ArticleBioinformatics advances2024
arcMS: transformation of multi-dimensional high-resolution mass spectrometry data to columnar format for compact storage and fast access.
Article in Bioinformatics advances, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 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
2 citing papers in PubMed.
- MassNet: billion-scale AI-ready mass spectral corpus empowering scalable deep learning in proteomics.Nature methods · 2026Article
- Storing Mass-Spectrometry Data in Simple Databases Enables Flexible and Intuitive Exploration without Time or Space Penalties.Journal of proteome research · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Summary: The arcMS R package addresses the challenges posed by proprietary and open-source high-resolution mass spectrometry data formats by providing functions to collect MS Availability and implementation: arcMS is an open-source R package and is available on GitHub at https://github.com/leesulab/arcMS. The complete documentation, including details on UNIFI configuration and tutorials for data conversion, access to Parquet files, and filtration of data, is available at https://leesulab.github.io/arcMS. An R/Shiny companion application is also provided for visualization of Parquet data and demonstration of data filtering with the Arrow library https://github.com/leesulab/arcms-dataviz.
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.