Career Spotlights: Inspirational Stories from Computational Biology

Judith Blake, Professor

What training did you receive to prepare you for this position?

Ph.D.

Do you get further training on the job?

Not specifically; more involvement in development of new methods and procedures as part of human genome project and related bioinformatics efforts.

How do you make sure your knowledge stays current?

Everything is new in what do. Attend presentations, collaborate with others to advance the field.

Are you still in the primary field where you received your academic training?

Yes

Did you ever make a career change?

Yes

What led to the change in career? Do you have any tips for making a career change?

Well somewhat, from evolutionary biology using molecular techniques to comparative and functional analysis reflective of new genomics information streams

What do you know now that you wish you’d known when you started out down this career path?

How much social interactions play a role in scientific advances and support

What, if anything, else would you like to tell us about your career in bioinformatics?

sometimes have to let go of legacy implementations of resources and knowledge representations

What was the biggest obstacle you've faced in your career as a bioinformatician?

Lack of recognition of bioinformatics as a research area worthy of academic faculty consideration....

What advice do you have to give to new/entering bioinformaticians?

Follow the questions that interest you.learn what you to so to answer the big questions. nothing will stay the same, so keep learning new things. consider the completeness of the data you are working with in interpreting the output of programs.

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Predrag Radivojac, Professor

What training did you receive to prepare you for this position?

Ph.D. in computer science, prior education in Electrical Engineering. Post-doc in comp bio.

Do you get further training on the job?

No.

How do you make sure your knowledge stays current?

Constant struggle, but a variety of methods. Reading papers, reviewing papers (increasingly) and grants, editing papers all help. Word of mouth. More recently I pick up things on social media and sometimes go down the rabbit hole of reading stuff.

Are you still in the primary field where you received your academic training?

Yes

Did you ever make a career change?

Yes

What led to the change in career? Do you have any tips for making a career change?

My job is the same, but I did switch institutions. Still a big change with adjustments necessary and proving yourself all over again. What prompted me was that I was approached by my current institution, but looks like I was ready to move on.

What do you know now that you wish you’d known when you started out down this career path?

I heard of many things but didn't understand them until I felt them. What surprised me was the extent to which science is effected by hype, self-promotion, and short attention span of the entire community.

What, if anything, else would you like to tell us about your career in bioinformatics?

My career in the field started randomly. My advisor had an RA position on a new grant and I was happy to give it a shot. It opened my eyes and I've been in the field ever since. It's sort of embarrassing that my first goal was other than bioinfo.

What was the biggest obstacle you've faced in your career as a bioinformatician?

Difficulty to fit in conventional depts or institutional groupings. My CS colleagues think I am a biologist and biologists often think I am just a computer scientist. Difficulty in recruiting students is a consequence of the still existing silos.

What advice do you have to give to new/entering bioinformaticians?

There are many ways to be successful in the field, yours might be unique. Be motivated by contributing to science much more than by potential success if you do it well. Allow others to change your views on what good science is, you won't regret that.

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Hui Cai, Scientific SDET

What training did you receive to prepare you for this position?

PhD in Molecular Biology

Do you get further training on the job?

Computer Science, Bioinformatics

How do you make sure your knowledge stays current?

Learn everyday from hands-on tasks, take occasionally online courses

Are you still in the primary field where you received your academic training?

Yes

Did you ever make a career change?

No

What do you know now that you wish you’d known when you started out down this career path?

I wish that I knew computer skills are critical, so I could have learned them early on

What was the biggest obstacle you've faced in your career as a bioinformatician?

Get software engineers to understand certain issues in the product are critical

What advice do you have to give to new/entering bioinformaticians?

Learn new things everyday

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Russel Schwartz, Professor

What training did you receive to prepare you for this position?

I received in PhD in Computer Science, but on work in computational biophysics. I also did postdoctoral work largely on problems in data analysis and spent two years in industry working on computational biology related to sequencing and genetic variation analysis before becoming a professor.

Do you get further training on the job?

Formal training on the job is largely on professional rather than strictly technical topics. This includes training in effective teaching and advising, as well as training required for regulatory compliance on research I do, such as in data security or protections for human research subjects.

