Complete List of Winners - ISCB Art in Science Competition

Hai Fang
Julian Gough
University of Bristol, United Kingdom

supraHex

This artwork called ‘supraHex’ is inspired by the prevalence of natural objects such as a honeycomb or at Giant’s Causeway. supraHex has architectural design of a supra-hexagonal map: symmetric beauty around the center, from which smaller hexagons radiate circularly outwards. In addition to this architectural layout, supraHex also captures mechanistic nature of these objects: formation in a self-organising manner. For this, supraHex is able to self-organise the input data (eg transcriptome data). In doing so, genes with similar data patterns are clustered to the same or nearby nodes (hexagons). The map distance (the hexagon size) tells how far each node is away from its neighbors, thus characterising relationships between clustered genes. Based on this map distance, supraHex is also able to partition the map to obtain gene meta-clusters covering continuous regions, as colour-coded by the ‘potato-peach-tomato’ colormap. This artwork is generated by an open-source R/Bioconductor package ‘supraHex’ (http://bioconductor.org/packages/release/bioc/html/supraHex.html).

Luke Wilson
Jim Procter
Geoff Barton
University of Dundee, United Kingdom

Analogue Alignment

“Multiple sequence alignments were once performed manually, and even today, we still examine automatically computed alignments to check that we can't do better.” –Jim Procter This is an image of the Jalview Abacus, a sculptural attempt to visually represent the function of the Jalview protein alignment program. The program can be used to find alignments of amino acids in similar proteins. These alignments are then used to find similarities and differences between these proteins.

This object expresses the core process of Jalview in a physical space, and plays on the relationship between high tech and low tech solutions. It is a functioning abacus built by hand from wood and steel. Each row is an extract from different similar proteins (cysteine proteases) and an alignment can be found by lining up the beads of like amino acids in the columns. If it was long enough it could be used to align the entire sequence manually.

Photography: Luke Wilson
Design and Construction: Luke Wilson in collaboration w. Jim Procter and Geoff Barton

Sean O'Donoghue, CSIRO & Garvan Institute, Australia
Christopher Hammang, Garvan Institute, Australia
Julian Heinrich, CSIRO, Australia

The Dark Proteome

Here, we use light and darkness to represent the known and unknown proteome of structural biology. Currently, only 12% of the human proteome has been observed with experimental structure determination methods such as crystallography or NMR spectroscopy. For a further 36%, structural information can be inferred by homology modelling. The remaining 52% of the proteome is 'dark', i.e., has completely unknown molecular conformation.

Nick Schurch
Chris Cole
University of Dundee, United Kingdom

ImpactFactor

impactFactor is a perspective on the use of the Journal Impact Factor scores in science. In taking a literal interpretation of this score we, as scientists, are questioning its use as a measure of scientific quality or importance.

Journal Impact Factor aims to reflect the importance of a journal, however it is now used to assess the quality of the scientists who publish within it. Employers and funding bodies conflate this artificial metric as a simplistic judge of a scientist’s quality with their choice of publisher. With this perspective for modern scientists, it could be argued that where they publish has become more important than the science itself! We feel it would be much better to judge both the scientist and their publications on merit alone.

The use of artificial quality metrics is an issue for all scientists and this piece is part of the discussion of moving scientific publishing away from its 17th Century roots. Publication remains a cornerstone of scientific research but impactFactor highlights the need for a better way to judge, and, perhaps publish, impactful science.

The original acrylic on canvas artwork was exhibited at Symbiosis, a local collaborative Science/Art exhibition in Dundee. The piece measures 2m x 2m and is too big to bring to the conference, instead, we present here a 70cm x70cm photographic interpretation of the piece.

