ISMB/ECCB 2021 Committees

• Steering Committee
• COVID-19 Track Committee
• Fellowship Committee
• Proceedings Committee
• Proceedings Reviewers
• Proceedings Sub-Reviewers
• Special Sessions Committee
• Technology Track
• Tutorials Committee

ISMB/ECCB 2021 Steering Committee

Rita Casadio, Conference Co-chair, University of Bologna, Italy
Christophe Dessimoz, University of Lausanne; University College London; Swiss Institute for Bioinformatics, Switzerland
Bruno Gaeta, ISCB Treasurer, University of New South Wales, Australia
Janet Kelso, ISCB Conference Advisory Council Co-chair, Max Planck Institute for Evolutionary Anthropology, Germany
Diane E. Kovats, ISCB Chief Executive Officer, United States
Steven Leard, ISMB Conference Director, Canada
Thomas Lengauer, ISCB Past-President, Max Planck Institute for Informatics, Germany
Christine Orengo, ISCB President, University College London, United Kingdom
Teresa M. Przytycka, NCBI, NLM, NIH, United States
Pat Rodenburg, Conference Administrator, Canada
Marie-France Sagot, Conference Co-chair, Claude Bernard University; French Institute for Research in Computer Science and Automation (INRIA); University of Lyon, France
Torsten Schwede, ECCB Liaison, Biozentrum, University of Basel & SIB Swiss Institute of Bioinformatics
Switzerland
Jacques Van Helden, Conference Co-chair, Aix-Marseille Université (AMU), France

COVID-19 Track Committee

Thomas Lengauer, Max Planck Institute for Informatics, Germany
Shoshana Wodak, VIB‐VUB Center for Structural Biology, Belgium

Rita Casadio, University of Bologna, Italy
Christophe Dessimoz, University College London, United Kingdom
Bruno Gaeta, The University of New South Wales, Australia
Wataru Iwaskai, The University of Tokyo, Japan
Janet Kelso, Max Planck Institute for Evolutionary Anthropology, Germany
Stephen MacKinnon, Cyclica, Canada
Christine Orengo, University College London, United Kingdom
Teresa Przytycka, National Center of Biotechnology Information, NLM, NIH, United States
Sushmita Roy University of Wisconsin-Madison, United States
Marie-France Sagot, Claude Bernard University; French Institute for Research in Computer Science and Automation (INRIA); University of Lyon, France
Russell Schwartz, Carnegie Mellon University, United States
Jinbo Xu, Toyota Technological Institute at Chicago, United States

Fellowship Committee

Chair: Dimitri Perrin, Queensland University of Technology, Australia
Co-chair: Catherine Putonti, Loyola University Chicago, United States

Proceedings Committee

Proceedings Co-chairs

Christophe Dessimoz, University of Lausanne; University College London; Swiss Institute for Bioinformatics, Switzerland
Teresa M. Przytycka, NCBI, NLM, NIH, United States

Area Chairs

Bioinformatics Education

Sarah L. Morgan, EMBL-EBI, United Kingdom
Russel Schwartz, Carnegie Mellon University, United States

Bioinformatics of Microbes and Microbiomes

Bernhard Renard, Hasso Plattner Institure
Hélène Touzet, CNRS, CRIStAL, France

Biomedical Informatics

Maria Secrier, University College London, United Kingdom
Marinka Zitnik, Harvard University, United States

Evolutionary, Comparative, and Population Genomics

Dannie Durand, Carnegie Mellon University, United States
Wataru Iwasaki, University of Tokyo, Japan

Genome Privacy and Security

Bonnie Berger, Massachusetts Institute of Technology, United States

Genomic Sequence Analysis

Can Alkan, Bilkent University, Turkey
Adam Phlilippy, NIH, United States

Macromolecular Sequence, Structure, and Function

Yann Ponty, CNRS/LIX, Polytechnique, France
Jinbo Xu , Toyota Technological Institute at Chicago, United States

Regulatory and Functional Genomics

Shaun Mahony, Penn State, United States
Yvan Saeys, VIB Ghent, Belgium

Systems Biology and Networks

Laurent Jacob, CNRS, France
Tamer Kahveci, University of Florida, United States

General Computational Biology

Mohammed El-Kebir, University of Illinois, United States
Cagatay Turkay, University of Warwick, United Kingdom

