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iPathwayGuide, an advanced AI platform, uses the most advanced pathway analysis approach that considers the role, positioning, and relationships of a given gene within a pathway, resulting in a significant reduction in false positives associated with pathway analysis. Simple, easy, web-based application that provides you with publication-ready results in minutes.
What is Pathway Analysis?
High-throughput technologies (e.g. RNA sequencing, microarray, etc) currently enable us to measure gene expression levels of tens of thousands of genes in the scope of a single experiment. Many such experiments involve the comparison of two phenotypes, such as disease vs. control, treated vs. not treated, drug A vs. drug B, etc. Various statistical approaches are subsequently used to identify the genes which are differentially expressed (DE) between these phenotypes, such as t test, Z-score, and ANOVA. Although such lists of DE genes (100’s to 1000’s) provide valuable information regarding the changes across phenotypes, and play important roles in the downstream analysis, they alone cannot explain the complex mechanisms that are involved in the given condition.
The goal of pathway analysis is to translate the list of genes that are differentially expressed across the given phenotypes (e.g. disease vs healthy, treated vs non-treated, disease subtype A vs disease subtype B, etc.) into meaningful biological phenomena.
By using a novel system biology approach called Impact Analysis, iPathwayGuide considers the role, position and relationships of each gene within a pathway, which results in a significant reduction in false positives and identify the truly impacted pathways and putative mechanisms that can explain all measured gene expression changes.
How iPathwayGuide differs from competitors?
Best-in-class Impact Analysis for Pathway Analysis
The iPathwayGuide’s impact analysis uses a systems biology approach that takes into consideration the direction and type of every edge on every pathway, the location of every gene, etc. This has been shown to both eliminate many of the false positives produced by the other approaches, as well as correctly identify true positives that are otherwise missed.
A comprehensive benchmarking and comparison of 13 widely-used pathway analysis methods across more than 1,000 analyses shows that the topology-based method used in iPathwayGuide ranked the best with highest median value of AUC.
The comparison is described in the following publication:
Nguyen TM, Shafi A, Nguyen T, Draghici S. Identifying significantly impacted pathways: a comprehensive review and assessment. Genome Biol. 2019 Oct 9;20(1):203. doi: 10.1186/s13059-019-1790-4. Erratum in: Genome Biol. 2019 Nov 12;20(1):234. PMID: 31597578; PMCID: PMC6784345.
We help you understand, not just visualize
For example, when creating a network diagram, it is difficult to obtain a complicated entanglement interpretation as shown in the left figure, but the iPathway Guide (right figure) can help you to visually understand the phenomenon.
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Pathway Analysis with proprietary Impact Analysis
All other pathway analysis approaches (e.g. Ingenuity Pathway Analysis or IPA) consider the pathways as simply sets of genes ignoring the biological processes that the pathways are meant to describe. iPathwayGuide is the only pathway analysis tool that uses a system biology approach and includes in the analysis the type, function and interactions between the genes on all pathways.
Impact Analysis is described in the following publication:
Sorin Draghici, Purvesh Khatri, Adi Laurentiu Tarca, Kashayp Amin, Arina Done, Calin Voichita, Constantin Georgescu, and Roberto Romero. A systems biology approach for pathway level analysis. Genome Research, 17(10):1537–1545, 2007.
iPathwayGuide helps us identify the actual process, and it helps us develop hypotheses we could not otherwise develop… iPathwayGuide’s pathway analysis works differently and better than other available systems of which I’m aware.
We were very excited to see the very positive clinical results provided by methylprednisolone in the Henry Ford Health System. We have seen a dramatic reduction in the number of deaths after we started treating patients with this drug. We are looking forward to exploring the clinical efficacy of the other repurposed drugs identified by Advaita’s drug repurposing platform.
What’s really nice about it [iPathwayGuide] still is… we can run an analysis and send it to a collaborator and they can play all day. And there is none of this nonsense about ‘having another seat’ that the competition is requiring. I mean, that’s the biggest thing. I would definitely recommend Advaita to other organizations. No product is perfect, but Advaita iPathwayGuide serves our needs very well, allows for flexibility and does not cost a huge amount of money. This is an excellent choice for a department like ours.
“iPathway[Guide] is very good at doing things quickly. It’s very intuitive to upload your dataset. It’s very intuitive at the gene set breakdown. Especially with how you change the cutoff and the types of statistical benchmarks you can use. For instance, it’s very easy to choose several different cutoffs when it comes to criteria and stringency for the types of statistics you’re doing. If you want to make things more stringent or you want to do a p-value correction, it’s easy to adjust for that.”
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Draghici, S., Khatri, P., Tarca, A. L., Amin, K., Done, A., Voichita, C., … Romero, R. (2007). A systems biology approach for pathway level analysis. Genome Research, 17(10), 1537–1545. doi:10.1101/gr.6202607. Full paper here.
Tarca, A. L., Draghici, S., Khatri, P., Hassan, S. S., Mittal, P., Kim, J., … Romero, R. (2008). A novel signaling pathway impact analysis. Bioinformatics, 25(1), 75–82. doi:10.1093/bioinformatics/btn577. Full paper here.
Ahsan, S., & Drăghici, S. (2017). Identifying Significantly Impacted Pathways and Putative Mechanisms with iPathwayGuide. Current Protocols in Bioinformatics, 7.15.1–7.15.30. doi:10.1002/cpbi.24. Full paper here.
Nguyen, T.-M., Shafi, A., Nguyen, T., & Draghici, S. (2019). Identifying significantly impacted pathways: a comprehensive review and assessment. Genome Biology, 20(1). doi:10.1186/s13059-019-1790-4. Full paper here.
About Advaita Bioinformatics
Title: Using bioinformatics to shorten your path to discovery and improve patient care
Date/Time: 30th June 2021 (WED), 900AM – 1030AM
Organized by: Department of Pathology, Faculty of Medicine and Health Sciences, UPM and Mbioscience Solutions Sdn Bhd
Invited Speaker: Professor Sorin Draghici, Computer Science and Obstetrics and Gynecology, Wayne State University (USA)
Host: Professor Dr. Johnson Stanslas, Researcher in Translational Pharmacology and Therapeutics, Department of Medicine, UPM
For more information, please visit here
Back in May 2021, we hosted a webinar demonstrating how iPathwayGuide identified the key processes and mechanisms in severe cased of COVID-19.
We heard from world expert immunologist Dr. Gil Mor about the key mechanisms identified in this disease, and showed how iPathwayGuide used those mechanisms to predict an existing drug with clinical efficacy against COVID-19.
We also presented results from an independent clinical study, which showed in a cohort of over 200 patients, that a short course of methylprednisolone was able to significantly reduce transfers to ICU, requirements for ventilation, as well as reduce mortality by 44%.
What could iPathwayGuide help you see in your data? Watch the webinar to get some ideas about what more you can mine from your experiments.
Please fill in a simple form to view the recording here.