Ruth Callaway
Swansea University, Biosciences, UK

Mondrian’s Sum of Squares

Science inspired many artist, but here it was the other way around. The visualisation of marine biodiversity data was modelled on paintings by the early 20th century Dutch artist Piet Mondrian. It is an ongoing challenge for ecologists to compress and simplify complex data and to illustrate patterns in marine ecosystems. Differently coloured and sized rectangles and squares were assembled in this Mondrian’s Sum of Squares and simultaneously shows numerical and taxonomic information of a benthic invertebrate seafloor community (Swansea Bay, Wales, UK). Each field, large or small, represents a different species. The size of the square or rectangle indicates how numerically common a species was, and colours indicate taxonomic or functional groups (blue: polychaete worms, yellow: bivalves, red: crustaceans, white: other mobile species, grey: other sessile species). The few large squares highlight that the seafloor community consists of just a handful of common species, while most occur in low densities. The overwhelming number of blue fields shows the importance of worm species for biodiversity. Like many of Mondrian’s paintings, this artwork is an abstract representation of the natural world. It differs in that Piet Mondrian deliberately stepped away from reality, while this work translates scientific data into art.

Alaa Abi Haidar
University of Pierre and Marie Curie

dEYEversity

The two contrasted eyes are composed of the same ingredients and diversity of eyes, ad infinitum. The artist owns all images’ rights.

Featured at La Nuit de la Photographie Contemporaine and soon in a gallery.

As for the technique, I developed image processing algorithms to crop the eyes from the 1001faces.org project to have them automatically reassembled in this mosaic using another algorithm that optimizes the images' position according to the best matching pixel intensities.

Marwan Abdellah
École polytechnique fédérale de Lausanne (EPFL)

In Silico Brainbow

In silico brainbow optical section of a neocortical slice (920 × 640 × 1740 μm3) created with a virtual light-sheet fluorescence microscope (LSFM).

The simulation of the LSFM is performed on a physically-plausible basis using Monte Carlo ray tracing and geometric optics. The tissue model is reconstructed in a three-step process: 1) converting the morphological skeletons of the neurons into piecewise surface meshes that represent their membranes, 2) reconstructing a volumetric model of the tissue using solid voxelization and finally 3) tagging the neurons with the optical properties of the neocortical tissue and also the spectroscopic properties of different fluorescent dyes.

The slice is virtually-tagged with six different fluorescent proteins (GFP, CFP, eCFP, mBanana, mCherry and mPlum) and illuminated at the maximum excitation wavelength of each respective dye

Dr. Kliment Olechnovic
Department of Bioinformatics, Life Sciences Center
Vilnius University
Lithuania

Disassembled Tessellation

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2017 ISCB Art in Science Competition

Nick Schurch
Chris Cole
University of Dundee, United Kingdom

ImpactFactor

impactFactor is a perspective on the use of the Journal Impact Factor scores in science. In taking a literal interpretation of this score we, as scientists, are questioning its use as a measure of scientific quality or importance.

Journal Impact Factor aims to reflect the importance of a journal, however it is now used to assess the quality of the scientists who publish within it. Employers and funding bodies conflate this artificial metric as a simplistic judge of a scientist’s quality with their choice of publisher. With this perspective for modern scientists, it could be argued that where they publish has become more important than the science itself! We feel it would be much better to judge both the scientist and their publications on merit alone.

The use of artificial quality metrics is an issue for all scientists and this piece is part of the discussion of moving scientific publishing away from its 17th Century roots. Publication remains a cornerstone of scientific research but impactFactor highlights the need for a better way to judge, and, perhaps publish, impactful science.

The original acrylic on canvas artwork was exhibited at Symbiosis, a local collaborative Science/Art exhibition in Dundee. The piece measures 2m x 2m and is too big to bring to the conference, instead, we present here a 70cm x70cm photographic interpretation of the piece.

ISCB Mentored Computational Biology Programming

The ISCB Mentored Computational Biology Programming Award was created to provide up to two students with a fellowship to learn scientific programming and software engineering.

The winning submission will receive a cash award of $2000 USD and will be announced at ISMB/ECCB 2021.

SUBMISSIONS

How to Submit:

The mentee should submit a work proposal via the link below to include brief proposal (similar to a grant proposal) that is no more than 6 pages.  The proposal should clearly state the goals of the project, the timetable for development, the tools (language, libraries, etc..) that were used, a brief architectural overview and clear deliverables.  Make sure to submit the project along with support letter from your mentors.

