Gene Sets Analysis using Network Patterns

Linkowski, Gregory; Blatti, Charles; Kalari, Krishna R.; Sinha, Saurabh; Vasudevan, Shobha

doi:10.1101/629816

Cited by 4 publications

(4 citation statements)

References 41 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…We have also speculated on a more iterative version of propagating information along network edges: once pathways have been scored for relevance to the query set of SNPs, perhaps the SNPs could be scored (also via RWR) for relevance to the top-ranking pathways, and by iterating pathway and SNP ranking multiple times we may identify a subset of the query set that is strongly connected with a subset of the pathways. Finally, in contrast to the ‘unsupervised’ approach adopted in VarSAn, future work may considering deploying supervised learning methods to characterize patterns of network connectivity that are predictive of membership in the query set, and infer pathway relevance scores from these patterns; such an approach was used for gene set characterization in recent work ( 79 ).…”

Section: Discussionmentioning

confidence: 99%

VarSAn: associating pathways with a set of genomic variants using network analysis

Xie

Kendzior

et al. 2021

Nucleic Acids Research

Self Cite

View full text Add to dashboard Cite

There is a pressing need today to mechanistically interpret sets of genomic variants associated with diseases. Here we present a tool called ‘VarSAn’ that uses a network analysis algorithm to identify pathways relevant to a given set of variants. VarSAn analyzes a configurable network whose nodes represent variants, genes and pathways, using a Random Walk with Restarts algorithm to rank pathways for relevance to the given variants, and reports P-values for pathway relevance. It treats non-coding and coding variants differently, properly accounts for the number of pathways impacted by each variant and identifies relevant pathways even if many variants do not directly impact genes of the pathway. We use VarSAn to identify pathways relevant to variants related to cancer and several other diseases, as well as drug response variation. We find VarSAn's pathway ranking to be complementary to the standard approach of enrichment tests on genes related to the query set. We adopt a novel benchmarking strategy to quantify its advantage over this baseline approach. Finally, we use VarSAn to discover key pathways, including the VEGFA-VEGFR2 pathway, related to de novo variants in patients of Hypoplastic Left Heart Syndrome, a rare and severe congenital heart defect.

show abstract

Section: Discussionmentioning

confidence: 99%

VarSAn: associating pathways with a set of genomic variants using network analysis

Xie

Kendzior

et al. 2021

Nucleic Acids Research

Self Cite

View full text Add to dashboard Cite

show abstract

“…We have also speculated on a more iterative version of propagating information along network edges: once pathways have been scored for relevance to the query set of SNPs, perhaps the SNPs could be scored (also via RWR) for relevance to the top-ranking pathways, and by iterating pathway and SNP ranking multiple times we may identify a subset of the query set that is strongly connected with a subset of the pathways. Finally, in contrast to the “unsupervised” approach adopted in VarSAn, future work may considering deploying supervised learning methods to characterize patterns of network connectivity that are predictive of membership in the query set, and infer pathway relevance scores from these patterns; such an approach was used for gene set characterization in recent work (76).…”

Section: Discussionmentioning

confidence: 99%

VarSAn: Associating pathways with a set of genomic variants using network analysis

Xie

Kendzior

et al. 2020

Preprint

Self Cite

View full text Add to dashboard Cite

There is a pressing need today to mechanistically interpret sets of genomic variants associated with diseases. Here we present a tool called ‘VarSAn’ that uses a network analysis algorithm to identify pathways relevant to a given set of variants. VarSAn analyzes a configurable network whose nodes represent variants, genes and pathways, using a Random Walk with Restarts algorithm to rank pathways for relevance to the given variants, and reports p-values for pathway relevance. It treats non-coding and coding variants differently, properly accounts for the number of pathways impacted by each variant and identifies relevant pathways even if many variants do not directly impact genes of the pathway. We use VarSAn to identify pathways relevant to variants related to cancer and several other diseases, as well as drug response variation. We find VarSAn’s pathway ranking to be complementary to the standard approach of enrichment tests on genes related to the query set. We adopt a novel benchmarking strategy to quantify its advantage over this baseline approach. Finally, we use VarSAn to discover key pathways, including the VEGFA-VEGFR2 pathway, related to de novo variants in patients of Hypoplastic Left Heart Syndrome, a rare and severe congenital heart defect.

show abstract

“…The KnowEnG pipeline uses an implementation called "DRaWR" [24], the main advantage of which compared to enrichment tests is that it examines not only properties with which the given genes are annotated but also the properties with which genes related to the given genes are annotated (Appendix C in S4 File). We have previously used DRaWR to characterize gene sets in Drosophila development [24] and cancer [63]. Here, we used the DRaWR-based knowledgeguided gene set characterization pipeline with the HumanNet Integrated network [39] as the underlying network to identify, for ESCC subtype-related genes, the most related pathways in the Enrichr Pathways Collection [64].…”

Section: Case Study 3: Signature Analysis and Gene Set Characterizatimentioning

confidence: 99%

Knowledge-guided analysis of "omics" data using the KnowEnG cloud platform

et al. 2020

Self Cite

View full text Add to dashboard Cite

We present Knowledge Engine for Genomics (KnowEnG), a free-to-use computational system for analysis of genomics data sets, designed to accelerate biomedical discovery. It includes tools for popular bioinformatics tasks such as gene prioritization, sample clustering, gene set analysis, and expression signature analysis. The system specializes in "knowledge-guided" data mining and machine learning algorithms, in which user-provided data are analyzed in light of prior information about genes, aggregated from numerous knowledge bases and encoded in a massive "Knowledge Network." KnowEnG adheres to "FAIR" principles (findable, accessible, interoperable, and reuseable): its tools are easily portable to diverse computing environments, run on the cloud for scalable and cost-effective execution, and are interoperable with other computing platforms. The analysis tools are made available through multiple access modes, including a web portal with specialized visualization modules. We demonstrate the KnowEnG system's potential value in democratization of advanced tools for the modern genomics era through several case studies that use its tools to recreate and expand upon the published analysis of cancer data sets.

show abstract

Gene Sets Analysis using Network Patterns

Cited by 4 publications

References 41 publications

VarSAn: associating pathways with a set of genomic variants using network analysis

VarSAn: associating pathways with a set of genomic variants using network analysis

VarSAn: Associating pathways with a set of genomic variants using network analysis

Knowledge-guided analysis of "omics" data using the KnowEnG cloud platform

Contact Info

Product

Resources

About