Thank you and goodnight

It transpires my previous blog is the last of the series. At the risk of sounding a bit ‘music award’ I just have some important thank yous (well only two, but they are important). Firstly, to Cambio, who have been absolutely great to me over the last year or so.  As I transitioned from PhD student to research fellow, the support offered to me by the company has been second to none. I will no doubt catch up with the guys over a beer or two at many conferences to come. Secondly (and lastly), to all the people who have read my blogs, tweeted them, re-tweeted them, and shared them on Facebook. I hope some of my rambles were of some practical use and if nothing else entertaining. Science is facing tough times with funding cuts and arguably more PhD graduates than ever before.

Science is great. We do it because we love it. But it’s competitive and if you’re not moving forward you’re moving backward. There are highs and lows. At times, major lows. We might not agree with the system but it’s a system we have to work in. Through everything have faith in yourself, work hard, and enjoy life.

If you ever see me at a conference please introduce yourself, the best thing about science is meeting awesome people!

I wish you all the best of luck with everything.

Thank you all again,


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What is ‘Omics’? Part 2


MetaboLomics is often confused with metaboNomics, probably because they mean exactly the same thing. They were both terms coined around a similar time, so pick your favourite and run with it. Metabolomics is probably more common and is usually used in conjunction with liquid chromatography mass spectrometry (LCMS) experiments, whereas metabonomics is usually used in NMR based studies. The technology has existed for some time but has gained significant momentum in recent years, coupled with higher resolution mass spectrometers. If you believe everything Wikipedia tells you (who doesn’t?) metabolomics is the study of all metabolites in a biological cell, tissue, organ or organism, which are the end products of cellular processes. So essentially it is the study of small molecules, typically less than 1500Da, which are produced by cells.

There are broadly two ways to obtain metabolomics data; by NMR or mass spectrometry (MS). NMR has been used more historically, but MS based approaches are increasingly common and will be the focus here. Bias is introduced with any single technique so it is advisable to use both techniques where possible, but of course this is not always practical and typically not a requirement for publication. The potential applications for metabolomics are expansive. It has been employed in single/co-culture, plant and crop, bio fluids, and organ based experiments. Not at the same time, of course. Like the boom in gut microbiome studies in all aspects of disease, I expect metabolomics to follow suit. In my primary research I have employed LCMS based metabolomics to explore the metabolites in stool, in the hope of increasing the understanding of cellular processes in the gut and developing potential biomarkers to aid in disease diagnosis. This type of work is not to be undertaken lightly and requires expansive optimisation. The analysis of the resulting data is also hugely complex and requires many user and computational hours to be invested. Following initial analysis it is also important to return to the samples and carry out MSn based targeted analysis on the compounds of interest. For absolute quantification and identification a standard of the compound should be run at various concentrations. As this technology grows, so too will methods with groups dedicating years to developing techniques, which everyone can benefit from. An example of this is passing a sample through a C18 column and HILIC column before injection into the MS separating both the hydrophobic and hydrophilic compounds, respectively.


Proteomics, the study of all proteins in any given sample, can be seen as the link between genomics and metabolomics. It is assumed that an increase/decrease in protein abundance reflects potentially important up-/down- regulation in response to a variable, for example disease or nutrient availability. There is an overwhelming amount of methods and techniques for proteomics, the choice of which should be dictated by the primary research question. Unlike genomics, gel based methods still provide good resolution and feature commonly in publications over the last decade. Two-dimensional (2D) gels separate proteins based on charge and size, where the proteins are visualised as spots. This is semi-quantitative as, all things even, the intensity of a spot reflects the relative abundance of the protein. SDS-PAGE gels are one-dimensional where proteins are separated electrophoretically according to size. Identification of spots or bands is usually necessary and involves excision from the gel and digestion, giving smaller peptide fragments. For multiple reasons, which won’t be discussed here, gel-based methods can be circumvented with samples digested in-solution into peptides and processed directly by liquid chromatography mass spectrometry (LCMS). Following acquisition of MS/MS data, peptides can identified using online databases, such as MASCOT, which maps the MS/MS fingerprint of peptides against genome sequence data to infer protein identity and cellular function. This overview is just the tip of the proteomics iceberg and I encourage anyone thinking of embarking in proteomics to understand the relative merits of all available methods.

