Meet the Trainers – Raffaele Calogero, Jeroen Krijgsveld, Lennart Martens

This month we have not one but three trainers we would like to introduce you to. Meet Raffaele Calogero, Jeroen Krijgsveld and Lennart Martens, organisers and trainers at the EMBL Course: Analysis and Integration of Transcriptome and Proteome Data (2 – 7 February 2020).

What is your research focus and why did you choose to become a scientist?

RC: I have a background in molecular biology, but since 1999 I have been working in transcriptomics data analysis and tool development. I became a scientist because I loved the idea of investigating new unknown topics every day.

LM: My research focus is bioinformatics, which stems directly from becoming completely taken in by computers at an early age. Like many of my generation, I started out with a Commodore 64 when I was 8, and had written my first (very, very simple :)) program in BASIC after two weeks. But my link with science came about after I stumbled across an episode of Jacob Bronowski’s ‘The Ascent of Man’ a few years later (I think I was 10 or 11) on Saturday morning on television, and this inspired a lifelong interest in anything scientific. All in all, I consider myself extremely lucky to be able to combine my passion for computers with my passion for science, and make a living out of it!

JK: My research focus is on proteomics, where we develop and apply mass spectrometry-based technologies to understand the intricate machineries used by cells to respond to their environment. Curiosity to understand how things work is as an important motivation for our current research as it was for me during my studies. Becoming a scientist has always felt a natural path for me to follow, and I have never seriously considered doing anything else. Although at the same time, when looking back, I realise how fortunate I have been being given the opportunity to follow my interest and move between fields. This is due in no small part to having training opportunities and stimulating environments at the various stages of my career!

Where do you see this field heading in the future?

RC: Data integration is the new upcoming frontier for biological studies, and transcriptomics and proteomics are going to be important players in the game.

JK: Proteomics has come a long way in the last 2 decades, reaching its current ability to characterise thousands of proteins in a single experiment, and do this across multiple experiments. I expect that during the next 5-10 years we will see how these investments will pay off, helping to understand processes at the protein level very similar to the way this has been achieved in genomics. After transitioning from a technology exclusively for physicists to a useful tool used by biologists, the next big step to be made is to implement mass spectrometry in the clinical arena. Integration with genomic methods and data types will be key to drive this forward.

LM: Bioinformatics is slowly taking over much of large-scale biology. Of course, focused biochemical and molecular biology research will remain the staple of the life sciences, but our ability to process and interrogate very large amounts of (potentially even heterogeneous) data is transforming our ability to generate new leads, new ideas, and to shed a much more holistic view on life at the molecular level. And with the advent of extremely powerful parallel computing, the field of machine learning has received an enormous boost, further increasing our ability to spot previously hidden patterns in our data. So it’s an exciting time in bioinformatics, with lots of new areas of research opening up to us for the first time! We’re going to continue having a lot of fun for a long time!

How has training influenced your career?

JK: This has been fundamental, in different ways. One is in a formal way, where classes and courses have been key to shaping a foundation, and helping to fill specific knowledge gaps. The second is less formal, which to me has been at least equally important, where I got trained by working in different research environments together with people of different backgrounds.

RC: In my role as an associate professor, training is part of my everyday life. I am involved in many advanced courses through my university, both in Europe and in Asia, and I think that courses are useful for both participants and instructors. The former grab the knowledge from the latter and the latter get new fresh view points from the former.

LM: Many of the excellent teachers I have studied with have left a deep impression on me, and guided my career path indirectly. As to providing training, it is one of the aspects of my job that I most cherish. For me, the importance of sharing and spreading knowledge resonates very closely to the core principles of science: by sharing and exchanging knowledge, all participants in the teaching experience gain something. In all, the biggest contribution that training has had to my career, is in greatly enhancing my ability to be creative in a productive way.

What is your number one tip for people looking for scientific training?

LM: Go for it, and seek out courses taught by those rare experts that both intimately know their field, but can also clearly communicate about the essence of that field. You don’t want to only grasp the current ‘hot’ method at a user level, but rather want to understand how that field works, how it has evolved, and how the current methods fit in with this. Courses that focus on these aspects deliver knowledge that lasts for much longer!

