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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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