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A dependency-free, streamable format and cross-language toolkit for scalable LC-MS feature detection: reading only what you need

Osorio Mosquera, J.; Lawler, N. G.; Cox, M.; Osorio Mosquera, J.; Moreno L, W. A.; Sala, S.; Nambiar, V.; Whiley, L.; Nicholson, J. K.; Holmes, E.; Wist, J.

2026-09-22 bioinformatics
10.64898/2026.09.15.751169 bioRxiv
Show abstract

Mass spectrometry generates data faster than it can be read, and the exchange standard, mzML, is text-based and must be parsed in full before any spectrum is accessible. Binary alternatives are smaller but depend on storage engines such as HDF5, so access is dictated by the engine, not the file. We present Ionic (.ion), an open-source, compact, streamable binary format, and Quant{middle dot}ion, a processing toolkit built on the former. Ionic stores spectra, chromatograms and metadata as independently compressed, indexed blocks, so a reader retrieves only the bytes it needs, even inside a web browser, and converts losslessly to and from mzML. Ionic was smaller than compressed mzMLb on every acquisition type tested, and extracting one compound took under 40 ms, 35 to 90 times faster than an mzML reader. Quant{middle dot}ion exposes one core to R, Python, and JavaScript with identical results, and recovered 97% of true features on a ground-truth benchmark.

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The top 3 journals account for 50% of the predicted probability mass.

1
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"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.