Using BioPAX-Parser (BiP) to enrich lists of genes or proteins with pathway data

Agapito, Giuseppe; Cannataro, Mario

doi:10.1186/s12859-021-04297-z

Cited by 7 publications

(6 citation statements)

References 130 publications

(134 reference statements)

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“…As it is easy to understand, the type of analysis depends also on the type of the data one would like to analyze. For unordered lists of genes, researchers can use g:Profiler g:GOSt [8][9][10], Enrichr [28,29], and BioPAX-Parser [35,47]. If the genes are ranked, g: Profiler g:GOSt can treasure this information and generate rank-based functional enrichment results.…”

Section: Tip 1: Before Starting Clarify Which Analysis You Would Like...mentioning

confidence: 99%

“…To perform pathway enrichment by employing more than a single database, users can employ cPEA [ 48 ], a software tool able to deal with several pathway databases using the BioPAX language [ 61 ] to store and represent pathways. Or they can use BiP [ 47 ] by selecting the “Whole PathwayCommon Data” option that will perform cross enrichment using the whole collection of automatically downloaded locally Pathway Common databases [ 62 ] coded in BioPAX. It is worth noticing that evaluating the similarity among pathways may be helpful to compare the genes within each pathway.…”

Section: Introductionmentioning

confidence: 99%

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Nine quick tips for pathway enrichment analysis

Chicco

Agapito

2022

PLoS Comput Biol

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Pathway enrichment analysis (PEA) is a computational biology method that identifies biological functions that are overrepresented in a group of genes more than would be expected by chance and ranks these functions by relevance. The relative abundance of genes pertinent to specific pathways is measured through statistical methods, and associated functional pathways are retrieved from online bioinformatics databases. In the last decade, along with the spread of the internet, higher availability of computational resources made PEA software tools easy to access and to use for bioinformatics practitioners worldwide. Although it became easier to use these tools, it also became easier to make mistakes that could generate inflated or misleading results, especially for beginners and inexperienced computational biologists. With this article, we propose nine quick tips to avoid common mistakes and to out a complete, sound, thorough PEA, which can produce relevant and robust results. We describe our nine guidelines in a simple way, so that they can be understood and used by anyone, including students and beginners. Some tips explain what to do before starting a PEA, others are suggestions of how to correctly generate meaningful results, and some final guidelines indicate some useful steps to properly interpret PEA results. Our nine tips can help users perform better pathway enrichment analyses and eventually contribute to a better understanding of current biology.

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Section: Tip 1: Before Starting Clarify Which Analysis You Would Like...mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Nine quick tips for pathway enrichment analysis

Chicco

Agapito

2022

PLoS Comput Biol

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“…An enrichment result could help identify interesting pathways not previously associated with the experimental context, for which a more careful evaluation is necessary to be validated as potential discoveries [ 17 ].…”

Section: Methodsmentioning

confidence: 99%

Risk Alleles for Multiple Myeloma Susceptibility in ADME Genes

Scionti

Agapito

Caracciolo

et al. 2022

Cells

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The cause of multiple myeloma (MM) remains largely unknown. Several pieces of evidence support the involvement of genetic and multiple environmental factors (i.e., chemical agents) in MM onset. The inter-individual variability in the bioactivation, detoxification, and clearance of chemical carcinogens such as asbestos, benzene, and pesticides might increase the MM risk. This inter-individual variability can be explained by the presence of polymorphic variants in absorption, distribution, metabolism, and excretion (ADME) genes. Despite the high relevance of this issue, few studies have focused on the inter-individual variability in ADME genes in MM risk. To identify new MM susceptibility loci, we performed an extended candidate gene approach by comparing high-throughput genotyping data of 1936 markers in 231 ADME genes on 64 MM patients and 59 controls from the CEU population. Differences in genotype and allele frequencies were validated using an internal control group of 35 non-cancer samples from the same geographic area as the patient group. We detected an association between MM risk and ADH1B rs1229984 (OR = 3.78; 95% CI, 1.18–12.13; p = 0.0282), PPARD rs6937483 (OR = 3.27; 95% CI, 1.01–10.56; p = 0.0479), SLC28A1 rs8187737 (OR = 11.33; 95% CI, 1.43–89.59; p = 0.005), SLC28A2 rs1060896 (OR = 6.58; 95% CI, 1.42–30.43; p = 0.0072), SLC29A1 rs8187630 (OR = 3.27; 95% CI, 1.01–10.56; p = 0.0479), and ALDH3A2 rs72547554 (OR = 2.46; 95% CI, 0.64–9.40; p = 0.0293). The prognostic value of these genes in MM was investigated in two public datasets showing that shorter overall survival was associated with low expression of ADH1B and SLC28A1. In conclusion, our proof-of-concept findings provide novel insights into the genetic bases of MM susceptibility.

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“…To identify the biological mechanisms and/or functions affected by the identified communities of genes, we used the Reactome pathway database [26]. In particular, we describe the enrichment performed using the communities with identifier 10 using the software tool BiopaxParser (BiP-v.1) [27].…”

Section: Pathway Enrichment Analysismentioning

confidence: 99%

An Exploratory Application of Multilayer Networks and Pathway Analysis in Pharmacogenomics

Milano,

Agapito,

Cannataro

2023

Genes

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Over the years, network analysis has become a promising strategy for analysing complex system, i.e., systems composed of a large number of interacting elements. In particular, multilayer networks have emerged as a powerful framework for modelling and analysing complex systems with multiple types of interactions. Network analysis can be applied to pharmacogenomics to gain insights into the interactions between genes, drugs, and diseases. By integrating network analysis techniques with pharmacogenomic data, the goal consists of uncovering complex relationships and identifying key genes to use in pathway enrichment analysis to figure out biological pathways involved in drug response and adverse reactions. In this study, we modelled omics, disease, and drug data together through multilayer network representation. Then, we mined the multilayer network with a community detection algorithm to obtain the top communities. After that, we used the identified list of genes from the communities to perform pathway enrichment analysis (PEA) to figure out the biological function affected by the selected genes. The results show that the genes forming the top community have multiple roles through different pathways.

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Using BioPAX-Parser (BiP) to enrich lists of genes or proteins with pathway data

Cited by 7 publications

References 130 publications

Nine quick tips for pathway enrichment analysis

Nine quick tips for pathway enrichment analysis

Risk Alleles for Multiple Myeloma Susceptibility in ADME Genes

An Exploratory Application of Multilayer Networks and Pathway Analysis in Pharmacogenomics

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