Proceedings Reviewers

Jan Aerts, Katholieke Universiteit Leuven
Tatsuya Akutsu, Kyoto University
Alexander Alekseyenko, Medical University of South Carolina
Mohammed Alquraishi, Columbia University
Emily Alsentzer, MIT
Mohammed Alser, ETH Zurich
Manimozhiyan Arumugam, University of Copenhagen
Volkan Atalay, Middle East Technical University
Ferhat Ay, La Jolla Institute
Erman Ayday, Case Western Reserve University and Bilkent University
Chloé Azencott, CBIO; Mines ParisTech; Institut Curie; INSERM
Pedro Ballester, Cancer Research Centre of Marseille
Vikas Bansal, University of California San Diego
Ziv Bar-Joseph, Carnegie Mellon University
Jakub Bartoszewicz, HPI
Anais Baudot, CNRS
Brett Beaulieu-Jones, University of Pennsylvania
Niko Beerenwinkel, ETH Zurich
Asa Ben-Hur, Colorado State University
Takis Benos, University of Pittsburgh
Etienne Birmele, Université Paris Descartes
Isabell Bludau, ETH Zurich
Sebastian Böcker, Friedrich Schiller University Jena
Valentina Boeva, Institut Cochin/INSERM/CNRS
Karsten Borgwardt, ETH Zurich
Christina Boucher, University of Florida
Patrick Bradley, The Ohio State University
Edward Braun, Univeristy of Florida
Michael Brent, WUSTL
Karel Brinda, Harvard T.H. Chan School of Public Health
Yana Bromberg, Rutgers University
Dongbo Bu, Insitute of Computing Technology, Chinese Academy of Sciences
Melissa Burke, Australian BioCommons
Laura Cantini, Institut de Biologie de l'Ecole Normale Superiore
Stefan Canzar, Gene Center, LMU
Alessandra Carbone, Sorbonne Université
Hannah Carter, University of California San Diego
Frédéric Cazals, INRIA
Mark Chaisson, University of Washington
Isaure Chauvot de Beauchêne, CAPSID team, CNRS, LORIA
Dexiong Chen, INRIA
Jianlin Cheng, University of Missouri Columbia
Rayan Chikhi, CNRS
Maria Chikina, University of Pittsburgh
Leonid Chindelevitch, Imperial College London
Hyunghoon Cho, Broad Institute of MIT and Harvard
A. Ercument Cicek, Bilkent University
Phillip Compeau, Carnegie Mellon University
Ana Conesa, University of Florida
James Costello, University of Colorado Anschutz Medical Campus
Lenore Cowen, Tufts University
Lorin Crawford, Microsoft Research New England
Felipe da Veiga Leprevost, University of Michigan
Piotr Wojciech Dabrowski, HTW Berlin University of Applied Sciences
Noah Daniels, The University of Rhode Island
Jeroen de Ridder, Delft Bioinformatics Lab
Charlotte Deane, University of Oxford
Dan DeBlasio, University of Texas at El Paso
Viraj Deshpande, Illumina Inc.
Didier Devaurs, The University of Edinburgh
Jo Dicks, Public Health England
Ying Ding, The University of Texas at Austin
Robin Dowell, University of Colorado Boulder
Jingcheng Du, UTHealth
Ray Enke, James Madison University
Damien Eveillard, Nantes University
Dirk Evers, Molecular Health GmbH
Gang Fang, Mount Sinai School of Medicine
Sofia Kirke Forslund, MDC/Charité/ECRC
Caroline Friedel, Ludwig Maximilian University of Munich
Fabian Froehlich, Harvard University
Tsukasa Fukunaga, The University of Tokyo
Kenji Fukushima, University of Würzburg
Nils Gehlenborg, Harvard University
Dario Ghersi, University of Nebraska at Omaha
Vladimir Gligorijevic, Institut Jozef Stefan
Omer Gokcumen, University at Buffalo
Sergei Grudinin, INRIA
Attila Gursoy, Koc University
Gamze Gursoy, Yale University
Faraz Hach, University of British Columbia and Vancouver Prostate Centre
Iman Hajirasouliha, Cornell University
Dominik Heider, Philipps-University of Marburg
Mikel Hernaez, University of Illinois, at Urbana-Champaign
Michael Hiller, MPI CBG
Ivo Hofacker, University of Vienna
Liisa Holm, University of Helsinki
Fereydoun Hormozdiari, University of Washington
Kexin Huang, Harvard University
Wolfgang Huber, EMBL
Lawrence Hunter, UC Denver
Ignacio Ibarra Del Río, EMBL Heidelberg
Trey Ideker, University of California San Diego
Pierre-Étienne Jacques, Université de Sherbrooke
Chirag Jain, National Institutes of Health
Peng Jiang, National Cancer Institute
Tao Jiang, University of California, Riverside
Yuxiang Jiang, Indiana University Bloomington
Lars Kaderali, University Medicine Greifswald
Andre Kahles, ETH Zurich
Akshay Kakumanu, The Pennsylvania State University
Lukas Käll, KTH Royal Institute of Technology
Emre Karakoc, University of Washington
Birte Kehr, Berlin Institute of Health / Charité - Universitätsmedizin Berlin
Sunduz Keles, University of Wisconsin-Madison
David Kelley, Calico Life Sciences
Chris Kennedy, Harvard Medical School
Daisuke Kihara, Purdue University
Judith Klein, Colorado School of Mines
David Knowles, Columbia University
Dave Koes, University of Pittsburgh
Mikhail Kolmorgorov, University of California San Diego
Rachel Kolodny, University of Haifa
Peter Koo, Cold Spring Harbor Laboratory
Sergey Koren, NHGRI/NIH
David Koslicki, Penn State University
Dennis Kostka, University of Pittsburgh
Mehmet Koyuturk, Case Western Reserve University
Roland Krause, University of Luxembourg
Arjun Krishnan, Michigan State University
Smita Krishnaswamy, Yale University
Anshul Kundaje, Stanford University
Lukasz Kurgan, Virginia Commonwealth University
Manuel Lafond, Université de Sherbrooke
Lydie Lane, SIB
Ben Langmead, Johns Hopkins University
Keren Lasker, Stanford University
Mathieu Lavallée-Adam, University of Ottawa
Dominique Lavenier, CNRS / IRISA
Heewook Lee, Carnegie Mellon University
Jingyi Jessica Li, University of California, Los Angeles
Le Li, Cornell University
Michelle Li, Harvard University
Maxwell Libbrecht, University of Washington Genome Sciences
Olivier Lichtarge, Baylor College of Medicine
Antoine Limasset, CNRS
Gerton Lunter, University of Oxford
Ruibang Luo, The University of Hong Kong
Jian Ma, Carnegie Mellon University
Jianzhu Ma, Purdue University
François Major, University of Montreal
William Majoros, Duke University
Salem Malikic, Simon Fraser University
Serghei Mangul, University of California, Los Angeles
Guillaume Marcais, Carnegie Mellon University
Florian Markowetz, University of Cambridge
Tobias Marschall, Saarland University / Max Planck Institute for Informatics
Tristan Mary-Huard, INRAE
David Mathews, University of Rochester
Matthew McDermott, MIT
Alejandra Medina-Rivera, Universidad Nacional Autónoma de México
Pall Melsted, University of Iceland
Irmtraud Meyer, Max-Delbrück-Centrum für Molekulare Medizin (MDC); and Free University
Michelle Meyer, Boston College
Tom Michoel, University of Bergen
Olgica Milenkovic, University of Illinois at Urbana-Champaign
Siavash Mirarab, The University of Texas at Austin
Hosein Mohimani, Carnegie Melon University
Erin Molloy, UCLA/UMD
Jonathan Monk, UCSD
Jean Monlong, University of California, Santa Cruz
Deisy Morselli Gysi, Northeastern University
Nicola Mulder, University of Cape Town
T.M. Murali, Virginia Tech
Chad Myers, University of Minnesota
Niranjan Nagarajan, Genome Institute of Singapore
Luay Nakhleh, Rice University
Leelavati Narlikar, National Chemical Laboratory
Kay Nieselt, University of Tübingen
William Noble, University of Washington
Ibrahim Numanagic, Massachusetts Institute of Technology
Claire O'Donovan, EBI
Layla Oesper, Carleton College
Yaron Orenstein, Ben-Gurion University
Hatice Osmanbeyoglu, University of Pittsburgh
Aida Ouangraoua, Université de Sherbrooke
Arzucan Ozgur, Bogazici University
Joshua Pan, Broad Institute
Akanksha Pandey, VIT University
Gaurav Pandey, Mount Sinai School of Medicine
Laxmi Parida, IBM
Rob Patro, University of Maryland
Loïc Paulevé, CNRS/LaBRI, Bordeaux
Eric Pelletier, CEA / Genoscope
Theodore Perkins, Ottawa Hospital Research Institute
Mihaela Pertea, Johns Hopkins University
Dmitri Pervouchine, Skolkovo Institute for Science and Technology
Anton I. Petrov, European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI)
Nico Pfeifer, University of Tübingen
Roger Pique-Regi, Wayne State University
Mihai Pop, University of Maryland
James Procter, University of Dundee
Simon Puglisi, University of Helsinki
Elizabeth Purdom, University of California, Berkeley
Xiaoning Qian, Texas A&M University
Gerald Quon, University of California, Davis
Predrag Radivojac, Northeastern University
Jean Louis Raisaro, Lausanne university hospital (CHUV)
Huzefa Rangwala, George Mason University
Verena Ras, University of Cape Town
Thomas Rattei, University of Vienna
Magnus Rattray, The University of Manchester
Vladimir Reinharz, Université du Québec à Montréal
Hugues Richard, University Pierre and Marie Curie
Lila Rieber, The Pennsylvania State University
Anna Ritz, Reed College
Elena Rivas, Janelia Farm Research Campus, HHMI
Marc Robinson-Rechavi, Universite de Lausanne
Miguel Rocha, University of Minho
Sushmita Roy, University of Wisconsin-Madison
Camilo Ruiz, Stanford University
Cenk Sahinalp, Indiana University Bloomington
Leena Salmela, University of Helsinki
Venkata Satagopam, University of Luxembourg and ELIXIR-Luxembourg
Gryte Satas, Princeton University
Alejandro Schaffer, National Institutes of Health
Michael Schatz, Cold Spring Harbor Laboratory
Melanie Schirmer, Technical University of Munich
Alexander Schoenhuth, Centrum Wiskunde & Informatica
Marcel Schulz, Goethe University
Michelle Scott, University of Sherbrooke
Alexander Sczyrba, Bielefeld University
Fritz Sedlazeck, Baylor College of Medicine
Nicola Segata, University of Trento
Mingfu Shao, Carnegie Mellon University
Roded Sharan, Tel Aviv University
Vishakha Sharma, Stevens Institute of Technology
Amarda Shehu, George Mason University
Yang Shen, Texas A&M University
Ilan Shomorony, University of Illinois at Urbana-Champaign
Avanti Shrikumar, Stanford University
Anne Siegel, IRISA - CNRS
Sean Simmons, Massachusetts Institute of Technology
Jared Simpson, Ontario Institute for Cancer Research
Johannes Soeding, MPI BPC
Giltae Song, Pusan National University
Jens Stoye, Bielefeld University
Mahito Sugiyama, National Institute of Informatics
Krister Swenson, CNRS, Université de Montpellier
Ewa Szczurek, University of Warsaw
Haixu Tang, Indiana University Bloomington
Eric Tannier, INRIA
Jaclyn Taroni, Alex's Lemonade Stand Foundation
Oznur Tastan, Sabanci University
Fabian Theis, Helmholtz Zentrum München – German Research Center for Environmental Health
Todd Treangen, Rice University
Olivier Tremblay-Savard, University of Manitoba
Yatish Turakhia, University of California, Santa Cruz
Celia van Gelder, DTL
Fabio Vandin, University of Padova
Nelle Varoquaux, University of California, Berkeley
Susana Vinga, Universidade de Lisboa
Martin Vingron, Max Planck Institut fuer molekulare Genetik
Max von Kleist, Freie Universität Berlin
Yijie Wang, Indiana University
Tandy Warnow, University of Illinois at Urbana-Champaign
Wyeth Wasserman, The University of British Columbia
Sebastian Will, Ecole Polytechnique
Phillip Wilmarth, OHSU
Yu Xia, McGill University
Guanjue Xiang, The Pennsylvania State University
Naomi Yamada, The Pennsylvania State University
Vicky Yao, Rice University
Byung-Jun Yoon, Texas A&M University
William Yu, University of Toronto
Simone Zaccaria, UCL Cancer Institute
Jianyang Zeng, Tsinghua University
Louxin Zhang, National University of Singapore
Xiang Zhang, Harvard University
Ralf Zimmer, Ludwig Maximilian University of Munich
Ali Zomorrodi, Massachusetts General Hospital & Harvard Medical School
Blaz Zupan, University of Ljubljana