*Special consideration will be given to mentor-mentee collaborations across disparate geographic regions, and to mentees from low-income countries*

Mentee/Mentor Eligibility Requirements:

• Student mentee needs to be a current ISCB member.
• Mentors need sufficient software development experience.
• If the student’s existing supervisor is to be a mentor, an additional co-mentor is mandatory.  

Submission Requirements:

• The projects can be part of the graduate project of the applicant.
• The project should include a timetable & be finite: no more than 3 months.  

Submissions Closed.  Check back for updates.

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ISCB Excellent Student and Postdoctoral Paper Highlight Award

The Excellent Student and Postdoctoral Paper Highlight Award was created to celebrate the achievements of Student and Postdoc members throughout their training even during the years they are not able to attend ISCBs flagship conference, ISMB.   

The winner will receive:

• One (1) year complimentary ISCB membership
• An invitation to present the awarded work as a highlights talk at ISMB 2022 with complimentary meeting registration

How to Submit Your Paper for Consideration

Are you an ISCB Member in good standing?  

Did you submit your research to a peer review journal and get accepted?

If you have answered yes to both of the above questions, you are eligible for consideration.  Simply submit your paper using the online submission portal below.  If your paper was accepted but not yet available online, please submit a copy along with the submission.  In addition to the paper, be prepared to submit a set of 2 to 3 statements of support for each paper to be submitted.

Submission Requirements
a. First (or co-first) author on the paper being reviewed. 
b. A current ISCB member in good standing at the time of submission and acceptance.
c. A student at an accredited degree-granting institution/postdoctoral researcher at the time of initial manuscript submission.

Self nominations will be accepted for this award.

Submit your Paper 

For all submission questions, please reach out to This email address is being protected from spambots. You need JavaScript enabled to view it..

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2018 ISCB Accomplishment by a Senior Scientist Award Ruth Nussinov

2018 ISCB Accomplishment by a Senior Scientist Award: Ruth Nussinov

Christiana N. Fogg¹, Diane Kovats ^2*, Ron Shamir<sup>3

1</sup> Freelance Writer, Kensington, Maryland, United States of America,
² International Society for Computational Biology,
³ Blavatnik School of Computer Science, Tel Aviv University, Tel Aviv, Israel
^* This email address is being protected from spambots. You need JavaScript enabled to view it.

Each year, the International Society for Computational Biology (ISCB) recognizes a leader in the fields computational biology and bioinformatics with the Accomplishments by a Senior Scientist Award. This is the highest award bestowed by ISCB in recognition of a scientist’s significant research, education, and service contributions. Ruth Nussinov, Senior Principal Scientist and Principal Investigator at the National Cancer Institute, National Institutes of Health and Professor Emeritus in the Department of Human Molecular Genetics & Biochemistry, School of Medicine at Tel Aviv University, Israel, was honored as the 2018 winner of the Accomplishment by a Senior Scientist Award. She received her award and presented a keynote address at ISCB’s premiere annual meeting, the 2018 Intelligent Systems for Molecular Biology (ISMB) conference in Chicago, Illinois,  held on July 6–10, 2018.

RUTH NUSSINOV: IN SEARCH OF BIOLOGICAL SIGNIFICANCE

Ruth Nussinov is a computational biologist with research interests that have touched every aspect of the field, from her PhD research on RNA secondary structure prediction to her visionary work on DNA sequence analysis, to proposing that all protein (and other biomacromolecules) conformations preexist and that all dynamic proteins are allosteric, to her current studies focused on Ras signaling in cancer. Nussinov’s deep intellectual curiosity has guided her research interests throughout her career.

Nussinov was raised in Rehovot, Israel, and attributes her early interest in science to watching her father conduct pioneering agricultural research that focused on adapting crops to the Israeli climate [1,2]. Nussinov’s father, Shmuel Hurwitz, was born in Minsk, Russia, and studied chemistry at Moscow University but later immigrated to Palestine (present day Israel) after his arrest for Zionist activities. It was here he discovered the great need for agricultural research. He pursued these studies at Berlin University but left Nazi Germany after his graduation in 1933 to found the Agricultural Research Station in Rehovot. Hurwitz was a founding member of the Faculty of Agriculture at the Hebrew University and was recognized for his significant contributions to advancing Israel agriculture with the 1957 Israeli Prize. As a child, Nussinov often joined her father on trips to his field sites, and his devotion to research and intense work ethic influenced her deeply and shaped how she approaches her work.

