Mass Dynamics 1.0: A streamlined, web-based environment for analyzing, sharing and integrating Label-Free Data.
Bloom, J. I.; Triantafyllidis, A.; Burton, P.; Infusini, G.; Webb, A. I.
Show abstract
Label Free Quantification (LFQ) of shotgun proteomics data is a popular and robust method for the characterization of relative protein abundance between samples. Many analytical pipelines exist for the automation of this analysis and some tools exist for the subsequent representation and inspection of the results of these pipelines. Mass Dynamics 1.0 (MD 1.0) is a web based analysis environment that can analyze and visualize LFQ data produced by software such as Maxquant. Unlike other tools, MD 1.0 utilizes cloud-based architecture to enable researchers to store their data, enabling researchers to not only automatically process and visualize their LFQ data but annotate and share their findings with collaborators and, if chosen, to easily publish results to the community. With a view toward increased reproducibility and standardisation in proteomics data analysis and streamlining collaboration between researchers, MD 1.0 requires minimal parameter choices and automatically generates quality control reports to verify experiment integrity. Here, we demonstrate that MD 1.0 provides reliable results for protein expression quantification, emulating Perseus on benchmark datasets over a wide dynamic range. The MD 1.0 platform is available globally via: https://app.massdynamics.com/. Contactwebb@wehi.edu.au
Matching journals
The top 3 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- prolfquapp - A User-Friendly Command-Line Tool Simplifying Differential Expression Analysis in Quantitative Proteomics 97%
- Quality control for the target decoy approach for peptide identification 96%
- mzMLb: a future-proof raw mass spectrometry data format based on standards-compliant mzML and optimized for speed and storage requirements 96%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.