Max Planck Institute for Molecular Biomedicine, Roentgenstrasse 20, 48149, Muenster, Germany.
MRC London Institute of Medical Sciences, Institute of Clinical Sciences, Faculty of Medicine, Imperial College London, Du Cane Road, London, W12 0NN, UK.
Genome Biol. 2020 Dec 17;21(1):303. doi: 10.1186/s13059-020-02215-9.
Chromosome conformation capture data, particularly from high-throughput approaches such as Hi-C, are typically very complex to analyse. Existing analysis tools are often single-purpose, or limited in compatibility to a small number of data formats, frequently making Hi-C analyses tedious and time-consuming. Here, we present FAN-C, an easy-to-use command-line tool and powerful Python API with a broad feature set covering matrix generation, analysis, and visualisation for C-like data ( https://github.com/vaquerizaslab/fanc ). Due to its compatibility with the most prevalent Hi-C storage formats, FAN-C can be used in combination with a large number of existing analysis tools, thus greatly simplifying Hi-C matrix analysis.
染色质构象捕获数据,特别是来自高通量方法(如 Hi-C)的数据,通常非常复杂,难以分析。现有的分析工具通常是单一用途的,或者与少数数据格式兼容,这使得 Hi-C 分析繁琐且耗时。在这里,我们展示了 FAN-C,这是一个易于使用的命令行工具和强大的 Python API,具有广泛的功能集,涵盖了 C 类数据的矩阵生成、分析和可视化(https://github.com/vaquerizaslab/fanc)。由于它与最流行的 Hi-C 存储格式兼容,FAN-C 可以与大量现有的分析工具结合使用,从而大大简化了 Hi-C 矩阵分析。