A statistical framework for protein quantitation in bottom-up MS-based proteomics.
BACKGROUND
Quantitative mass spectrometry-based proteomics requires protein-level estimates and associated confidence measures. Challenges include the presence of low quality or incorrectly identified peptides and informative missingness. Furthermore, models are required for rolling peptide-level information up to the protein level.


RESULTS
We present a statistical model that carefully accounts for informative missingness in peak intensities and allows unbiased, model-based, protein-level estimation and inference. The model is applicable to both label-based and label-free quantitation experiments. We also provide automated, model-based, algorithms for filtering of proteins and peptides as well as imputation of missing values. Two LC/MS datasets are used to illustrate the methods. In simulation studies, our methods are shown to achieve substantially more discoveries than standard alternatives.


BACKGROUND
The software has been made available in the open-source proteomics platform DAnTE (http://omics.pnl.gov/software/).
Karpievitch
Yuliya
Y
Department of Statistics, Texas A&M University, 3143 TAMU, College Station, TX 77843, USA.
Stanley
Jeff
J
Taverner
Thomas
T
Huang
Jianhua
J
Adkins
Joshua N
JN
Ansong
Charles
C
Heffron
Fred
F
Metz
Thomas O
TO
Qian
Wei-Jun
WJ
Yoon
Hyunjin
H
Smith
Richard D
RD
Dabney
Alan R
AR
D030562:Databases, Protein; D013058:Mass Spectrometry / Q000379:methods*; D015233:Models, Statistical; D011506:Proteins / Q000032:analysis*; D020543:Proteome / Q000032:analysis; D040901:Proteomics / Q000379:methods*
D011506:Proteins; D020543:Proteome