As systems biology is increasingly applied to complex research questions, the field of omics will continue to expand and evolve. While these technologies become more accessible, with capacity to generate huge amounts of data, it is important researchers understand the technologies and implement them accordingly. Undertaking any omics experiment is NOT a quick way of amassing data, not when done properly anyway. To quote a leading metabolomics expert from the recent proteomics methods forum – “If you are thinking about doing metabolomics, don’t”. I wouldn’t go this far, my advice would be – “If you are thinking about doing any omics, do so with a hypothesis in mind, appreciate the need to develop and optimise methods, and understand analysis of data is hugely complex and time consuming”.

Let me know in the comments section how you perceive ‘omics’, how you are getting on in your research, and if you have any questions I might be able to address.

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What is ‘Omics’? Part 1

Given the huge rise of ‘omics’ technologies in recent years, there is a good chance you have at least come across the term, even if you don’t understand what it encompasses. Love it or hate it, omics is essentially a term used to imply the study of the entirety of something. It is typically high throughput and a vast amount of data can be generated in a relatively small time frame. Typically omics technologies are suffixed by ‘ome’ – microbiome, metabolome, proteome, transcriptome to name but a few from the ever expanding list. There are many reasons these technologies are gaining profound interest from a vast array of scientific fields. They can complement just about any study providing data from DNA/RNA content and expression levels, through the protein regulation and ultimately to the functional compounds (termed metabolites) which are produced by cells. In this blog I will give a flavour of a few of these technologies, which will hopefully provide a foundation for understanding of the principal and potential.


Metagenomics involves DNA and RNA based studies. There is a huge array of potential studies in this field of next generation sequencing (NGS) including 16S rRNA / ITS profiling, whole genome sequencing, and whole transcriptome sequencing. I am hasty to note that I use profiling to describe the use of universal microbial genes (e.g 16S rRNA for bacteria) and clarify this is not technically metagenomics, which is a shotgun approach and does not aim to sequence a single gene. Metagenomics will thus provide more information than simply who is there, and is not limited to specific domains, but a huge amount of data is needed. This means sequencing runs cannot be multiplexed to the same extent as in specific gene based profiling, raising the cost substantially. While the cost of sequencing continues to fall, one approach might be to use, for example, 16S rRNA profiling on all samples and deep metagenomic sequencing on the most informative 10%. Important and often difficult enrichment of samples should also be considered when carrying out metagenomics. NGS profiling overcomes this due to the use of specific primers, but noteworthy is that this amplicon based approach is still subject to inherent PCR bias. I find a lot of published NGS profiling data which I am sceptical about. This relates primarily to over simplifying highly complex data, such as knowing each patient differs hugely but amalgamating data into pie charts to mask the variability in an attempt to show a simplified figure. The other end of the spectrum involved using figures which are hugely complex and offer little information, or require readers to invest too much time in what feels like deciphering the Da Vinci code, and often are the result of following an R script or similar. I myself am guilty of this over-elaborate figure production, but have refrained from taking them to publication.

A couple of important notes about metagenomics before you plough in head first, only to be so snowed under by data you won’t see the light for many a year. While acquiring data is arguably easier than ever, the opposite is probably true for the processing. The handling of NGS data really needs a bioinformatician. Trust me; I have spent too many sleepless nights self-teaching myself the methods of processing and analysis. Briefly, while on the subject of handling the data, a couple of good pipelines exist. I like pipelines but am almost ashamed to say so as any hardcore bioinformaticians out there will probably have head in hands at the thought. Still, I appreciate them for what they are, which is a tool allowing easy access to a range of commands to take raw data to publication quality figures. The two most common pipelines are Mothur which can be run on all operating systems (even windows, which for the newbie is a bonus) and QIIME which can be ran on MAC and Linux (and via virtual box on windows). Both have hugely useful online tutorials and workflows, as well as dedicated support forums. Another point of consideration is the still relatively short read lengths when doing NGS profiling. This means that analysis can only typically go to genus level, which from a microbial ecology point of view is not ideal given the huge variation of functions exerted between strains of the same species. To demonstrate, E. coli 0157 (nasty) and E. coli K12 (lab strain) have the same 16S rRNA gene sequence. Thus, it is important not to forget the power of traditional techniques, such as culturing an organism. I like to use 16S profiling to guide my research and understand more about the ecology in preterm disease. But I also take this information further. For example, guided by my NGS data, I have cultured E. coli from preterm stool that was dominated in both healthy and diseased infants. Doing so has revealed some interesting results – all babies have unique strains of the bacteria, but the E. coli cultured from diseased babies are comparable in some respects, such as the same antibiotic sensitivity. In a typical circular fashion, the next phase may involve whole genome sequencing the isolates to detect shared relevant genes between the E. coli in diseased babies. Other ‘omics’ technologies are also being implemented in this complex research question, going beyond simply what bacteria are present and explore the mechanisms and functional potential. This work will involve metabolomics and proteomics and these technologies will be discussed in part 2.

