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Benchmarking differential expression, imputation and quantification methods for proteomics data

ABSTRACT: Data analysis is a critical part of quantitative proteomics studies in interpreting biological questions. Numerous computational tools including protein quantification, imputation, and differential expression (DE) analysis were generated in the past decade. However, searching optimized tools is still an unsolved issue. Moreover, due to the rapid development of RNA-Seq technology, a vast number of DE analysis methods are created. Applying these newly developed RNA-Seq-oriented tools to proteomics data is still a question that needs to be addressed. In order to benchmark these analysis methods, a proteomics dataset constituted the proteins derived from human, yeast, and drosophila with different ratios were generated. Based on this dataset, DE analysis tools (including array-based and RNA-Seq based), imputation algorithms, and protein quantification methods were compared and benchmarked. This study provided useful information on analyzing quantitative proteomics datasets. All the methods used in this study were integrated into Perseus which are available at https://www.maxquant.org/perseus.

ORGANISM(S): Homo Sapiens (human) Saccharomyces Cerevisiae (baker's Yeast) Drosophila Melanogaster (fruit Fly)

SUBMITTER: Miao-Hsia Lin

PROVIDER: PXD029891 | JPOST Repository | Wed Nov 16 00:00:00 GMT 2022

REPOSITORIES: jPOST

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Benchmarking differential expression, imputation and quantification methods for proteomics data.

Lin Miao-Hsia MH Wu Pei-Shan PS Wong Tzu-Hsuan TH Lin I-Ying IY Lin Johnathan J Cox Jürgen J Yu Sung-Huan SH

Briefings in bioinformatics 20220501 3

Data analysis is a critical part of quantitative proteomics studies in interpreting biological questions. Numerous computational tools for protein quantification, imputation and differential expression (DE) analysis were generated in the past decade and the search for optimal tools is still going on. Moreover, due to the rapid development of RNA sequencing (RNA-seq) technology, a vast number of DE analysis methods were created for that purpose. The applicability of these newly developed RNA-seq- ...[more]

PMID: 35397162

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Project description:Purpose: Next-generation sequencing (NGS) has revolutionized systems-based analysis of cellular pathways. An integrated systems approach was used with bioinformatics tools to understand the the role of aldehyde dehydrogenase 1A1 (ALDH1A1) in human colon cancer cells. We combined transcriptomics, proteomics and untargeted metabolomics to gain insight into the mechanisms affected by the suppression of the ALDH1A1 gene in COLO320 cells Methods: COLO 320 cells. Cellular ALDH1A1 expression was suppressed using MISSION shRNA lentiviral transduction particles containing validated ALDH1A1 shRNA in a pLKO plasmid vector. Scramble control shRNA in pLKO plasmid vector was used to generate control cells. Signal intensities were converted to individual base calls during a run using the system's Real Time Analysis (RTA) software. Primary analysis sample de-multiplexing and alignment to the human genome was performed using Illumina's CASAVA 1.8.2 software suite. The reads were trimmed for quality and aligned with the hg19 reference genome using TopHat2. The transcripts were assembled using Cufflinks. The assembled transcripts were used to estimate transcript abundance and differential gene expression using Cuffdiff. The results were visualized using R (CRAN) and CummeRbund Results: We combined transcriptomics, proteomics and untargeted metabolomics to gain insight into the mechanisms affected by the suppression of the ALDH1A1 gene in COLO320 cells. RNA-seq and proteomics analyses revealed 1747 transcripts and 336 proteins that were differentially expressed in cells in which the ALDH1A1 had been suppressed by shRNA transfection. Transcriptomics pathway analysis showed significant signaling pathways, such as Wnt/β-catenin (p=3.47E-6), molecular mechanisms of cancer (7.41E-5) and others relevant to lipid metabolism, such as cholesterol biosynthesis I, II and III (p=1.82E-4). Proteomics pathway analysis identified oxidative phosphorylation to be the most highly significant pathway (p=5.01E-31). Pathway analysis of the genes common to the transcriptomics and proteomics analyses revealed the asparagine biosynthesis (p=3.16E-3) and cholesterol biosynthesis the most statistically significant (p=3.36E-3). Univariate analysis of the untargeted metabolomics dataset revealed 859 ions statistically significant. Network analysis showed alterations in pathways linked to energy and lipid metabolism, such carnitine shuttle (p=5.3E-4) and fatty acid oxidation (p=2.9E-2). A systems biology approach was used to integrate all three datasets and a total of 61 pathways were generated. A direct association between the suppression of ALDH1A1 and the Vitamin A (retinol) metabolism pathway and downregulation of retinol and the UGT2B17, UGT2A3 and PRDX6 genes was shown Conclusions: the present results confirm the role of ALDH1A1 in retinol metabolism and shows for the first time the influence of this gene on lipid metabolism pathways that may be crucial for cholesterol synthesis in human colon cancer cells. In addition, they demonstrate how an integrated systems approach using unbiased bioinformatics tools can be used to understand the interplay of cellular pathways.

Dataset Information

Benchmarking differential expression, imputation and quantification methods for proteomics data

Publications

Benchmarking differential expression, imputation and quantification methods for proteomics data.

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