How do you make sure your knowledge stays current?

My technical training is largely self-driven at this point, including reading current literature in my area and related fields, attending conferences, and attending seminars at my own institution, as well as other independent reading as needed.

Are you still in the primary field where you received your academic training?

Yes

Did you ever make a career change?

Yes

What led to the change in career? Do you have any tips for making a career change?

I have moved from industry to academia and between various subfields in each, although always in computational biology. There are many career paths by which one can make valuable contributions to science, so my best advice would be to keep an open mind and follow where the science takes you.

What do you know now that you wish you’d known when you started out down this career path?

I wish I had had more training in how to teach and to manage people before I started as a professor instead of having to figure out a lot for myself. The technical landscape is always changing and there are topics I wish I had known better starting out, but that is just the nature of science.

What, if anything, else would you like to tell us about your career in bioinformatics?

I probably did not appreciate enough early on the many ways one can make a difference in science. I am proud of the work I have done as a researcher, but I have learned to appreciate much more the impact one can make as an educator, administrator, advisor, advocate, policy-maker, etc.

What was the biggest obstacle you've faced in your career as a bioinformatician?

At least as a US-based academic researcher, I have found that the biggest obstacle to getting good science done is the outsized amount of effort one has to devote to raising money to support the science and the ways that process can sometimes stifle the most innovative and important directions.

What advice do you have to give to new/entering bioinformaticians?

This is a field rich with great problems that is only going to grow in importance over time. I would recommend that you never stop learning and always keep an open mind about how you can contribute to the science and by what career path you pursue it.

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Patricia M. Palagi, Team Leader, SIB Training

What training did you receive to prepare you for this position?

I have obtained a BSc in Biology and a MSc in Computational Sciences, both from the University of Brasilia (Brazil). I have then obtained a PhD in Cognitive Sciences from the INPG-Grenoble. I think all those have contributed to this position, but were not intentionally targeted for this position.

Do you get further training on the job?

Yes, I have received training on time management, project management, leadership and management.

How do you make sure your knowledge stays current?

I attend congresses, eg ISMB, ECCB, BC2, and I read scientific articles. I'm also involved in Training/Education communities (ISCB Education COSI, ELIXIR Training, GOBLET). I’m also Deputy Editor of the Education track of PLoS CB journal. All those are endless sources of knowledge and inspiration.

Are you still in the primary field where you received your academic training?

Yes

Did you ever make a career change?

Yes

What led to the change in career? Do you have any tips for making a career change?

I had 2 main career changes in my life: 1st from being a plant pathologist to a computer scientist, 2nd from being a scientist, those who carry own research, to become an Education and Training leader. My tips: follow your heart, your instinct, be open to new opportunities and give them a try.

What do you know now that you wish you’d known when you started out down this career path?

I wished I knew that teaching and facilitating teaching could be so joyful and enlightening. I had this biased view that I could only be happy by being a scientist.

What, if anything, else would you like to tell us about your career in bioinformatics?

There is one thing that I miss from being a bioinformatician and that thing is coding. I miss spending hours searching that little bug in my code!

What was the biggest obstacle you've faced in your career as a bioinformatician?

My biggest obstacle was to be confronted along my career with people who can be toxic and compelled to prove their superiority by crushing their colleagues. Luckily there weren't many, and I also was surrounded by empathic and caring mentors who compensated.

What advice do you have to give to new/entering bioinformaticians?

Network and learn from others. Share your ideas, code, research results with others. In other words, be Open and FAIR, and here I mean in the scientific and human sense of these terms.

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Francisco De La Vega, Vice President of Hereditary Disease

What training did you receive to prepare you for this position?

Doctor of Science degree in Genetics and Molecular Biology; Executive Certificate in Management and Leadership (MIT) later in career.

Do you get further training on the job?

Industry jobs regularly provided leadership, management, and product development process trainings

How do you make sure your knowledge stays current?

Following science Twitter; review TOC from top journals, attended relevant conferences

Are you still in the primary field where you received your academic training?

Yes

Did you ever make a career change?

Yes

What led to the change in career? Do you have any tips for making a career change?

From academia to industry early on my career

What do you know now that you wish you’d known when you started out down this career path?

