A Review on Bioinformatics Enrichment Analysis Tools Towards Functional Analysis of High Throughput Gene Set Data

ABSTRACTGene functional enrichment analysis represents one of the most popular bioinformatics methods for annotating the pathways and function categories of a given gene list. Current algorithms for enrichment computation such as Fisher’s exact test and hypergeometric test totally depend on the category count numbers of the gene list and one gene set. In this case, whatever the genes are, they were treated equally. However, actually genes show different scores in their essentiality in a gene list and in a gene set. It is thus hypothesized that the essentiality scores could be important and should be considered in gene functional analysis. For this purpose, here we proposed WEAT (https://www.cuilab.cn/weat/), a weighted gene set enrichment algorithm and online tool by weighting genes using essentiality scores. We confirmed the usefulness of WEAT using two case studies, the functional analysis of one aging-related gene list and one gene list involved in Lung Squamous Cell Carcinoma (LUSC). Finally, we believe that the WEAT method and tool could provide more possibilities for further exploring the functions of given gene lists.

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YQFC: a web tool to compare quantitative biological features between two yeast gene lists

Database ◽

10.1093/database/baaa076 ◽

2020 ◽

Vol 2020 ◽

Author(s):

Wei-Sheng Wu ◽

Lai-Ji Wang ◽

Han-Chen Yen ◽

Yan-Yuan Tseng

Keyword(s):

High Throughput ◽

Half Life ◽

Enrichment Analysis ◽

Biological Research ◽

Translational Efficiency ◽

Yeast Gene ◽

Mrna Isoforms ◽

Omics Technologies ◽

Quantitative Feature ◽

Analysis Tools

Abstract Nowadays high-throughput omics technologies are routinely used in biological research. From the omics data, researchers can easily get two gene lists (e.g. stress-induced genes vs. stress-repressed genes) related to their biological question. The next step would be to apply enrichment analysis tools to identify distinct functional/regulatory features between these two gene lists for further investigation. Although various enrichment analysis tools are already available, two challenges remain to be addressed. First, most existing tools are designed to analyze only one gene list, so they cannot directly compare two gene lists. Second, almost all existing tools focus on identifying the enriched qualitative features (e.g. gene ontology [GO] terms, pathways, domains, etc.). Many quantitative features (e.g. number of mRNA isoforms of a gene, mRNA half-life, protein half-life, transcriptional plasticity, translational efficiency, etc.) are available in the yeast, but no existing tools provide analyses on these quantitative features. To address these two challenges, here we present Yeast Quantitative Features Comparator (YQFC) that can directly compare various quantitative features between two yeast gene lists. In YQFC, we comprehensively collected and processed 85 quantitative features from the yeast literature and yeast databases. For each quantitative feature, YQFC provides three statistical tests (t-test, U test and KS test) to test whether this quantitative feature is statistically different between the two input yeast gene lists. The distinct quantitative features identified by YQFC may help researchers to study the underlying molecular mechanisms that differentiate the two input yeast gene lists. We believe that YQFC is a useful tool to expedite the biological research that uses high-throughput omics technologies. Database URL http://cosbi2.ee.ncku.edu.tw/YQFC/

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modEnrichr: a suite of gene set enrichment analysis tools for model organisms

Nucleic Acids Research ◽

10.1093/nar/gkz347 ◽

2019 ◽

Vol 47 (W1) ◽

pp. W183-W190 ◽

Cited By ~ 7

Author(s):

Maxim V Kuleshov ◽

Jennifer E L Diaz ◽

Zachary N Flamholz ◽

Alexandra B Keenan ◽

Alexander Lachmann ◽

...

Keyword(s):

Data Integration ◽

Expression Profiles ◽

Enrichment Analysis ◽

Gene Set Enrichment Analysis ◽

Protein Domain ◽

Model Organisms ◽

Gene Set Enrichment ◽

Gene Set ◽

Analysis Tools ◽

Gene Sets

Abstract High-throughput experiments produce increasingly large datasets that are difficult to analyze and integrate. While most data integration approaches focus on aligning metadata, data integration can be achieved by abstracting experimental results into gene sets. Such gene sets can be made available for reuse through gene set enrichment analysis tools such as Enrichr. Enrichr currently only supports gene sets compiled from human and mouse, limiting accessibility for investigators that study other model organisms. modEnrichr is an expansion of Enrichr for four model organisms: fish, fly, worm and yeast. The gene set libraries within FishEnrichr, FlyEnrichr, WormEnrichr and YeastEnrichr are created from the Gene Ontology, mRNA expression profiles, GeneRIF, pathway databases, protein domain databases and other organism-specific resources. Additionally, libraries were created by predicting gene function from RNA-seq co-expression data processed uniformly from the gene expression omnibus for each organism. The modEnrichr suite of tools provides the ability to convert gene lists across species using an ortholog conversion tool that automatically detects the species. For complex analyses, modEnrichr provides API access that enables submitting batch queries. In summary, modEnrichr leverages existing model organism databases and other resources to facilitate comprehensive hypothesis generation through data integration.

