S. elongatus PCC 7942 Carbon Metabolism Proteomics (MC-DP2)

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Created on 2024-10-14T20:33:54+00:00 by LN Anderson; Last Updated 2024-11-13T15:01:44+00:00

S. elongatus PCC 7942 Carbon Metabolism Proteomics (MC-DP2)

The purpose of this experiment was to understand the regulatory dynamics of carbon fixation in S. elongatus PCC 7942 under light and dark treatment conditions using a tandem mass tag (TMT) and global analysis approach. TMT16 and global proteomic data was acquired using a Q-Exactive HF-X mass spectrometer and processed using MSGF+ for downstream RedoxProteomics proteome expression analysis.

Accessible Digital Data Downloads (pending)*

Processed datasets are openly accessible from the download button and contain secondary processed TMT16 and global proteomics results files, sample naming key, and supporting metadata materials. See corresponding primary data accessions below and relevant computational source code at RedoxProteomics01 supporting data transparency and reuse.

*Corresponding primary publication pending.

Linked Primary Data

Primary liquid chromatography-mass spectrometry (LC-MS) raw measurement data are openly accessible for download at the Mass Spectrometry Interactive Virtual Environment (MassIVE) community repository under the accession MSV000096184 and can be formally cited using the MassIVE registered digital object identifier at https://doi.org/10.25345/C5HX1630P.

 

Funding Acknowledgments

The research data described here was funded in whole or in part by the Predictive Phenomics Initiative (PPI) at Pacific Northwest National Laboratory (PNNL). This work was conducted under the Laboratory Directed Research and Development Program at PNNL. A portion of this research was performed in the Environmental Molecular Sciences Laboratory, a national scientific user facility sponsored by the U.S. Department of Energy (DOE) Office of Science located at PNNL. PNNL is a multiprogram national laboratory operated by Battelle for the DOE under Contract No. DE-AC05-76RL01830.

Citation Policy

In efforts to enable discovery, reproducibility, and reuse of PPI-funded project dataset citations in accordance with best practices (as outlined by the FORCE11 Data Citation Principles), we ask that all reuse of project data and metadata download materials acknowledge all primary and secondary dataset citations and corresponding journal articles where applicable.

Data Licensing

CC BY 4.0 (dataset DOI downloads), CC0 1.0 (PNNL DataHub policy default)

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Margaret S. Cheung is a biological physicist and a computational scientist on the Computing, Analytics, and Modeling team at EMSL. She graduated from the National Taiwan University in 1994 and went on to obtain a Ph.D. degree from the University of California at San Diego in 2003. She was then...

Lindsey Anderson’s research has been dedicated to the identification and characterization of novel, targeted and non-targeted, functional metabolic interactions using a high-throughput systems biology and computational biology approach. Her expertise in functional metabolism and multidisciplinary...

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