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

> 商业许可源文 · EUROPE_PMC · [CC-BY](https://creativecommons.org/licenses/by/)

书目信息

引用：Baxi EG, Thompson T, Li J, Kaye JA, Lim RG, Wu J, Ramamoorthy D, Lima L, Vaibhav V, Matlock A, Frank A, Coyne AN, Landin B, Ornelas L, Mosmiller E, Thrower S, Farr SM, Panther L, Gomez E, Galvez E, Perez D, Meepe I, Lei S, Mandefro B, Trost H, Pinedo L, Banuelos MG, Liu C, Moran R, Garcia V, Workman M, Ho R, Wyman S, Roggenbuck J, Harms MB, Stocksdale J, Miramontes R, Wang K, Venkatraman V, Holewenski R, Sundararaman N, Pandey R, Manalo DM, Donde A, Huynh N, Adam M, Wassie BT, Vertudes E, Amirani N, Raja K, Thomas R, Hayes L, Lenail A, Cerezo A, Luppino S, Farrar A, Pothier L, Prina C, Morgan T, Jamil A, Heintzman S, Jockel-Balsarotti J, Karanja E, Markway J, McCallum M, Joslin B, Alibazoglu D, Kolb S, Ajroud-Driss S, Baloh R, Heitzman D, Miller T, Glass JD, Patel-Murray NL, Yu H, Sinani E, Vigneswaran P, Sherman AV, Ahmad O, Roy P, Beavers JC, Zeiler S, Krakauer JW, Agurto C, Cecchi G, Bellard M, Raghav Y, Sachs K, Ehrenberger T, Bruce E, Cudkowicz ME, Maragakis N, Norel R, Van Eyk JE, Finkbeiner S, Berry J, Sareen D, Thompson LM, Fraenkel E, Svendsen CN, Rothstein JD. (2022). Answer ALS, a large-scale resource for sporadic and familial ALS combining clinical and multi-omics data from induced pluripotent cell lines. Nature neuroscience. PMID 35115730 · PMC8825283 · DOI 10.1038/s41593-021-01006-0

证据类型：DATABASE_RESOURCE

主题：multi-omics

被引次数（采集时）：152

原始记录：[Europe PMC](https://europepmc.org/article/MED/35115730)

来源许可：[CC-BY](https://creativecommons.org/licenses/by/)

作者摘要（按来源许可复用）

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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