Evidence map›Paper›PMID 37680201›Full record

ArticleFrontiers in genetics2023

PSF toolkit: an R package for pathway curation and topology-aware analysis.

Siras Hakobyan, Ani Stepanyan, Lilit Nersisyan, Hans Binder, Arsen Arakelyan

Abstract read
In one paragraph

Article in Frontiers in genetics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

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2 · The registry

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3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

  1. Article
  2. Article
  3. Pan-cancer analysis of telomere maintenance mechanisms.The Journal of biological chemistry · 2024
    Article
4 · The record

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PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Siras HakobyanBioinformatics Group, Institute of Molecular Biology, Armenian National Academy of Sciences, Yerevan, Armenia.
Ani StepanyanArmenian Bioinformatics Institute, Yerevan, Armenia.
Lilit NersisyanArmenian Bioinformatics Institute (ABI), Yerevan, Armenia.
Hans BinderArmenian Bioinformatics Institute, Yerevan, Armenia.
Arsen ArakelyanBioinformatics Group, Institute of Molecular Biology, Armenian National Academy of Sciences, Yerevan, Armenia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Most high throughput genomic data analysis pipelines currently rely on over-representation or gene set enrichment analysis (ORA/GSEA) approaches for functional analysis. In contrast, topology-based pathway analysis methods, which offer a more biologically informed perspective by incorporating interaction and topology information, have remained underutilized and inaccessible due to various limiting factors. These methods heavily rely on the quality of pathway topologies and often utilize predefined topologies from databases without assessing their correctness. To address these issues and make topology-aware pathway analysis more accessible and flexible, we introduce the PSF (Pathway Signal Flow) toolkit R package. Our toolkit integrates pathway curation and topology-based analysis, providing interactive and command-line tools that facilitate pathway importation, correction, and modification from diverse sources. This enables users to perform topology-based pathway signal flow analysis in both interactive and command-line modes. To showcase the toolkit's usability, we curated 36 KEGG signaling pathways and conducted several use-case studies, comparing our method with ORA and the topology-based signaling pathway impact analysis (SPIA) method. The results demonstrate that the algorithm can effectively identify ORA enriched pathways while providing more detailed branch-level information. Moreover, in contrast to the SPIA method, it offers the advantage of being cut-off free and less susceptible to the variability caused by selection thresholds. By combining pathway curation and topology-based analysis, the PSF toolkit enhances the quality, flexibility, and accessibility of topology-aware pathway analysis. Researchers can now easily import pathways from various sources, correct and modify them as needed, and perform detailed topology-based pathway signal flow analysis. In summary, our PSF toolkit offers an integrated solution that addresses the limitations of current topology-based pathway analysis methods. By providing interactive and command-line tools for pathway curation and topology-based analysis, we empower researchers to conduct comprehensive pathway analyses across a wide range of applications.

Indexed as

biological networksgene expressionpathway analysissingle cell omicssystems biology

Identifiers

PMID37680201
PMCPMC10482229

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.