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1.
Figure 2

Figure 2. Correlation between protein length and both genes expression and protein abundance. From: Comparative analysis of different label-free mass spectrometry based protein abundance estimates and their correlation with RNA-Seq gene expression data.

(A) Correlation between protein length and spectral counts (NSAF). (B) Same for protein abundance by msInspect - top 3 normalized area intensities. (C) Same for gene expression by RPKM, log transformed. (D) Same for gene expression by microarrays, log transformed.

Kang Ning, et al. J Proteome Res. ;11(4):2261-2271.

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2.
Figure 6

Figure 6. The change of protein abundance by msInspect and gene expression by mRNA-Seq (RPKM) across brainstem and liver tissues. From: Comparative analysis of different label-free mass spectrometry based protein abundance estimates and their correlation with RNA-Seq gene expression data.

Consistency between fold changes in protein and gene abundances across brainstem and liver tissues, log(protein_abundancebrainstem / protein_abundanceliver) vs. log(gene_expressionbrainstem / gene_expressionliver). Protein estimates are by msInspect using top 3 normalized peptide area intensities, and gene expression by mRNA-Seq (RPKM).

Kang Ning, et al. J Proteome Res. ;11(4):2261-2271.

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3.
Figure 4

Figure 4. Correlation of gene expression based on RPKM by RNA-Seq and protein abundance by label-free method. From: Comparative analysis of different label-free mass spectrometry based protein abundance estimates and their correlation with RNA-Seq gene expression data.

(A) MS1 based quantification by msInspect plotted against RPKM, log transformed. (B) Normalized MS2 spectral counts (NSAF)) plotted against RPKM, log transformed. Data for mouse mitochondrial genes in brainstem tissue. Protein abundance by msInspect is based on top 3 normalized peptide area intensities.

Kang Ning, et al. J Proteome Res. ;11(4):2261-2271.

CitationFull text

4.
Figure 1

Figure 1. Framework for the comparison between gene expression and protein abundance based on different methods. From: Comparative analysis of different label-free mass spectrometry based protein abundance estimates and their correlation with RNA-Seq gene expression data.

(A) Generation of protein abundance and analysis of the correlation between protein abundance and gene expression. (B) 8 configurations for MS1 based label-free protein quantification. (C) Analysis of the changes of gene expression and protein abundance across different tissues.

Kang Ning, et al. J Proteome Res. ;11(4):2261-2271.

CitationFull text

5.
Figure 3

Figure 3. Comparison between different quantitative measures at gene and protein levels. From: Comparative analysis of different label-free mass spectrometry based protein abundance estimates and their correlation with RNA-Seq gene expression data.

(A) Protein abundance estimates by MS2 spectral counts (NSAF) plotted against protein abundance estimates from MS1 intensity data by msInspect, log transformed. Data for mitochondrial genes in brainstem tissue. Protein abundance by msInspect is based on top 3 normalized peptide area intensities. (B) Correlation of gene expression based on RPKM by mRNA-Seq and based on microarrays, log transformed.

Kang Ning, et al. J Proteome Res. ;11(4):2261-2271.

CitationFull text

6.
Figure 5

Figure 5. The analysis of the “coding region dominant” genes and selected example. From: Comparative analysis of different label-free mass spectrometry based protein abundance estimates and their correlation with RNA-Seq gene expression data.

(a) Comparison of normalized number of short reads in coding regions (log transformed) and log(RPKM). Data for mitochondrial genes in brainstem tissue. The number of short reads in coding regions is normalized and it is always smaller than RPKM for the same gene. The boundaries of 90% and 99% confidence intervals are shown in solid and dashed lines. (b) The screen shots of UCSC genome browser of the outlier gene Chchd2. Most of the short reads for this gene are aligned to untranslated regions.

Kang Ning, et al. J Proteome Res. ;11(4):2261-2271.

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