新兴的高通量技术导致药物转录组数据集规模显着激增，特别是在肿瘤学方面。特征搜索方法（SSM）利用差异表达基因通过测序形成的致癌特征，在不依赖先验知识的情况下进行抗癌药物筛选和识别作用机制方面发挥了重要作用。然而，各种研究发现不同的 SSM 在药物转录组数据集中表现出不同的性能。此外，致癌特征的大小也会显着影响药物再利用的结果。因此，为特定疾病找到最佳的 SSM 和定制的致癌特征仍然是一个挑战。为了解决这个问题，我们推出了 Signature Search Polestar (SSP)，这是一个网络服务器，集成了 LINCS L1000 中最大的抗癌药物药物转录组数据集和五个最先进的 SSM（XSum、CMap、GSEA、ZhangScore、XCos）。 SSP 提供三个主要模块：Benchmark、Robustness 和 Application。 Benchmark 使用两个指数（曲线下面积和富集分数）基于药物注释来评估不同致癌特征大小的 SSM。稳健性，适用于药物注释不充分的情况，使用基于药物自我检索的性能评分进行评估。应用程序提供三种筛选策略，单一方法、SS_all 和 SS_cross，允许用户自由利用具有定制致癌特征的最佳 SSM 进行药物再利用。SSP 在 https://web.biotcm.net/SSP/ 上免费。当前版本的 SSP 存档于 https://doi.org/10.6084/m9.figshare.26524741.v1，允许用户直接使用或定制自己的 SSP Web 服务器。补充数据可在 Bioinformatics online 获取。© 作者（ s) 2024 年。由牛津大学出版社出版。

The burgeoning high-throughput technologies have led to a significant surge in the scale of pharmacotranscriptomic datasets, especially for oncology. Signature search methods (SSMs), utilizing oncogenic signatures formed by differentially expressed genes through sequencing, have been instrumental in anti-cancer drug screening and identifying mechanisms of action without relying on prior knowledge. However, various studies have found that different SSMs exhibit varying performance across pharmacotranscriptomic datasets. In addition, the size of the oncogenic signature can also significantly impact the result of drug repurposing. Therefore, finding the optimal SSMs and customized oncogenic signature for a specific disease remains a challenge. To address this, we introduce Signature Search Polestar (SSP), a webserver integrating the largest pharmacotranscriptomic datasets of anti-cancer drugs from LINCS L1000 with five state-of-the-art SSMs (XSum, CMap, GSEA, ZhangScore, XCos). SSP provides three main modules: Benchmark, Robustness, and Application. Benchmark uses two indices, Area Under the Curve and Enrichment Score, based on drug annotations to evaluate SSMs at different oncogenic signature sizes. Robustness, applicable when drug annotations are insufficient, uses a performance score based on drug self-retrieval for evaluation. Application provides three screening strategies, single method, SS_all, and SS_cross, allowing users to freely utilize optimal SSMs with tailored oncogenic signature for drug repurposing.SSP is free at https://web.biotcm.net/SSP/. The current version of SSP is archived in https://doi.org/10.6084/m9.figshare.26524741.v1, allowing users to directly use or customize their own SSP webserver.Supplementary data are available at Bioinformatics online.© The Author(s) 2024. Published by Oxford University Press.