with Trello, the ability to modularly (and very visually) manage and monitor my projects was a great tool. Since then, I have expanded my use of Trello boards to include the peripherals necessary for managing life as an ECR. Things like how to prioritise your workload, manage your short-term and long-term goals and keep on top of your reading list all become easier using Trello. My most recent addition was a prioritisation board, based on the priorities list shared by Corey J. A. Bradshaw in his amazing book “The Effective Scientist” (stay tuned for a book review - I really cannot recommend this book highly enough!). In short, Bradshaw runs through an exemplary list of how to prioritise the many competing interests vying for the time of an academic to get the maximum bang-for-your-buck in terms of career returns. These include things like writing articles, doing experiments (duh), helping students and colleagues, social media and writing grants.

When I first filled in all of the elements of the board, I realised just how unbalanced I have been in allocating time to each of these aspects. It was a great way to put into perspective where I should be spending the majority of my time and revisiting this structured big-picture priority list once a fortnight has helped refocus my research output.

Resources

That’s enough from me for now - if I have convinced you to give Trello a go, there are a couple of handy links below. I have included template examples for each of the board types that I mentioned above: feel free to use, abuse, copy and manipulate them to fit your own workflow, I honestly believe one of the best assets of Trello is it’s customisability - so don’t be afraid to make it your own. Just be sure to let me know if you find it helpful!

Template boards:

Additional info (from those who’ve said it better than me!):

Happy organising!

Image credit: @sidneyperryphotography via @unsplash

a weekend walk around the Bokeh library

Sun, 13 May 2018 11:13:53 +0000

If you’ve read my , you will know that I (somewhat arrogantly) consider myself “a passionate advocate for the use of programming by life scientists”. This came about following my Cambridge trip during my doctoral degree, in which I began to code analysis programs to derive meaning from the large (tens of gigabyte) datasets that we were collecting on a daily basis. At the time, I first learnt IgorPro - a proprietary language used by the other members of my host lab. While it was a steep learning curve, I was hooked. The power of being able to analyse huge reams of data without a single copy-paste-formula manouver normally completed in spreadsheets? Sign me up! Take out the human error of transferring data from program to program (Excel, GraphPad…) to achieve a result? Tell me more!

After my initial introduction, I quickly went in search of a more versatile language to learn. I disliked the feeling of being locked into the facilities provided within the commercial platform, and was tired of not being able to easily find support for this program online. And so, I met Python. And boy, did we hit it off! Python is easily the most friendly programming language I have come across, it is versatile and open source, the community is amazingly friendly, and I am yet to come across a problem I can’t solve via a quick trip to StackOverflow (seriously, where would I be without you guys?).

Little by little, I have worked on building my skills and I learn a little more each day. For a while now I have been comfortable writing small scripts that assist in my data analysis and plotting needs. However, I was yet to venture into the world of interactive plotting. I have always been intrigued by all of the different ways we can visualise data, and believe interactive plotting is a great way to allow both myself and someone else to explore a dataset. With this in mind, I set out this weekend to explore an interactive plotting library in python - Bokeh.

“Bokeh is an interactive visualization library that targets modern web browsers for presentation… Bokeh can help anyone who would like to quickly and easily create interactive plots, dashboards, and data applications.”

Sounds pretty good right? So, I gave it a go. As a test dataset, I decided to recreate the scatter plot from a published by our group for which the supplementary data is readily available. This plot displays the proteins whose cysteine residues significantly changed their reactivity following treatment with the drug Tunicamycin (an inducer of endoplasmic reticulum stress). Below you can find my version of Figure 6b, constructed as it is displayed in the article using Excel and GraphPad.

To recreate this plot in python, I took the raw data and constructed the scatter plot, colouring each datapoint according to the level of significance (as determined by the p-value). I also mapped the gene names associated with each datapoint via the UniProt API (a second learning exercise for this weekend!). This provides a second layer of data which I used to create the interactive feature of the graph - instead of displaying static names for only a few of the genes (which is limited by the need to not overcrowd the plotting area), gene names and ontology information is displayed by hovering over the point of interest. My final product is below - go ahead, give it a try! I found it super simple to embed the output here, plus there is also some handy toolbars provided by Bokeh out of the box that allow the plot to be saved to png or svg from the current view.

