About the ALS Knowledge Portal project

Data in the ALS Knowledge Portal

Data used in the ALS Knowledge Portal were analyzed by the Neale lab. For details about quality control, please visit: Farhan et al., 2018 BioRxiv: https://www.biorxiv.org/content/10.1101/307835v2. Sali Farhan was supported by the ALS Canada Tim E. Noël Postdoctoral Fellowship.

Data in the ALS Knowledge Portal were collected from the Familial ALS (FALS) Consortium and the ALS Genetics (ALSGENS) Consortium.

The members of the FALS Consortium are Giuseppe Lauria, Orla Hardiman, Russell L McLaughlin, Letizia Mazzini, Stefano Duga, Anneloor L M A ten Asbroek, Frank Baas, Lucia Corrado, Sandra D'Alfonso, Jonathan D Glass, Meraida Polak, Seneshaw Asress, Antonia Ratti, Cinzia Tiloca, Claudia Colombrita, Daniela Calini, Federico Verde, Nicola Ticozzi, Vincenzo Silani, Claudia Fallini, Diane McKenna-Yasek, John E. Landers, Kevin P. Kenna, Pamela Keagle, Peter C. Sapp, Robert H. Brown Jr, Cinzia Bertolin, Gianni Sorarù, Giorgia Querin, Giacomo P. Comi, Roberto Del Bo, Stefania Corti, Cristina Cereda, Mauro Ceroni, Stella Gagliardi, Garth A. Nicholson, Ian P. Blair, Kelly L. Williams, Karen E. Morrison, Hardev Pall, Ammar Al-Chalabi, Andrew King, Athina Soragia Gkazi, Bradley N. Smith, Caroline Vance, Christopher E. Shaw, Claire Troakes, Jack W. Miller, Safa Al-Sarraj, Simon D. Topp, Michael Simpson, Nick W. Parkin, Claire S. Leblond, Guy A. Rouleau, Patrick A. Dion, Jacqueline de Belleroche, Kevin Talbot, Martin R. Turner, Pamela J. Shaw, P. Nigel Leigh, Alberto García-Redondo, Jesús Esteban-Pérez, José Luis Muñoz-Blanco, Barbara Castellotti, Cinzia Gellera, Franco Taroni, and Viviana Pensato.

The members of the ALS Sequencing Consortium are Andrew S. Allen, Stanley Appel, Robert H. Baloh, Richard S. Bedlack, Braden E. Boone, Robert Brown, John P. Carulli, Alessandra Chesi, Wendy K. Chung, Elizabeth T. Cirulli, Gregory M. Cooper, Julien Couthouis, Aaron G. Day-Williams, Patrick A. Dion, Summer Gibson, Aaron D. Gitler, Jonathan D. Glass, David B. Goldstein, Yujun Han, Matthew B. Harms, Tim Harris, Sebastian D. Hayes, Angela L. Jones, Jonathan Keebler, Brian J. Krueger, Brittany N. Lasseigne, Shawn E. Levy, Yi-Fan Lu, Tom Maniatis, Diane McKenna-Yasek, Timothy M. Miller, Richard M. Myers, Slavé Petrovski, Stefan M. Pulst, Alya R. Raphael, John M. Ravits, Zhong Ren, Guy A. Rouleau, Peter C. Sapp, Neil A. Shneider, Ericka Simpson, Katherine B. Sims, John F. Staropoli, Lindsay L. Waite, Quanli Wang, Jack R. Wimbish, and Winnie W. Xin.

The Knowledge Portal Framework

The Knowledge Portal framework is being developed as part of the Accelerating Medicines Partnership, a public-private partnership between the National Institutes of Health (NIH), the U.S. Food and Drug Administration (FDA), 10 biopharmaceutical companies, and multiple non-profit organizations that is managed through the Foundation for the NIH (FNIH). AMP seeks to harness collective capabilities, scale, and resources toward improving current efforts to develop new therapies for complex, heterogeneous diseases. The ultimate goal is to increase the number of new diagnostics and therapies for patients while reducing the time and cost of developing them, by jointly identifying and validating promising biological targets for several diseases, including type 2 diabetes.

Knowledge Portals are intended to serve three key functions:

  1. To be central repositories for large datasets of human genetic information linked to complex diseases and related traits.
  2. To function as scientific discovery engines that can be harnessed by the community at large, and assist in the selection of new targets for drug design.
  3. Eventually, to facilitate the conduct of customized analyses by any interested user around the world, doing so in a secure manner that provides high quality results while protecting the integrity of the data.

Knowledge Portals are intended to be secure, compliant with pertinent ethical regulations, accessible to a wide user base, inviting to researchers who may want to contribute data and participate in analyses, organic in the continuous incorporation of scientific advances, modular in their analytical capabilities and user interfaces, automated, rigorous in the quality of data aggregation and returned results, versatile, and sustainable.