Matteo Di Bernardo, Claire Peterson, Ege Topkoc, Matt Might, Iain M. Cheeseman · 2026 · arXiv (Cornell University)
DOI: 10.48550/arxiv.2610.22617 · cited by 0 works (OpenAlex) · open-access PDF
光学固定スクリーンは細胞形態や行動の変化に遺伝的障害を接続しますが、顕微鏡データを解釈可能な結果に変換するには複雑な、複数の日間の分析が必要です。確立されたパイプラインはこれらのデータを処理しますが、研究者はまだパラメータを選択し、出力を検査し、各実験の失敗を解決します。ここでは、Brieflow-auto、インタラクティブなノートブックでパイプライン特有の利用方法を紹介し、コード大言語モデル(LLM)がオープンソースのエンド-エンドパイプラインであるBrieflow、オープンソースのエンド-エンドパイプラインを操作することを可能にします。研究者は実験的な事実を提供し、
Machine translation (Meta M2M100); the English original follows.
Optical pooled screens connect genetic perturbations to changes in cell morphology and behavior, but turning the microscopy data into interpretable results takes complex, multi-day analyses. Established pipelines process this data, yet researchers still select parameters, inspect outputs, and resolve failures for each experiment. Here, we present Brieflow-auto, a pipeline-specific harness implemented as a Claude Code plugin that lets a coding large language model (LLM) operate Brieflow, an open-source end-to-end pipeline for optical pooled screening data. A researcher supplies experimental facts and approves the launch. An agent tunes parameters in an interactive notebook, and the harness validates the configuration, monitors execution, recovers from common failures, and checks output quality. For pipeline defects, the agent opens upstream issues proposing backward-compatible fixes for the broader community. In five previously analyzed screens, harness runs yielded more cells with a confident single-gene assignment than the original hand analyses. On a screen the pipeline was not designed for, the harness turned each failure into a reviewed upstream fix. More broadly, we propose that wrapping an established pipeline in a harness can substantially accelerate its use and the long-term reliability of its codebase.
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Topics: Pipeline (software), Coding (social sciences), Computer science, Process (computing), Reliability (semiconductor), Upstream (networking)
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