Nussinov also attributes her success as a scientist to the unwavering support from her husband, Shmuel Nussinov. They married just after she completed her service in the Israeli Army, during which time he was pursuing his graduate studies in particle physics at the Weizmann Institute. Her husband’s research advisor moved to the University of Washington, so Nussinov continued her undergraduate studies there (in microbiology) and went on to pursue her master’s degree in biochemistry at Rutgers University while her husband pursued postdoctoral research at Princeton University. They returned to Israel when Shmuel Nussinov joined the faculty at Tel Aviv University. When they came back to the United States several years later for his sabbatical, Ruth Nussinov enrolled in a PhD program in biochemistry at Rutgers and was mentored by a newly arrived assistant professor named George Pieczenik who had just come from Cambridge (United Kingdom). Nussinov recalled, “He said, ‘You know Ruth, Fred Sanger has just developed a DNA sequencing method and consequently there will be RNA sequences, and we will need an algorithm for the prediction of the secondary structure of RNA.’” She ran with this idea and worked tirelessly to develop the foundational Nussinov dynamic programming algorithm that is still in use today [3]. Nussinov’s PhD research has driven her career long search for questions that tackle issues of biological significance. She worked relatively independently on her project and was able to graduate in two years, and this early autonomy was critical to shaping her career path as an independent researcher.

Nussinov and her family returned to Israel, and she pursued postdoctoral studies in the Structural Chemistry Department of the Weizmann Institute and made several seminal contributions to DNA sequence analysis. She also worked as a visiting scientist in the Chemistry Department at the University of California, Berkeley, and in the Biochemistry Department at Harvard University. In spite of her impressive body of work and concept driven approach to scientific inquiry, Nussinov faced difficulties in securing a position at Tel Aviv University in the mid 1980s due to her husband’s existing position at the university and her unconventional, independent career path [4]. In 1985, Nussinov was finally appointed as an associate professor at Tel Aviv University and also became affiliated with the National Cancer Institute (NCI)/National Institutes of Health (NIH). During these early years, she credits her husband for giving her valuable advice about handling criticism from manuscript reviewers. He urged her to trust in her work and to reflect on and revise her manuscripts and resubmit them, as publications matter to the progress of a junior and unknown scientist [2].

One of Nussinov’s most profound contributions to the field is the “conformational selection and population shift” model of molecular recognition [5 9]. She and her colleagues first proposed this model in 1999 as an alternative paradigm to the “induced fit” model of protein–protein interactions. The induced fit model hypothesizes that conformational changes to a protein occur in a stepwise fashion upon binding to a ligand. In contrast, the conformational selection model portends that unbound molecules exist in all possible structural conformations, but some unbound higher energy conformations preferentially associate with a binding partner and cause a shift in equilibrium that favors this conformation. This model can explain numerous interactions observed for protein–ligand, RNA–ligand, protein–protein, protein–DNA, and protein–RNA interactions and can explain mechanisms of biological regulation, including oncogenic signaling. Nussinov is currently focused on the Ras protein and its interactions with effectors, with a particular interest in KRAS driven adenocarcinomas. She observed that self as sociation of GTP dependent K Ras dimers at different interfaces regulates which effectors bind to the dimers, which can alter downstream activity [10]. Nussinov and her team have also described the critical role of calmod ulin selectively binding to the GTP bound K Ras4B onco genic isoform, which promotes the initiation and progres sion of adenocarcinomas due to full activation of PI3Kα/Akt signaling in addition to the mitogen activated protein kinase (MAPK) pathway. These mechanistic insights are critical to developing better cancer drugs, and this work was recognized in the “Best of the AACR Journals Collec tion 2015.” Nussinov is also starting to explore interac tions between the human proteome and pathogens, given the growing appreciation of the microbiome on human health. Nussinov’s impact to the fields of computational biology and bioinformatics is notable. She has published more than 500 articles and has been ranked as a Highly Cited Researcher (ranking among the top 3,000 researchers or 1% across all fields according to Thomson Reuters Essen tial Science Indicators, http://highlycited.com/ December 2015) with more than 43,000 citations to date. Nussinov has also given over 300 invited talks and continues to maintain an active speaker schedule.