Industry development processes; financial skills to manage budgets and understand valuation of technologies; project management skills.

What, if anything, else would you like to tell us about your career in bioinformatics?

Technolgy development drives directions of research and new products. As new technologies become available and scale up everyone shifts to leverage these data streams. One needs to be always aware of what new technology is emerging to take advantage of such shifts in advancing one's career.

What was the biggest obstacle you've faced in your career as a bioinformatician?

Be present and considered at the inception of projects to ensure a proper study design and that the necessary data and metadata are collected to improve outcomes.

What advice do you have to give to new/entering bioinformaticians?

Industry is a viable and rewarding career alternative. As such try ti explore early on, e.g. via internships to appreciate and make an informed decision on career choices.

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Alex Bateman, Head of Protein Sequencing Resources

What training did you receive to prepare you for this position?

Throughout my career I have had access to very good management training, which has been essential to manage and prioritise the wide variety of tasks at hand.

Do you get further training on the job?

One of the reasons that I became a scientist was to discover things for myself. I really like to learn while doing something. If I get stuck I'll ask someone for advice.

How do you make sure your knowledge stays current?

I simply embrace my ignorance. As Ernest says in the Admirable Crichton, "I'm not young enough to know everything". Working with students is a great way to learn about new areas.

Are you still in the primary field where you received your academic training?

Yes

Did you ever make a career change?

No

What do you know now that you wish you’d known when you started out down this career path?

Spend less time worrying about things you cannot change.

What, if anything, else would you like to tell us about your career in bioinformatics?

As a former Director of Graduate studies I often told students they should travel internationally to get experience. I have never left the UK, or even Cambridge for my work. Draw your own conclusions.

What was the biggest obstacle you've faced in your career as a bioinformatician?

My own fear of failure has led me to not take risks and perhaps missing some interesting opportunities. I will not step into something new unless I have a strong conviction it is important.

What advice do you have to give to new/entering bioinformaticians?

Make sure you work on an important problem and be able to explain why it is important.

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Tell us your story

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Published Rules Regarding Authorship

• International Committee of Medical Journal Editors
• PLOS Computational Biology
• Bioinformatics
• University of Cambridge Guidelines on Authorship 
• Council of Science Editors - Authorship and Authorship Responsibilities

Large Collaborative Projects

Collaborations at the European or US level are generally orchestrated via grants from government or foundation agencies (the European Commission, the National Institutes of Health, the National Science Foundation, etc).  When such agencies put out calls for proposals, focus groups (scientists known to each other, say, via societies and conferences) are likely to be first responders. The calls are usually tightly aligned with particular research areas and are often quite prescriptive. When core groups do not have sufficient expertise, additional project partners are invited from their peer groups to bridge the gaps, or wider calls for participation are put out to the community (it is not uncommon for ‘brokering’ companies to do this), soliciting additional scientists, or in-kind contributors, who bring hardware platforms, software packages, or other accessory contributions. The same principles apply to establishing collaborations in Africa, where researchers, having worked together previously, reunite to submit collaborative proposals. In bioinformatics, there have been informal interactions between geographically distributed groups through the African Society for Bioinformatics and Computational Biology, which runs a conference every 2 years, and more formal partnerships via the H3ABioNet Pan African bioinformatics network.

Ultimately, assembling a team requires balancing the requisite expertise with an appropriate number of partners, so that each receives realistic and sufficient funds to be able to deliver the tasks at hand (as detailed in a contract of work). It is important to try to achieve equity, and to ensure that each partner adheres to their required contributions so that others don’t have to carry their load. All partners should have clearly defined roles, should feel like they belong and that their contributions are essential.

European grants are highly structured, broken down into a series of interdependent ‘work packages’, each of which has one or more leads, plus a set of participants with expertise relevant to the work package theme. Partners can contribute to more than one work package, although leading more than one is generally discouraged. The leads are responsible for ensuring that their tasks get done; along the way, they are required to commit to specific deliverables, documented via detailed milestones, upon which they are required to report (in writing) at regular intervals - often quarterly. The project coordinator’s responsibility is to collate all of the mid-term reports, to compile an annual report (detailing the scientific achievements and the project’s financial status), and to submit them to the Commission for review.

