PROTREC: A probability-based approach for recovering missing proteins based on biological networks

Kong, Weijia; Wong, Bertrand Jern Han; Gao, Huanhuan; Guo, Tiannan; Liu, Xianming; Du, Xiaoxian; Wong, Limsoon; Goh, Wilson Wen Bin

doi:10.1016/j.jprot.2021.104392

Cited by 10 publications

(18 citation statements)

References 33 publications

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“…The top two heatmaps show the missing values in DDA datasets and have been published in the Supplementary Fig. 1 in the associated research article [1] . The bottom two heatmaps were added to this data article to show the distribution of missing values in the corresponding DIA datasets.…”

Section: Data Descriptionmentioning

confidence: 99%

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Proteomic datasets of HeLa and SiHa cell lines acquired by DDA-PASEF and diaPASEF

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Section: Data Descriptionmentioning

confidence: 99%

“… We present four datasets on proteomics profiling of HeLa and SiHa cell lines associated with the research described in the paper “PROTREC: A probability-based approach for recovering missing proteins based on biological networks” [1] . Proteins in each cell line were acquired by two different data acquisition methods.…”

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Proteomic datasets of HeLa and SiHa cell lines acquired by DDA-PASEF and diaPASEF

et al. 2022

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“…To help users, ProInfer’s methodology is easily understood; involving no complex calculations while possessing reasonable assumptions. Additionally, ProInfer borrows similar principles from our missing protein prediction method PROTREC [ 25 ] that leverages on the phenomenon that proteins forming a stable protein complex (or constituting part of a tightly clustered network module) are more likely to be co-expressed [ 26 ]. Specifically, it incorporates protein complex information to rescue proteins with weak signals themselves but have neighbors with strong evidence.…”

Section: Introductionmentioning

confidence: 99%

ProInfer: An interpretable protein inference tool leveraging on biological networks

2023

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In mass spectrometry (MS)-based proteomics, protein inference from identified peptides (protein fragments) is a critical step. We present ProInfer (Protein Inference), a novel protein assembly method that takes advantage of information in biological networks. ProInfer assists recovery of proteins supported only by ambiguous peptides (a peptide which maps to more than one candidate protein) and enhances the statistical confidence for proteins supported by both unique and ambiguous peptides. Consequently, ProInfer rescues weakly supported proteins thereby improving proteome coverage. Evaluated across THP1 cell line, lung cancer and RAW267.4 datasets, ProInfer always infers the most numbers of true positives, in comparison to mainstream protein inference tools Fido, EPIFANY and PIA. ProInfer is also adept at retrieving differentially expressed proteins, signifying its usefulness for functional analysis and phenotype profiling. Source codes of ProInfer are available at https://github.com/PennHui2016/ProInfer.

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“…Examples of such algorithms include Functional Class Scoring (FCS) [21][22][23], Gene Set Enrichment Analysis (GSEA) [24,25] and PROTREC [26]. Dealing with coverage missingness is useful when we want to greatly expand the unobserved proteome but is challenging due to subsequent quantification confidence issues (i.e., we can determine the presence of the protein but do not have enough data to infer how much is present).…”

Section: Introductionmentioning

confidence: 99%

“…Dealing with coverage missingness is more complex and may involve the use of data integration strategies e.g., combining observed proteins with reference networks and other prior knowledge to predict presence. Examples of such algorithms include Functional Class Scoring (FCS) [21–23], Gene Set Enrichment Analysis (GSEA) [24, 25] and PROTREC [26]. Dealing with coverage missingness is useful when we want to greatly expand the unobserved proteome but is challenging due to subsequent quantification confidence issues (i.e., we can determine the presence of the protein but do not have enough data to infer how much is present).…”

Section: Introductionmentioning

confidence: 99%

Dealing with missing values in proteomics data

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Proteomics data are often plagued with missingness issues. These missing values (MVs) threaten the integrity of subsequent statistical analyses by reduction of statistical power, introduction of bias, and failure to represent the true sample. Over the years, several categories of missing value imputation (MVI) methods have been developed and adapted for proteomics data. These MVI methods perform their tasks based on different prior assumptions (e.g., data is normally or independently distributed) and operating principles (e.g., the algorithm is built to address random missingness only), resulting in varying levels of performance even when dealing with the same dataset. Thus, to achieve a satisfactory outcome, a suitable MVI method must be selected. To guide decision making on suitable MVI method, we provide a decision chart which facilitates strategic considerations on datasets presenting different characteristics. We also bring attention to other issues that can impact proper MVI such as the presence of confounders (e.g., batch effects) which can influence MVI performance. Thus, these too, should be considered during or before MVI.

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PROTREC: A probability-based approach for recovering missing proteins based on biological networks

Cited by 10 publications

References 33 publications

Proteomic datasets of HeLa and SiHa cell lines acquired by DDA-PASEF and diaPASEF

Proteomic datasets of HeLa and SiHa cell lines acquired by DDA-PASEF and diaPASEF

ProInfer: An interpretable protein inference tool leveraging on biological networks

Dealing with missing values in proteomics data

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