Nussinov serves as the Editor in Chief of PLOS Computational Biology, and she has also served as an editor and reviewer for numerous leading journals. Her scientific contributions have been recognized through her election as a Fellow of the Biophysical Society (2011) and an ISCB Fellow (2013). Nussinov has been a devoted mentor and advisor to graduate students and trainees throughout her career, and she has mentored dozens of PhD students, including numerous women. She has tried to model her mentorship to how she was trained, and she said, “I very much encourage independence and like for students to suggest a problem to study.”

Nussinov has always felt a close connection with ISCB, and her recognition with the 2018 ISCB Accomplishments by a Senior Scientist Award is a fitting tribute to her contributions to ISCB and to computational biology in general. She said, “I feel that’s where I belong and that’s where I want to be. I care very much about the development and sustainability and contribution of computational biology to all biological, chemical, and physical sciences.”

References
http://en.hafakulta.agri.huji.ac.il/people/shmuel hurwitz

Nussinov, Ruth. “TrendsTalk An Interview with Ruth Nussinov.” (2017) 761 763.

Nussinov, Ruth, et al. “Algorithms for loop matchings.” SIAM Journal on Applied mathematics (1978) 35.1: 68 82.

Shehu, Amarda. “Computational biologist in profile: Ruth Nussinov.” ACM SIGBioinformatics Record 3.3 (2013) 12 14.

Tsai CJ, Kumar S, Ma B, Nussinov R. “Folding funnels, binding funnels, and protein function.” Protein Sci. (1999) 8: 1181 90.

Ma B, Kumar S, Tsai CJ, Nussinov R. “Folding funnels and binding mechanisms.” Protein Eng. (1999) 12: 713 20.

Tsai CJ, Ma B, Nussinov R. Folding and binding cascades: shifts in energy landscapes. Proc Natl Acad Sci U S A. (1999) 96: 9970 72.

Ma B, Shatsky M, Wolfson HJ, Nussinov R. Multiple diverse ligands binding at a single protein site: a matter of pre existing populations. Protein Sci. (2002) 11: 184 97.

Boehr, David D., Ruth Nussinov, and Peter E. Wright. “The role of dynamic conformational ensembles in biomolecular recognition.” Nature Chemical Biology (2009) 5.11: 789.

Nussinov R, Tsai CJ, Jang H. Oncogenic Ras Isoforms Signaling Specificity at the Membrane. Cancer Res. 2018 Feb 1;78(3):593 602.
_______________________________________________________

Full bibliographical articles profiling the award recipients please see the ISMB 2018 focus issue of the ISCB newsletter as well as the ISCB Society Pages in PLOS Computational Biology and OUP Bioinformatics.  Later this year, articles will be publish in F1000 Research ISCB Community Journal.

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2019 ISCB Overton Prize: Christophe Dessimoz

Christophe Dessimoz, SNSF Professor, University of Lausanne; Associate Professor, University College London; Group Leader, Swiss Institute of Bioinformatics

The Overton Prize recognizes the research, education, and service accomplishments of early to mid-career scientists who are emerging leaders in computational biology and bioinformatics. The Overton Prize was instituted in 2001 to honor the untimely loss of G. Christian Overton, a leading bioinformatics researcher and a founding member of the ISCB Board of Directors. Christophe Dessimoz is being recognized as the 2019 winner of the Overton Prize.
 
Christophe Dessimoz is a SNSF Professor at the University of Lausanne; an Associate Professor at the University College London; a Group Leader at the Swiss Institute for Bioinformatics. Christophe obtained his Master in Biology (2003) and PhD in Computer Science (2009) from ETH Zurich, Switzerland. After a postdoc at the European Bioinformatics Institute near Cambridge (UK), he joined University College London as Lecturer (2013) and was promoted to a Reader in 2015. He joined the University of Lausanne as a SNSF Professor in 2015. With 70 papers published, Christophe has made varied and sustained contributions to bioinformatics.

