Integrative analysis of transcriptomic and proteomic data of Desulfovibrio vulgaris: a non-linear model to predict abundance of undetected proteins.

ABSTRACT:

Motivation

Gene expression profiling technologies can generally produce mRNA abundance data for all genes in a genome. A dearth of proteomic data persists because identification range and sensitivity of proteomic measurements lag behind those of transcriptomic measurements. Using partial proteomic data, it is likely that integrative transcriptomic and proteomic analysis may introduce significant bias. Developing methodologies to accurately estimate missing proteomic data will allow better integration of transcriptomic and proteomic datasets and provide deeper insight into metabolic mechanisms underlying complex biological systems.

Results

In this study, we present a non-linear data-driven model to predict abundance for undetected proteins using two independent datasets of co

SUBMITTER: Torres-Garcia W 

PROVIDER: S-EPMC2712339 | biostudies-literature | 2009 Aug

REPOSITORIES: biostudies-literature