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

Yue, Zongliang; Willey, Christopher D.; Hjelmeland, Anita B.; Chen, Jake Y.

doi:10.1093/nar/gkz428

Cited by 8 publications

(11 citation statements)

References 37 publications

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“…We begin the iterative prioritization and expansion process by using BEERE to prioritize the disease gene list, determining genes with significant p-value (p < 0.05). BEERE is a user-friendly implementation of the WINNER software [8, 18]. Although BEERE only outputs one p-value per gene [18], it is sufficient for the prioritization methods employed within this methodology.…”

Section: Methodsmentioning

confidence: 99%

“…BEERE is a user-friendly implementation of the WINNER software [8, 18]. Although BEERE only outputs one p-value per gene [18], it is sufficient for the prioritization methods employed within this methodology. We use Prioritization to select the top genes amongst the candidate and expand genes for further processing, yielding a more specific, biologically significant gene list.…”

Section: Methodsmentioning

confidence: 99%

“…Thus, the output of these tools is restricted entirely by the quality of the input data; insufficient input data will lead to low quality or, even worse, biased results [17]. While the BEERE software tool [18] can expand gene lists, it fails to consider pathways and other biological contexts. Correctly placing genes into a biological context can aid the determination of significant genes not found within the initial query list [19].…”

Section: Introductionmentioning

confidence: 99%

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Prioritizing Complex Disease Genes from Heterogeneous Public Databases

Gong

Chen

2023

Preprint

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Background: Complex human diseases are defined not only by sophisticated patterns of genetic variants/mutations upstream but also by many interplaying genes, RNAs, and proteins downstream. Analyzing multiple genomic and functional genomic data types to determine a short list of genes or molecules of interest is a common task called ″gene prioritization″ in biology. There are many statistical, biological, and bioinformatic methods developed to perform gene prioritization tasks. However, little research has been conducted to examine the relationships among the technique used, merged/separate use of each data modality, the gene list′s network/pathway context, and various gene ranking/expansions. Methods: We introduce a new analytical framework called ″Gene Ranking and Iterative Prioritization based on Pathways″ (GRIPP) to prioritize genes derived from different modalities. Multiple data sources, such as CBioPortal, PAGER, and COSMIC were used to compile the initial gene list. We used the PAGER software to expand the gene list based on biological pathways and the BEERE software to construct protein-protein interaction networks that include the gene list to rank order genes. We produced a final gene list for each data modality iteratively from an initial draft gene list, using glioblastoma multiform (GBM) as a case study. Conclusion: We demonstrated that GBM gene lists obtained from three modalities (differential gene expressions, gene mutations, and copy number alterations) and several data sources could be iteratively expanded and ranked using GRIPP. While integrating various modalities of data can be useful to generate an integrated ranked gene list related to any specific disease, the integration may also decrease the overall significance of ranked genes derived from specific data modalities. Therefore, we recommend carefully sorting and integrating gene lists according to each modality, such as gene mutations, epigenetic controls, or differential expressions, to procure modality-specific biological insights into the prioritized genes.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Prioritizing Complex Disease Genes from Heterogeneous Public Databases

Gong

Chen

2023

Preprint

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“…We took a sample list of PubMed IDs from each retrieved entry. To remove biases and further confirm the mentioned keywords, we applied the analysis described in the previously developed tool called biomedical entity expansion ranking and exploration (BEERE) ( Yue et al, 2019b ) to extract those semantic relationships that co-mention “melanoma” and the pathways’ keywords.…”

Section: Methodsmentioning

confidence: 99%

PAGER Web APP: An Interactive, Online Gene Set and Network Interpretation Tool for Functional Genomics

et al. 2022

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Functional genomics studies have helped researchers annotate differentially expressed gene lists, extract gene expression signatures, and identify biological pathways from omics profiling experiments conducted on biological samples. The current geneset, network, and pathway analysis (GNPA) web servers, e.g., DAVID, EnrichR, WebGestaltR, or PAGER, do not allow automated integrative functional genomic downstream analysis. In this study, we developed a new web-based interactive application, “PAGER Web APP”, which supports online R scripting of integrative GNPA. In a case study of melanoma drug resistance, we showed that the new PAGER Web APP enabled us to discover highly relevant pathways and network modules, leading to novel biological insights. We also compared PAGER Web APP’s pathway analysis results retrieved among PAGER, EnrichR, and WebGestaltR to show its advantages in integrative GNPA. The interactive online web APP is publicly accessible from the link, https://aimed-lab.shinyapps.io/PAGERwebapp/.

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“…In order to identify lncRNAs and their potential targets, we have devised an in silico approach using quasi-mapping of long, paired-end reads ( 19 ) combined with nucleic acid binding prediction software using thermodynamics-based algorithms to detect RNA:RNA duplex or RNA:DNA triplex formation ( 20 , 21 ) (see Methods section). We also combine traditional differential gene expression analysis (DE) with differential gene correlation analysis (DGCA) ( 22 ), machine learning ( 23 , 24 ), and semantic network construction ( 25 , 26 ) to further elucidate transcriptional mechanisms of therapy resistance. These approaches have revealed differential regulation of DNA damage response (DDR) pathways as well as stemness, cell cycle, and chromatin remodeling signatures.…”

Section: Introductionmentioning

confidence: 99%

An in vivo model of glioblastoma radiation resistance identifies long noncoding RNAs and targetable kinases

Stackhouse¹,

Anderson²,

Yue³

et al. 2022

JCI Insight

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Key molecular regulators of acquired radiation resistance in recurrent glioblastoma (GBM) are largely unknown with a dearth of accurate pre-clinical models. To address this, we generated 8 GBM patient-derived xenograft (PDX) models of acquired radiation therapy-selected (RTS) resistance compared with same-patient, treatment naïve (RTU) PDX. These unique models mimic the longitudinal evolution of patient recurrent tumors following serial radiation therapy. Indeed, while whole exome sequencing confirmed retention of major genomic alterations in the RTS lines, we did detect a chromosome 12q14 amplification that is associated with clinical GBM recurrence in two RTS models. A novel bioinformatics pipeline was applied to analyze phenotypic, transcriptomic and kinomic alterations, which identified long non-coding RNAs (lncRNAs) and targetable, PDX-specific kinases. We observed differential transcriptional enrichment of DNA damage repair (DDR) pathways in our RTS models which correlated with several lncRNAs. Global kinomic profiling separated RTU and RTS models, but pairwise analyses indicated that there are multiple molecular routes to acquired radiation-resistance. RTS model-specific kinases were identified and targeted with clinically relevant small molecule inhibitors (SMIs). This unique cohort of in vivo radiation therapy-selected patient-derived models will enable future preclinical therapeutic testing to help overcome the treatment resistance seen in GBM patients.

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

Cited by 8 publications

References 37 publications

Prioritizing Complex Disease Genes from Heterogeneous Public Databases

Prioritizing Complex Disease Genes from Heterogeneous Public Databases

PAGER Web APP: An Interactive, Online Gene Set and Network Interpretation Tool for Functional Genomics

An in vivo model of glioblastoma radiation resistance identifies long noncoding RNAs and targetable kinases

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