Title: DNAp: A Pipeline for DNA-seq Data Analysis.

Authors: Causey, Jason L; Ashby, Cody; Walker, Karl; Wang, Zhiping Paul; Yang, Mary; Guan, Yuanfang; Moore, Jason H; Huang, Xiuzhen

Published In Sci Rep, (2018 May 01)

Abstract: Next-generation sequencing is empowering genetic disease research. However, it also brings significant challenges for efficient and effective sequencing data analysis. We built a pipeline, called DNAp, for analyzing whole exome sequencing (WES) and whole genome sequencing (WGS) data, to detect mutations from disease samples. The pipeline is containerized, convenient to use and can run under any system, since it is a fully automatic process in Docker container form. It is also open, and can be easily customized with user intervention points, such as for updating reference files and different software or versions. The pipeline has been tested with both human and mouse sequencing datasets, and it has generated mutations results, comparable to published results from these datasets, and reproducible across heterogeneous hardware platforms. The pipeline DNAp, funded by the US Food and Drug Administration (FDA), was developed for analyzing DNA sequencing data of FDA. Here we make DNAp an open source, with the software and documentation available to the public at http://bioinformatics.astate.edu/dna-pipeline/ .

PubMed ID: 29717215

MeSH Terms: Animals; Computational Biology/methods; Datasets as Topic; Exome Sequencing/methods; Exome Sequencing/statistics & numerical data*; Genome*; High-Throughput Nucleotide Sequencing/methods; High-Throughput Nucleotide Sequencing/statistics & numerical data*; Humans; Information Dissemination; Internet; Mice; Mutation; Sequence Analysis, DNA/statistics & numerical data*; Software*