He is renowned for his contributions to and subsequently management of the OMA resource providing high quality information on orthologous proteins. OMA is a very highly regarded resource with important applications in protein function prediction.

Another important thread across Christophe’s work has been his pursuit of benchmarking. Christophe’s rigorous approach to benchmarking had a major impact on three key subfields of computational biology: orthology inference, sequence alignment, and gene ontology.

ISCB will present award winners Bonnie Berger (Accomplishments by a Senior Scientist Award), Christophe Dessimoz (Overton Prize), William Stafford Noble (Innovator Award) and Barbara Bryant (Outstanding Contributions to ISCB Award), at ISMB/ECCB 2019 (www.iscb.org/ismbeccb2019), which is being held in Basel, Switzerland, July 21-25. Berger, Dessimoz, and Noble will present keynote addresses during the conference.
 
Full bibliographical articles profiling the award recipients will be available in the ISMB/ECCB 2019 focus issue of the ISCB newsletter later this year, as well as the ISCB Society Pages in OUP Bioinformatics, and F1000 Research ISCB Community Journal.

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2020 ISCB Overton Prize: Jian Peng, PhD

Jian Peng, PhD, Assistant Professor, Department of Computer Science, College of Medicine (by courtesy), Institute of Genomic Biology (affiliate)
Cancer Center at Illinois (affiliate), National Center of Supercomputing and Applications (affiliate), University of Illinois at Urbana-Champaign

A Lifelong Love of the Sequence- Structure- Function Relationship

Jian Peng grew up in Yichang, Hubei Province, China to parents who were both university professors.  His earliest memories include taking pleasure in his t time spent reading from his parents’ home l library, even when he could not fully comprehend the content of some the books. He recalled, “My parents were college professors, who always gave me the freedom to choose what I Iiked to do.” Peng was 10 years old when his parents gave him his first personal computer. and he was quickly drawn to computer programming. He spent many hours teaching himself to program and read programming books on C/ C++, Windows, and data structures. In high school, Peng became interested in chemistry, but he returned to his early interest in computer programming while pursuing his bachelor’ s and master’ s degrees in computer science at Wuhan University. As an undergraduate, Peng became deeply interested in mathematical logic and i ts applications to programming languages and wanted to pursue this topic in graduate school. He said, “ I didn’t find many places to study this topic. I was fortunate to meet with Professor Jinbo Xu, who was giving a bioinformatics talk at Tsinghua University and kindly showed me several fascinating papers, including his seminal work on the protein side chain packing problem.  He suggested that I spend time reading textbooks on machine learning (ML), as he believed that ML would become a very useful tool in computational biology when more data become available.”

Peng went on to complete his PhD in 2013 at the Toyota Technological Institute at Chicago under Xu, where his research focused on protein structure prediction and modeling using ML methods. These methods, which are known as Raptor X and are still widely used today, have excelled at alignments of hard targets. Peng then joined Bonnie Berger’ s lab as a postdoc and expanded his research scope to include systems biology and functional genomics.

He recalled, “We have had a great time working on a variety of problems, including structural bioinformatics, compressive genomics, systems biology, and disease genomics. I also really appreciated my time in the lab of (the late) Susan Lindquist, where I learned a lot from experimental and wet lab biologists and found ways to help address important problems in neurodegenerative diseases using my computational skills.” He is deeply appreciative of his mentorship under Xu, Berger, and Lindquist not just for the areas of research he worked on with them but also for the lessons he learned in conducting experiments correctly and with rigor.

In 2015 , Peng was appointed as an assistant professor in the Department of Computer Science, and affiliated with the College of Medicine, at the University of Illinois at Urbana-Champaign. Peng's perspective in identifying new research topics has evolved with his maturation as an academic. As a student, he was more drawn to problems that he thought were highly interesting, or he was swayed by the “coolness” of a method. Now Peng appreciates that his research interests must also address important scientific problems, and he feels i t is critical to convey this concept to his trainees as they apply their knowledge in computation and biology to solve problems that deeply interest them.

