CHOmics: A web-based tool for multi-omics data analysis and interactive visualization in CHO cell lines

Dongdong Lin, Hima B. Yalamanchili, Xinmin Zhang, Nathan E. Lewis, Christina S. Alves, Joost Groot, Johnny Arnsdorf, Sara P. Bjørn, Tune Wulff, Bjørn G. Voldborg, Yizhou Zhou, Baohong Zhang

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Abstract

Chinese hamster ovary (CHO) cell lines are widely used in industry for biological drug production. During cell culture development, considerable effort is invested to understand the factors that greatly impact cell growth, specific productivity and product qualities of the biotherapeutics. While high-throughput omics approaches have been increasingly utilized to reveal cellular mechanisms associated with cell line phenotypes and guide process optimization, comprehensive omics data analysis and management have been a challenge. Here we developed CHOmics, a web-based tool for integrative analysis of CHO cell line omics data that provides an interactive visualization of omics analysis outputs and efficient data management. CHOmics has a built-in comprehensive pipeline for RNA sequencing data processing and multi-layer statistical modules to explore relevant genes or pathways. Moreover, advanced functionalities were provided to enable users to customize their analysis and visualize the output systematically and interactively. The tool was also designed with the flexibility to accommodate other types of omics data and thereby enabling multi-omics comparison and visualization at both gene and pathway levels. Collectively, CHOmics is an integrative platform for data analysis, visualization and management with expectations to promote the broader use of omics in CHO cell research.

Original languageEnglish
Article numbere1008498
JournalPLOS Computational Biology
Volume16
Issue number12
Number of pages13
ISSN1553-734X
DOIs
Publication statusPublished - 22 Dec 2020

Bibliographical note

Publisher Copyright:
© 2020 Lin et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

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