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Bioinformatics, 34(13), 2018, 2330–2331 doi: 10.1093/bioinformatics/bty360 Advance Access Publication Date: 2 June 2018 Message from the ISCB 2018 ISCB Overton Prize awarded to Cole Trapnell 1 2, 3 Christiana N. Fogg , Diane E. Kovats * and Ron Shamir 1 2 3 Kensington, 20895 MD, USA, Bethesda, 20814 MD, USA and Computational Genomics Group, Blavatnik School of Computer Science, Tel Aviv University, 69978 Tel Aviv, Israel *To whom correspondence should be addressed. Contact: executive.ofﬁce@iscb.org Each year the International Society for Computational Biology (ISCB) recognizes the achievements of an early to mid-career scien- tist with the Overton Prize. This prize honors the untimely death of Dr. G. Christian Overton, a respected computational biologist and founding ISCB Board member. The Overton Prize recognizes inde- pendent investigators who are in the early to middle phases of their careers and are selected because of their significant contributions to computational biology through research, teaching, and service. ISCB is pleased to recognize Dr. Cole Trapnell, Assistant Professor of Genome Sciences at the University of Washington as the 2018 winner of the Overton Prize. Trapnell will be presenting a keynote presentation at the 2018 International Conference on Intelligent Systems for Molecular Biology in Chicago, Illinois being held from July 6 to 10, 2018. 1 Cole Trapnell: building bridges to the lab bench Cole Trapnell’s earliest interest in science began at home. He was born in Cheverly, MD and spent his childhood living in College computer science and mathematics at the University of Maryland, Park, right near the University of Maryland. His father, Bruce College Park in 2005 and then began his PhD in computer science Trapnell, is a physician scientist, and Cole has fond memories of there as well. Trapnell thought he would work on problems in accompanying his father to the lab. Beyond the hands-on experien- supercomputing, but then he took Steven Salzberg’s class on bio- ces of doing restriction digests with his dad as young child, Trapnell informatics. This brought his attention to the emergence of ‘next- most appreciates how his father encouraged him to think scientific- generation’ sequencing technology, and he realized the potential for ally. He recalled, ‘One time we were playing a board game, and I high throughput computing to handle this sequence data. remarked that because the last dice roll was a six, the next one Trapnell’s PhD research focused on sequence alignment, and he wouldn’t be. My dad decided to correct my thinking, so the next adapted the Bowtie algorithm developed by Ben Langmead into a thing I knew, we were flipping a penny 1000 times to estimate the program called TopHat that could handle transcriptomic data. probability distribution of getting heads versus tails. I still have the During this time, Trapnell moved to the University of California, plot that we drew by hand on 1 mm graph paper.’ Trapnell was first interested in physics and abstract mathematics Berkeley, where his wife was pursuing her PhD in mathematics, and and was drawn to how these fields tackled complex ideas in terms of he started working with Lior Pachter, who became his co-advisor ‘first principles.’ He began learning programming as a high school with Salzberg at UMD. As Trapnell developed TopHat and the com- panion tool, Cufflinks, he tested them with datasets from Barbara student and worked as a student engineer on a robotics project for the US Army. Trapnell honed his coding skills as an undergraduate Wold’s lab, and he began to develop an appreciation for biological by working for a startup that developed software for the areas of re- questions, especially in gene regulation. Trapnell was drawn to tail stock, futures and foreign currency trading, and he learned how doing bench research, and his labmate Rob Bradley encouraged him to develop tools that can do complex calculations with large to take that leap. He recalled, ‘Rob Bradley convinced me that to amounts of data in real time. He completed a dual BS degree in become a really good biologist, I should learn to do experiments. V The Author(s) 2018. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: firstname.lastname@example.org 2330 Downloaded from https://academic.oup.com/bioinformatics/article-abstract/34/13/2330/5032530 by Ed 'DeepDyve' Gillespie user on 03 July 2018 2018 ISCB Overton Prize 2331 Rob, who trained as a biophysicist, had gone off to do a postdoc at experience, but he wants to them to learn to understand the culture the bench. I followed suit and joined John Rinn’s lab (at Harvard of these two realms and not just acquire the necessary skills to do University), where I worked to both do experiments and analyze experiments or develop algorithms. them myself.’ Trapnell’s time in Rinn’s lab not only helped him get Throughout his training, Trapnell has valued the guidance of his his hands dirty doing bench research, but gave him the unique per- mentors. His current lab is positioned between the labs of Stan spective of working under a scientist who pioneered the field of long Fields and Bob Waterson, both leaders in the field of genomics, and noncoding RNAs. they been invaluable advisors to Trapnell. He said, ‘Despite their Trapnell’s postdoctoral training opened his eyes to the realities fame and their busy lives, both go way out of their way to advise me of experimental biology and he acknowledges that these experien- on how to bring my research and lab to its potential.’ All of his men- ces have made him a better computational biologist. While tors have inspired Trapnell to build a lab culture that encourages Cufflinks could help him predict which individual splice isoforms open, inspiring and rigorous science. As he established his own lab may be elevated under certain disease conditions, he came to real- at the University of Washington, he has started to think differently ize how hard it can be to validate these observations at the lab as a PI and said, ‘I am continually faced with the question: What do bench: a specific antibody may not exist for a western blot or tech- I think is the most important scientific contribution I can make?’ nical difficulties may make it difficult to knock down a gene iso- Shifting his mindset has been a challenge, but he is still broadly form in a particular model system. Trapnell had to adjust to the interested in gene regulation, especially gaining a more quantitative different culture associated with working in a wet lab. He understanding of the epigenome. Trapnell considers the advances in recounted, ‘Computational people are often mystified and frus- single-cell measurements as critical to quantifying aspects of gene trated by how often their experiments fail. I like to tell them a story regulation, and his team is developing tools for single-cell measure- of my own frustration: A little while after starting my wet lab post- ments of gene expression, chromatin accessibility, and other features doc training, I was complaining to my labmate, Dave of the molecular state of the genome. Much of this work is in collab- Hendrickson, that my experiments were constantly failing. He oration with Jay Shendure, whose lab specializes in molecular bio- asked me how long I’d been at it, and I told him about six months. technology development. Trapnell is keen on this collaboration: ‘Jay He said, ‘Well, give it another six months.’ I thought he meant I and I have very different approaches but share a common goal to would get better at doing experiments but what he actually said transform our understanding of development and disease using next was, ‘It’ll hurt less when they don’t work.’ This was a tremen- single-cell technologies. Our collaboration has been fantastically dously eye opening thing for me, because he was trying to tell me productive and fun so far, and there’s a lot more to come.’ that being an effective experimentalist means anticipating failure, Trapnell is deeply honored to selected for the Overton Prize, and planning for it, designing controls that can detect it, and paralleliz- said, ‘I feel strongly that my success is at least as much a product of ing work within projects so that you can make progress in one dir- my being in the right place at the right time with the right collabora- ection even when you’re stuck in another. There are similar tors as from any choices I made. I have been repeatedly given great cultural differences that experimentalists encounter when learning opportunities and I’ve tried to make the best use of them, but I to program.’ As a PI, Trapnell is supportive of students and train- would have gotten nowhere if not for the generous help and creativ- ees that want to gain both experimental and computational ity of a long list of mentors, collaborators and colleagues.’ Downloaded from https://academic.oup.com/bioinformatics/article-abstract/34/13/2330/5032530 by Ed 'DeepDyve' Gillespie user on 03 July 2018
Bioinformatics – Oxford University Press
Published: Jun 2, 2018
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