JK: It is two-fold: find a course that gives you the theoretical foundation of an area or topic you would like to venture into. After this, I think it is essential to ‘internalise’ this knowledge, so I would advise you to find a collaborator or colleague to help you along the path to use the acquired knowledge. It is like driving a car: you need to get a driver’s license to be allowed on the road, but you really learn how to do it by practicing afterwards.

If you weren’t a scientist, what would you be?

RC: Definitively a Medical Doctor.

LM: Most likely a software developer or systems architect of some sort. I worked in industry for a few years after my Masters degree, and then chose to pursue a PhD rather than take a very nice position at Microsoft. So if I had made a different choice then, I’d probably still be in IT!

JK: I have never really been in a situation where I had to choose between being a scientist or do something else, but thinking about it now I would like to think I would be a writer, a novelist. The reason is that I like writing, and it is part of my every day job, however writing papers and proposals comes with a lot of constraints and requirements. Making up stories without the boundaries of page limits, allowing fantasy and imagination, in an expanded vocabulary and, above all, in my mother tongue sounds all very appealing to me. Maybe one day. 🙂

You are organising the EMBL Course: Analysis and Integration of Transcriptome and Proteome Data (2 – 7 February 2020). What is the greatest benefit of the course for the scientific community and what could the techniques in this course be used for in the bigger picture?

JK: The greatest benefit is that it allows participants to have a look ‘over the fence’: most participants usually have a background either in genomics or proteomics, and being exposed to both aspects – along with the technologies and methods used in these fields – allows them to ask questions they may not have thought about before. What we try to achieve is that they could even go a step further, and integrate the new knowledge in their research to gain a more complete picture of the biological systems they are using.

LM: The course is unique in that it brings together the two key fields of research to understand cellular functional: transcriptomics, which traces the usage of the information in the genome in a given cell, and proteomics, which shows the actual biochemical and signalling capability of a given cell. There is important information at each of these levels, and these are moreover more complementary than many might think.

Of course, gene regulation provides important insights into a cell’s internal processes, but proteins are not only made or broken down, they are also closely regulated at the protein level. A complete picture of a cellular process thus requires insight into (and thus data from) both the gene expression level as well as the protein level. This is where this course provides the right skills for researchers; teaching them how to integrate data from their transcriptomics and proteomics experiments.

The usefulness for the bigger picture is therefore more or less implicit: by combining transcriptomics and proteomics, a much more complete picture of cellular functioning can be obtained. And let’s be honest, isn’t that what all of us really want to obtain, after all? 🙂

RC: I think the phrase of Claudia Manzoni in the abstract of her paper in Briefing in Bioinformatics in 2016 (Volume 19, Issue 2, March 2018, Pages 286–302) “Our work intends to target students and researchers seeking knowledge outside of their field of expertise and fosters a leap from the reductionist to the global-integrative analytical approach in research” is a perfect explanation for our course.


Interested in this course? Apply by 10 November!

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We’ve proved it, biologists can also program

“Like punning, programming is a play on words.” Alan J. Perlis.

You don’t have to be a programmer to have programming skills. Writing code is an essential part of being a programmer (duh!), but is also a vital component of being a scientific developer, software developer or computer scientist. You can utilise computer programs to automate tedious and repetitive tasks, extract results from experimental data, apply models to solve your research questions or purely have fun with your own projects.

Today is Programmers’ Day (yay!🥳) and we want to recognise all those who submerge themselves in the deepest mysteries of code (especially their own) and aim to automate the future.

If you’re looking to start venturing into the programming world or embark on your next project, get some inspiration from some scientists who are helping out at our EMBL Events’ courses.

Florian Huber PHOTO: Marietta Schupp/EMBL

“What do I love about programming? It allows me to go from zero to one: gaining new biological insights from data.” Florian Huber (Postdoctoral Fellow, at the Typas Group in EMBL Heidelberg and the Beltrao Group at EMBL–EBI in Hinxton).

