Integrated analysis of transcriptomic and proteomic data

Abstract

Until recently, understanding the regulatory behavior of cells has been pursued through independent analysis of the transcriptome or the proteome. Based on the central dogma, it was generally assumed that there exist a direct correspondence between mRNA transcripts and generated protein expressions. However, recent studies have shown that the correlation between mRNA and Protein expressions can be low due to various factors such as different half lives and post transcription machinery. Thus, a joint analysis of the transcriptomic and proteomic data can provide useful insights that may not be deciphered from individual analysis of mRNA or protein expressions. This article reviews the existing major approaches for joint analysis of transcriptomic and proteomic data. We categorize the different approaches into eight main categories based on the initial algorithm and final analysis goal. We further present analogies with other domains and discuss the existing research problems in this area.

Keywords: Combined analysis review.; Data fusion approaches; Integrated omics; Joint modeling; Proteome; Transcriptome.

Fig. (1)

Integration of transcriptomic and proteomic dataset by simple union method.

Fig. (2)

Hidden node analysis reveals new node X5. The dotted line represents the connectivity before hidden node analysis and the solid line reresents the connectivity after hidden node analysis.

Fig. (3)

Flowchart of the method used by Garcia et al. [77].

Fig. (4)

Finding effect of sequence features on mRNA-protein correlation by multiple regression analysis.

Fig. (5)

Method for clustering individually.

Fig. (6)

Method for clustering after concatenation.

Fig. (7)

Coupled clustering method used by Rogers et al. [82].

Fig. (8)

The greedy hill-climbing algorithm for finding and modeling protein complexes and estimating a gene network.