PAGER 2.0: an update to the pathway, annotated-list and gene-signature electronic repository for Human Network Biology

Willey

Hjelmeland

et al. 2019

Nucleic Acids Research

Self Cite

BEERE (Biomedical Entity Expansion, Ranking and Explorations) is a new web-based data analysis tool to help biomedical researchers characterize any input list of genes/proteins, biomedical terms or their combinations, i.e. ‘biomedical entities’, in the context of existing literature. Specifically, BEERE first aims to help users examine the credibility of known entity-to-entity associative or semantic relationships supported by database or literature references from the user input of a gene/term list. Then, it will help users uncover the relative importance of each entity—a gene or a term—within the user input by computing the ranking scores of all entities. At last, it will help users hypothesize new gene functions or genotype–phenotype associations by an interactive visual interface of constructed global entity relationship network. The output from BEERE includes: a list of the original entities matched with known relationships in databases; any expanded entities that may be generated from the analysis; the ranks and ranking scores reported with statistical significance for each entity; and an interactive graphical display of the gene or term network within data provenance annotations that link to external data sources. The web server is free and open to all users with no login requirement and can be accessed at http://discovery.informatics.uab.edu/beere/.

Section: Discussionmentioning

confidence: 99%

BEERE: a web server for biomedical entity expansion, ranking and explorations

Willey

Hjelmeland

et al. 2019

Nucleic Acids Research

Self Cite

“…All PAGs are given a cohesion coefficient score (CoCo score) to help assess the degree of biological relevance for each PAG beyond random chance. Due to the limitation of time, we were not able to perform analysis on the updated PAGER version (26), published in 2017.…”

Section: Methodsmentioning

confidence: 99%

“Super Gene Set” Causal Relationship Discovery from Functional Genomics Data

IEEE/ACM Trans. Comput. Biol. and Bioinf.

Neylon

Nguyen

et al. 2018

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We present a computational framework to identify "causal relationships" among super gene sets. For "causal relationships", we refer to both stimulatory and inhibitory regulatory relationships, regardless of through direct or indirect mechanisms. For super gene sets, we refer to "pathways, annotated lists, and gene signatures", or PAGs. To identify causal relationships among PAGs, we extend the previous work on identifying PAG-to-PAG regulatory relationships by further requiring them to be significantly enriched with gene-to-gene co-expression pairs across the two PAGs involved. This is achieved by developing a quantitative metric based on PAG-to-PAG Co-expressions (PPC), which we use to infer the likelihood that PAG-to-PAG relationships under examination are causal-either stimulatory or inhibitory. Since true causal relationships are unknown, we approximate the overall performance of inferring causal relationships with the performance of recalling known r-type PAG-to-PAG relationships from causal PAG-to-PAG inference, using a functional genomics benchmark dataset from the GEO database. We report the area-under-curve (AUC) performance for both precision and recall to be 0.81. By applying our framework to a myeloid-derived suppressor cells (MDSC) dataset, we further demonstrate that this framework is effective in helping build multi-scale biomolecular systems models with new insights on regulatory and causal links for downstream biological interpretations.

“…To perform functional genomics downstream analysis such as the Gene Set Enrichment Analysis (GSEA) ( 10 ), users today rely on general-purpose gene set databases, e.g. MSigDB ( 11 ), KEGG ( 12 ), EnrichR ( 13 ) or PAGER ( 14 ). However, while these databases generally contain ‘immune response’ pathways or gene signatures based on prior studies of cancer, autoimmune disorders, or other infectious diseases, they lack specific SARS-CoV-2 gene sets identified in recent SARS-CoV-2 genomic or functional genomic studies.…”

Section: Introductionmentioning

confidence: 99%

PAGER-CoV: a comprehensive collection of pathways, annotated gene-lists and gene signatures for coronavirus disease studies

Zhang

et al. 2020

Nucleic Acids Research

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PAGER-CoV (http://discovery.informatics.uab.edu/PAGER-CoV/) is a new web-based database that can help biomedical researchers interpret coronavirus-related functional genomic study results in the context of curated knowledge of host viral infection, inflammatory response, organ damage, and tissue repair. The new database consists of 11 835 PAGs (Pathways, Annotated gene-lists, or Gene signatures) from 33 public data sources. Through the web user interface, users can search by a query gene or a query term and retrieve significantly matched PAGs with all the curated information. Users can navigate from a PAG of interest to other related PAGs through either shared PAG-to-PAG co-membership relationships or PAG-to-PAG regulatory relationships, totaling 19 996 993. Users can also retrieve enriched PAGs from an input list of COVID-19 functional study result genes, customize the search data sources, and export all results for subsequent offline data analysis. In a case study, we performed a gene set enrichment analysis (GSEA) of a COVID-19 RNA-seq data set from the Gene Expression Omnibus database. Compared with the results using the standard PAGER database, PAGER-CoV allows for more sensitive matching of known immune-related gene signatures. We expect PAGER-CoV to be invaluable for biomedical researchers to find molecular biology mechanisms and tailored therapeutics to treat COVID-19 patients.