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Review for "High‐throughput single‐cell sequencing of paired TCRα and TCRβ genes for the direct expression‐cloning and functional analysis of murine T cell receptors"

10.1002/eji.201848030/v1/review1 ◽

2019 ◽

Keyword(s):

Functional Analysis ◽

T Cell ◽

Single Cell ◽

High Throughput ◽

T Cell Receptors ◽

Expression Cloning ◽

Cell Receptors ◽

Single Cell Sequencing

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Topside Defect Localization Using OBIRCH Analysis

ISTFA 2005: Conference Proceedings from the 31st International Symposium for Testing and Failure Analysis ◽

10.31399/asm.cp.istfa2005p0095 ◽

2005 ◽

Author(s):

Clifford Howard ◽

Anusha Weerakoon ◽

Diana M. Mitro ◽

Dawn Glaeser

Keyword(s):

Functional Analysis ◽

Defect Location ◽

Analysis Tools ◽

Defect Localization ◽

Tools And Techniques

Abstract OBIRCH analysis is a useful technique for defect localization not only for parametric failures, but also for functional analysis. However, OBIRCH results do not always identify the exact defect location. OBIRCH analysis results must be used in conjunction with other analysis tools and techniques to successfully identify defect locations.

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Establishing a consensus for the hallmarks of cancer based on gene ontology and pathway annotations

BMC Bioinformatics ◽

10.1186/s12859-021-04105-8 ◽

2021 ◽

Vol 22 (1) ◽

Author(s):

Yi Chen ◽

Fons. J. Verbeek ◽

Katherine Wolstencroft

Keyword(s):

Gene Ontology ◽

Enrichment Analysis ◽

Biological Data ◽

Hallmarks Of Cancer ◽

High Throughput Analysis ◽

Knowledge Resources ◽

Gene Set ◽

Cancer Hallmarks ◽

Starting Point ◽

High Level

Abstract Background The hallmarks of cancer provide a highly cited and well-used conceptual framework for describing the processes involved in cancer cell development and tumourigenesis. However, methods for translating these high-level concepts into data-level associations between hallmarks and genes (for high throughput analysis), vary widely between studies. The examination of different strategies to associate and map cancer hallmarks reveals significant differences, but also consensus. Results Here we present the results of a comparative analysis of cancer hallmark mapping strategies, based on Gene Ontology and biological pathway annotation, from different studies. By analysing the semantic similarity between annotations, and the resulting gene set overlap, we identify emerging consensus knowledge. In addition, we analyse the differences between hallmark and gene set associations using Weighted Gene Co-expression Network Analysis and enrichment analysis. Conclusions Reaching a community-wide consensus on how to identify cancer hallmark activity from research data would enable more systematic data integration and comparison between studies. These results highlight the current state of the consensus and offer a starting point for further convergence. In addition, we show how a lack of consensus can lead to large differences in the biological interpretation of downstream analyses and discuss the challenges of annotating changing and accumulating biological data, using intermediate knowledge resources that are also changing over time.

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Higher Acid-Base Imbalance Associated with Respiratory Failure Could Decrease the Survival of Patients with Scrub Typhus during Intensive Care Unit Stay: A Gene Set Enrichment Analysis

Journal of Clinical Medicine ◽

10.3390/jcm8101580 ◽

2019 ◽

Vol 8 (10) ◽

pp. 1580 ◽

Cited By ~ 1

Author(s):

Kyoung Min Moon ◽

Kyueng-Whan Min ◽

Mi-Hye Kim ◽

Dong-Hoon Kim ◽

Byoung Kwan Son ◽

...

