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Proteomic profiling dataset of chemical perturbations in multiple biological backgrounds

While gene expression profiling has traditionally been the method of choice for large-scale perturbational profiling studies, proteomics has emerged as an effective tool in this context for directly monitoring cellular responses to perturbations. We previously reported a pilot library containing 340...

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Autores principales: Dele-Oni, Deborah O., Christianson, Karen E., Egri, Shawn B., Vaca Jacome, Alvaro Sebastian, DeRuff, Katherine C., Mullahoo, James, Sharma, Vagisha, Davison, Desiree, Ko, Tak, Bula, Michael, Blanchard, Joel, Young, Jennie Z., Litichevskiy, Lev, Lu, Xiaodong, Lam, Daniel, Asiedu, Jacob K., Toder, Caidin, Officer, Adam, Peckner, Ryan, MacCoss, Michael J., Tsai, Li-Huei, Carr, Steven A., Papanastasiou, Malvina, Jaffe, Jacob D.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8387426/
https://www.ncbi.nlm.nih.gov/pubmed/34433823
http://dx.doi.org/10.1038/s41597-021-01008-4
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author Dele-Oni, Deborah O.
Christianson, Karen E.
Egri, Shawn B.
Vaca Jacome, Alvaro Sebastian
DeRuff, Katherine C.
Mullahoo, James
Sharma, Vagisha
Davison, Desiree
Ko, Tak
Bula, Michael
Blanchard, Joel
Young, Jennie Z.
Litichevskiy, Lev
Lu, Xiaodong
Lam, Daniel
Asiedu, Jacob K.
Toder, Caidin
Officer, Adam
Peckner, Ryan
MacCoss, Michael J.
Tsai, Li-Huei
Carr, Steven A.
Papanastasiou, Malvina
Jaffe, Jacob D.
author_facet Dele-Oni, Deborah O.
Christianson, Karen E.
Egri, Shawn B.
Vaca Jacome, Alvaro Sebastian
DeRuff, Katherine C.
Mullahoo, James
Sharma, Vagisha
Davison, Desiree
Ko, Tak
Bula, Michael
Blanchard, Joel
Young, Jennie Z.
Litichevskiy, Lev
Lu, Xiaodong
Lam, Daniel
Asiedu, Jacob K.
Toder, Caidin
Officer, Adam
Peckner, Ryan
MacCoss, Michael J.
Tsai, Li-Huei
Carr, Steven A.
Papanastasiou, Malvina
Jaffe, Jacob D.
author_sort Dele-Oni, Deborah O.
collection PubMed
description While gene expression profiling has traditionally been the method of choice for large-scale perturbational profiling studies, proteomics has emerged as an effective tool in this context for directly monitoring cellular responses to perturbations. We previously reported a pilot library containing 3400 profiles of multiple perturbations across diverse cellular backgrounds in the reduced-representation phosphoproteome (P100) and chromatin space (Global Chromatin Profiling, GCP). Here, we expand our original dataset to include profiles from a new set of cardiotoxic compounds and from astrocytes, an additional neural cell model, totaling 5300 proteomic signatures. We describe filtering criteria and quality control metrics used to assess and validate the technical quality and reproducibility of our data. To demonstrate the power of the library, we present two case studies where data is queried using the concept of “connectivity” to obtain biological insight. All data presented in this study have been deposited to the ProteomeXchange Consortium with identifiers PXD017458 (P100) and PXD017459 (GCP) and can be queried at https://clue.io/proteomics.
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spelling pubmed-83874262021-09-14 Proteomic profiling dataset of chemical perturbations in multiple biological backgrounds Dele-Oni, Deborah O. Christianson, Karen E. Egri, Shawn B. Vaca Jacome, Alvaro Sebastian DeRuff, Katherine C. Mullahoo, James Sharma, Vagisha Davison, Desiree Ko, Tak Bula, Michael Blanchard, Joel Young, Jennie Z. Litichevskiy, Lev Lu, Xiaodong Lam, Daniel Asiedu, Jacob K. Toder, Caidin Officer, Adam Peckner, Ryan MacCoss, Michael J. Tsai, Li-Huei Carr, Steven A. Papanastasiou, Malvina Jaffe, Jacob D. Sci Data Data Descriptor While gene expression profiling has traditionally been the method of choice for large-scale perturbational profiling studies, proteomics has emerged as an effective tool in this context for directly monitoring cellular responses to perturbations. We previously reported a pilot library containing 3400 profiles of multiple perturbations across diverse cellular backgrounds in the reduced-representation phosphoproteome (P100) and chromatin space (Global Chromatin Profiling, GCP). Here, we expand our original dataset to include profiles from a new set of cardiotoxic compounds and from astrocytes, an additional neural cell model, totaling 5300 proteomic signatures. We describe filtering criteria and quality control metrics used to assess and validate the technical quality and reproducibility of our data. To demonstrate the power of the library, we present two case studies where data is queried using the concept of “connectivity” to obtain biological insight. All data presented in this study have been deposited to the ProteomeXchange Consortium with identifiers PXD017458 (P100) and PXD017459 (GCP) and can be queried at https://clue.io/proteomics. Nature Publishing Group UK 2021-08-25 /pmc/articles/PMC8387426/ /pubmed/34433823 http://dx.doi.org/10.1038/s41597-021-01008-4 Text en © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) applies to the metadata files associated with this article.
spellingShingle Data Descriptor
Dele-Oni, Deborah O.
Christianson, Karen E.
Egri, Shawn B.
Vaca Jacome, Alvaro Sebastian
DeRuff, Katherine C.
Mullahoo, James
Sharma, Vagisha
Davison, Desiree
Ko, Tak
Bula, Michael
Blanchard, Joel
Young, Jennie Z.
Litichevskiy, Lev
Lu, Xiaodong
Lam, Daniel
Asiedu, Jacob K.
Toder, Caidin
Officer, Adam
Peckner, Ryan
MacCoss, Michael J.
Tsai, Li-Huei
Carr, Steven A.
Papanastasiou, Malvina
Jaffe, Jacob D.
Proteomic profiling dataset of chemical perturbations in multiple biological backgrounds
title Proteomic profiling dataset of chemical perturbations in multiple biological backgrounds
title_full Proteomic profiling dataset of chemical perturbations in multiple biological backgrounds
title_fullStr Proteomic profiling dataset of chemical perturbations in multiple biological backgrounds
title_full_unstemmed Proteomic profiling dataset of chemical perturbations in multiple biological backgrounds
title_short Proteomic profiling dataset of chemical perturbations in multiple biological backgrounds
title_sort proteomic profiling dataset of chemical perturbations in multiple biological backgrounds
topic Data Descriptor
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8387426/
https://www.ncbi.nlm.nih.gov/pubmed/34433823
http://dx.doi.org/10.1038/s41597-021-01008-4
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