Proceedings Sub-Reviewers

Dhoha Abid
Sandeep Acharya
Michael Adamer, ETH Zurich
Constantin Ahlmann-Eltze, EMBL Heidelberg
Kyle Akers
Jarno Alanko, University of Helsinki; Dalhousie University
Michael Alonge, Johns Hopkins University
Israa Alqassem, Ludwig Maximilian University of Munich
Mohammed Alser, ETH Zurich
Yoann Anselmetti
Meltem Apaydin, Texas A&M University
Ricard Argelaguet, Babraham Institute
Angeles Arzalluz-Luque
Stefano Avanzini, Stanford University
Kerem Ayöz
Alex Baker
Brittany Baur
Fatemeh Behjati
Djamal Belazzougui
Felix Berkenkamp
Anushua Biswas, CSIR-National Chemical Laboratory; Academy of Scientific and Innovative Research
Nico Borgsmüller, ETH Zurich
Matthew Brendel
Douglas Brubaker, Weldon School of Biomedical Engineering
Diyue Bu, Indiana University Bloomington
Doruk Cakmakci, Bilkent University
Daniele Capocefalo, European Institute of Oncology; University of Milan
Claudia Cava, Institute of Molecular Bioimaging and Physiology; National Research Council (IBFM-CNR)
Shounak Chakraborty, Quantitative Biosciences Munich (QBM); LMU Munich
Shubham Chandak, Stanford University
Ibrahim Chegrane, University of Sherbrooke
Hanrong Chen, Genome Institute of Singapore
Julie Chow, University of California, Davis
Ratul Chowdhury
Sarah Christensen, DE Shaw Research
Simone Ciccolella, University of Milan-Bicocca
Helen Cook
Miklós Csürös, Université de Montréal
Alister D'Costa
Andy Dahl
Kushal Dey
Jun Ding, McGill University
Van Hoan Do, Ludwig-Maximilians-Universität München
Arthur Dondi, ETH Zurich
Daniel Dörr, Heinrich Heine University Düsseldorf
Dat Doung
Monica Dragan
Peter Ebert, Heinrich Heine University Düsseldorf
Jana Ebler, Heinrich Heine University Düsseldorf
Bowen Fan
Jason Fan
Mohsen Ferdosi, Carnegie Mellon University
Pedro Ferreira, ETH Zürich
Mattia Forcato, University of Modena and Reggio Emilia
Lisa Gai
Jay Ghurye, Verily Life Sciences, LLC
Gal Gilad
Jean-Sebastian Gounot
Stephane Guindon
Thomas Gumbsch, ETH Zürich; Swiss Institute of Bioinformatics
Anna Hake, Max Planck Institute for Informatics
Spencer Halberg
Išerić Hamza, University of Victoria
Ramin Hasibi
Dennis Hecker, Goethe University Hospital
Pingzhao Hu, University of Manitoba
Elham Jafari, Indiana University Bloomington
Mansoureh Jalilkhany, University of Victoria
Kiran Javkar, University of Maryland, College Park
Jeffery Jung
Mostafa Karimi, Microsoft Corporation
Arya Kaul, Harvard Medical School
Jamshed Khan, University of Maryland
Mikhail Khodak
Parsoa Khorsand
Michael Kleyman
Sara Knaack, University of Wisconsin-Madison
Maren Knop
Can Kockan
Noam Koren, Tel-Aviv University
David Koslicki
Thomas Krannich, Berlin Institute of Health; Charité - Universitätsmedizin Berlin
Roland Krause, University of Luxembourg
Nathan LaPierre, University of California, Los Angeles
Da-Inn Lee
Dongshunyi Li, Carnegie Mellon University
Le Li, Weill Institute for Cell & Molecular Biology; Cornell University
Feng (Ryan) Lin, University of Washington
Changchang Liu
Xiaowen (Kevin) Liu, Indiana University-Purdue University Indianapolis
Jennifer Lu, Johns Hopkins University
Junyan Lu, European Molecular Biology Laboratory
Adriaan-Alexander Ludl
Jose Lugo-Martinez
Xiao Luo, Bielefeld University
Cynthia Ma
Ranjan Kumar Maji, Uniklinikum and Goethe University Frankfurt
Muhammad Ammar Malik
Weiguang Mao, University of Pittsburgh
Sapir Margalit, Tel Aviv University
Rebecca Serra Mari, Heinrich Heine University Düsseldorf
John McBride, Ulsan National Institute for Science and Technology
Zachary McCaw, Google, Health
Lauren McIntyre, University of Florida
Alan Medlar, University of Helsinki
Jacquelyn Meisel, University of Maryland, College Park
Jane Merlevede, Institut Curie; PSL Research University; Mines Paris Tech; Inserm
Dmitrii Meleshko, Weill Cornell Medical College
Mike Molnar
Pablo Monteagudo
Marcin Możejko, University of Warsaw
Brian Nadel
Aya Narunsky, Yale University
Sebastian Niehus, Regensburg Center for Interventional Immunology
Seyednami Niyakan, Texas A&M University
Asma Nouira, Mines ParisTech; Institut Curie; INSERM
Idoia Ochoa
Baraa Orabi, The University of British Columbia;Vancouver Prostate Centre
Yaron Orenstein, Ben-Gurion University of the Negev
Rita Osadchy, University of Haifa
Furkan Özden
Ozan Ozisik, Aix-Marseille University; Inserm; MMG
Prashant Pandey, Lawrence Berkeley National Lab; UC Berkeley US
Laetitia Papaxanthos, Google Brain
Azam Peyvandipour
Kim Philipp Jablonski, ETH Zurich; SIB Swiss Institute of Bioinformatics
Oriol Pich
Christopher Pockrandt, Johns Hopkins University
Saptarshi Pyne, University of Wisconsin-Madison
Amatur Rahman, The Pennsylvania State University
Elior Rahmani, University of California Berkeley
Daniele Raimondi, KU Leuven
Sabrina Rashid, AI Therapeutics
Sumanta Ray, Aliah University
Justyna Resztak
Damian Roqueiro, ETH Zurich
Brin Rosenthal, University of California, San Diego
Massimiliano Rossi, University of Florida
Matthew Ruffalo
Steven Salzberg, Johns Hopkins University
Edward Sanders, University of Oxford
Hirak Sarkar, Harvard University
Gryte Satas, Memorial Sloan Kettering Cancer Center
Leah Schaffer, University of California, San Diego
Sebastian Schmidt, University of Helsinki
Tizian Schulz, Bielefeld University
Nidhi Shah, University of Maryland, College Park
Huwenbo Shi, Harvard T.H. Chan School of Public Health; Broad Institute of MIT and Harvard
Qian Shi
Rahul Siddharthan, The Institute of Mathematical Sciences
Tomer Sidi, University of Haifa
Dana Silverbush, Massachusetts General Hospital & Harvard Medical School; Broad Institute of Harvard & MIT
Akshat Singhal, University of California San Diego
Haris Smajlovic, University of Victoria
Nataliya Sokolovska, Sorbonne University
Harihara Subrahmaniam Muralidharan, University of Maryland, College Park
Chayaporn Suphavilai
Polina Suter, ETH Zürich
Nure Tasnina, Virginia Tech
Laurent Tichit
Marketa Tomkova, University of California, Davis
Nurcan Tuncbag, Koc University
Ales Varabyou, Johns Hopkins University
Paul Villoutreix, Aix-Marseille University
Xin Wang, City University of Hong Kong
Leah Weber
Julong Wei
Caroline Weis
Omer Weissbrod, Eleven TX
Yufeng Wu, University of Connecticut
Chencheng Xu, Tsinghua University
Harry Taegyun Yang, University of California, Los Angeles
Reyyan Yeniterzi, Sabancı University
Serhan Yılmaz, Case Western Reserve University
Kaan Yorgancioglu, Case Western Reserve University
Yuning You, Texas A&M University
Ke Yuan, University of Glasgow
Ye Yuan
Carl Zang, The Pennsylvania State University
Ron Zeira
Haowen Zhang, Georgia Institute of Technology
Jing Zhang, University of California, Irvine
Qimin Zhang
Ruochi Zhang
Sai Zhang, Stanford University School of Medicine
Yang Zhang, Carnegie Mellon University
Tianming Zhou, Carnegie Mellon University
Jennifer Zou, University of California, Los Angeles