 

 

 

 

Ullrich Köthe PHOTO: Ullrich Köthe

“Automated image analysis has always been an interesting and fun field of research, but thanks to the deep learning revolution and the wide availability of wonderful neural network libraries, we can now actually solve hard practical problems.” Ullrich Köthe (Group Leader in the Visual Learning Lab Heidelberg).

 

 

Valentyna Zinchenko PHOTO: Carolina Cuadras/EMBL

“Programming skills allow you to automate the routineparts of your job and focus more on the exciting ones. At some moment you just have so much data, that you would not want to process it manually. You would not wash your clothes by hand if you have a washing machine, would you? Then why analyzing your data manually, when you can have it done by a machine as well?” Valentyna Zinchenko (Predoctoral Fellow in the Kreshuk Group).

 

Adrian Wolny PHOTO: Carolina Cuadras/EMBL

“Whenever I build something, be it a new machine learning model or my pet project, I always try to make it easy to understand and generic enough so that other people could use it in their work. I try to open source my projects whenever I can and contribute back to the community. There is nothing more rewarding than seeing your little piece of software used by others to find answers to their own research questions.” Adrian Wolny (Visiting Researcher at EMBL and PhD candidate at Heidelberg University).

 

Pavel Baranov PHOTO: Pavel Baranov

“The relationship between computer science and modern biology is akin to that between mathematics and physics.” Pavel Baranov (Professor of Biomolecular Informatics, University College Cork, Ireland)

 

 

 

 

It’s no secret that managing biological data efficiently can be overwhelming and feel impossible. If you’re a biologist who’s interested in learning how to process, analyse, organise and interpret your almost innumerable data sets – preferably with the most suitable and state-of-the-art techniques and tools out there – EMBL Events has got you covered.

EMBL Course: Deep Learning for Image Analysis, Apply by 20 September 2019

EMBL Course: Exploratory Analysis of Biological Data: Data Carpentry, Apply by 5 November 2019

EMBL Course: Analysis and Integration of Transcriptome and Proteome Data, Apply by 10 November 2019

EMBL Course: Immune Profiling of Single Cells, Apply by 10 November 2019

EMBO Practical Course: Microbial Metagenomics: A 360º Approach, Apply by 27 January 2020

EMBO Practical Course: Measuring Translational Dynamics by Ribosome Profiling, Apply by 9 February 2020

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Meet the Trainer – Imre Gaspar

Meet Dr. Imre Gaspar, Senior Research Assistant in the Kikuë Tachibana Group at the Institute of Molecular Biotechnology in Vienna, Austria, which focuses on understanding how chromatin is spatially reorganised in totipotent cells.

What is your research focus and why did you choose to become a scientist?

I’m interested in the central dogma, that is how gene expression is regulated on the transcriptional and post-transcriptional levels and how these regulations allow development of an organism.

I became a scientist because I always fancied solving riddles – and as a scientist you get to work on solving the ultimate riddle that interests us, humans.

Where do you see this field heading in the future?

Right now, there is a boom of high-throughput and omics techniques in studying gene expression allowing us to create predictive quantitative models of regulatory networks, which will allow us to get mechanistic understanding of the processes underlying development, homeostasis and pathogenesis. Microscopy analysis is already essential for the latter and is also gaining importance also in the omics studies with the advent of high-throughput hybridisation techniques.

What is your number one tip for people looking for scientific training?

Being a microscopist, it was absolutely essential for my career to receive training in state-of-the-art imaging and image analysis technologies. Courses are important, of course, but I find that the best source of training a scientist can receive is core facilities, internal trainings, and of course close colleagues in the lab.

If you weren’t a scientist, what would you be?

I have a degree in medicine, so I probably would have become a medical software developer – that profession is closest to the work of a scientist and having a background in medicine would allow me to contribute to the development of medical instrumentation.

You are organising the EMBO Practical Course ”FISHing for RNAs: Classical to Single Molecule Approaches” (15 – 20 March 2020). What is the greatest benefit of the course for the scientific community and what could the techniques in this course be used for in the bigger picture?

