A Word From: Sofia Robb

Since 1999, Cold Spring Harbor Laboratory has offered an annual course in Programming for Biology, which runs for two weeks every October. The course underwent a major modification in 2017: Co-instructors Simon Prochnik and Sofia Robb changed the language being taught from Perl to Python. The switch came after 18 years because “more and more people are using Python for bioinformatics analysis,” as Sofia explains below. 

Both Simon and Sofia have been with the course since 2002. Simon first participated as a Teaching Assistant (TA) before taking on the role of lead instructor in 2004. Sofia, a 2002 course alumna, experienced the course from every role prior to joining Simon as a co-instructor in 2011. We met with Sofia last year to chat about the long-running course.

Can you talk us through what a typical day looks like for a course trainee?

We have three phases in the course, and Phase 1 is general programming. For the first week, we have a morning lecture each day that teaches programming followed by exercises on the computer. Then in the afternoon, we have another lecture and more time on the computer. We usually have a review session after dinner and then time again on the computer. These Phase 1 sessions cover the basics like how to do a for-loop and an if-statement, and we give them real-world problems for practice so they can understand why they’re doing what they’re doing. And that’s the way it goes for the first week. 

The second phase is similar but we bring in outside lecturers who talk about “bigger” topics. Like the first phase, these lectures are followed by programming exercises, but the exercises have the flair and flavor of the lecturer so the trainees can see how concepts can be applied to a different slice of bioinformatics. For example, we’ll look at sequence similarity, file formats and how to convert between them, or how to work with certain files from NGS (next-generation sequencing). The trainees are using a lot of the same concepts they learned from the first week, but practicing more to help cement what they’re learning. This course is like learning a foreign language: it’s awkward at first and just so new, but practice makes it a little more familiar. We’re not looking for perfection but general understanding. 

Phase 3 takes place during the last three or four days of the course and involves group projects. Simon and I have the trainees present ideas for individual projects and, oftentimes, the ideas are very similar so we rewrite them into five or six group projects. Each TA signs up for the project they feel they can help the most with, and the trainees break into groups with a TA and work on the project task from start to finish. The projects are too big for one person to complete in three days  so they have to work as a team, decide who’s doing what, and how they’re doing their individual parts. They also have to report to each other because, even though they work in parallel, the output from each part has to feed into the next steps. On the final day of the course, the groups present their projects and results to the class. It’s a good skill building exercise. A lot of times, the trainees can bring the projects home and use them on their data; they can send their data through pipelines built in the course. It’s amazing actually, what they can do after just two weeks.

You’ve experienced the course via all roles: student, TA, and instructor. What is your highlight from each role, and what brings you back year after year? 

Taking the course was amazing and life-changing for my career, in terms of learning how to program and using it in my work. I loved the course so much that, while I was a student, I asked Lincoln Stein, lead instructor in 2002 if I could return as a TA. He said, “Yes, email me in August and I'll give you the details.” So I did and I came back as a TA in 2003. It's been amazing to help the trainees learn since then. 

Most of the students come to the course with little programming background, without knowing that a terminal window exists on their computer, so we teach them from scratch. The course is challenging for trainees but it’s just as challenging for the instructors and TAs, who have to figure out what the trainees understand or don’t understand each year, and where they need help. But when they get it, they get it; you can see the light bulb and a sigh of relief. 

The course is challenging even from day 1. By the third day, the trainees feel like their heads will explode and they can’t take in any more new information. When that happens, we always make a point of reminding the students to take a step back and compare what they knew on the first day to what they know now. That usually helps.  

What was the reasoning behind changing the programming language taught in the course? 

It’s a big change. The course started in 1999 and taught Perl exclusively until 2017. But more and more people are using Python for bioinformatics analyses now, so we thought we’d try out teaching it in the course. So far, it’s going well.

Besides switching from Perl to Python, have you noticed any other changes in the course over the past fifteen years?

The biggest change I’ve seen is a shift away from people building tools. When the course started, there weren’t many tools available and so a lot of people were interested in building tools for the community. Now, there are so many tools out there that this isn’t as necessary, and I’ve seen this shift reflected in the course.

And of course our topics change. We evaluate what topics are in most need and, when reviewing applications, we get a feel for what methodologies the trainees are using. Oftentimes there’s a consensus on what the students are interested in, so we try to invite a speaker to cover that topic. 

What do you and Simon look for when reviewing applications? 

