研究突变，包括单核苷酸变异 (SNV)、基因融合、选择性剪接和拷贝数变异 (CNV)，是癌症研究的基础。最近的计算方法和生物学研究已经证明了从单细胞转录组数据中检测突变的可靠性和生物学意义。然而，缺乏包含所有类型癌症的全面突变信息的单细胞水平数据库。从大量新兴的单细胞转录组数据中建立单细胞突变景观可以为阐明肿瘤发生和进化机制提供关键资源。在这里，我们开发了 scTML (http://sctml.xglab.tech/)，这是第一个提供多种突变类型的泛癌单细胞景观的数据库。它包括 SNV、插入/删除、基因融合、选择性剪接和 CNV，以及基因表达、细胞状态和其他表型信息。数据来自 74 个包含 2 582 633 个细胞的数据集，包括 35 个全长（Smart-seq2）转录组单细胞数据集（所有公开数据均带有原始测序文件）、来自 10X 技术的 23 个数据集和 16 个空间转录组数据集。 scTML 使用户能够交互式地探索跨肿瘤或细胞类型的多种突变景观，分析单细胞水平的突变表型关联并检测感兴趣的细胞亚群。 scTML 是一种重要资源，将显着促进破译肿瘤内和肿瘤间异质性，以及突变如何塑造细胞表型。© 作者 2024。由牛津大学出版社代表核酸研究出版。

Investigating mutations, including single nucleotide variations (SNVs), gene fusions, alternative splicing and copy number variations (CNVs), is fundamental to cancer study. Recent computational methods and biological research have demonstrated the reliability and biological significance of detecting mutations from single-cell transcriptomic data. However, there is a lack of a single-cell-level database containing comprehensive mutation information in all types of cancer. Establishing a single-cell mutation landscape from the huge emerging single-cell transcriptomic data can provide a critical resource for elucidating the mechanisms of tumorigenesis and evolution. Here, we developed scTML (http://sctml.xglab.tech/), the first database offering a pan-cancer single-cell landscape of multiple mutation types. It includes SNVs, insertions/deletions, gene fusions, alternative splicing and CNVs, along with gene expression, cell states and other phenotype information. The data are from 74 datasets with 2 582 633 cells, including 35 full-length (Smart-seq2) transcriptomic single-cell datasets (all publicly available data with raw sequencing files), 23 datasets from 10X technology and 16 spatial transcriptomic datasets. scTML enables users to interactively explore multiple mutation landscapes across tumors or cell types, analyze single-cell-level mutation-phenotype associations and detect cell subclusters of interest. scTML is an important resource that will significantly advance deciphering intra-tumor and inter-tumor heterogeneity, and how mutations shape cell phenotypes.© The Author(s) 2024. Published by Oxford University Press on behalf of Nucleic Acids Research.