Bokeh Plot

I must say, I have been thoroughly impressed so far. Bokeh makes it extremely easy to build simple plots from existing pandas dataframes (think: spreadsheets) and to overlay interactivity without too much extra hassle. It offers the ability to completely customise the plots and layouts with widgets and extra functionality, although I think even the standard plotting elements I used here look pretty stunning in comparison to the original version!

So, here it is - my first passionate post about the benefits of programming for life scientists. Even if you take away the amazing benefits of reproducibilty, shareability, scalability and efficiency - the sheer ability to easily produce stunning visualisations that can be shared is a great asset. If you haven’t jumped on the programming bandwagon yet, what are you waiting for??

what to do when you can't get a word in edge ways

Sat, 28 Apr 2018 03:45:17 +0000

I don’t believe in resolutions, mostly because I have finally learnt (after 20-something years) that I will never stick to them. However, January 1st this year saw me looking to make a few key changes in my lifestyle. A few of these resulted from lessons I learnt throughout 2017 (you can read more about those ), and some were personal decisions that I felt would contribute to my mental, professional and physical health. One of those decisions was a commitment to reading and writing more. What you see here is my attempt at writing - but what about reading? I consume a steady stream of media everyday, and I decided it was high time that I put the constant onslaught of information to better use - to make it work for me, instead of feeling like I was drowning under it’s weight. My answer to this challenge came in a few forms:

1. A new system for prioritising and tracking purely scientific reading. This includes journal articles and media pieces with direct relevance to my current project, or project ideas that I hope to pursue one day. For more on my new system, and how I automate some of the process, keep an eye out for a future post coming soon.

2. A more balanced and positive social media environment. For me, this meant migrating to Instagram and Twitter (in favour of Facebook). Given I was a relative late comer to Twitter, I was able to curate my social network to generate an environment filled with positive stories, useful information, scicomm and professional opportunities etc. Not only does my time spent on these social channels no longer feel cumbersome and negative, but I have also found a brand new way to stay up to date with new science and funding opportunities, as well as a group of positive uplifting scientists working to effect real change in the systems they see failing scientists (and particular female scientists) worldwide.

3. A conscious effort to read something for leisure. While I allowed myself to choose books loosely based in science, it had to be in an expanded real-life paperback-in-hand book (no online journal articles) that I would sit with and focus solely on for at least half an hour at a time. I have only managed a single book this year (more on that ), but these ‘unplugged’ moments have helped anchor my self-care for the past few months. I find it refreshing to have a single focus for a defined time period (no electronic attention-grabbing distractions), and it was great to stretch my mind and take in broad science outside my niche that was designed for a popular audience.

After almost four months, my media-rich life is finally beginning to feel manageable. Tackling the barrage of text and images head on, I have found a balance between reading I have to do (articles), reading I am inevitably going to do (socials) and reading I would like to do (books). And if nothing else, this really reinforces for me the biggest lesson of 2017 - call it yin and yang, call it … call it whatever you like - it’s all about the balance!

the nomadic scientist: blessing or curse?

Thu, 05 Apr 2018 10:36:32 +0000

From the very beginning of my undergraduate degree, I knew that I wanted to pursue research overseas. Not so much a permanent relocation, but I was very keen to leverage the international nature of science to see the world. I secured a post-graduate position with a group leader who was open and supportive of this goal, and was instrumental in helping me go abroad during my PhD research. What followed was a whirlwind trip to Cambridge, UK, where I worked on acquiring a new technique to add to my developing skills arsenal. Despite the short stint, it was hands down the most personally and professionally life changing experience.