We like it when an applicant understands their problem and presents a solution, but knows they can’t obtain the solution unless they learn something more. It’s not enough for an applicant to only say, “Bioinformatics is important and I want to learn it.” 

We also look for enthusiasm. It’s nice to have students who are enthusiastic about what they’re working on and learning something new. Beyond that, we try to do some group building. We like when our students hail from diverse projects because it broadens the awareness of everyone else in the class. The students come in with limited bioinformatics experience, and they don’t know what tools are available or what problems there are except for their own. It’s beneficial for them to see all the different issues people have using different systems and methodologies. 

And there have been all kinds of people who take this course while working on projects that require very different perspectives and approaches. We had a trainee once who was studying biology and bioinformatics with a focus on human language. She was working with octopus---specifically octopus tentacles---because the muscles in tentacles are similar to muscles in the human tongue.

Despite these differences however, the trainees become a little family. One of the reasons the course is successful is because the trainees feel comfortable with each other, the instructors, and the TAs. We always try to incorporate activities to ensure the trainees aren’t just sitting next to each other and typing on computers. In addition to the group projects, we dine together, have group runs or walks on campus, attend the CSHL Halloween party together, and go out one evening in Huntington (the local town).  

These are chances for the trainees to meet, interact, and become a little community. And as a community, they are more comfortable asking questions in class. This course is a little stressful. Strangers are more stressful than friends, so they become friends pretty fast.

It’s really sad when the course ends and everybody leaves. Actually, it’s more emotional when you get home and don’t have twenty people to eat or take a coffee break with. A lot of the trainees find that a difficult transition because we do become like family here. 

The Programming for Biology course returns to the Laboratory this October and is accepting applications here until this Sunday, July 15th. To learn about the course from the perspective of a former student, read this Q&A with 2016 and 2017 course alumna Shasta Webb.

For more conversations with other course instructors, check out the rest of our A Word From series. 

Photo: Constance Brukin

Visitor of the Week: Jonathan Diedrich

Meet Jonathan Diedrich of the St. Jude Children’s Research Hospital (SJCRH) in Memphis, TN. Jonathan is a postdoctoral fellow in Dr. Daniel Savic in the Pharmaceutical Sciences Department. He is on campus for the Statistical Methods for Functional Genomics course and we’re hoping to have him back for The Biology of Genomes meeting next year. 

What are your research interests? What are you working on?
My project employs functional genomic techniques to identify non-coding regulatory elements involved in chemotherapeutic drug resistance in childhood acute lymphoblastic leukemia.  

How did you decide to make this the focus of your research? 
I have always wanted to transition into the functional genomic field because I thoroughly believe that functional genomics and large-scale data analysis is the future of cancer research and precision medicine. As a postdoc in an extremely collaborative and supportive environment, I am in an excellent position to learn cutting edge functional genomic techniques and hone my analytical skills. 

How did your scientific journey begin? 
I actually started my research “career” in zoology! I studied comparative anatomy of carnivore skulls at Michigan State University where I fell in love with the research aspect of science and quickly knew I wanted to transition into the cancer field. I completed my PhD in Cancer Biology at Wayne State University with Dr. Izabela Podgorski as my mentor. My thesis project was to interrogate the effects of bone marrow adipocytes on metastatic prostate tumor progression. Dr. Podgorski’s exceptional mentoring and support during my PhD inspired me to pursue a career in academia; I am now in the first year of my postdoc and very excited for a career in science!

What and/or how will you apply what you've learned from the course to your work? 
Being competent in statistical analysis in the field of functional genomics is extremely crucial and this course has helped me develop my skills to become the expert in Dr. Savic’s laboratory regarding various statistical analyses for large-scale RNA-seq, ATAC-seq, ChIP-seq, and 3-dimensional profiling datasets. My participation in this course will benefit my data analysis as well as the members of my laboratory and department at SJCRH. I will be able to assist in the analysis and scientific discussions of my team and become an integral asset in functional genomic analyses.  

What is your key takeaway from the course?
The most important takeaway from this course is to understand the conceptual reasoning behind the complex analyses performed to evaluate large-scale genomic datasets, and to efficiently utilize the programs to visualize and interpret your data in a reproducible and reliable manner.  

How many CSHL courses have you attended?
This is my first course at CSHL and I am planning on hopefully attending next year’s Biology of Genomes meeting. I am very excited about the possibility of going!

