Correction to: Identifying significantly impacted pathways: a comprehensive review and assessment

Nguyen, Tuan-Minh; Shafi, Adib; Nguyen, Tin; Drăghici, Sorin

doi:10.1186/s13059-019-1882-1

Cited by 3 publications

(6 citation statements)

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“…Scientists can use pathway enrichment analysis to acquire mechanistic insight into gene lists generated by genome-scale (omics) investigations. This approach finds biological pathways that are more enriched in a gene list than is expected by chance ( Nguyen et al, 2019 ). Innovative pathway enrichment analysis methodologies and provide a step-by-step guidance for interpreting gene lists generated by RNA-seq and genome-sequencing research.…”

Section: Proteomicsmentioning

confidence: 99%

Innovative in Silico Approaches for Characterization of Genes and Proteins

et al. 2022

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Bioinformatics is an amalgamation of biology, mathematics and computer science. It is a science which gathers the information from biology in terms of molecules and applies the informatic techniques to the gathered information for understanding and organizing the data in a useful manner. With the help of bioinformatics, the experimental data generated is stored in several databases available online like nucleotide database, protein databases, GENBANK and others. The data stored in these databases is used as reference for experimental evaluation and validation. Till now several online tools have been developed to analyze the genomic, transcriptomic, proteomics, epigenomics and metabolomics data. Some of them include Human Splicing Finder (HSF), Exonic Splicing Enhancer Mutation taster, and others. A number of SNPs are observed in the non-coding, intronic regions and play a role in the regulation of genes, which may or may not directly impose an effect on the protein expression. Many mutations are thought to influence the splicing mechanism by affecting the existing splice sites or creating a new sites. To predict the effect of mutation (SNP) on splicing mechanism/signal, HSF was developed. Thus, the tool is helpful in predicting the effect of mutations on splicing signals and can provide data even for better understanding of the intronic mutations that can be further validated experimentally. Additionally, rapid advancement in proteomics have steered researchers to organize the study of protein structure, function, relationships, and dynamics in space and time. Thus the effective integration of all of these technological interventions will eventually lead to steering up of next-generation systems biology, which will provide valuable biological insights in the field of research, diagnostic, therapeutic and development of personalized medicine.

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Section: Proteomicsmentioning

confidence: 99%

Innovative in Silico Approaches for Characterization of Genes and Proteins

et al. 2022

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“…By casting gene-level measurements in a broader biological context, this approach allows researchers to interpret their data in terms of a great variety of gene sets that may represent different functions, processes, components, or associations with disease. The success of pathway enrichment analysis, however, led to a very complex state of affairs with more than 70 different methods and hundreds of variants published to date [1][2][3][4] compounded by more than 33,000 human gene sets available from the Molecular Signatures Database (MSigDB) [5] alone, a scenario that has become extremely difficult to navigate.…”

Section: Introductionmentioning

confidence: 99%

“…Existing methods have been categorized in various ways; for the purpose of our work, we are primarily concerned with over-representation analysis (ORA), whose input is an unranked list of genes (selected from the full list of measured genes by imposing a threshold criterion), and functional class scoring (FCS), whose input is the full list of measured genes (ranked based on quantitative scores) [6]. In recent years, several groups have discussed and implemented benchmarks to compare findings from different tools as well as to evaluate multiple factors that affect the results of enrichment analysis [1,[7][8][9][10][11][12][13][14][15]. Typically, these benchmarking efforts were aimed at comparing many different tools belonging to one or more broad categories, such as ORA and FCS.…”

Section: Introductionmentioning

confidence: 99%

Assessment of Gene Set Enrichment Analysis using curated RNA-seq-based benchmarks

Candia,

Ferrucci

2024

PLoS ONE

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Pathway enrichment analysis is a ubiquitous computational biology method to interpret a list of genes (typically derived from the association of large-scale omics data with phenotypes of interest) in terms of higher-level, predefined gene sets that share biological function, chromosomal location, or other common features. Among many tools developed so far, Gene Set Enrichment Analysis (GSEA) stands out as one of the pioneering and most widely used methods. Although originally developed for microarray data, GSEA is nowadays extensively utilized for RNA-seq data analysis. Here, we quantitatively assessed the performance of a variety of GSEA modalities and provide guidance in the practical use of GSEA in RNA-seq experiments. We leveraged harmonized RNA-seq datasets available from The Cancer Genome Atlas (TCGA) in combination with large, curated pathway collections from the Molecular Signatures Database to obtain cancer-type-specific target pathway lists across multiple cancer types. We carried out a detailed analysis of GSEA performance using both gene-set and phenotype permutations combined with four different choices for the Kolmogorov-Smirnov enrichment statistic. Based on our benchmarks, we conclude that the classic/unweighted gene-set permutation approach offered comparable or better sensitivity-vs-specificity tradeoffs across cancer types compared with other, more complex and computationally intensive permutation methods. Finally, we analyzed other large cohorts for thyroid cancer and hepatocellular carcinoma. We utilized a new consensus metric, the Enrichment Evidence Score (EES), which showed a remarkable agreement between pathways identified in TCGA and those from other sources, despite differences in cancer etiology. This finding suggests an EES-based strategy to identify a core set of pathways that may be complemented by an expanded set of pathways for downstream exploratory analysis. This work fills the existing gap in current guidelines and benchmarks for the use of GSEA with RNA-seq data and provides a framework to enable detailed benchmarking of other RNA-seq-based pathway analysis tools.

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“…The first such method, impact analysis ( Draghici et al, 2007 ; Tarca et al, 2009 ), was soon followed by a plethora of over 20 other approaches ( Khatri et al, 2012 ; Mitrea et al, 2013 ; Nguyen et al, 2018 ). Many of these methods have been bench-marked recently ( Nguyen et al, 2019 ).…”

mentioning

confidence: 99%

“…Despite the existence of a large number of gene set analysis methods, there is little consistency among different methods when analyzing the same gene expression dataset ( Maleki et al, 2019b ; Nguyen et al, 2019 ). Although gene set overlap is a common phenomenon in gene set databases, most gene set analysis methods disregard such an overlap.…”

mentioning

confidence: 99%

Editorial: Advancement in Gene Set Analysis: Gaining Insight From High-Throughput Data

et al. 2022

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Correction to: Identifying significantly impacted pathways: a comprehensive review and assessment

Abstract: Following publication of the original paper [1], the authors reported the following update to the competing interests declaration.

Cited by 3 publications

References 1 publication

Innovative in Silico Approaches for Characterization of Genes and Proteins

Innovative in Silico Approaches for Characterization of Genes and Proteins

Assessment of Gene Set Enrichment Analysis using curated RNA-seq-based benchmarks

Editorial: Advancement in Gene Set Analysis: Gaining Insight From High-Throughput Data

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