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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...
Autores principales: | , , , , , , , , , , , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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Nature Publishing Group UK
2021
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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. |
format | Online Article Text |
id | pubmed-8387426 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
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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