Patel Yash, Zhu Chenghao, Yamaguchi Takafumi N, Wang Nicholas K, Wiltsie Nicholas, Zeltser Nicole, Gonzalez Alfredo E, Winata Helena K, Pan Yu, Mootor Mohammed Faizal Eeman, Sanders Timothy, Fitz-Gibbon Sorel T, Kandoth Cyriac, Livingstone Julie, Liu Lydia Y, Carlin Benjamin, Holmes Aaron, Oh Jieun, Sahrmann John, Tao Shu, Eng Stefan, Hugh-White Rupert, Pashminehazar Kiarod, Park Andrew, Beshlikyan Arpi, Jordan Madison, Wu Selina, Tian Mao, Arbet Jaron, Neilsen Beth, Haas Roni, Bugh Yuan Zhe, Kim Gina, Salmingo Joseph, Zhang Wenshu, Anand Aakarsh, Hwang Edward, Neiman-Golden Anna, Steinberg Philippa, Zhao Wenyan, Anand Prateek, Agrawal Raag, Tsai Brandon L, Boutros Paul C
bioRxiv. 2025 Apr 25:2024.09.04.611267. doi: 10.1101/2024.09.04.611267.
The price, quality and throughout of DNA sequencing continue to improve. Algorithmic innovations have allowed inference of a growing range of features from DNA sequencing data, quantifying nuclear, mitochondrial and evolutionary aspects of both germline and somatic genomes. To automate analyses of the full range of genomic characteristics, we created an extensible Nextflow meta-pipeline called metapipeline-DNA. Metapipeline-DNA analyzes targeted and whole-genome sequencing data from raw reads through pre-processing, feature detection by multiple algorithms, quality-control and data- visualization. Each step can be run independently and is supported robust software engineering including automated failure-recovery, robust testing and consistent verifications of inputs, outputs and parameters. Metapipeline-DNA is cloud-compatible and highly configurable, with options to subset and optimize each analysis. Metapipeline-DNA facilitates high-scale, comprehensive analysis of DNA sequencing data.
Metapipeline-DNA is an open-source Nextflow pipeline under the GPLv2 license and is available at https://github.com/uclahs-cds/metapipeline-DNA .
DNA测序的价格、质量和通量持续提高。算法创新使得能够从DNA测序数据中推断出越来越多的特征，对种系和体细胞基因组的核、线粒体及进化方面进行量化。为了实现对全基因组特征的自动化分析，我们创建了一个名为metapipeline-DNA的可扩展Nextflow元管道。metapipeline-DNA可分析来自原始读数的靶向和全基因组测序数据，包括预处理、通过多种算法进行特征检测、质量控制和数据可视化。每个步骤都可以独立运行，并得到强大的软件工程支持，包括自动故障恢复、严格测试以及对输入、输出和参数的一致性验证。metapipeline-DNA与云兼容且高度可配置，具有对每个分析进行子集化和优化的选项。metapipeline-DNA有助于对DNA测序数据进行大规模、全面的分析。
metapipeline-DNA是一个遵循GPLv2许可的开源Nextflow管道，可在https://github.com/uclahs-cds/metapipeline-DNA获取。
