Dataset Information

ProteiNorm – A user-friendly tool for normalization and analysis of TMT and label-free protein quantification

ABSTRACT: The technological advances in mass spectrometry allow us to collect more comprehensive data with higher quality and increasing speed. With the rapidly increasing amount of data generated, the need for streamlining analyses becomes more apparent. Proteomic data is known to be often affected by systemic bias from unknown sources, and failing to adequately normalize the data can lead to erroneous conclusions. To allow researchers to easily evaluate and compare different normalization methods via a user-friendly interface, we have developed “proteiNorm”. The current implementation of proteiNorm accommodates preliminary filter on peptide and sample level, followed by an evaluation of several popular normalization methods and visualization of missing value. The user then selects an adequate normalization method and one of several imputation methods used for the subsequent comparison of different differential abundance/expression methods and estimation of statistical power. The application of proteiNorm and interpretation of its results is demonstrated on a Tandem Mass Tag mass spectrometry example data set, where the proteome of three different breast cancer cell lines was profiled with and without hydroxyurea treatment. With proteiNorm, we provide a user-friendly tool to identify an adequate normalization method and to select an appropriate method for a differential abundance/expression analysis.

INSTRUMENT(S): Orbitrap Eclipse

ORGANISM(S): Homo Sapiens (human)

TISSUE(S): Breast

DISEASE(S): Breast Cancer

SUBMITTER: Stephanie Byrum

LAB HEAD: Stephanie Diane Byrum

PROVIDER: PXD018152 | Pride | 2021-09-09

REPOSITORIES: Pride

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Dataset's files

Source:

			Action	DRS
	Byrd_100719_TMT3_SF01.raw	Raw
	Byrd_100719_TMT3_SF02.raw	Raw
	Byrd_100719_TMT3_SF03.raw	Raw
	Byrd_100719_TMT3_SF04.raw	Raw
	Byrd_100719_TMT3_SF05.raw	Raw

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Publications

proteiNorm - A User-Friendly Tool for Normalization and Analysis of TMT and Label-Free Protein Quantification.

Graw Stefan S Tang Jillian J Zafar Maroof K MK Byrd Alicia K AK Bolden Chris C Peterson Eric C EC Byrum Stephanie D SD

ACS omega 20200930 40

The technological advances in mass spectrometry allow us to collect more comprehensive data with higher quality and increasing speed. With the rapidly increasing amount of data generated, the need for streamlining analyses becomes more apparent. Proteomics data is known to be often affected by systemic bias from unknown sources, and failing to adequately normalize the data can lead to erroneous conclusions. To allow researchers to easily evaluate and compare different normalization methods via a ...[more]

PMID: 33073088

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Project description:Background Array-based comparative genome hybridization (aCGH) is commonly used to determine the genomic content of bacterial strains. In aCGH data, systematic errors are comparable to those occurring in transcriptome data. However, especially for microbes, an additional source of variation exists: differences in hybridization due to gene sequence divergence between the strains hybridized. Current normalization methods do not take this source of variation into consideration. Results We present Supervised Lowess, or S-Lowess, an application of the subset Lowess normalization method that does take difference in genomic content into account. The performance of S-Lowess was assessed on aCGH experiments in which differentially labeled genomic DNA fragments of Lactococcus lactis IL1403 and L. lactis MG1363 strains were hybridized to IL1403 microarrays. Since both genome sequences are known (they have only on average 85 % sequence identity), the success rate in detecting deletions in the MG1363 genome of different aCGH normalization methods can be compared. S-Lowess detects 97% of the deletions, whereas other aCGH normalization methods detect up to only 60% of the deletions. Conclusions S-Lowess removes systematic errors from dual-dye aCGH data in two steps: (i) determination of likely homologous genes (LHG); and (ii) estimation of correction factors for systematic errors from spots of LHG and subset Lowess normalization of the remaining spots using these correction factors. It is implemented in a user-friendly web-tool accessible from http://bioinformatics.biol.rug.nl/websoftware/s-lowess. We demonstrate that it outperforms existing normalization methods and maximizes the number of detectable genomic deletions or duplications from microbial aCGH data. Keywords: comparative genome hybridisation In this study, 4 aCGH comparisons (slides) between L. lactis MG1363 and L. lactis IL1403 were performed (including dye swap with biological replicates; see also supplementary materials). The resulting aCGH slide signals were normalized using the different 'likely homologous gene' (LHG) sets (see above) yielding ratios of signals of labelled DNA of MG1363 over those of IL1403. A maximum of 8 ratios per amplicon (gene) were obtained for the 4 hybridized slides (each with 2 replicate spots per amplicon). Only genes with at least 5 measurements were used in this study. The normalization methods evaluated in this study are: a) no normalization, b) total signal normalization, c) grid-based Lowess (implemented in PreP; f = 0.7) [GarcM-CM--a de la Nava, J et al. 2003. Bioinformatics 19:2328-2329], and d) S-Lowess using different subsets of conserved lactococcal genes (for details see above). Results with the MANOR R package (spatial normalization; standard parameters) [Neuvial P, et al. 2006. BMC Bioinformatics 7:264; Liva S, et al. 2006. Nucleic Acids Res 34:W477-W481] are shown in our supplementary materials.