{
  "id": 16801338,
  "title": "Asking the Data: Conversational Interrogation of LC-MS/MS Proteomics Data Sets with an LLM Agent",
  "url": "https://urgent.news/2026/10/27/asking-the-data-conversational-interrogation-of-lc-ms-ms-proteomics",
  "topic": "ai",
  "section": "AI",
  "published": "2026-10-27T00:00:00.000Z",
  "source": {
    "name": "bioRxiv",
    "slug": "biorxiv",
    "url": "https://www.biorxiv.org/content/10.64898/2026.10.22.760949v1?rss=1"
  },
  "original_language": "en",
  "account": "In the realm of proteomics research, traditional workflows are tailored to address preconceived questions, rendering it challenging to investigate novel analytical problems that arise following initial findings without supplementary bioinformatics efforts. A groundbreaking approach, data set interrogation, emerges in the form of a conversational framework. Here, a researcher collaborates with a large language model (LLM) agent to transform spontaneous inquiries into executable tests, validate the answers through controls, and maintain a chronological record of code, inputs, and decisions. Through this innovative method, four liquid chromatography tandem mass spectrometry (LC-MS/MS) data sets underwent analysis, revealing previously undetected issues that altered the interpretation of three in-house studies. Among them, a search database was found to be missing one of two symbionts, a pair of tandem mass tag (TMT) channels were erroneously swapped, inflated statistics were attributed to non-host proteins, and inaccurate deamidation assignments stemmed from the selection of the 13C isotope peak as the monoisotopic peak. Intriguingly, a public TMT data set was reanalyzed from raw files in merely 24 minutes, without any human comparison. Remarkably, 99.2% of the spectra identified by both workflows yielded identical peptide assignments when compared to Proteome Discoverer, and protein fold changes exhibited a remarkable correlation coefficient of 0.93. Moreover, a custom-built channel assignment check identified all 216 between-strain channel swaps and none of the 90 within-strain swaps during seeded tests. This innovative framework seamlessly merges natural language interaction with executable analyses, explicit controls, and traceable decisions. It empowers researchers to delve into unforeseen questions that were not initially considered within the original pipeline. The results affirm the practicality of this approach in a confined retrospective setting, involving a single researcher. However, an extensive evaluation encompassing diverse users, models, and data sets is imperative to ascertain its general performance.",
  "summary": "Proteomics workflows are designed to answer predefined questions, leaving analytical problems that emerge after first results difficult to investigate without additional bioinformatics work. We introduce data set interrogation, a conversational framework in which a researcher uses a large language model (LLM) agent to translate emerging questions into executable tests, check the answers against…",
  "key_points": [],
  "editors_take": null,
  "illustration": null,
  "coverage": {
    "outlets": 1,
    "also_reported_by": []
  },
  "ai_generated": true,
  "disclaimer": "Summaries, key points and the editor’s take are written by software from other outlets’ reporting and may contain errors — always check the linked original."
}