I hope this has given a basic description of metagenomics. There is a wealth of information available on the internet and please leave additional comments or related questions in the discussion. If you are reading this and thinking “there is no chance my PI will invest in omics” – with the current downward projection of sequencing costs, I like to think that metagenomics of huge cohorts will be achievable, even to the little guys.

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The DOs and DON’Ts of science poster presentations

When done well a scientific poster can be a hugely informative and useful research dissemination tool. However, despite a wealth of poster advice tips available on the internet and in books, posters presentations are typically substandard. Here I present a view DOs and DON’Ts of scientific poster presentations from poster design to presentation. These are my personal views based on the 4 years I have attended conferences, I am sure there will be many more suggestions which I encourage as comments.


  • Look at the poster guidelines for the specific conference.  It is a good idea to re-use posters if possible, but often poster boards are specific sizes so check any existing posters are suitable
  • Include a sufficiently concise title and use the largest font size for the title (for A1 I like a minimum font size of 60)
  • Have clear subheadings on each section (Introduction, Methodology, etc)
  • Keep text as minimal as possible.  It is easy to include lots of information, what requires much greater skill is condensing the appropriate information into a succinct section
  • Where possible, use images in place of text.  A picture says a thousand words
  • Ensure the font size of all text (including text embedded in images!) is large enough to be read at an appropriate distance, such as 6 ft. (for A1 I like a minimum text size of 28)
  • Don’t overload the poster and leave lots of white space
  • Use colour
  • Be consistent with the use of punctuation, especially full stops at the end of bullet points (I would say they are not needed)
  • Include your contact details, especially an email address
  • Tweet details of your poster and presentation data and time with the conference hashtag
  • Looking engaging when standing next your poster
  • If interest in your poster is low, talk to other presenters around you and invite them to ask you about your research 
  • Ask interested parties about their work.  Sometimes it is not clear in passing how relevant a poster is to research interests, but once you have a chat you can often find parallels and who knows, maybe even collaborations!



  • Leave designing the poster until the last minute
  • Include an abstract on the poster (unless specifically requested).  This will be available through the abstract book and takes up space unnecessarily
  • Use lots of references.  Again this can take up considerable space.  I would say 10 max but ideally close to 5.
  • Play on your phone while standing next to your poster.  Facebook can wait
  • Talk negatively about your results
  • Don’t leave your  poster for any length of time during your session.  It’s very annoying when you specifically go to a poster when the presenter should be there and they never bother to show up
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Work/Life balance

We have probably all heard the expression…

“Love what you do and you will never have to work another day in your life”

This is, in my completely biased opinion, truer of science than any other line of work. Science rarely feels like a ‘job’. I base this statement in comparison to a previous job that I worked to support my undergraduate studies, working in a car park collecting trollies. This was by no means a bad job and I worked with a good friend. But I would never look forward to going into work and while there would spend much of my day counting down the hours until finish. In science it’s quite the opposite, I am battling to do as much as possible in the day and wish the time would slow down! In university I hear people say, and indeed do, something I would never hear in my old job…

“I would do this job for free”

