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Exploratory data analysis and visualization tool for time-series multi-omics data sets.
This tool is created for the following paper:
MOVIS: A Multi-Omics Software Solution for Multi-modal Time-Series Clustering and Embedding Tasks Aleksandar Anžel, Dominik Heider, and Georges Hattab
Please cite the paper as:
@article{ANZEL20221044,
title = {MOVIS: A multi-omics software solution for multi-modal time-series clustering, embedding, and visualizing tasks},
journal = {Computational and Structural Biotechnology Journal},
volume = {20},
pages = {1044-1055},
year = {2022},
issn = {2001-0370},
doi = {https://doi.org/10.1016/j.csbj.2022.02.012},
url = {https://www.sciencedirect.com/science/article/pii/S2001037022000526},
author = {Aleksandar Anžel and Dominik Heider and Georges Hattab},
keywords = {Time-series, Multi-omics, Visualization, Data exploration, Temporal multi-omics, Longitudinal multi-omics},
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).}
}You can see how MOVIS works by clicking on the following link https://movis.mathematik.uni-marburg.de/
Time-series multi-omics data sets are becoming more accessible and available than ever before. Besides this, the big increase in computational power in recent years allows researchers to work with those big data sets in a much more streamlined manner than before. However, today's tools do not properly capture the real power of multi-modalities of those data sets is not properly captured by today's tools.
In order to pinpoint anomalies and patterns in time-series multi-omic data sets, researchers often rely on collaborations with other experts and many complex software solutions. To enable easier manipulation and capture of the real power of time-series multi-omic data sets, we developed a web-based tool 'MOVIS'.
MOVIS uses the latest technologies and libraries in the Python ecosystem joined with the state-of-the-art front-end library Streamlit. MOVIS is built from the ground up with modularity in mind so that new functionalities could be easily incorporated into it. MOVIS can also be run locally as we also provide a DOCKER container.
The goal of this Wiki is to provide easy access to the methodologies used to design MOVIS. It should also guide users on how to use this tool to capture its true potential.