Through his research experiences, Peng has learned that scientists are often surprised by unexpected findings. He said, “ What I’ve learned in these years from successes and failures is how capable (and incapable) computational methods can be. Like many artificial intelligence/ ML researchers, I was initially focused on developing powerful ML models for problems with large datasets, which hopefully can provide us new biological insights. However, in many important problems, such as those related to protein function and design, disease mutations studies, and functional genomics, the effective sample sizes are much smaller than what we expect for ML.”  Peng's research has always been driven by understanding the sequence- structure- function relationship. Currently, Peng’s research has shifted directions towards using biological insights for developing advanced machine learning models. Like Bayesian methods, he uses known biological insights to serve as the “structural” prior to constrain ML models and generate new hypotheses in line with existing knowledge. Two recent notable projects in this line are the Deep Contact algorithm for protein contact map prediction and the Mashup algorithm (with Berger and Cho) for heterogeneous biological network data integration.

He is interested in understanding the functional and structural consequences of protein mutations. Peng appreciates the importance of this area in terms of designing proteins with better and more biologically relevant functions, but also improving the annotation of missense mutations in human genomes for gaining insights in molecular mechanisms of human diseases.

Peng is greatly humbled and honored to receive the 2020 ISCB Overton Prize as it is a recognition from his peers within the ISCB community, and he shares his gratitude with the mentors, students and collaborators that have brought his work to fruition.
 

ISCB will present the Accomplishments by a Senior Scientist Award, Overton Prize, Innovator Award and Outstanding Contributions to ISCB Award, at ISMB 2020 (www.iscb.org/ismb2020), which will take place in Montreal, Quebec, Canada, July 12-16, 2020 where, in addition, Peng, Liu, and Salzberg will present keynote addresses during the conference.

Full bibliographical articles profiling the award recipients will be available in the ISMB 2020 focus issue of the ISCB newsletter later this year, as well as the ISCB Society Pages in OUP Bioinformatics, and F1000 Research ISCB Community Journal.

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2018 Overton Prize Winner: Cole Trapnell

2018 ISCB Overton Prize: Cole Trapnell

Each year the International Society for Computational Biology (ISCB) recognizes the achievements of an early to mid-career scientist 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 independent 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 presented a keynote presentation at the 2018 International Conference on Intelligent Systems for Molecular Biology in Chicago, Illinois, held from July 6-10, 2018.

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 Park, right near the University of Maryland. His father, Bruce Trapnell, is a physician scientist, and Cole has fond memories of accompanying his father to the lab. Beyond the handson experiences of doing restriction digests with his dad as young child, Trapnell most appreciates how his father encouraged him to think scientifically. He recalled, “One time we were playing a board game, and I remarked that because the last dice roll was a six, the next one wouldn’t be. My dad decided to correct my thinking, so the next thing I knew, we were flipping a penny 1,000 times to estimate the probability distribution of getting heads vs. tails. I still have the plot that we drew by hand on 1mm graph paper.”

Trapnell was first interested in physics and abstract mathematics and was drawn to how these fields tackled complex ideas in terms of “first principles.” He began learning programming as a high school student and worked as a student engineer on a robotics project for the US Army. Trapnell honed his coding skills as an undergraduate by working for a startup that developed software for the areas of retail stock, futures and foreign currency trading, and he learned how to develop tools that can do complex calculations with large amounts of data in real time. He completed a dual BS degree in computer science and mathematics at the University of Maryland, College Park in 2005 and then began his PhD in computer science there as well. Trapnell thought he would work on problems in supercomputing, but then he took Steven Salzberg’s class on bioinformatics. This brought his attention to the emergence of “next-generation” sequencing technology, and he realized the potential for high throughput computing to handle this sequence data.