Although working for nothing is often the case upon completion of PhD funding, it is clearly not sustainable. But practicality aside, no one really goes into science for the money – exactly – what money? People need to be paid but with a pay cheque in science often comes considerable pressure and demanding schedules. Science will never be a job where you leave work at work. I work more evenings than not. I work throughout lunch more days than not. Morning or afternoon tea breaks are typically saved for the most special of occasions, but there will always be other people who work longer hours or continually complain about how busy they are. That doesn’t mean more is being achieved, often these people are on first name terms with the staff at the coffee shop. So, it is often all too easy to get sucked into endless working and, when you enjoy aspects of it, what’s the harm? Well, from someone who feels guilty if they don’t work endlessly, I am still aware and in agreement with another common saying…

“Work to live, don’t live to work”

With the modern pressures on scientists to get funding and publish in high impact factor journals (on top of everything else!), it is still important to have a life outside of work. Very few of my friends are involved in science, which helps put work to the back of my mind when socialising. I think playing sport and/or exercising is important too. Stress arises from inescapable negative thoughts and blogs about stress in academia are all too common. It is important to work hard and play hard. Make sure there are things outside of work that make you happy and provide you with satisfaction. Like golf, science can be the best job when it goes well and the worst when it doesn’t. Take the rough with the smooth. Finally, the last saying I want to note…

“Nobody lies on their death bed wishing they had worked harder”

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PhD viva

As my previous two blogs focused on preparing a PhD thesis, it seems logical to advance to the PhD viva. If there’s one thing I learnt from asking numerous people what their viva was like, is that vivas (vivae?) differ vastly from one to the other. So with that in mind let me describe my personal story and resulting emotions elucidated in the run up to, during, and following the big day!

Upon completion of my thesis I immediately printed, bound, and submitted them to the graduate school. All 5 copies at >300 pages per thesis. Having dragged them around Newcastle upon Tyne trying to find a place capable of binding such large documents, finally submitting them was a huge weight of my back. Literally. But that was it, there was no real sense of achievement just another, albeit relatively big, box ticked. I know this stage is only a necessity to get the cogs turning toward the viva, but even so a firework or two would have been nice.

The availability of my external examiner wasn’t great and as a result I would have to wait around 3 month before my viva. This also meant I would not be finished before Christmas as I had hoped. Nonetheless I was able to put the looming viva day to the back of my mind and focus on other matters of life for much of the build-up. But there comes a time, a time which I suspect happens to many people for many things, when all of a sudden the once seemingly distant date of something is approaching uncomfortably quickly. This occurred for me about 2 weeks before the viva, which coincided with job interviews and deadlines for fellowship applications. I did bits of reading around the subject, mostly focusing on my external examiner’s papers, at weeks 2-1 before in the time I had between interview and application preparation. Then in the final week before I kept my diary largely free. My viva was on a Friday – through personal choice, based purely on Friday being a better night for a party than the other dates. In the main I was able to focus the majority of my attention on all things viva from the Monday onwards. This is probably the first time I really went inside my brain and asked myself “what do you not know?”. As my PhD progressed from day 1 until this penultimate stage I could easily answer “what do I know?”. But “what do I NOT know” is a daunting question and one which would keep me awake at night.

I felt I understood the work in my thesis well; I had after all lived it for the best part of three years. I thus opted to focus my reading on subjects around my research rather than directly relating to my work. It wasn’t completely irrelevant and was more an attempt for me to put my results in a wider context – particularly the context of the research interests of my examiners. Ultimately I was asked little about much of what I read in this time but I don’t regret this approach and I learnt a great deal of useful information and developed a range of further studies I think would be important. I also refreshed myself with my thesis work by having a relatively quick read through it. In all I find it pretty funny that I could be literally surrounded by publications and still spend a vast amount of time on Wikipedia, the very source of knowledge prohibited to mere undergrads. Whether academics admit it or not, everyone uses Wikipedia for fast and dirty information, which I feel is fine when you know a topic.

The viva itself was an enjoyable process. It’s not often you will get the opportunity to discuss your research in such a manner. My viva was over in little over 2.5 hours which is probably on the quick side. My general guesstimate would be a viva typically lasts around 3.5 – 4 hours. Every viva will be different so I have avoided discussing the exact style of questions here, but if you would like some more specific information please leave a comment.

As far as advice goes, I can tell you what everyone is told – you will be fine and try not to stress too much about it – but I know the likelihood is you will always worry. I had 7 publications going into my viva, the last getting accepted on the morning of the viva itself (nice omen surely), but I still worried. So I won’t say don’t worry, but in worrying it is important not to concentrate on what you don’t know but appreciate what you do know.

