ReviewAmino acids2012
Current challenges in software solutions for mass spectrometry-based quantitative proteomics.
Review in Amino acids, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 42 papers, 1 of them a synthesis that pooled it.
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
42 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Guidelines for the design, analysis and interpretation of 'omics' data: focus on human endometrium.Human reproduction updatePooled it
- Peptide Mapping for Sequence Confirmation of Therapeutic Proteins and Recombinant Vaccine Antigens by High-Resolution Mass Spectrometry: Software Limitations, Pitfalls, and Lessons Learned.International journal of molecular sciences · 2025Article
- Applying LFQRatio Normalization in Quantitative Proteomic Analysis of Microbial Co-culture Systems.Bio-protocol · 2025Article
- LFQRatio: A Normalization Method to Decipher Quantitative Proteome Changes in Microbial Coculture Systems.Journal of proteome research · 2024Article
- ReducingAnalytical chemistry · 2023Article
- Program for Integration and Rapid Analysis of Mass Isotopomer Distributions (PIRAMID).Bioinformatics (Oxford, England) · 2023Article
- Challenges and Opportunities for Single-cell Computational Proteomics.Molecular & cellular proteomics : MCP · 2023Review
- Deep Learning Based MS2 Feature Detection for Data-Independent Shotgun Proteomics.Proceedings. IEEE International Conference on Bioinformatics and Biomedicine · 2022Article
- Article
- LipidFinder 2.0: advanced informatics pipeline for lipidomics discovery applications.Bioinformatics (Oxford, England) · 2021Article
- MS Amanda 2.0: Advancements in the standalone implementation.Rapid communications in mass spectrometry : RCM · 2021Article
- ANPELA: analysis and performance assessment of the label-free quantification workflow for metaproteomic studies.Briefings in bioinformatics · 2020Article
- New label-free methods for protein relative quantification applied to the investigation of an animal model of Huntington Disease.PloS one · 2020Article
- The Human Immunopeptidome Project: A Roadmap to Predict and Treat Immune Diseases.Molecular & cellular proteomics : MCP · 2020Article
- Comparative Proteomic Analysis of Nodulated and Non-NodulatedInternational journal of molecular sciences · 2019Article
- DeepIso: A Deep Learning Model for Peptide Feature Detection from LC-MS map.Scientific reports · 2019Article
- LipidFinder on LIPID MAPS: peak filtering, MS searching and statistical analysis for lipidomics.Bioinformatics (Oxford, England) · 2019Article
- PANDA-view: an easy-to-use tool for statistical analysis and visualization of quantitative proteomics data.Bioinformatics (Oxford, England) · 2018Article
- Establishment of Dimethyl Labeling-based Quantitative Acetylproteomics in Arabidopsis.Molecular & cellular proteomics : MCP · 2018Article
- Integrated Chemometrics and Statistics to Drive Successful Proteomics Biomarker Discovery.Proteomes · 2018Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
Abstract
Mass spectrometry-based proteomics has evolved as a high-throughput research field over the past decade. Significant advances in instrumentation, and the ability to produce huge volumes of data, have emphasized the need for adequate data analysis tools, which are nowadays often considered the main bottleneck for proteomics development. This review highlights important issues that directly impact the effectiveness of proteomic quantitation and educates software developers and end-users on available computational solutions to correct for the occurrence of these factors. Potential sources of errors specific for stable isotope-based methods or label-free approaches are explicitly outlined. The overall aim focuses on a generic proteomic workflow.
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.