MALBoost: a web-based application for gene regulatory network analysis in Plasmodium falciparum
Abstract Background Gene Regulatory Networks (GRN) produce powerful insights into transcriptional regulation in cells. The power of GRNs has been underutilized in malaria research. The Arboreto library was incorporated into a user-friendly web-based application for malaria researchers ( http://malbo...
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ftdoajarticles:oai:doaj.org/article:8efba26fe16948119e723120d4a29099 2023-05-15T15:13:29+02:00 MALBoost: a web-based application for gene regulatory network analysis in Plasmodium falciparum Roelof van Wyk Riëtte van Biljon Lyn-Marie Birkholtz 2021-07-01T00:00:00Z https://doi.org/10.1186/s12936-021-03848-2 https://doaj.org/article/8efba26fe16948119e723120d4a29099 EN eng BMC https://doi.org/10.1186/s12936-021-03848-2 https://doaj.org/toc/1475-2875 doi:10.1186/s12936-021-03848-2 1475-2875 https://doaj.org/article/8efba26fe16948119e723120d4a29099 Malaria Journal, Vol 20, Iss 1, Pp 1-9 (2021) Gene regulatory network Malaria Plasmodium falciparum Machine learning Artificial intelligence Arctic medicine. Tropical medicine RC955-962 Infectious and parasitic diseases RC109-216 article 2021 ftdoajarticles https://doi.org/10.1186/s12936-021-03848-2 2022-12-31T06:32:14Z Abstract Background Gene Regulatory Networks (GRN) produce powerful insights into transcriptional regulation in cells. The power of GRNs has been underutilized in malaria research. The Arboreto library was incorporated into a user-friendly web-based application for malaria researchers ( http://malboost.bi.up.ac.za ). This application will assist researchers with gaining an in depth understanding of transcriptomic datasets. Methods The web application for MALBoost was built in Python-Flask with Redis and Celery workers for queue submission handling, which execute the Arboreto suite algorithms. A submission of 5–50 regulators and total expression set of 5200 genes is permitted. The program runs in a point-and-click web user interface built using Bootstrap4 templates. Post-analysis submission, users are redirected to a status page with run time estimates and ultimately a download button upon completion. Result updates or failure updates will be emailed to the users. Results A web-based application with an easy-to-use interface is presented with a use case validation of AP2-G and AP2-I. The validation set incorporates cross-referencing with ChIP-seq and transcriptome datasets. For AP2-G, 5 ChIP-seq targets were significantly enriched with seven more targets presenting with strong evidence of validated targets. Conclusion The MALBoost application provides the first tool for easy interfacing and efficiently allows gene regulatory network construction for Plasmodium. Additionally, access is provided to a pre-compiled network for use as reference framework. Validation for sexually committed ring-stage parasite targets of AP2-G, suggests the algorithm was effective in resolving “traditionally” low-level signatures even in bulk RNA datasets. Article in Journal/Newspaper Arctic Directory of Open Access Journals: DOAJ Articles Arctic Malaria Journal 20 1 |
institution |
Open Polar |
collection |
Directory of Open Access Journals: DOAJ Articles |
op_collection_id |
ftdoajarticles |
language |
English |
topic |
Gene regulatory network Malaria Plasmodium falciparum Machine learning Artificial intelligence Arctic medicine. Tropical medicine RC955-962 Infectious and parasitic diseases RC109-216 |
spellingShingle |
Gene regulatory network Malaria Plasmodium falciparum Machine learning Artificial intelligence Arctic medicine. Tropical medicine RC955-962 Infectious and parasitic diseases RC109-216 Roelof van Wyk Riëtte van Biljon Lyn-Marie Birkholtz MALBoost: a web-based application for gene regulatory network analysis in Plasmodium falciparum |
topic_facet |
Gene regulatory network Malaria Plasmodium falciparum Machine learning Artificial intelligence Arctic medicine. Tropical medicine RC955-962 Infectious and parasitic diseases RC109-216 |
description |
Abstract Background Gene Regulatory Networks (GRN) produce powerful insights into transcriptional regulation in cells. The power of GRNs has been underutilized in malaria research. The Arboreto library was incorporated into a user-friendly web-based application for malaria researchers ( http://malboost.bi.up.ac.za ). This application will assist researchers with gaining an in depth understanding of transcriptomic datasets. Methods The web application for MALBoost was built in Python-Flask with Redis and Celery workers for queue submission handling, which execute the Arboreto suite algorithms. A submission of 5–50 regulators and total expression set of 5200 genes is permitted. The program runs in a point-and-click web user interface built using Bootstrap4 templates. Post-analysis submission, users are redirected to a status page with run time estimates and ultimately a download button upon completion. Result updates or failure updates will be emailed to the users. Results A web-based application with an easy-to-use interface is presented with a use case validation of AP2-G and AP2-I. The validation set incorporates cross-referencing with ChIP-seq and transcriptome datasets. For AP2-G, 5 ChIP-seq targets were significantly enriched with seven more targets presenting with strong evidence of validated targets. Conclusion The MALBoost application provides the first tool for easy interfacing and efficiently allows gene regulatory network construction for Plasmodium. Additionally, access is provided to a pre-compiled network for use as reference framework. Validation for sexually committed ring-stage parasite targets of AP2-G, suggests the algorithm was effective in resolving “traditionally” low-level signatures even in bulk RNA datasets. |
format |
Article in Journal/Newspaper |
author |
Roelof van Wyk Riëtte van Biljon Lyn-Marie Birkholtz |
author_facet |
Roelof van Wyk Riëtte van Biljon Lyn-Marie Birkholtz |
author_sort |
Roelof van Wyk |
title |
MALBoost: a web-based application for gene regulatory network analysis in Plasmodium falciparum |
title_short |
MALBoost: a web-based application for gene regulatory network analysis in Plasmodium falciparum |
title_full |
MALBoost: a web-based application for gene regulatory network analysis in Plasmodium falciparum |
title_fullStr |
MALBoost: a web-based application for gene regulatory network analysis in Plasmodium falciparum |
title_full_unstemmed |
MALBoost: a web-based application for gene regulatory network analysis in Plasmodium falciparum |
title_sort |
malboost: a web-based application for gene regulatory network analysis in plasmodium falciparum |
publisher |
BMC |
publishDate |
2021 |
url |
https://doi.org/10.1186/s12936-021-03848-2 https://doaj.org/article/8efba26fe16948119e723120d4a29099 |
geographic |
Arctic |
geographic_facet |
Arctic |
genre |
Arctic |
genre_facet |
Arctic |
op_source |
Malaria Journal, Vol 20, Iss 1, Pp 1-9 (2021) |
op_relation |
https://doi.org/10.1186/s12936-021-03848-2 https://doaj.org/toc/1475-2875 doi:10.1186/s12936-021-03848-2 1475-2875 https://doaj.org/article/8efba26fe16948119e723120d4a29099 |
op_doi |
https://doi.org/10.1186/s12936-021-03848-2 |
container_title |
Malaria Journal |
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20 |
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1 |
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1766344040595324928 |