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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.

bioinformatics

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

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