Special Sessions

Co-Chair: Céline Brochier-Armanet, Université de Lyon, France
Co-Chair: Yves Moreau, KU Leuven, Belgium

COSI Board Representatives:
Tijana Milenkovic, University of Notre Dame, United States
Saurabh Sinha, University of Illinois at Urbana-Champaign, United States 
Mark Wass, University of Kent, United Kingdom

Technology Track Committee

Chair: Hagit Shatkay, University of Delaware, United States

Tutorials

Chair: Annette McGrath, Commonwealth Scientific and Industrial Research Organisation (CSIRO), Australia
Co-chair: Michelle Brazas, Ontario Institute for Cancer Research, Canada
Co-chair: Patricia M. Palagi, SIB Swiss Institute of Bioinformatics, Switzerland

COSI TRACK LEADS & ABSTRACT CHAIRS - ISMB/ECCB 2021

3D-SIG: Structural Bioinformatics and Computational Biophysics

Iris Antes, Technical University of Munich, Germany
Douglas Pires, The University of Melbourne, Australia
Rafael Najmanovich, University of Montreal, Canada

BIOINFO-CORE

Madelaine Gogol, Stowers Institute, United States
Rodrigo Ortega Polo, Agriculture and Agri-Food Canada
Alberto Riva, University of Florida, United States

Bio-Ontologies

Tiffany Callahan, University of Colorado Denver, United States
Robert Hoehndorf, King Abdullah University of Science & Technology, Saudi Arabia

BOSC: Bioinformatics Open Source Conference

Peter Cock, James Hutton Institute, United Kingdom
Chris Fields, University of Illinois, United States
Nomi L. Harris, Lawrence Berkeley National Laboratory, United States
Karsten Hokamp, Trinity College Dublin, Ireland

BioVis: Biological Data Visualizations

Danielle Albers Szafir, University of Colorado at Boulder, United States
Jan Byška, Masaryk University, Czech Republic
Helena Jambor, TU Dresden, Germany
Cagatay Turkay, University of Warwick, United Kingdom

CAMDA: Critical Assessment of Massive Data Analysis

David Kreil, Boku University Vienna, Austria
Joaquin Dopazo, Fundación Progreso y Salud, Spain
Paweł P Łabaj, Austrian Academy of Sciences, and Jagiellonian University, Poland 
Wenzhong Xiao, Harvard Medical School, United States

CompMS: Computational Mass Spectrometry

Wout Bittremieux, University of California San Diego, United States
Isabell Bludeau, Max Planck Institute of Biochemistry, Germany
Lindsay Pino, University of Pennsylvania, United States
Timo Sachsenberg, University of Tübingen, Germany

Education: Computational Biology and Bioinformatics Education and Training

Annette McGrath, Australian Commonwealth Scientific and Industrial Research Organisation (CSIRO), Australia
Patricia M. Palagi, SIB Swiss Institute of Bioinformatics, Switzerland
Russell Schwartz, Carnegie Mellon University, United States

EvolCompGen: Evolution & Comparative Genomics

Edward L. Braun, University of Florida, United States
Janani Ravi, Michigan State University, United States
Giltae Song, Pusan National University, Korea

Function: Gene and Protein Function Annotation

Iddo Friedberg, Iowa State University, United States
Casey S. Greene, University of Colorado, United States
Sean D Mooney, University of Washington, United States
Kimberly Reynolds, University of Texas Southwestern Medical Center, United States
Mark Wass, University of Kent, United Kingdom
Predrag Radivojac, Northeastern University, United States

HiTSeq: High Throughput Sequencing Algorithms & Applications

Can Alkan, Bilkent University, Turkey
Ana Conesa, University of Florida, United States
Francisco M. De La Vega, Stanford University, United States
Dirk Evers, Dr. Dirk Evers Consulting, Germany
Gang Fang, Mount Sinai School of Medicine, United States
Kjong Lehmann, ETH-Zürich, Switzerland
Layla Oesper, Carleton College, United States
Gunnar Rätsch, ETH-Zürich, Switzerland

iRNA: Integrative RNA Biology

Athma Pai, University of Massachusetts Medical School, United States
Klemens Hertel, UC Irvine, United States
Michelle Scott, University of Sherbrooke, Canada
Yoseph Barash, University of Pennsylvania, United States

MLCSB: Machine Learning in Computational and Systems Biology

Anshul Kundaje, Stanford University, United States
Gabriele Schweikert, Dundee University, Scotland

Microbiome

Aaron Darling, University of Technology Sydney, Australia
Alice McHardy, Helmholtz Centre for Infection Research, Germany
Alexander Sczyrba, Bielefeld University, Germany
Thea Van Rossum, EMBL Heidelberg, Germany
Zhong Wang, Joint Genome Institute, United States

NetBio: Network Biology

Martina (Tina) Kutmon, Maastricht University, Netherlands
Tijana Milenkovic, University of Notre Dame, United States
Natasa Przulj, ICREA Research Professor, Catalan Institution for Research and Advanced Studies (ICREA); Group Leader, Barcelona Supercomputing Center; Professor of Computer Science, University College London.

