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

“…Technological developments have led to the development of higher throughput techniques such as the Data-Independent Acquisition-based Sequential Window Acquisition of all Theoretical ion spectra (SWATH) [4] and ion mobility shift assays such as Parallel Accumulation-Serial Fragmentation (PASEF) [5]. The pairing of DDA and DIA data on the latest PASEF technologies also presents powerful analytical possibilities [6][7][8]. Algorithmic developments, in turn, resulted in powerful open-source software such as MS-Fragger [9], OpenMS [10] and OpenSWATH [11].…”

Section: Missing Protein Problem (Mpp) and Algorithm Solutionsmentioning

confidence: 99%

“…= N − number of proteins observed in the screen (8) c. Validation based on the assembled protein list obtained using the two-peptide rule Assembled proteins are built from observed peptides or PSMs.…”

Section: Number Of Predicted Mpsmentioning

confidence: 99%

Optimizing the PROTREC network‐based missing protein prediction algorithm

Wu,

Huang,

Kong

et al. 2023

Proteomics

Self Cite

View full text Add to dashboard Cite

This article summarizes the PROTREC method and investigates the impact that the different hyper‐parameters have on the task of missing protein prediction using PROTREC. We evaluate missing protein recovery rates using different PROTREC score selection approaches (MAX, MIN, MEDIAN, and MEAN), different PROTREC score thresholds, as well as different complex size thresholds. In addition, we included two additional cancer datasets in our analysis and introduced a new validation method to check both the robustness of the PROTREC method as well as the correctness of our analysis. Our analysis showed that the missing protein recovery rate can be improved by adopting PROTREC score selection operations of MIN, MEDIAN, and MEAN instead of the default MAX. However, this may come at a cost of reduced numbers of proteins predicted and validated. The users should therefore choose their hyper‐parameters carefully to find a balance in the accuracy‐quantity trade‐off. We also explored the possibility of combining PROTREC with a p‐value‐based method (FCS) and demonstrated that PROTREC is able to perform well independently without any help from a p‐value‐based method. Furthermore, we conducted a downstream enrichment analysis to understand the biological pathways and protein networks within the cancerous tissues using the recovered proteins. Missing protein recovery rate using PROTREC can be improved by selecting a different PROTREC score selection method. Different PROTREC score selection methods and other hyper‐parameters such as PROTREC score threshold and complex size threshold introduce accuracy‐quantity trade‐off. PROTREC is able to perform well independently of any filtering using a p‐value‐based method. Verification of the PROTREC method on additional cancer datasets. Downstream Enrichment Analysis to understand the biological pathways and protein networks in cancerous tissues.

show abstract

“…Thus far, one of the best acquisition methods with promising selectivity, sensitivity and dynamic quantitation is diaPASEF, which captures and releases the precursor synchronously, resulting in the almost complete sampling of the precursor ion beam [16]. Compared to DDA acquisition, diaPASEF is able to identify and quantify 20-60% more proteins using the same cell line [17].…”

Section: Introductionmentioning

confidence: 99%

“…Indeed, missing data is a common occurrence, according to the literature, 70‐90% proportion of peptides contain at least one MV [28], leading to 10–50% of proteins being unobserved [29]. Even for diaPASEF, the missing proportion also reaches 10% [17]. MVs can have a significant effect on the conclusions that can be drawn from the data.…”

Section: Introductionmentioning

confidence: 99%

Dealing with missing values in proteomics data

et al. 2022

Self Cite

View full text Add to dashboard Cite

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.

show abstract

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

Cited by 6 publications

References 6 publications

An angled-shape tip-based strategy for highly sensitive proteomic profiling of a low number of cells

An angled-shape tip-based strategy for highly sensitive proteomic profiling of a low number of cells

Optimizing the PROTREC network‐based missing protein prediction algorithm

Dealing with missing values in proteomics data

Contact Info

Product

Resources

About