Prediction and Characterization of Missing Proteomic Data in Desulfovibrio vulgaris
Joint Authors
Qiao, Jianjun
Li, Feng
Nie, Lei
Zhang, Weiwen
Wu, Gang
Source
Comparative and Functional Genomics
Issue
Vol. 2011, Issue 2011 (31 Dec. 2011), pp.1-16, 16 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2011-05-04
Country of Publication
Egypt
No. of Pages
16
Main Subjects
Abstract EN
Proteomic datasets are often incomplete due to identification range and sensitivity issues.
It becomes important to develop methodologies to estimate missing proteomic data, allowing better interpretation of proteomic datasets and metabolic mechanisms underlying complex biological systems.
In this study, we applied an artificial neural network to approximate the relationships between cognate transcriptomic and proteomic datasets of Desulfovibrio vulgaris, and to predict protein abundance for the proteins not experimentally detected, based on several relevant predictors, such as mRNA abundance, cellular role and triple codon counts.
The results showed that the coefficients of determination for the trained neural network models ranged from 0.47 to 0.68, providing better modeling than several previous regression models.
The validity of the trained neural network model was evaluated using biological information (i.e.
operons).
To seek understanding of mechanisms causing missing proteomic data, we used a multivariate logistic regression analysis and the result suggested that some key factors, such as protein instability index, aliphatic index, mRNA abundance, effective number of codons (Nc) and codon adaptation index (CAI) values may be ascribed to whether a given expressed protein can be detected.
In addition, we demonstrated that biological interpretation can be improved by use of imputed proteomic datasets.
American Psychological Association (APA)
Li, Feng& Nie, Lei& Wu, Gang& Qiao, Jianjun& Zhang, Weiwen. 2011. Prediction and Characterization of Missing Proteomic Data in Desulfovibrio vulgaris. Comparative and Functional Genomics،Vol. 2011, no. 2011, pp.1-16.
https://search.emarefa.net/detail/BIM-497470
Modern Language Association (MLA)
Li, Feng…[et al.]. Prediction and Characterization of Missing Proteomic Data in Desulfovibrio vulgaris. Comparative and Functional Genomics No. 2011 (2011), pp.1-16.
https://search.emarefa.net/detail/BIM-497470
American Medical Association (AMA)
Li, Feng& Nie, Lei& Wu, Gang& Qiao, Jianjun& Zhang, Weiwen. Prediction and Characterization of Missing Proteomic Data in Desulfovibrio vulgaris. Comparative and Functional Genomics. 2011. Vol. 2011, no. 2011, pp.1-16.
https://search.emarefa.net/detail/BIM-497470
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-497470