If someone curious in attending this course asked you for feedback or advice on it, what would you tell him/her?
Be prepared to learn a lot! This is an amazing opportunity for anyone interested in functional genomics to learn directly from the experts in the field, so take full advantage of the course while you are here! To keep pace, practice maneuvering through R and get familiar with basic R syntax ahead of time. Also, don’t be afraid to ask as many questions as possible! The instructors are amazing, very eager to help, and want you to get as much as you can out of this course. 

What do you like most about your time at CSHL?
The best (non-science related) aspect of this course is the ability to meet and network with people from all over the world and who have similar interests and experiences. I have met so many great people within the first (of two) weeks I have been here and hopefully will have lasting friendships and future collaborations!

Thank you to Jonathan for being this week's featured visitor. To meet other featured scientists - and discover the wide range of science that takes part in a CSHL meeting or course - go here.

Visitor of the Week: Claire Olingy

Meet Claire Olingy of La Jolla Institute for Allergy and Immunology (LJI). Claire is a postdoctoral fellow in Catherine “Lynn” Hedrick’s lab in the Division of Inflammation Biology. She’s participating in her first course at CSHL: Single Cell Analysis. 

What are your research interests? What are you working on?
I am interested in the role myeloid immune cells play in anti-tumor immunity during cancer progression and metastasis. I’m currently working to understand whether we can leverage these cells as early diagnostics and to enhance patient responses to immunotherapies.

How did you decide to make this the focus of your research? 
Cancer is the second leading cause of death in the United States after heart disease, yet there is still so much we don’t understand about this diverse group of diseases. During my PhD in biomedical engineering, I became increasingly interested in the role that the immune system plays in health and disease. Our immune systems are really involved in everything! I closely followed the success stories of cancer immunotherapies and recognized the immense potential the field holds, both for treating cancer and many other diseases. This inspired me to pursue a postdoctoral fellowship at La Jolla Institute for Allergy and Immunology in the lab of Lynn Hedrick, who is a leader in myeloid cell biology. LJI is a great place to learn about and conduct immunology research because you are surrounded by experts and have access to the resources necessary to study important questions in immunology.

How did your scientific journey begin? 
I have always loved math and knew I wanted to study engineering in college. However while I was in high school and my grandfather was diagnosed with colon cancer, I realized I also wanted to work in a field where I could have an impact on human health. I majored in biomedical engineering at my grandfather’s alma mater, Washington University in St. Louis. I became involved in undergraduate research and recognized that research would give me the opportunity to better understand and contribute to unanswered questions in biology and medicine.

Was there something specific about the Single Cell Analysis course that drew you to apply?
I applied to the Single Cell Analysis course because of the diversity of techniques the course offered to study cells at the single cell level, including bioinformatics tools to analyze single cell data. Some of these techniques can be intimidating for a first-timer like myself so I was looking for exposure, as well as how to appropriately design experiments in my research. Initially, I was most interested in the single cell sequencing approaches this course covers, but many of the skills I’ve so far learned in the first week could be applied to my research. 

What and/or how will you apply what you've learned from the course to your work? 
The immune system is extremely heterogeneous so that it can respond to the wide range of pathogens and diseases our bodies face. The techniques I’ve learned in this course will enable me to study this diversity during the progression of cancer, which is really only possible with single cell approaches. And I’m looking forward to sharing what I’ve learned with my labmates and collaborators at my home institution.

What is your key takeaway from the course?
The instructors did a great job designing this course so that it highlights a lot of exciting technologies that are being used in a wide range of biological fields. My key takeaway is that it can be very valuable to look to other fields for inspiration that can be applied to my own work. I’ve also learned that understanding how new technologies work before applying them to my research is critical when it comes to designing experiments that will actually answer the scientific questions I’m interested in.
 
If someone curious in attending this course asked you for feedback or advice on it, what would you tell him/her?
I think it’s important to come to this course with a willingness to learn new techniques outside your immediate field and to consider how you may be able to expand your own research. Many of my peers (including myself) came primarily to learn about a specific technique, but to me, one of the most valuable takeaways is the new ideas we’re bringing back to our home institution. Also, to ensure you get the most out of this course, spend time before the course learning some basic programming! 

What do you like most about your time at CSHL?
I have most enjoyed getting to know a really diverse group of scientists who are at different stages in their scientific journey, studying different fields, and from all over the world. Everyone is here to learn and the beautiful CSHL environment makes it a really enjoyable experience!