This is not to say that my home institute isn’t producing to good science - it is - but through this experience I was immersed in a city bursting at the seams with passionate experts, cutting-edge research and world-leading techniques. To say I fell in love would be an understatement. The cohesiveness and intertwined networks I observed whilst there were an incredible, eyeopening window into the collaboration that comes from bringing together world-leading experts from a broad range of disciplines in a melting pot dedicated to the pursuit of knowledge.

After returning to Australia, I worked towards the holy grail - thesis submission - and dreamed of the postdoctoral jobs I could chase internationally. One year on I am still here, and in a position I am thoroughly enjoying, but you better believe I still have my eye on an overseas position in the next 5 years. I do wholeheartedly believe that a diverse research training period is a critical foundation for a well rounded researcher, giving context and cultivating understanding of the abundant theories and approaches one can take to answer the big scientific questions. Diverse experiences offer the chance to explore the problem at hand from a new perspective, leading to unique insight.

And as excited as I still am about all the places science could take me in the next few chapters, my extra-continental daydreams have been tainted with an air of urgency of late. I am beginning to feel as though, with the current funding climate, the only way I am going to get a foot in the door is to do my time in a country where the pay is less, the work-life balance a little more unbalanced and my family a little(lot) further away. Even worse, I am acutely aware that in this situation I am the privileged one. Assuming I am lucky enough the secure a position in a good lab group in my area of research, I have very few restrictions or barriers to relocation. Sure, money is always an issue. And there might be a few personal hiccups (my partner is not infected with my incurable case of wanderlust), but I am (relatively) young, without kids and with no significant financial constraints (other than the misguided hope to someday get on the property ladder).

Many of my colleagues, and friends, do not enjoy the same level of flexibility.

And so, I suppose this post is more about what you think - is it possible to progress in the Australian scientific research landscape without being a nomad, at least for a little while? And what effect does this have on the diversity retained in the upper echelons of the academic institutions?

lifeguards of the cell

Sun, 11 Mar 2018 09:39:27 +0000

I believe communicating science with a broad and diverse audience is our responsibility as researchers. One of my most important audiences has always been my friends and family, many of whom didn’t do or don’t remember a single high school biology lesson. Needless to say, they’ve been a great testing ground for honing my elevator pitch - and I’ve discovered a neat metaphor makes it so much easier to describe complex scientific concepts without the dumbfounded look that normally accompanies any mention of protein homeostasis at family BBQ’s. Thankfully, our beach side home of Wollongong provides plenty of inspiration!

My scientific passion is understanding how each cell in our body achieves the world’s greatest balancing act - keeping all of its basic building blocks, proteins, in their correct three-dimensional conformation and therefore a functional state. Cells have an intricate network of ’lifeguards’, the proteostasis network, who monitor the proteins (or swimmers), keeping them folded and active or sending them to be degraded when they are no longer functional or needed. When there are disruptions to these lifeguards, the swimmers begin to drown (misfold) and can take others down with them (non-functional proteins accumulate) - at which point our cells begin to die. In the case of neurons, this cell death manifests as diseases like Alzheimer’s, ALS and Parkinson’s disease. It is therefore immensely important for us to understand why proteins begin to drown, how the lifeguards work and what contributes to their failure.

Much of my PhD work centered on one type of lifeguard - the small heat shock molecular chaperones. These lifeguards essentially patrol the beach, watching the swimmers, and spring into action when they detect a swimmer in trouble (a misfolded protein). Unfortunately they don’t have the ability to completely rescue a drowning swimmer (refold the protein), but they are quite good at bringing them back to the clubhouse and keeping them safe until help arrives (holding the protein in a folding-competent state). I focused my research on understanding how the small heat shock proteins help the swimmers (do they give them a life-ring? a pair of floaties? when do they hand them over to the experts?), and whether there was anything they could do once the drowning swimmers started to accumulate.

As a result of my PhD research, we now know that there are many ways the small heat shock proteins can interact with a particular type of swimmer (a-synuclein, whose misfolding and accumulation leads to Parkinson’s disease). Understanding these fundamental mechanisms is a crucial step in developing therapeutics to target the cellular lifeguards, to help treat or cure the diseases that result from their failure.