We are at the onset of quantitative analysis in biology: many labs have already implemented corresponding work-flows, but this principle should be spread widely, especially in the fields working on the understanding of gene expression. I expect that the single molecule techniques we will cover during the course will serve as mind-changers to help people embrace the concept of quantitative biology.

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Meet the Trainer – Anna Kreshuk

PHOTO: EMBL/Marietta Schupp

Meet Dr. Anna Kreshuk, a group leader in the EMBL Cell Biology and Biophysics unit, whose group uses machine learning to develop automated methods to help biologists speed up image analysis. Anna joined EMBL in 2018 and has since been very active in building up training opportunities in her research field.

What is your research focus and why did you choose to become a scientist?

My research is concerned with developing new machine learning-based methods of the analysis of biological images. I enjoy doing science, both for the thrill of finding new things and the joy of seeing others do that in their domain with the help of our tools.

Where do you see this field heading in the future?

I hope to see most of the routine image analysis automated in the future. This will hopefully raise new research questions in biology which can only be answered by imaging at scale, creating, in its turn, more exciting research questions for us.

How has training influenced your career?

We develop software for end users without computational expertise, who want to solve biological problems we don’t quite understand. Participating in training has provided a lot of insight to the user side of things, brought new collaborations and even new research directions for me and for my group.

What is your number one tip for people looking for scientific training?

A one-week course can be a great start, however, it’s important to find out how you can get support with the new technology in your everyday work. Try to stay in contact with your course buddies, but also look for online communities. For image analysis, for example, there is a great forum connecting all the popular tools.

If you weren’t a scientist, what would you be?

My 7-year-old recently asked: “you say I can become anything I want to be, but then why didn’t you become an astronaut?”. Seriously though, I’d probably be a programmer, I love automating things.

You are organising the EMBL Course: Deep Learning for Image Analysis (20 – 24 January 2020). What is the greatest benefit of the course for the scientific community and what could the techniques in this course be used for in the bigger picture?

Deep learning has brought an enormous advance in computer vision. We can now analyse microscopy images in ways no one thought possible just 10 years ago. While the technology is getting more accessible every year, it’s still difficult even for computationally savvy biologists to apply state-of-the-art methods to their image analysis problems. This is exactly the gap we intend to close.

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Meet the Trainer – Katharina Danielski

Meet Katharina Danielski, Field Application Scientist at 10x Genomics, who is an organiser and trainer at the EMBL Course: Immune Profiling of Single Cells (10 – 13 February 2020).

 

 

 

What is the greatest benefit of the course for the scientific community?

It provides researchers with an overview of what is currently possible when studying the immune system at the single-cell level. There are so many new technologies and methods available these days that scientists are overwhelmed with keeping track of everything new. The 10x Genomics Single-Cell Immune Profiling solution allows you to study a broad range of aspects all derived from the same single cell: sequence information of paired full-length T cell or B cell receptor transcripts; gene expression profile; cell surface protein markers; antigen specificity. Linking all these pieces of information back to the same cell is opening a lot of new ways to study the adaptive immune response that were just not possible before.

Are the methods used in this course unusual or new?

The ability to study single cells to the extent as it is currently possible with various assays on the market is still very recent. We are only beginning to scratch the surface of the biological information that will be uncovered in the coming years thanks to the methods discussed in this course, among others.

In comparison to other training environments, what do you enjoy most about teaching at EMBL?

I enjoy teaching at EMBL because of the high level of organisation that the EMBL team displays. The EMBL Heidelberg Campus is also a particularly beautiful location situated on top of a hill surrounded by forests. But most importantly: the food in the canteen is legen- wait for it -dary.

What is your number one tip related to the course?

Don’t be shy. The trainers are more than happy to answer your questions and discuss your projects and  experiments…but skip breakfast so you can fill up on lunch at the EMBL Canteen. You will thank me later.

What, in your opinion, is the most crucial scientific discovery of the past 100 years?

I don’t think any single discovery on its own could be labeled as “the most crucial”. Science in the past 100 years has made so many giant leaps for mankind.

Where is science heading in your opinion?

Studying gene expression of (single) cells spatially resolved within their morphological context of an intact tissue section.

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