Keyword(s):

Intensive Care Unit ◽

Intensive Care ◽

Respiratory Failure ◽

Scrub Typhus ◽

Enrichment Analysis ◽

Gene Set Enrichment Analysis ◽

Acid Base ◽

Gene Set Enrichment ◽

Gene Set ◽

Gene Sets

Ninety percent of patients with scrub typhus (SC) with vasculitis-like syndrome recover after mild symptoms; however, 10% can suffer serious complications, such as acute respiratory failure (ARF) and admission to the intensive care unit (ICU). Predictors for the progression of SC have not yet been established, and conventional scoring systems for ICU patients are insufficient to predict severity. We aimed to identify simple and robust indicators to predict aggressive behaviors of SC. We evaluated 91 patients with SC and 81 non-SC patients who were admitted to the ICU, and 32 cases from the public functional genomics data repository for gene expression analysis. We analyzed the relationships between several predictors and clinicopathological characteristics in patients with SC. We performed gene set enrichment analysis (GSEA) to identify SC-specific gene sets. The acid-base imbalance (ABI), measured 24 h before serious complications, was higher in patients with SC than in non-SC patients. A high ABI was associated with an increased incidence of ARF, leading to mechanical ventilation and worse survival. GSEA revealed that SC correlated to gene sets reflecting inflammation/apoptotic response and airway inflammation. ABI can be used to indicate ARF in patients with SC and assist with early detection.

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Gene set enrichment analysis for genome-wide DNA methylation data

Genome Biology ◽

10.1186/s13059-021-02388-x ◽

2021 ◽

Vol 22 (1) ◽

Author(s):

Jovana Maksimovic ◽

Alicia Oshlack ◽

Belinda Phipson

Keyword(s):

Dna Methylation ◽

Enrichment Analysis ◽

R Package ◽

Gene Set Enrichment Analysis ◽

Methylation Array ◽

Gene Set ◽

Genome Wide ◽

Genome Methylation ◽

Unbiased Gene ◽

Gene Set Testing

AbstractDNA methylation is one of the most commonly studied epigenetic marks, due to its role in disease and development. Illumina methylation arrays have been extensively used to measure methylation across the human genome. Methylation array analysis has primarily focused on preprocessing, normalization, and identification of differentially methylated CpGs and regions. GOmeth and GOregion are new methods for performing unbiased gene set testing following differential methylation analysis. Benchmarking analyses demonstrate GOmeth outperforms other approaches, and GOregion is the first method for gene set testing of differentially methylated regions. Both methods are publicly available in the missMethyl Bioconductor R package.

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IKBIP is a novel EMT-related biomarker and predicts poor survival in glioma

Translational Neuroscience ◽

10.1515/tnsci-2021-0002 ◽

2021 ◽

Vol 12 (1) ◽

pp. 009-019

Author(s):

Ying Yang ◽

Jin Wang ◽

Shihai Xu ◽

Wen Lv ◽

Fei Shi ◽

...

Keyword(s):

Epithelial To Mesenchymal Transition ◽

Molecular Subtype ◽

Enrichment Analysis ◽

Gene Set Enrichment Analysis ◽

Transcriptional Level ◽

Who Grade ◽

Interacting Protein ◽

Mesenchymal Transition ◽

Gene Set ◽

Substrate Adhesion

Abstract Background In cancer, kappa B-interacting protein (IKBIP) has rarely been reported. This study aimed at investigating its expression pattern and biological function in brain glioma at the transcriptional level. Methods We selected 301 glioma patients with microarray data from CGGA database and 697 glioma patients with RNAseq data from TCGA database. Transcriptional data and clinical data of 998 samples were analyzed. Statistical analysis and figure generating were performed with R language. Results We found that IKBIP expression showed positive correlation with WHO grade of glioma. IKBIP was increased in isocitrate dehydrogenase (IDH) wild type and mesenchymal molecular subtype of glioma. Gene ontology analysis demonstrated that IKBIP was profoundly associated with extracellular matrix organization, cell–substrate adhesion and response to wounding in both pan-glioma and glioblastoma. Subsequent gene set enrichment analysis revealed that IKBIP was particularly correlated with epithelial-to-mesenchymal transition (EMT). To further elucidate the relationship between IKBIP and EMT, we performed gene set variation analysis to screen the EMT-related signaling pathways and found that IKBIP expression was significantly associated with PI3K/AKT, hypoxia and TGF-β pathway. Moreover, IKBIP expression was found to be synergistic with key biomarkers of EMT, especially with N-cadherin, vimentin, snail, slug and TWIST1. Finally, higher IKBIP indicated significantly shorter survival for glioma patients. Conclusions IKBIP was associated with more aggressive phenotypes of gliomas. Furthermore, IKBIP was significantly involved in EMT and could serve as an independent prognosticator in glioma.

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