Trapnell’s PhD research focused on sequence alignment, and he adapted the Bowtie algorithm developed by Ben Langmead into a program called TopHat that could handle transcriptomic data. During this time, Trapnell moved to the University of California, Berkeley, where his wife was pursuing her PhD in mathematics, and he started working with Lior Pachter, who became his co-advisor with Salzberg at UMD. As Trapnell developed TopHat and the companion tool, Cufflinks, he tested them with datasets from Barbara Wold’s lab, and he began to develop an appreciation for biological questions, especially in gene regulation. Trapnell was drawn to doing bench research, and his labmate Rob Bradley encouraged him to take that leap. He recalled, “Rob Bradley convinced me that to become a really good biologist, I should learn to do experiments. Rob, who trained as a biophysicist, had gone off to do a postdoc at the bench. I followed suit and joined John Rinn’s lab (at Harvard University), where I worked to both do experiments and analyze them myself.” Trapnell’s time in Rinn’s lab not only helped him get his hands dirty doing bench research, but gave him the unique perspective of working under a scientist who pioneered the field of long noncoding RNAs.

Trapnell’s postdoctoral training opened his eyes to the realities of experimental biology and he acknowledges that these experiences have made him a better computational biologist. While Cufflinks could help him predict which individual splice isoforms may be elevated under certain disease conditions, he came to realize how hard it can be to validate these observations at the lab bench: a specific antibody may not exist for a western blot or technical difficulties may make it difficult to knock down a gene isoform in a particular model system. Trapnell had to adjust to the different culture associated with working in a wet lab. He recounted, “Computational people are often mystified and frustrated by how often their experiments fail. I like to tell them a story of my own frustration: A little while after starting my wet lab postdoc training, I was complaining to my labmate, Dave Hendrickson, that my experiments were constantly failing. He asked me how long I’d been at it, and I told him about six months. He said, “Well, give it another six months.” I thought he meant I would get better at doing experiments but what he actually said next was, “It’ll hurt less when they don’t work.” This was a tremendously eye opening thing for me,  because he was trying to tell me that being an effective experimentalist means anticipating failure, planning for it, designing controls that can detect it, and parallelizing work within projects so that you can make progress in one direction even when you’re stuck in another. There are similar cultural differences that experimentalists encounter when learning to program.” As a PI, Trapnell is supportive of students and trainees that want to gain both experimental and computational experience, but he wants to them to learn to understand the culture of these two realms and not just acquire the necessary skills to do experiments or develop algorithms.

Throughout his training, Trapnell has valued the guidance of his mentors. His current lab is positioned between the labs of Stan Fields and Bob Waterson, both leaders in the field of genomics, and they been invaluable advisors to Trapnell. He said, “Despite their fame and their busy lives, both go way out of their way to advise me on how to bring my research and lab to its potential.” All of his mentors have inspired Trapnell to build a lab culture that encourages open, inspiring and rigorous science. As he established his own lab at the University of Washington, he has started to think differently as a PI and said, “I am continually faced with the question: What do I think is the most important scientific contribution I can make?” Shifting his mindset has been a challenge, but he is still broadly interested in gene regulation, especially gaining a more quantitative understanding of the epigenome. Trapnell considers the advances in single-cell measurements as critical to quantifying aspects of gene regulation, and his team is developing tools for single-cell measurements of gene expression, chromatin accessibility, and other features of the molecular state of the genome. Much of this work is in collaboration with Jay Shendure, whose lab specializes in molecular biotechnology development. Trapnell is keen on this collaboration: “Jay and I have very different approaches but share a common goal to transform our understanding of development and disease using single-cell technologies. Our collaboration has been fantastically productive and fun so far, and there’s a lot more to come.”

Trapnell is deeply honored to selected for the Overton Prize, and said, “I feel strongly that my success is at least as much a product of my being in the right place at the right time with the right collaborators as from any choices I made. I have been repeatedly given great opportunities and I’ve tried to make the best use of them, but I would have gotten nowhere if not for the generous help and creativity of a long list of mentors, collaborators and colleagues.”
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Full bibliographical articles profiling the award recipients please see the ISMB 2018 focus issue of the ISCB newsletter as well as the ISCB Society Pages in PLOS Computational Biology and OUP Bioinformatics.  Later this year, articles will be publish in F1000 Research ISCB Community Journal.

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