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easyMF: A Web Platform for Matrix Factorization-Based Gene Discovery from Large-scale Transcriptome Data

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Abstract

With the development of high-throughput experimental technologies, large-scale RNA sequencing (RNA-Seq) data have been and continue to be produced, but have led to challenges in extracting relevant biological knowledge hidden in the produced high-dimensional gene expression matrices. Here, we develop easyMF (https://github.com/cma2015/easyMF), a web platform that can facilitate functional gene discovery from large-scale transcriptome data using matrix factorization (MF) algorithms. Compared with existing MF-based software packages, easyMF exhibits several promising features, such as greater functionality, flexibility and ease of use. The easyMF platform is equipped using the Big-Data-supported Galaxy system with user-friendly graphic user interfaces, allowing users with little programming experience to streamline transcriptome analysis from raw reads to gene expression, carry out multiple-scenario MF analysis, and perform multiple-way MF-based gene discovery. easyMF is also powered with the advanced packing technology to enhance ease of use under different operating systems and computational environments. We illustrated the application of easyMF for seed gene discovery from temporal, spatial, and integrated RNA-Seq datasets of maize (Zea mays L.), resulting in the identification of 3,167 seed stage-specific, 1,849 seed compartment-specific, and 774 seed-specific genes, respectively. The present results also indicated that easyMF can prioritize seed-related genes with superior prediction performance over the state-of-art network-based gene prioritization system MaizeNet. As a modular, containerized and open-source platform, easyMF can be further customized to satisfy users’ specific demands of functional gene discovery and deployed as a web service for broad applications.

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Acknowledgements

We thank High-Performance Computing (HPC) of Northwest Agriculture and Forestry University for providing computing resources.

Funding

This work was supported by the National Natural Science Foundation of China (31570371), the Youth 1000-Talent Program of China, the Hundred Talents Program of Shaanxi Province of China, Projects of Youth Technology New Star of Shaanxi Province (2017KJXX-67), and the Fundamental Research Funds for the Central Universities (2452020041).

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Author notes

  1. Wenlong Ma

    Present address: Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, 518120, China

  2. Siyuan Chen

    Present address: Senior Department of Infectious Diseases, The Fifth Medical Center of PLA General Hospital, Bei**g, 100039, China

  3. Wenlong Ma, Siyuan Chen and Yuhong Qi have contributed equally to this work.

Authors and Affiliations

  1. State Key Laboratory of Crop Stress Biology for Arid Areas, Center of Bioinformatics, College of Life Sciences, Northwest Agriculture and Forestry University, Yangling, 712100, China

    Wenlong Ma, Siyuan Chen, Yuhong Qi, Minggui Song, **g**g Zhai, Ting Zhang, Shang **e & Chuang Ma

  2. Key Laboratory of Biology and Genetics Improvement of Maize in Arid Area of Northwest Region, Ministry of Agriculture, Northwest Agriculture and Forestry University, Yangling, 712100, China

    Wenlong Ma, Siyuan Chen, Yuhong Qi, **g**g Zhai, Ting Zhang & Chuang Ma

  3. National Key Laboratory of Wheat and Maize Crops Science, Collaborative Innovation Center of Henan Grain Crops, College of Agronomy, Henan Agricultural University, Zhengzhou, 450002, China

    Guifeng Wang

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Contributions

C.M. conceived the project; W.M. and S.C. developed the software; M.S., J.Z., T.Z. and S.X. tested the software. W.M. and S.C. performed the bioinformatics analysis; S.C., C.M., Y.Q. and G.W. interpreted the bioinformatics analysis results; W.M., S.C. and C.M. wrote the article.

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Correspondence to Chuang Ma.

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Ma, W., Chen, S., Qi, Y. et al. easyMF: A Web Platform for Matrix Factorization-Based Gene Discovery from Large-scale Transcriptome Data. Interdiscip Sci Comput Life Sci 14, 746–758 (2022). https://doi.org/10.1007/s12539-022-00522-2

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