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Moderator: Laurent Gatto, Senior Research Associate, University of Cambridge and member of the eLife Early-Career Advisory Group.
Speakers: C. Titus Brown, Associate Professor, UC Davis Genome Center; Aedin Culhane, Research Scientist, Harvard T.H. Chan School of Public Health; and Mick Watson, Director of ARK-Genomics and Research Group Leader, Roslin Institute.
As the amount of data produced in biological experiments increases, bioinformatics techniques are becoming ever more crucial for data analysis and modelling. The support available to bioinformaticians can make it difficult for them to follow a standard academic career path – although, as our panellists discussed, this does not prevent them from being successful.
Titus Brown has found that the most effective way of introducing biologists to the in-depth computational skills required for bioinformatics are intensive 1–2 week workshops “where they do nothing but computing”. Shorter courses, like those available from Data Carpentry and Coursera are also good for polishing skills and becoming aware of particular approaches.
But there’s no real substitute for practical experience. “My favourite thing to set people to do is [to reproduce] Figure 1 from your favourite paper,” reveals Aedin Culhane. “It really is a good way to get people down and dirty with the data because they have to download it and they have to work out what to do”. If you encounter difficulties, Stack Overflow is a good source of help, as are your colleagues.
“Your technical skills are going to become defunct at some point”, says Mick Watson: new technologies will develop and become the new standards. “The skills that are going to make you more successful in bioinformatics are more generic”. All of the panelists agree that bioinformaticians must be able to communicate well, particularly as they work at the intersection of vastly different fields.
Other important skills to cultivate include producing reproducible workflows, and being able to query your data before you start analysing it. But the most important skill for Watson is writing: “I spend half of my time, if not more than that writing. If you don’t like it then perhaps a career in academic research is not for you”.
“The barriers between academia and industry are less than what they used to be”, says Culhane. “I’ve seen several people who’ve worked with me move into industry and even move back again”. The main differences in working environment come at the leadership stages – academics have more freedom to choose what to research, as long as they can get funding for it.
Taking on technical roles (for example, computational support or running the sequencing facility) at research institutes can also be a way to carve out a secure career, and will in many cases still allow you time for your own projects. Watson’s career “has always toed that line between support and research”. He advises people who are in support roles who want to go into research to frame their work with researchers as collaborations: what seems like support to one person can seem like working together to others.
Collaborations are crucial because they provide the data that you can build your tools and models on, but you also need to make sure you have time for your own first author publications. “You have to be able to juggle many, many projects,” says Culhane. But the impact that bioinformaticians can have extends beyond their formal collaborations. Brown once went to a conference and saw a number of researchers present work that used software he’d developed, but who he’d never interacted with. “I don’t like talking to people, but I love helping other people,” says Brown. Since then, focusing on writing easy-to-use software and tutorials on how to use it “has been a consistent focus that has yielded positive results for my career”.