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MOVIS:一种用于多模态时间序列聚类、嵌入和可视化任务的多组学软件解决方案。

MOVIS: A multi-omics software solution for multi-modal time-series clustering, embedding, and visualizing tasks.

作者信息

Anžel Aleksandar, Heider Dominik, Hattab Georges

机构信息

Department of Mathematics and Computer Science, University of Marburg, Hans-Meerwein-Strasse 6, Marburg 35032, Hesse, Germany.

出版信息

Comput Struct Biotechnol J. 2022 Feb 22;20:1044-1055. doi: 10.1016/j.csbj.2022.02.012. eCollection 2022.

DOI:10.1016/j.csbj.2022.02.012
PMID:35284047
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC8886009/
Abstract

Thanks to recent advances in sequencing and computational technologies, many researchers with biological and/or medical backgrounds are now producing multiple data sets with an embedded temporal dimension. Multi-modalities enable researchers to explore and investigate different biological and physico-chemical processes with various technologies. Motivated to explore multi-omics data and time-series multi-omics specifically, the exploration process has been hindered by the separation introduced by each omics-type. To effectively explore such temporal data sets, discover anomalies, find patterns, and better understand their intricacies, expertise in computer science and bioinformatics is required. Here we present MOVIS, a modular time-series multi-omics exploration tool with a user-friendly web interface that facilitates the data exploration of such data. It brings into equal participation each time-series omic-type for analysis and visualization. As of the time of writing, two time-series multi-omics data sets have been integrated and successfully reproduced. The resulting visualizations are task-specific, reproducible, and publication-ready. MOVIS is built on open-source software and is easily extendable to accommodate different analytical tasks. An online version of MOVIS is available under https://movis.mathematik.uni-marburg.de/ and on Docker Hub (https://hub.docker.com/r/aanzel/movis).

摘要

得益于测序和计算技术的最新进展,许多具有生物学和/或医学背景的研究人员现在正在生成具有嵌入式时间维度的多个数据集。多模态使研究人员能够使用各种技术探索和研究不同的生物和物理化学过程。特别是出于探索多组学数据和时间序列多组学的动机,探索过程受到每种组学类型所引入的分离的阻碍。为了有效地探索此类时间数据集、发现异常、找到模式并更好地理解其复杂性,需要计算机科学和生物信息学方面的专业知识。在这里,我们展示了MOVIS,这是一个具有用户友好型网页界面的模块化时间序列多组学探索工具,它便于对此类数据进行数据探索。它让每个时间序列组学类型平等参与分析和可视化。截至撰写本文时,已经集成并成功重现了两个时间序列多组学数据集。生成的可视化结果是针对特定任务的、可重现的且可用于发表的。MOVIS基于开源软件构建,并且易于扩展以适应不同的分析任务。MOVIS的在线版本可在https://movis.mathematik.uni-marburg.de/以及Docker Hub(https://hub.docker.com/r/aanzel/movis)上获取。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4fa9/8886009/e1fbbbe9cedc/gr11.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4fa9/8886009/9008b2f31543/gr3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4fa9/8886009/753d1936f211/gr5.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4fa9/8886009/afe5ab6e21fa/gr8.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4fa9/8886009/24d37933da35/gr9.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4fa9/8886009/b312feaa922a/gr10.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4fa9/8886009/e1fbbbe9cedc/gr11.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4fa9/8886009/9008b2f31543/gr3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4fa9/8886009/753d1936f211/gr5.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4fa9/8886009/afe5ab6e21fa/gr8.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4fa9/8886009/24d37933da35/gr9.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4fa9/8886009/b312feaa922a/gr10.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4fa9/8886009/e1fbbbe9cedc/gr11.jpg

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