Cargando…
MOVIS: A multi-omics software solution for multi-modal time-series clustering, embedding, and visualizing tasks
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 physic...
Autores principales: | , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Research Network of Computational and Structural Biotechnology
2022
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8886009/ https://www.ncbi.nlm.nih.gov/pubmed/35284047 http://dx.doi.org/10.1016/j.csbj.2022.02.012 |
_version_ | 1784660555588435968 |
---|---|
author | Anžel, Aleksandar Heider, Dominik Hattab, Georges |
author_facet | Anžel, Aleksandar Heider, Dominik Hattab, Georges |
author_sort | Anžel, Aleksandar |
collection | PubMed |
description | 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). |
format | Online Article Text |
id | pubmed-8886009 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Research Network of Computational and Structural Biotechnology |
record_format | MEDLINE/PubMed |
spelling | pubmed-88860092022-03-11 MOVIS: A multi-omics software solution for multi-modal time-series clustering, embedding, and visualizing tasks Anžel, Aleksandar Heider, Dominik Hattab, Georges Comput Struct Biotechnol J Research Article 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). Research Network of Computational and Structural Biotechnology 2022-02-22 /pmc/articles/PMC8886009/ /pubmed/35284047 http://dx.doi.org/10.1016/j.csbj.2022.02.012 Text en © 2022 The Author(s) https://creativecommons.org/licenses/by/4.0/This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Research Article Anžel, Aleksandar Heider, Dominik Hattab, Georges MOVIS: A multi-omics software solution for multi-modal time-series clustering, embedding, and visualizing tasks |
title | MOVIS: A multi-omics software solution for multi-modal time-series clustering, embedding, and visualizing tasks |
title_full | MOVIS: A multi-omics software solution for multi-modal time-series clustering, embedding, and visualizing tasks |
title_fullStr | MOVIS: A multi-omics software solution for multi-modal time-series clustering, embedding, and visualizing tasks |
title_full_unstemmed | MOVIS: A multi-omics software solution for multi-modal time-series clustering, embedding, and visualizing tasks |
title_short | MOVIS: A multi-omics software solution for multi-modal time-series clustering, embedding, and visualizing tasks |
title_sort | movis: a multi-omics software solution for multi-modal time-series clustering, embedding, and visualizing tasks |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8886009/ https://www.ncbi.nlm.nih.gov/pubmed/35284047 http://dx.doi.org/10.1016/j.csbj.2022.02.012 |
work_keys_str_mv | AT anzelaleksandar movisamultiomicssoftwaresolutionformultimodaltimeseriesclusteringembeddingandvisualizingtasks AT heiderdominik movisamultiomicssoftwaresolutionformultimodaltimeseriesclusteringembeddingandvisualizingtasks AT hattabgeorges movisamultiomicssoftwaresolutionformultimodaltimeseriesclusteringembeddingandvisualizingtasks |