Claire received a fellowship from the Helmsley Charitable Trust to cover a portion of her course tuition. On behalf of Claire, thank you to the Helmsley Charitable Trust for supporting and enabling our young scientists to attend a CSHL course where they expand their skills, knowledge, and network.

Thank you to Claire for being this week's featured visitor. To meet other featured scientists - and discover the wide range of science that takes part in a CSHL meeting or course - go here.

Visitor of the Week: Felix Chan

Meet Felix Chan of Brown University. Felix is a postdoctoral research associate in Judy Liu’s lab within the Department of Molecular, Cell Biology, and Biochemistry. He was on campus for three weeks last month participating in the third iteration of the Metabolomics course. 

What are your research interests? What are you working on?
My interest is in characterizing the metabolic activity of the brain during physiological and pathological brain activity. Currently, I am working to characterize the link between pathological seizure activity and sleep in the context of tissue metabolism. 

How did you decide to make this the focus of your research? 
The brain is a unique organ with a high metabolic demand - it only takes up about 2% of the body weight but consumes 20% of the body’s oxygen supply. Yet, not much is understood about the metabolic change that occurs in the brain during a period of intense brain activation, whether in a physiological condition (during cognition, for example) or pathological condition (during a seizure, for example). 

How did your scientific journey begin? 
I have always been interested in psychological theories and research, and the leap I took to study neuroscience stems from how the brain -- as a single organ -- can be responsible for many different functions from crude ones like movement, sensation, and speech to fine-tuned ones like emotion, cognition, and perception. I owe my commitment to an academic research career to the many researchers I interacted with as I earned my Masters and PhD in Newcastle University; particularly my graduate mentors Dr. Gavin Clowry and Professor Mark Cunningham. 

Felix with fellow coursemates Karin Mitosch and Smitha Pillai getting hands-on practice at operating and analyzing liquid chromatography.

Was there something specific about the Metabolomics course that drew you to apply?
I was attracted to the course by the wide range of techniques listed on its overview (here); such as GC-MS to LC-MS, and even Seahorse metabolic flux analyzer. In addition to picking up new techniques, I acquired hands-on experience on instrumentation use and data analysis which was really helpful in learning the theory behind the instruments and practical applications of the techniques. 

What and/or how will you apply what you've learned from the course to your work? 
The knowledge and techniques I acquired from the course will be implemented to design a metabolomics experiment to answer my research question regarding metabolic changes in the brain. 

What is your key takeaway from the course?
I came into the course with a naive perception that metabolomics is an all-encompassing technique to dissect metabolism in a comprehensive manner. Whilst it remains a powerful technique, as with other techniques, it cannot measure every metabolite. Careful thought into the experimental design is what can lead to precise measurements of the metabolites in which you are interested. The course has well-equipped me with skills, knowledge, and techniques to consider my experimental design so that it can answer the scientific hypothesis I have in mind. 

How many CSHL courses have you attended?
This is the first CSHL course that I have attended and it hopefully won’t be the last! 

If someone curious in attending this course asked you for feedback or advice on it, what would you tell him/her?
Come in with an open mind and curiosity to learn about metabolomics. The instructors have worked really hard to design a comprehensive and meaningful course that addresses a wide range of aspects about metabolomics. The course schedule will be intense but with a strong passion in metabolomics, you will get the most out of this course.

What do you like most about your time at CSHL?
I enjoyed the fact that I am learning cool science in a beautiful, serene, and picturesque place that has a rich history in advancing science and technology. You can easily walk around CSHL and see the many evidences of their involvement in advancing biomedical sciences. It helps also that the food served is top-notch and you can eat your meals on a balcony overlooking the beautiful Cold Spring Harbor - never gets old!

Thank you to Felix for being this week's featured visitor. To meet other featured scientists - and discover the wide range of science that takes part in a CSHL meeting or course - go here.

Repeat Visitor: Shasta Webb

Since 1999, Cold Spring Harbor Laboratory has run an annual course in Programming for Biology aimed at lab biologists with no prior coding experience. The course provides trainees with the bioinformatics and scripting skills necessary to design biological data analysis pipelines using computer code. In 2017, lead instructors Simon Prochnik and Sofia Robb changed the language being taught from Perl to Python, a popular scripting language with a growing community of users. 

Python’s increasing popularity is one of the reasons Shasta Webb, a PhD student at the University of Calgary, returned to CSHL last October and took part in her second consecutive Programming for Biology course. Shasta is now an alum of both the 2016 and 2017 courses, where she learned to program in Perl and Python, respectively. We sat down with her to chat about how she uses scripting languages in her work, why she first attended the Programming for Biology course, why she returned, and her thoughts on the changes Simon and Sofia made to the course. 

