English

COGEDAP: A COmprehensive GEnomic Data Analysis Platform

Genomics 2023-01-02 v1

Abstract

Non-sharable sensitive data collection and analysis in large-scale consortia for genomic research is complicated. Time consuming issues in installing software arise due to different operating systems, software dependencies and running the software. Therefore, easier, more standardized, automated protocols and platforms can be a solution to overcome these issues. We have developed one such solution for genomic data analysis using software container technologies. The platform, COGEDAP, consists of different software tools placed into Singularity containers with corresponding pipelines and instructions on how to perform genome-wide association studies (GWAS) and other genomic data analysis via corresponding tools. Using a provided helper script written in Python, users can obtain auto-generated scripts to conduct the desired analysis both on high-performance computing (HPC) systems and on personal computers. The analyses can be done by running these auto-generated scripts with the software containers. The helper script also performs minor re-formatting of the input/output data, so that the end user can work with a unified file format regardless of which genetic software is used for the analysis. COGEDAP is actively being used by users from different countries/projects to conduct their genomic data analyses. Thanks to this platform, users can easily run GWAS and other genomic analyses without spending much effort on software installation, data formats, and other technical requirements.

Keywords

Cite

@article{arxiv.2212.14103,
  title  = {COGEDAP: A COmprehensive GEnomic Data Analysis Platform},
  author = {Bayram Cevdet Akdeniz and Oleksandr Frei and Espen Hagen and Tahir Tekin Filiz and Sandeep Karthikeyan and Joelle Pasman and Andreas Jangmo and Jacob Bergsted and John R. Shorter and Richard Zetterberg and Joeri Meijsen and Ida Elken Sonderby and Alfonso Buil and Martin Tesli and Yi Lu and Patrick Sullivan and Ole Andreassen and Eivind Hovig},
  journal= {arXiv preprint arXiv:2212.14103},
  year   = {2023}
}
R2 v1 2026-06-28T07:55:25.399Z