RegSys: Regulatory and Systems Genomics

Shaun Mahony, Penn State University, United States
Anthony Mathelier, University of Oslo, Norway
Lonnie Welch, Ohio University, United States
Judith Zaugg, EMBL, Germany

SysMod: Computational Modeling of Biological Systems

Laurence Calzone, Institut Curie, France
Claudine Chaouiya, Aix-Marseille Université, France
Andreas Dräger, University of Tübingen, Germany
María Rodríguez Matínez, IBM Research Europe, Switzerland
Juilee Thakar, University of Rochester Medical Center, United States

Text Mining

Cecilia Arighi, University of Delaware, United States
Lars Juhl Jensen, University of Copenhagen, Denmark
Robert Leaman, NCBI/NLM/NIH, United States
Zhiyong Lu, NCBI/NLM/NIH, United States

TransMed: Translational Medicine Informatics & Applications

Irina Balaur, University of Luxembourg
Wei Gu, University of Luxembourg
Venkata Satagopam, University of Luxembourg
Mansoor Saqi, Kings College London, United Kingdom
Maria Secrier, University College London, United Kingdom

VarI: Variant Interpretation

Emidio Capriotti, University of Bologna, Italy
Hannah Carter, University of California, San Diego, United States
Antonio Rausell, Imagine Institute for Genetic Diseases, France

General Computational Biology

Xin Gao, King Abdullah University of Science and Technology (KAUST), Saudi Arabia
Xuegong Zhang, Tsinghua University, China

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ISCB Overton Prize Keynote

Barbara Engelhardt

Associate Professor
Computer Science Department
Princeton University
United States
https://beehive.cs.princeton.edu

Introduced by: Rita Casadio, Conference Co-chair
Time: Monday, July 26, 16:20 – 17:20 UTC
Presentation Title: Cells in Space: Methods to investigate local neighborhoods of cells

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. Barbara Engelhardt is being recognized as the 2021 winner of the Overton Prize.

Abstract:

Spatial genomics is a burgeoning field but the data come with some challenges for analytic methods, including registering images across samples, small numbers of cells and genes, and a lack of normalization techniques. In this talk, I draw inspiration from the spatial statistics community to describe methods we have developed to study spatial genomics data. In particular, we develop two approaches to partition variation in gene expression into variation attributed to the cell's neighborhood and intrinsic to cell type, and we find differences in the impact of local neighborhoods on cells given location and cell type.

Biography:

Barbara Engelhardt is an Associate Professor in the Princeton Computer Science Department. Prior to that, she was an assistant professor at Duke University in Biostatistics and Bioinformatics and Statistical Sciences. Professor Engelhardt’s research has contributed to statistical models and methods that capture patterns in high-dimensional biomedical data and use those patterns to make discoveries and hypotheses. Her broad application areas include genetic and genomic data, spatial and time series sequencing and imaging data, and hospital patient records. Her approaches have been used to model the state of a system accurately, predict how that system will respond to interventions, and make decisions to push the system into desirable states. Her longer term goals include understanding the underlying biological mechanisms of complex phenotypes and human disease, and making academia and the US a more diverse and inclusive place.

ISCB Accomplishments by a Senior Scientist Award Keynote

Peer Bork

Director (Scientific Activities)
EMBL Heidelberg
Germany
https://www.embl.de/research/units/scb/bork/

Introduced by: Martin Vingron, ISCB Awards Committee Chair
Time: Friday, July 30, 15:20 – 16:20 UTC
Presentation Title: Analyzing microbes in us and on our planet

The ISCB Accomplishments by a Senior Scientist Award recognizes a member of the computational biology community who is more than two decades post-degree and has made major contributions to the field of computational biology. Peer Bork is being honored as the 2021 winner of the ISCB Accomplishments by a Senior Scientist Award.­­

Abstract

Environmental sequencing, that is metagenomics, has become a major driver for uncovering microbial biodiversity and increasingly also for cataloging molecular functions on our planet. The exponentially increasing metagenomes need computational tools and resources to allow researchers to access and digest these valuable data. Based on methods and resources, developed in our group, but also utilizing public bioinformatics resources, here I (i) introduce into our work on the gut microbiome, aimed at basic understanding, but also at medical applications, (ii) show a few examples from tracing the structure and function of microbiomes in different habitats on earth (ocean and soil) and (iii) briefly outline our concept of interacting computational resources, developed and maintained by a network of researchers across Europe.

Biography:

Peer directs the Heidelberg site of EMBL (European Molecular Biology laboratory), focusing on scientific activities. He is also senior group leader and head of the Structural and Computational Biology unit (with C. Müller). In addition, he is honorary professor at the universities of Heidelberg and Würzburg as well as the Fudan university of Shanghai.

Peer received his PhD in Biochemistry (1990) and his Habilitation in Theoretical Biophysics (1995). His research group works in various areas of computational and systems biology, currently with a focus on microbiomes. He has published more than 600 research articles, among them more than 80 in Nature, Science or Cell, and is among the most cited researchers in life sciences (>250.000 citations, Hfactor of 211 beginning of 2021). He is or has been on the editorial board of various journals, including Science and Cell, and functions as senior editor of the journal Molecular Systems Biology.

Peer co-founded five successful biotech companies, two of which went public. More than 50 of his former associates now hold professorships or other group leader positions in prominent institutions all over the world. He received a number of awards, among them the "Nature award for creative mentoring" for his achievements in nurturing and stimulating young scientists and the prestigious "Royal Society and Académie des Sciences Microsoft award" for the advancement of science using computational methods. He further obtained two competitive ERC advanced investigator grants and is elected member of the German national academy of sciences (Leopoldina), the European molecular biology organization (EMBO) and the Academia Europaea.

ISCB Innovator Award Keynote

Ben Raphael

Professor, Computer Science
Lewis-Sigler Institute, Princeton University
United States
https://lsi.princeton.edu/ben-raphael

Introduced by: Christine Orengo, ISCB President
Time: Thursday, July 29, 16:20 – 17:20 UTC
Presentation Title: Quantifying Tumor Heterogeneity across Time and Space

The year 2016 marked the launch of the ISCB Innovator Award, which is given to a leading scientist who is within two decades of receiving the PhD degree, has consistently made outstanding contributions to the field, and continues to forge new directions. Ben Raphael is the 2021 winner of the ISCB Innovator Award.

