Submitting Author: Jonny Tran (@JonnyTran)
All current maintainers: @JonnyTran
Package Name: openomics
One-Line Description of Package: Library for integration of multi-omics, annotation, and interaction data
Repository Link: https://github.com/JonnyTran/OpenOmics
Version submitted: 0.8.4
Editor: @NickleDave
Reviewer 1: @gawbul
Reviewer 2: @ksielemann
Archive:
JOSS DOI:
Version accepted: v 0.8.8
Date accepted (month/day/year): 04/17/2021
OpenOmics is a Python library to assist integration of heterogeneous multi-omics bioinformatics data. By providing an API of data manipulation tools as well as a web interface (WIP), OpenOmics facilitates the common coding tasks when preparing data for bioinformatics analysis. It features support for:
OpenOmics also has an efficient data pipeline that bridges the popular data manipulation Pandas library and Dask distributed processing to address the following use cases:
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OpenOmics' core functionalities are to provide a suite of tools for data preprocessing, data integration, and public database retrieval. Its main goal is to maximize the transparency and reproducibility in the process of multi-omics data integration.
OpenOmics' primary target audience are computational bioinformaticians, and the scientific application of this package is to provide scalable ad-hoc data-frame manipulation for multi-omics data integration in a reproducible manner. Also, we are currently developing an interactive web dashboard and interfaces to the Galaxy Tool Shed, disseminating the tool to biologists without a programming background.
Existing PyPI Python packages within the scope of multi-omics data analysis are "pythomics" and "omics". Their functions appear to be lacking support for manipulation of integrated multi-omics dataset, retrieval of public databases, and extensible OOP design. OpenOmics aims to follow modern software best-practices and package publishing standards.
Aside from multi-omics integration tools, several specialized Python packages exists for single omics data, such as ScanPy's "AnnData" and "Loom" files. They provide an intuitive data structure for expression arrays and side annotations, and Loom file even allows for out-of-core data-frame processing. However, they don't yet provide mechanisms for multi-omics data integration, where each omics data may have overlapping samples or varying row/column sizes.
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