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Preparing a PhD thesis (part 2)

HAPPY NEW YEAR!! I hope you had a great Christmas and New Year. In this blog I will continue where I left off, providing some more specific comments relating to the typical sections of a thesis and the joys of formatting that provide a constant form of frustration to PhD students worldwide…

Also known as the literature review, this section is perhaps the most challenging to write. Usually ‘mini’ literature reviews are required for internal check points throughout the PhD, so it might be at least a small part of this has been done when the time comes to sit down and really have a go at writing it. Previous work might provide a foundation, but look to improve it and make sure references are up to date and all information is still true and accurate. Science is a fast moving field and what you wrote in year 1 may no longer apply. I found it useful to have a rough plan before I started writing my introduction. For this I spent time laying out subheadings in a document and structuring it how I felt would best provide a nice flow. Inevitably bits will get added/removed/swapped around so don’t spend too long planning, but if nothing else it helps to keep focus when sections are clearly laid out.

Depending on the structure of the thesis, which usually relates to how much the method is altered between chapters; the methods section may stand alone or be included in each results chapter. Because the methods were generally comparable between studies in my thesis I generated a large methods section. The idea is that anyone should be able to follow the methods and reproduce the work. While this is also the case in publications, the space is often limited and some element of mystery remains. However, thesis methods can be much more descriptive and should make following the exact procedure easier. It might also be useful document to have if you come back to a technique in the future and need a quick reference on how it was done last time. The methods is not going to be the most fun chapter to write, but might represent a substantial amount of the word count. So once complete you can tell yourself that you have done X% of your thesis. Every little helps…

Results chapters
These were my favourite chapters to write. Once data has been generated, the next step is analysis/statistics. This can often take as long as the generation of data itself. I looked at each results chapter like a ‘story’ I wanted to tell. I carried out a range of analyses and added copies of the figures I generated into a word document. I then added notes to the document describing what the results meant and what the statistical significance of the results was. This is perhaps a long way of doing things and if a particular analysis was integrated into the study design then doing this is obviously redundant. But for me it helped to get a feel for the data and allowed me to drill down into what I was showing and what was important. From this I pieced together the ‘story’ and began to write the chapter. How you present your data is important so take time to make the figures inviting and easy to understand. Figure legends in my thesis also tended to have added information compared to my publications to help the ease of interpretation. Results chapters are what you have added to the world. This is data you have produced with your very own hands!

This section fills any holes which are still left after completion of the main document and can range from media and reagent recipes to tables of patient information, and everything in between. It’s also nice to include representative gels in these sections, just make sure the best/prettiest gels are selected. There were times when I would stare at a figure/gel image/table wondering “are you worthy of the main thesis?”. This is subjective and ultimately you should just do what feels right. Your supervisor will probably tell you if what felt right is wrong anyway…

While typically done last, I think it is at least worth getting the basic formatting set up before beginning. Get hold of the guidelines for submission (or equivalent) document and format the word document to the criteria, such as margins, font, spacing, reference style ect. I wrote each chapter in separate word documents and then combined all the documents as late as I could. This helped prevent the document getting to large which can cause it to be less responsive.

If you need to start a new page, for a figure or new chapter for example, resist inputting many lines with enter and instead create a page break. This will help avoid massive shifts in figures/tables when the document is edited. There is little worse than spending 3+ years working on a thesis and just when you think it’s finally finished you realise a duplicate word in the text. Then when you delete the duplicate word a figure from page 186 moves to page 8, a figure from page 56 moves to page 200, a table on page 137 disappears completely… Trust me, at some point you will wish word was a physical object so you could punch it! But precautions can be taken to limit the frustration and page breaks are definitely one of those. Other useful features include table of contents/figures/tables which automatically inserts a table detailing each and updates the page numbers as the document is changed. I didn’t use the table of figures/tables option and have subsequently spent more time then I care to imagine editing the table so the page numbers correspond!

Final suggestions
Simple but effective – back up regularly! And, give your supervisor the chapters one by one – don’t try and write the whole thing before you’ve had some useful feedback!

The hardest part of writing my thesis was getting started. So what are you waiting for?!

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