Abstract:

Tumors are heterogeneous mixtures of normal and cancerous cells with distinct genetic and transcriptional profiles.  In this talk, I will present several computational approaches to quantify tumor heterogeneity and to study tumor evolution using single-cell and spatial sequencing technologies.  For single-cell DNA sequencing data, I will describe algorithms to reconstruct tumor evolution from multiple types of somatic mutations and will use these approaches to analyze changes in tumor genomes over time.  For spatial transcriptomics data, I will introduce algorithms to detect genomic aberrations and to align and integrate data from multiple adjacent tissue sections leveraging both spatial and transcriptional similarity.  I will illustrate applications of these methods to quantify spatial heterogeneity in several cancer types.

Biography:

Ben Raphael is a Professor of Computer Science at Princeton University.  His research focuses on the design of combinatorial and statistical algorithms for the interpretation of biological data.  Recent areas of emphasis include cancer evolution, network/pathway analysis of germline and somatic mutations, single-cell and spatial DNA/RNA sequencing, and structural variation in human and cancer genomes.  His group’s algorithms have been used in multiple projects from The Cancer Genome Atlas (TCGA) and the International Cancer Genome Consortium (ICGC).  He received an S.B. in Mathematics from MIT, a Ph.D. in Mathematics from the University of California, San Diego (UCSD), completed postdoctoral training in Bioinformatics and Computer Science at UCSD, and was on the faculty of Brown University (2006-2016).  He is an elected Fellow of the International Society for Computational Biology (2020) and a recipient of the Alfred P. Sloan Research Fellowship, the NSF CAREER award, and a Career Award at the Scientific Interface from the Burroughs Wellcome Fund.

ISMB/ECCB 2021 - Fellowship Application Process

Chair: Dimitri Perrin, Queensland University of Technology, Australia
Co-chair: Catherine Putonti, Loyola University Chicago, United States

• Sponsors
• Funding Information
• Fellowship Application Overview
• Fellowship Key Dates
• Maximum Award Amounts
• Application Process
• Eligibility Requirements
• Fellowship Contact

SPONSORS

FUNDING INFORMATION:

ISCB is pleased to offer registration fellowships to any member in good standing with priority going to members from low or middle income countries as well as students and postdoctoral fellows to attend the ISMB/ECCB 2021 virtual conference. Funding sources for Fellowships are very limited and we regret that we are not able to fund all applicants. The conference organizers are committed to providing support to as many eligible applicants as possible. Fellowship consideration is based on membership, accepted work to ISMB/ECCB 2021 and country economic status based on World Bank rankings.

FELLOWSHIP APPLICATION OVERVIEW:

1. ISCB members in-need may submit an application for consideration;
2. Applicant must be a current member of ISCB prior to submitting an application;
3. All applicants must attend all six (6) conference days and secure additional funding from other sources in order to be able to cover any additional add-ons when registering as the waiver is for conference registration only;
4. The deadline to submit a fellowship application is June 14, 2021, by 5:00 pm Eastern Daylight Time. No exceptions will be made.

MAXIMUM AWARD AMOUNTS

The maximum fellowship award is the cost of the registration fee for the applicants type and World Bank economic status. A complimentary registration code will be given to accepted applicants to register for ISMB/ECCB 2021. We kindly request that if you submit a fellowship award application that you not register until after the notification deadline (June 18, 2021). ISMB/ECCB 2021 has one standard rate thus there is no penalty to you for delaying your registration.

APPLICATION PROCESS

Fellowships for ISMB/ECCB 2021 are open to all ISCB members in need of registration support.

ELIGIBILITY REQUIREMENTS

Applicant must be a current ISCB member whose membership does not expire prior to December 31, 2021. Applications will not be accepted from non-members; pending memberships do not qualify and must be paid in full prior to submission of an application.

NOTIFICATION

Applicants will be notified no later than June 18, 2021 of the funding status. In some cases applicants may be notified they are on a wait list for funding, which means that ISCB is fully expecting but still awaiting the formal confirmation of our grant award from one or more granting agency, and that awarding of those funds will not be possible until the grant needed to fund the fellowship is confirmed. Any waitlisted applicant that is eventually awarded funds will be offered reimbursed registration if the funding is awarded.

CONTACTS

Questions regarding fellowships should be addressed to: This email address is being protected from spambots. You need JavaScript enabled to view it.

The information on this page is subject to change without notice, and all changed information will be considered final for the purposes of awarding and funding ISMB/ECCB 2021 Fellowships.

Programme Menu

• Birds of a Feather
• COSI Tracks & Other Abstracts
• COVID-19 Special Track
• Hackathon - Tools for Protein Analysis
• Hackathon - ISCB Wikipedia
• ISCB Town Hall
• ISMB/ECCB 2021 Distinguished Keynotes
• Posters
• Proceedings
• Schedule at a Glance
• Science Communication and Science Journalism
• Special Sessions
• Special Track: NIH/OD Office of Data Science Strategy (ODSS)
• Student Council Symposium
• Technology Track
• Tutorials
• WEB 2021

ISMB/ECCB 2021 Virtual Tutorial Program

All times are UTC

Half Day Tutorials:

• Tutorial 1: tidytranscriptomics : introduction to tidy analysis of single-cell and bulk RNA sequencing data
• Tutorial 2: Comprehensive analysis of immunogenomics sequencing data in the cloud to facilitate reproducibility and rigor of immunogenomics research
• Tutorial 3: Meta-learning for Bridging Labeled and Unlabeled Data in Biomedicine

Full Day Tutorials (presented over two half-days):

• Tutorial 4: A practical introduction to multi-omics integration and network analysis (SOLD OUT)
• Tutorial 5: Inside the ‘Black Box’: Explainable Deep Learning Models For Image and Sequence Classification (SOLD OUT)
• Tutorial 6: Nextflow and nf-core: Scalable and FAIR Biomedical Analysis Workflows
• Tutorial 7: The state-of-the-art in microbial community bioinformatics (SOLD OUT)
• Tutorial 8: Reproducible omics data analysis workflows with the COVID-19 Disease Map, WikiPathways and Cytoscape

Thursday, July 22, 11:00 - 15:00 UTC

Presenters:

Maria Doyle, Peter MacCallum Cancer Centre, Australia
Stefano Mangiola, The Walter and Eliza Hall Institute of Medical Research, Australia

This tutorial will present how to perform analysis of single-cell and bulk RNA sequencing data following the tidy data paradigm (Wickham and others 2014). The tidy data paradigm provides a standard way to organise data values within a dataset, where each variable is a column, each observation is a row, and data is manipulated using an easy-to-understand vocabulary. Most importantly, the data structure remains consistent across manipulation and analysis functions.

This can be achieved with the integration of packages present in the R CRAN and Bioconductor ecosysten, including tidyseurat, tidySingleCellExperiment, tidybulk, tidyHeatmap (Mangiola and Papenfuss 2020) and tidyverse (Wickham et al. 2019). These packages are part of the tidytranscriptomics suite that introduces a tidy approach to RNA sequencing data representation and analysis.

Pre-requisites:
• Basic knowledge of RStudio
• Some familiarity with tidyverse syntax
• Some familiarity with bulk RNA-seq and single cell RNA-seq
Recommended Background Reading Introduction to R for Biologists

Learning goals:
• To understand the key concepts and steps of RNA sequencing data analysis.
• To approach data representation and analysis though a tidy data paradigm, integrating tidyverse with tidybulk, tidyseurat, tidySingleCellExperiment and tidyHeatmap.

Learning objectives:
• Recall the key concepts of RNA sequencing data analysis.
• Apply the concepts to publicly available data.
• Create plots that summarise the information content of the data and analysis results.