Results of European projects may be published at the level of individual work packages and/or of the whole consortium. Consortium-level papers include all project partners; those emanating from work packages will depend on the policy of the lead(s): e.g., whether they adhere to strict authorship policies (See section on Published Rules Regarding Authorship), in terms of who contributed to the study design, who did the work, wrote the paper, etc., or whether they simply include all named work-package participants, regardless of their individual contributions. Authorship  and the order of inclusion should be agreed upon prior to the completion of the paper to avoid later disagreements.

Collaborations within (fee-based) membership organisations (like ISCB, GOBLET, EMBnet, ISB, etc.) function a little differently, although there are clear overlaps. Membership of such organisations may be at the level either of individuals (as is typical of societies like ISCB and ISB) or of institutions or groups spread across the globe (as is the case for Foundations like GOBLET and EMBnet). Members generally contribute to the work of these organisations as volunteers, and the projects they undertake are most often not funded by granting agencies. The projects, and interactions between participants, therefore tend to be more fluid, driven more by people’s bandwidths than by tight deliverable or reporting deadlines - projects thus tend to grow and shrink according to the time people have available to contribute.

Results from collaborations like this may also be published at project- or full-consortium levels. Here, consortium-level papers are likely to include all current members of the organisation; papers resulting from focused interest groups within the consortium, on the other hand, are likely to depend on the collective decision of the project participants (whether to include authors strictly according to their specific input, or whether simply to include everyone who expressed an interest, regardless of their actual contributions).

Some fee-based organisations operate at a national rather than institutional or individual level: in ELIXIR for example, countries within Europe (and beyond), may become members on payment of an annual national subscription fee. Here, collaborative projects are likely to be funded either by the organisation’s central administration (or ‘hub’) or by separate grants held by groups of member countries (these may or may not be coordinated by the central administrative hub). Inevitably, national consortia like this involve many additional (often rather convoluted) levels of management: policies and processes regarding who may contribute to which project, and how individual contributions to resulting publications are recognised, are therefore likely to be much more bureaucratic.

Running large collaborative networks can be challenging, as inevitably there tend to be some key contributors and some “passengers”. Having clearly defined roles and deliverables for each member is essential, as is a clear process to follow for lack of delivery. Large consortium publications can be difficult to manage fairly owing to the limited level of granularity for defining author roles and contributions offered by some journals. However, it is important to try to represent diverse author contributions adequately, to ensure recognition for those who put in the most effort.

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Bioinformatics Core Facilities

A Core Facility is a centralized institutional resource and includes a team of computational biologists available to work on a variety of projects with lab scientists. The descriptions here are drawn from experiences at an independent research institute (Whitehead Institute for Biomedical Research) and in a university setting (University of Cape Town). The funding model of the Core can be quite different depending on where it is located. It could be anything from funded by an institution in whole or part, partially or fully grant supported, fee for service or any combination of the above. Our focus here is how collaborations work in these facilities and how authorship is addressed.

Core facilities engage with the lab scientists at multiple stages of their research - preparation for a presentation, for grant proposals, and, to analyze data and co-author manuscripts. The group leader of the Core would likely promote the group's accomplishments with faculty and lab members and encourage them to include Core members as co-authors if they've made a significant contribution to their research.

Lab scientists often opt for a collaboration agreement, which means the analysis/support is free, but contributors get authorship on publications arising from the research. It is important for scientists to understand that the contributions from Core members are not just technical but also scientific. Few workflows are “one size fits all”, and algorithms are constantly evolving. Therefore, Core members must keep up-to-date with the latest methods and usually need to customize pipelines. Determining the most appropriate tool to use, including parameters, reporting on and presenting the results in a usable format, are key roles they play; they may also sometimes provide guidance on data interpretation. These all require intellectual input, making the Core member role more than just about technical assistance.

Core members should have a good overview of different bioinformatics techniques, specialist knowledge in one or more specific data-analysis workflows, some coding and statistical expertise, and good interpersonal skills. Ideally, they should want to help and be willing to work on other people’s projects rather than forge academic careers of their own. They still derive rewards from and recognition for their contributions, and increase their skills and experience in the process.

