4.7 Article

Answer ALS, a large-scale resource for sporadic and familial ALS combining clinical and multi-omics data from induced pluripotent cell lines

期刊

NATURE NEUROSCIENCE
卷 25, 期 2, 页码 226-+

出版社

NATURE PORTFOLIO
DOI: 10.1038/s41593-021-01006-0

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资金

  1. Robert Packard Center for ALS Research at Johns Hopkins
  2. Travelers Insurance
  3. ALS Finding a Cure Foundation
  4. Stay Strong Vs. ALS
  5. Answer ALS Foundation
  6. Microsoft
  7. Caterpillar Foundation
  8. American Airlines
  9. Team Gleason
  10. National Institutes of Health
  11. Fishman Family Foundation
  12. Aviators Against ALS
  13. AbbVie Foundation
  14. Chan Zuckerberg Initiative
  15. ALS Association
  16. National Football League
  17. F. Prime
  18. Bruce Edwards Foundation
  19. Judith and Jean Pape Adams Charitable Foundation
  20. Muscular Dystrophy Association
  21. Les Turner ALS Foundation
  22. PGA Tour
  23. Bari Lipp Foundation

向作者/读者索取更多资源

Answer ALS is a resource that includes patient-derived iPS cell lines, multi-omic data from iPS neurons, and clinical and smartphone data from over 1,000 ALS patients. This data can be used to identify distinct disease subgroups in ALS.
Answer ALS is a resource of patient-derived iPS cell lines, multi-omic data derived from iPS neurons and longitudinal clinical and smartphone data from over 1,000 patients with ALS. This serves as a foundation to identify distinct disease subgroups. Answer ALS is a biological and clinical resource of patient-derived, induced pluripotent stem (iPS) cell lines, multi-omic data derived from iPS neurons and longitudinal clinical and smartphone data from over 1,000 patients with ALS. This resource provides population-level biological and clinical data that may be employed to identify clinical-molecular-biochemical subtypes of amyotrophic lateral sclerosis (ALS). A unique smartphone-based system was employed to collect deep clinical data, including fine motor activity, speech, breathing and linguistics/cognition. The iPS spinal neurons were blood derived from each patient and these cells underwent multi-omic analytics including whole-genome sequencing, RNA transcriptomics, ATAC-sequencing and proteomics. The intent of these data is for the generation of integrated clinical and biological signatures using bioinformatics, statistics and computational biology to establish patterns that may lead to a better understanding of the underlying mechanisms of disease, including subgroup identification. A web portal for open-source sharing of all data was developed for widespread community-based data analytics.

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