Maximum Participants: 100

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Thursday, July 22, 15:00 - 19:00 UTC

Presenters:
Victor Greiff, University of Oslo, Norway
Kenneth B. Hoehn, Yale University, United States
Steven H. Kleinstein, Yale University, United States 
Serghei Mangul, University of Southern California, United Statea
Milena Pavlovic, University of Oslo, Norway
Kerui Peng, University of Southern California, United States

Immunogenomics is a field in which genetic information at different levels of biological organization (epigenetics, transcriptomics, metabolomics, cells, tissues, and clinical data) has been characterized and utilized to understand the immune system and immune responses. Immunogenomics studies have offered new opportunities for deepening our understanding of adaptive immune receptors (B-cell receptors, antibodies, T-cell receptors) in the context of a variety of human diseases, such as infectious diseases, cancer, autoimmune conditions, and neurodegenerative disease. Given the importance of adaptive immune receptor research for drug and vaccine discovery, the field is growing at an exponential pace, as exemplified by the user statistics of several immune receptor sequence analysis software suites and databases (Immcantation: >52,000 downloads, >14,000 unique visitors in 2019. VDJTools: >10,000 visitors per year, VDJdb: > 19,000 visitors in 2019 and >40,000 views in 2019). With the number of users exploding, there is a need for software tutorials that lay focus on both rigorous analysis methods as well as reproducibility and interoperability.

We will cover the current stage of immunogenomics methods by providing hybrid lectures and hands-on training sessions. The audience will be equipped with knowledge in this field and the essential skills to conduct adaptive immune receptor analysis independently.

Learning Objectives:

(1) Understand the basics of immunogenomics and its implications for disease diagnosis, drug discovery, and vaccine development.
(2) Understand the basics of computational analysis that facilitate the immunogenomics big data research.
(3) Understand the commonly used computational tools and available datasets for promoting reproducibility and transparency in immunogenomics research.
(4) Understand the cutting edge machine learning approaches for immunogenomics research.

Audience and level:
Beginner or intermediate level. This tutorial is designed for a broader audience interested in immunogenomics research by providing an introduction, demonstration of existing tools, and publicly available datasets.

Maximum Participants: 60

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Friday, July 23, 15:00 - 19:00 UTC

Presenters:

Maria Brbic, Stanford University, United States
Chelsea Finn, Stanford University, United States
Jure Leskovec, Stanford University, United States

Additional Tutorial details are available at: http://snap.stanford.edu/metalearning-ismb/

In biomedical domains labeled datasets are often very difficult and time-consuming to obtain, requiring a lot of costly manual effort and expert knowledge to hand-label classes before machine learning methods can even be used. This results in many scarcely labeled or completely unlabeled datasets. For instance, in protein function prediction a large number of functional labels have only a few labeled genes, or in single-cell transcriptomics novel and rare cell types appear across large, heterogeneous single-cell datasets. While machine learning methods excel on tasks with a large number of labeled datasets that can support learning of highly parameterized models, to solve central problems in biomedicine we need methods that can generalize to unseen domains and datasets given only a few labeled training examples, or in the extreme case to completely unlabeled datasets. Meta-learning methods solve this challenge by acquiring prior knowledge over previously labeled tasks in order to learn to generalize to a new task with insufficient labeled data.

This tutorial will cover principles and recent advancements of meta-learning with the case studies designed based on their high relevance for advancing new biomedical discoveries. We will present representation learning methods that bridge labeled and unlabeled data by learning to generalize across datasets given only a few labeled examples or extremely without any labeled data with an emphasis on interpretability. The tutorial will equip participants with the ability to understand fundamentals and state-of-the-art meta-learning methods and to utilize the learned concepts and methods in their own research.

Learning objectives:

At the completion of the tutorial, the participants will gain understanding and broad knowledge about the basic concepts and recent advances in the meta-learning techniques:
(1) How can we effectively learn from scarcely labeled datasets, e.g., protein functions or structures with a few labeled examples? How can we use prior knowledge to learn to generalize, i.e., meta-learn?
(2) How can we utilize knowledge from existing knowledge bases, such as Gene Ontology and Cell Ontology, to provide interpretations behind decisions based on only few-labeled examples?
(3) How can we learn without any labeled examples? How can we discover new, never-before-seen categories/classes, such as rare and unseen cell types across single-cell experiments?
(4) How can we transfer knowledge across different species, tissues, or sequencing technologies?
(5) What fundamental open problems in biology can benefit from meta-learning techniques? How can meta-learning be applied to these problems?
(6) What frameworks, tools and libraries are available to use meta-learning methods on new datasets and applications?

Maximum Participants: 100

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Thursday, July 22, 11:00 - 15:00 UTC
Friday, July 23, 11:00 - 15:00 UTC

Presenters:

Ashfaq Ali, National Bioinformatics Infrastructure Sweden, Science for Life Laboratory, Lund University
Rui Benfeitas, National Bioinformatics Infrastructure Sweden, Science for Life Laboratory, Stockholm University
Nikolay Oskolkov, National Bioinformatics Infrastructure Sweden, Science for Life Laboratory, Lund University

Advances in next generation sequencing (NGS) and mass spectrometry have recently allowed us to probe deeper and systematically into different layers of biological information flow. We can now capture snapshots of cellular states at single-cell or tissue levels on genomic, transcriptomic, metabolomic, and proteomic levels, to examine relationships between thousands of features in each of these omics and a given phenotype or disease. However, characterization beyond individual omic levels to understand how multi-omic relationships jointly relate with a given phenotype remains a challenge. How may identify the features with the largest phenotypic impact, and how can we identify patterns among the different layers?

In this tutorial we will introduce several different approaches for integration of multi-omics data including supervised and unsupervised learning and network analyses. We will highlight some of the key issues in dealing with the high multidimensionality that characterizes multi-omic data and techniques to address them. We will also discuss some of the most successful methods for multi-omic data abstraction, and how machine learning approaches can be used in unraveling biological relationships. We will show how biological network analyses can be used to identify patterns within and between omics, and how communities of features may be related with phenotypic data and biologic functions. Finally, we will discuss how meta-analyses and network meta-analyses can be used in analyzing studies from independent experiments.

Learning Objectives:
(1) Identify common issues in integration of highly multidimensional omics data.
(2) Identify key methods for data integration through supervised and unsupervised machine learning approaches.
(3) Understand how biological network analysis may assist in identifying coordinated patterns between features and associating feature communities with phenomic and biological functions.
(4) Hands-on experience in supervised/unsupervised integration and biological network analysis.

Audience and level:
Aimed at bioinformaticians and computational biologists with experience in analysis of highthroughput data and basic statistics knowledge, with R or Python coding experience. Knowledge of machine learning techniques is advantageous. Hands-on sessions will comprise both R and Python coding.

Maximum Participants: 30

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Thursday, July 22, 11:00 - 15:00 UTC
Friday, July 23, 11:00 - 15:00 UTC

Presenters:

Panagiotis Alexiou, Central European Institute of Technology, Masaryk University, Czech Republic
David Cechak, Central European Institute of Technology, Masaryk University, Czech Republic
Filip Jozefov, Faculty of Informatics, Masaryk University, Czech Republic
Vlastimil Martinek, Central European Institute of Technology, Masaryk University, Czech Republic
Petr Simecek, Central European Institute of Technology, Masaryk University, Czech Republic

Computational Biologists have been using Machine Learning techniques based on Artificial Neural Networks for decades. New developments in the Machine Learning field over the past years have revolutionized the efficiency of Neural Networks and bring us to the era of Deep Learning. In the news, you can read about Deep Learning beating experts in Go, Chess and StarCraft, translating texts and speech between languages, turning the steering wheels of self-driving cars and even to tag kittens, Not-Hotdogs, and tumours in images. In our field, we have witnessed such systems reaching competitive accuracy with experienced radiologists, predicting folding of proteins and calling single nucleotide polymorphisms in genomic data better than any other method.

