ArticleProteomics2015
Workflows for automated downstream data analysis and visualization in large-scale computational mass spectrometry.
Article in Proteomics, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 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
8 citing papers in PubMed.
- Comprehensive Peptide Mapping Is Crucial for Proteogenomics and Proteomics.Methods in molecular biology (Clifton, N.J.) · 2025Article
- DEIMoS: An Open-Source Tool for Processing High-Dimensional Mass Spectrometry Data.Analytical chemistry · 2022Article
- Integration of the ImageJ Ecosystem in the KNIME Analytics Platform.Frontiers in computer science · 2020Article
- A Critical Evaluation of Wet Biomarkers for Huntington's Disease: Current Status and Ways Forward.Journal of Huntington's disease · 2018Review
- ImmunoNodes - graphical development of complex immunoinformatics workflows.BMC bioinformatics · 2017Article
- Systems biology guided by XCMS Online metabolomics.Nature methods · 2017Article
- Public proteomics data: How the field has evolved from sceptical inquiry to the promise ofEuPA open proteomics · 2016Article
- Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
12 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
MS-based proteomics and metabolomics are rapidly evolving research fields driven by the development of novel instruments, experimental approaches, and analysis methods. Monolithic analysis tools perform well on single tasks but lack the flexibility to cope with the constantly changing requirements and experimental setups. Workflow systems, which combine small processing tools into complex analysis pipelines, allow custom-tailored and flexible data-processing workflows that can be published or shared with collaborators. In this article, we present the integration of established tools for computational MS from the open-source software framework OpenMS into the workflow engine Konstanz Information Miner (KNIME) for the analysis of large datasets and production of high-quality visualizations. We provide example workflows to demonstrate combined data processing and visualization for three diverse tasks in computational MS: isobaric mass tag based quantitation in complex experimental setups, label-free quantitation and identification of metabolites, and quality control for proteomics experiments.
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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.