I am housed in an anthropology department but the work I do spans anthropology, biology, and metagenomics. My work focuses on how gut bacterial communities (the microbiome) change throughout pregnancy and lactation. I study this question using a wild system of white-faced capuchin monkeys in Costa Rica. One of my goals is to unite field methods that look at behavior, ecology, and diet in this primate species, and then combine those methods with metagenomics to see how, internally, the primates are responding to social cues.

A big part of my work involves raw sequencing data from the microbiome. We extract and sequence bacterial DNA, which results in huge data files that we need to parse, organize, and clean up for downstream analysis. As I was launching into this work, it became clear that I needed some way to do all of that by myself. That was the initial impetus for taking a programming course. Plus, personally, I prefer to work closely with my own data as opposed to outsourcing its analysis. I want to understand what's going on behind the scenes with my sequencing data.

In the 2016 course, you learned how to program in Perl. What was your motivation in coming back for the 2017 course?

Part of the reason I came back was that we started some new collaborations, and a lot of the people we now work with code in Python. As a result, it became increasingly clear that Python would be a really useful language to learn in order to maintain and grow the lab’s collaborations. I don’t have the authority to say whether Perl or Python is better, whether the core fundamentals of one language are better or worse than the other. It’s just out of convenience and the way my research trajectory has gone that I decided to pursue Python as well.

L to R: Shasta Webb, Meredith Cenzer, Drew Behrens, Jared Brewer, Adam Blanchard

But there are other things that drew me back. The way that the course is designed is a really nice fit for my learning style. In the first week, it’s a mixture of lectures and putting into practice what we learned during the lectures. It’s so well-balanced: you never feel like the lecture chunks or the programming-practice chunks are too long. In the second week, we work on direct applications. I’ve attended smaller workshops for other languages and, oftentimes, you’re given coding practice but there’s a missing component in how to link it to your own research. In this course, we get to not only hear about other projects that leaders in bioinformatics have done, but we also get to do our own projects where we put into practice all the basic stuff we learned in the first week. Overall, the organization is just really good.

Also, the community facilitated by Simon and Sofia is really positive and fun. They are great about reminding us that no one is falling behind, that everyone is improving and doing well. I have never felt intimidated by Simon, Sofia, or the TAs, and I get the sense that they want us to have fun learning and coding. I had a great time at the 2016 course so when I saw they switched to Python, I jumped at the chance to come back.

Although prior coding experience isn’t a prerequisite for the course, did your participation in the 2016 course help you in the 2017 course? 

Initially, it was a little more hectic because it was all so new. It’s pretty humbling to come in and learn a totally new skill, to look at a blank screen and have to write a script. I remember that from 2016. At the beginning of every problem set, before I got warmed up, I felt really intimidated, stressed out, and concerned that I wasn't becoming a programmer fast enough. But once you have a foundation and some of the core components of programming, learning another language feels easier. In 2017, I sat down and was able to quickly produce a script for one of the first Python problem sets. It was this nice moment of confidence that set the tone for the rest of the course. I suddenly felt like I would be able to handle even challenging scripts, because if you can get something down in the text editor, then you have something to build off of.

There are still challenges when learning another scripting language. The problems are new, and the syntax between languages are different. Once we started getting into the more advanced stuff – like complicated data structures – it became difficult and challenging, but in a good way. To take the time to attend this course for two weeks, I don’t want it to be easy. Feeling challenged is good because you feel so accomplished when you actually get through a difficult script.

Besides switching to Python from Perl, was there anything else introduced in 2017 that you found helpful?

What I've found really useful is the course’s inclusion of GitHub, which is a place for anyone to submit and openly share code, text files, or anything to do with programming. It was really nice to have a formal introduction to GitHub because I’d been meaning to learn it for a long time. In 2017, the entire course was run through it – our access to all of the course documents, problem sets, and example code was through GitHub. It’s something I will use daily at my home institution because I want to be able to share my code easily with other people.

The Programming for Biology course returns to the Laboratory this October. Applications for this year's course are being accepted until July 15, 2018 here. 

Thank you to Shasta for sharing with us her experience, and we look forward to having her back for at the Laboratory again. To meet other featured scientists - and discover the wide range of science that takes part in a CSHL meeting or course - go here.