In this tutorial we utilize three powerful components that are freely available for use: TensorFlow is an open source library for deep learning and machine learning in general. Thanks to the second one, Google Collaboratory, computational resources needed to train TensorFlow models are available without cost. And finally, TensorFlow.js, will enable us to deploy the trained model as a static web page that can be easily hosted, e.g. on GitHub Pages. We will demonstrate Google Collaboratory + TensorFlow + TensorFlow.js on two examples: classification of images (cells & tissues) and classification of genomic sequences.

The key part of the tutorial will be evaluation and interpretation of the trained model. What could go wrong and how to diagnose it? We will start with simple techniques, like measuring the impact of simple perturbation, and end with an Integrated Gradient method to identify part of input mostly contribution to the decision, introduced in a paper “Axiomatic Attribution for Deep Networks”.

Audience and level: This tutorial is intended for students and practitioners interested in getting their hands dirty with neural networks. It is designed to be an introduction and a starting point for further work and study. Beginners are welcome. Familiarity with Python is necessary, experience with Jupyter Notebooks, pandas & numpy will be useful.

Maximum Participants: 70

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Thursday, July 22, 15:00 - 19:00 UTC
Friday, July 23, 15:00 - 19:00 UTC

Presenters:

Phil Ewels, nf-core creator; Bioinformatics Team Leader, SciLifeLab, Sweden 
Evan Floden, Nextflow co-creator; Seqera Labs, Spain
Paolo Di Tommaso, Nextflow co-creator; Seqera Labs, Spain

Nextflow is an open-source workflow management system that prioritizes portability and reproducibility. It enables users to develop and seamlessly scale genomics workflows locally, on HPC clusters, or in major cloud providers’ infrastructures. Developed since 2014 and backed by a fast-growing community, the Nextflow ecosystem is made up of users and developers across academia, government and industry. It counts over 1M downloads and over 10K users worldwide.

nf-core is a framework for the development of collaborative, peer-reviewed, best-practice analysis pipelines. All nf-core pipelines are written in Nextflow and benefit from the ability to be executed on most computational infrastructures, as well as having native support for container technologies such as Docker and Singularity. The nf-core community has developed a suite of tools that automate pipeline creation, testing, deployment and synchronization. The goal is to provide a framework for high-quality bioinformatics pipelines that can be used across all institutions and research facilities.

This intensive tutorial is targeted at bioinformaticians and will cover everything to get users started with Nextflow and nf-core.

Audience: This tutorial is targeted and bioinformaticians and developers interested in writing and deploying biomedical analysis pipelines with Nextflow.

Requirements: Participants should have a basic knowledge of Linux shell programming. Virtual environments will be provided for registered participants. 

Maximum Participants: 100

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Thursday, July 22, 15:00 - 19:00 UTC
Friday, July 23, 15:00 - 19:00 UTC

Organizers & Presenters:

Curtis Huttenhower, Harvard T.H. Chan School of Public Health, United States
Melanie Schirmer, Technical University of Munich, Germany
Nicola Segata, University of Trento, Italy

Presenters:

Eric Franzosa, Harvard T.H. Chan School of Public Health, United States
Philipp Muench, Helmholtz Centre for Infection Research, Germany
Kelsey Thompson, Harvard T.H. Chan School of Public Health, United States
Aaron Walsh, Broad Institute of MIT and Harvard, United States

This tutorial will introduce attendees to the current state-of-the-art in computational and quantitative methods for microbial community analyses. These will focus on integrating modern culture-independent sequencing (shotgun metagenomics and metatranscriptomics) with other molecular data (metabolomics, metaproteomics) and applying appropriate, accurate upstream bioinformatics and downstream biostatistics. This will include both human microbiome epidemiology and environmental microbial ecological, phylogenetic, and toxicology applications.

Attendees are assumed to be familiar with basic microbial community concepts and with command line environments, ideally with some facility in Python and/or R, but are not required to have extensive prior experience with metagenomics. The tutorial will mix lectures introducing important current analysis concepts with hands-on labs using pre-built cloud instances including demonstration data and bioBakery software tools. It will conclude with a discussion of gaps, needs, challenges, and potential next steps for bioinformaticians interested in the field of microbial community research.

Learning Objectives:
(1) Understand the breadth of available microbial community molecular profiling data and computationalanalysis approaches to it.
(2) Apply bioBakery tools for basic microbiome analysis tasks (e.g. taxonomic and functional profiling).
(3) Integrate them with external tools and advanced statistical and visualization techniques for multi-omic integration and downstream analysis.
(4) Recognize and avoid common pitfalls in microbial community bioinformatics, particularly with respect to statistical gotchas, false positives, and noise characteristics of microbiome data.
(5) Discuss gaps in the field and opportunities for future work in microbiome bioinformatics.

Audience: Should be familiar with basic microbial community concepts and, importantly, have facility with command line computing environments. We will provide prebuilt cloud instances for each participant, and participants should be able to manipulate command line tools and data within these instances with little to no introduction. However, extensive familiarity with current microbial community bioinformatics is not required, as these will be introduced (briefly) at the beginning of the tutorial.

Maximum Participants: 60

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Thursday, July 22, 15:00 - 19:00 UTC
Friday, July 23, 15:00 - 19:00 UTC

Presenters:

Lauren Dupuis, Maastricht University, Netherlands
John “Scooter” Morris, UCSF, United States
Martina Summer Kutmon, Maastricht University, Netherlands

During the COVID19 pandemic, an international group of over 200 researchers started a collaboration to build a comprehensive map of SARS-CoV-2 related processes from virus uptake and virus replication to host immune response. In this tutorial session, we will highlight some of the use cases of this collection of highly curated pathway models for omics data analysis using pathway and network approaches.

Given the constant influx of new knowledge and data, the development of automated and reproducible data analysis workflows is essential. After a short introduction of the COVID-19 Disease Map project and the WikiPathways community curated pathway database, the tutorial will start with a session focused on Cytoscape, one of the most popular tools for network analysis and visualization, and its automation features. During the hands-on session in the afternoon, we will instruct participants on how to make use of three automated R-based transcriptomics data analysis workflows focused on pathway enrichment, tissue-specific pathway activity, network visualization, and network extension.

Importantly, while we will focus on the COVID-19 Disease Map and COVID-19 related transcriptomics datasets, the majority of the workflows can be easily utilized for other applications.

Audience: The audience for this tutorial session are bioinformaticians or life scientists interested in learning to use automation in R to perform pathway and network analysis of transcriptomics data. Participants should have some prior experience with R data analysis. Participants are required to install a Cytoscape 3.8, R and RStudio, and optionally Jupyter notebooks installed. Detailed instructions will be provided in the weeks prior to the tutorial.

Learning Objectives:
(1) Participants will understand the basics of pathway and network analysis.
(2) Participants will be able to perform reproducible workflows in R for transcriptomics data analysis using pathway information from the COVID19 Disease Map and WikiPathways.
(3) Participants will be able to set up and perform an automated network analysis in Cytoscape.

Maximum Participants: 30

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