ArticleBriefings in bioinformatics2018
A comprehensive evaluation of popular proteomics software workflows for label-free proteome quantification and imputation.
Article in Briefings in bioinformatics, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 68 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
68 citing papers in PubMed.
- Suspect screening-based LC-HRMS characterization of short-chain peptide hydrolysates from marine macroalgae.Analytical and bioanalytical chemistry · 2026Article
- Potential of SERS and proteomics for biomarker detection in cancer cells.Analytical and bioanalytical chemistry · 2026Article
- CLASPP: A unified model for predicting post-translational modifications.PLoS computational biology · 2026Article
- CLASPP: A unified model for predicting post-translational modifications.bioRxiv : the preprint server for biology · 2026Article
- Article
- AUGMENTED DOUBLY ROBUST POST-IMPUTATION INFERENCE FOR PROTEOMIC DATA.The annals of applied statistics · 2025Article
- Open-Source and FAIR Research Software for Proteomics.Journal of proteome research · 2025Review
- Systematic evaluation of normalization approaches in tandem mass tag and label-free protein quantification data using PRONE.Briefings in bioinformatics · 2025Article
- Assessing the impact of batch effect associated missing values on downstream analysis in high-throughput biomedical data.Briefings in bioinformatics · 2025Article
- AUGMENTED DOUBLY ROBUST POST-IMPUTATION INFERENCE FOR PROTEOMIC DATA.bioRxiv : the preprint server for biology · 2025Article
- PEPerMINT: peptide abundance imputation in mass spectrometry-based proteomics using graph neural networks.Bioinformatics (Oxford, England) · 2024Article
- Apple Blossom Agricultural Residues as a Sustainable Source of Bioactive Peptides through Microbial Fermentation Bioprocessing.Antioxidants (Basel, Switzerland) · 2024Article
- APIR: Aggregating Universal Proteomics Database Search Algorithms for Peptide Identification with FDR Control.Genomics, proteomics & bioinformatics · 2024Article
- Imputation of label-free quantitative mass spectrometry-based proteomics data using self-supervised deep learning.Nature communications · 2024Article
- Optimizing differential expression analysis for proteomics data via high-performing rules and ensemble inference.Nature communications · 2024Article
- An Enzymatic and Proteomic Analysis ofJournal of fungi (Basel, Switzerland) · 2024Article
- bootGSEA: a bootstrap and rank aggregation pipeline for multi-study and multi-omics enrichment analyses.Frontiers in bioinformatics · 2024Article
- The plasma proteome reveals markers of recent and repeated stress in free-ranging seals.Conservation physiology · 2024Article
- Improvement in the prediction power of an astrocyte genome-scale metabolic model using multi-omic data.Frontiers in systems biology · 2024Article
- Evaluating Proteomics Imputation Methods with Improved Criteria.Journal of proteome research · 2023Article
8 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
Label-free mass spectrometry (MS) has developed into an important tool applied in various fields of biological and life sciences. Several software exist to process the raw MS data into quantified protein abundances, including open source and commercial solutions. Each software includes a set of unique algorithms for different tasks of the MS data processing workflow. While many of these algorithms have been compared separately, a thorough and systematic evaluation of their overall performance is missing. Moreover, systematic information is lacking about the amount of missing values produced by the different proteomics software and the capabilities of different data imputation methods to account for them.In this study, we evaluated the performance of five popular quantitative label-free proteomics software workflows using four different spike-in data sets. Our extensive testing included the number of proteins quantified and the number of missing values produced by each workflow, the accuracy of detecting differential expression and logarithmic fold change and the effect of different imputation and filtering methods on the differential expression results. We found that the Progenesis software performed consistently well in the differential expression analysis and produced few missing values. The missing values produced by the other software decreased their performance, but this difference could be mitigated using proper data filtering or imputation methods. Among the imputation methods, we found that the local least squares (lls) regression imputation consistently increased the performance of the software in the differential expression analysis, and a combination of both data filtering and local least squares imputation increased performance the most in the tested data sets.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.