A computational strategy to analyze label-free temporal bottom-up proteomics data.
Biological systems are in a continual state of flux, which necessitates an understanding of the dynamic nature of protein abundances. The study of protein abundance dynamics has become feasible with recent improvements in mass spectrometry-based quantitative proteomics. However, a number of challenges still remain related to how best to extract biological information from dynamic proteomics data, for example, challenges related to extraneous variability, missing abundance values, and the identification of significant temporal patterns. This paper describes a strategy that addresses these issues and demonstrates its values for analyzing temporal bottom-up proteomics data using data from a Rhodobacter sphaeroides 2.4.1 time-course study.
Du
Xiuxia
X
Fundamental and Computational Sciences Directorate, Pacific Northwest National Laboratory, Richland, Washington 99352, USA.
Callister
Stephen J
SJ
Manes
Nathan P
NP
Adkins
Joshua N
JN
Alexandridis
Roxana A
RA
Zeng
Xiaohua
X
Roh
Jung Hyeob
JH
Smith
William E
WE
Donohue
Timothy J
TJ
Kaplan
Samuel
S
Smith
Richard D
RD
Lipton
Mary S
MS
D001426:Bacterial Proteins / Q000378:metabolism; D019295:Computational Biology; D003205:Computing Methodologies; D020543:Proteome / Q000378:metabolism*; D040901:Proteomics; D012242:Rhodobacter sphaeroides / Q000378:metabolism; D013997:Time Factors
D001426:Bacterial Proteins; D020543:Proteome