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Small-Scale Collaborations

Small-scale collaborations tend to be academic focused. In the early days of bioinformatics, such collaborations were needed to get access to data, as the only open bioinformatics database was GenBank. This started to change as more and more databanks adopted open-access policies. However, data are often downloaded in a raw format that needs the requisite expertise to be able to integrate them into larger projects: this is typically where bioinformaticians come into academic collaborations. Bioinformaticians may provide services via a core facility (as described elsewhere), but may also be part of small research groups within universities, research institutes or companies; sometimes, a bioinformatican may be the only individual with appropriate bioinformatics training and/or experience. Where this is the case, collaborations often form as the bioinformatician is sought out to join groups of researchers who lack the expertise to manage and run their own analyses.

These kinds of collaboration can be based on receiving a ‘fee for service’, or built on the understanding of co-authorship of publications that may arise from the work (the determining factor is usually whether the research question is determining new bioinformatics methods/tools or is using established bioinformatics protocols to answer broader biological questions). In the latter scenario, the place of bioinformaticians in the author list will reflect the level of their contribution to the project. The former scenario is akin to purchasing an antibody: the service is a well-defined product with an established fee, and authorship is unlikely. Regardless, it is best to define the expectations on both sides clearly and early in the collaboration. Generally, if a project involves computational analyses, then the nature of the analyses should be written into the grant upfront; this, in turn, means that the person responsible for performing the analyses should also have been identified. If a collaboration is between two PIs and includes students or post-docs, the PIs will typically be senior (last) authors, and the students/post-docs first authors. Here again, it is important to get agreement in advance about potential authorship, while the collaboration is being established and not after the project has finished.

Given the variable nature of expertise from lab to lab, no specific skill-set can be expected. Instead, the pattern of expertise becomes apparent from bioinformaticians’ publication records. Within a campus, the individual or small group will often become known among their peers for their bioinformatics focus, and collaborations develop organically based on the need for their particular skill-sets. Longer term collaborations, supported by grants, typically involve a discussion of the budget before the grant is submitted. This involves quantifying the percent effort for the bioinformatics components, which can include part of a programmer, or all of a student or post-doc. It may be that a figure for the bioinformatics work has already been set; in this case, it is essential either to work out a way of shaping the commitment to make that figure work, or to point out – upfront – that the figure is an underestimate that will not be convincing in the grant. Once the budget is fixed, however, it stays fixed.

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Share Your
Career Spotlights: Inspirational Stories from Computational Biology

Questions for Personae:

• What training did you receive to bring you here?
• Do you get training on the job?
• How do you make sure your knowledge stays current?
• Are you still in the primary field where you received your academic training?
• Did you ever make a career change?
• What do you know now that you wish you’d known when you started out down this career path?
• Anything else you want to tell us about your career in bioinformatics?
• Advice to new/entering bioinformaticians?
• What was the biggest obstacle you faced?
• Tips for making a career change?

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Example Career Paths

• Biocurator
• Principal Investigator, Academia
• Data Engineering (workflow, application, algorithm development)
• Data Scientist (analysis, hypothesis testing, hypothesis generation)
• Scientific Software Engineering Manager
• Product Owner/Product Development Leader
• Quality Assurance/Test Engineering
• Scientific Communication
1. Scientific Writing (journalism, blog/book, copy-editing, documentation)
2. Journal Editor
• Administrative liaison
• Bioinformatics Core Facilities

Tell us your story

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Collaborations and Publishing: How This Works

Computational biologists engage in collaborations of different size and scope. Various types of collaborations are described in more detail below. Publishing is often the end product of a collaboration. It is important to consider how people have contributed to a collaboration and who should be included as co-authors in a resulting publication.

The importance of publishing may be largely dependent on the position you hold. Researchers in academia are often evaluated and promoted based on the publications they co-author. Other positions may be less focused on one’s publication record to be promoted. However, if an individual makes an important contribution to a project, they should be included in the author list. Their position in the list should be based on their contribution.

Links within this section: Small Scale Collaborations | Bioinformatics Core Facilities | Large Collaborative Projects | Published Rules Regarding Authorship