NoRCE: non-coding RNA sets cis enrichment tool

Abstract Background While some non-coding RNAs (ncRNAs) are assigned critical regulatory roles, most remain functionally uncharacterized. This presents a challenge whenever an interesting set of ncRNAs needs to be analyzed in a functional context. Transcripts located close-by on the genome are often regulated together. This genomic proximity on the sequence can hint at a functional association. Results We present a tool, NoRCE, that performs cis enrichment analysis for a given set of ncRNAs. Enrichment is carried out using the functional annotations of the coding genes located proximal to the input ncRNAs. Other biologically relevant information such as topologically associating domain (TAD) boundaries, co-expression patterns, and miRNA target prediction information can be incorporated to conduct a richer enrichment analysis. To this end, NoRCE includes several relevant datasets as part of its data repository, including cell-line specific TAD boundaries, functional gene sets, and expression data for coding & ncRNAs specific to cancer. Additionally, the users can utilize custom data files in their investigation. Enrichment results can be retrieved in a tabular format or visualized in several different ways. NoRCE is currently available for the following species: human, mouse, rat, zebrafish, fruit fly, worm, and yeast. Conclusions NoRCE is a platform-independent, user-friendly, comprehensive R package that can be used to gain insight into the functional importance of a list of ncRNAs of any type. The tool offers flexibility to conduct the users’ preferred set of analyses by designing their own pipeline of analysis. NoRCE is available in Bioconductor and https://github.com/guldenolgun/NoRCE.

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NoRCE: Non-coding RNA Sets Cis Enrichment Tool

10.1101/663765 ◽

2019 ◽

Author(s):

Gulden Olgun ◽

Afshan Nabi ◽

Oznur Tastan

Keyword(s):

Expression Patterns ◽

Target Prediction ◽

Enrichment Analysis ◽

Fruit Fly ◽

Relevant Information ◽

R Package ◽

Spatial Proximity ◽

Biologically Relevant ◽

Gene Sets ◽

Data Files

AbstractSummaryWhile some non-coding RNAs (ncRNAs) are assigned to critical regulatory roles, most remain functionally uncharacterized. This presents a challenge whenever an interesting set of ncRNAs needs to be analyzed in a functional context. Transcripts located close-by on the genome are often regulated together. This genomic spatial proximity can lead to a functional association. Based on this idea, we present a tool, NoRCE, that performs cis enrichment analysis for a given set of ncRNAs. Enrichment is carried out using the functional annotations of the coding genes located proximal to the input ncRNAs. NoRCE allows incorporating other biologically relevant information such as topologically associating domain (TAD) boundaries, co-expression patterns, and miRNA target prediction information. NoRCE repository provides several data, such as cell-line specific TAD boundaries, functional gene sets, and expression data for coding and ncRNAs specific to cancer for the analysis. Additionally, users can utilize their custom data files in their investigation. Enrichment results can be retrieved in a tabular format or visualized in several different ways. NoRCE is currently available for the following species: human, mouse, rat, zebrafish, fruit fly, worm, and yeast. NoRCE is a platform-independent, user-friendly, comprehensive R package that could be used to gain insight into the functional importance of a list of any type of interesting ncRNAs. Users can run the pipeline in a single function; also, the tool offers flexibility to conduct the users’ preferred analysis in a single base and design their pipeline. It is available in Bioconductor and https://github.com/guldenolgun/NoRCE.

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Learning Dysregulated Pathways in Cancers from Differential Variability Analysis

Cancer Informatics ◽

10.4137/cin.s14066 ◽

2014 ◽

Vol 13s5 ◽

pp. CIN.S14066 ◽

Cited By ~ 4

Author(s):

Bahman Afsari ◽

Donald German ◽

Elana J. Fertig

Keyword(s):

Expression Patterns ◽

Enrichment Analysis ◽

R Package ◽

Computationally Efficient ◽

Variability Analysis ◽

Software Packages ◽

Pathway Inference ◽

Gene Sets ◽

Multigene Expression ◽

Differential Variability

Analysis of gene sets can implicate activity in signaling pathways that is responsible for cancer initiation and progression, but is not discernible from the analysis of individual genes. Multiple methods and software packages have been developed to infer pathway activity from expression measurements for set of genes targeted by that pathway. Broadly, three major methodologies have been proposed: over-representation, enrichment, and differential variability. Both over-representation and enrichment analyses are effective techniques to infer differentially regulated pathways from gene sets with relatively consistent differentially expressed (DE) genes. Specifically, these algorithms aggregate statistics from each gene in the pathway. However, they overlook multivariate patterns related to gene interactions and variations in expression. Therefore, the analysis of differential variability of multigene expression patterns can be essential to pathway inference in cancers. The corresponding methodologies and software packages for such multivariate variability analysis of pathways are reviewed here. We also introduce a new, computationally efficient algorithm, expression variation analysis (EVA), which has been implemented along with a previously proposed algorithm, Differential Rank Conservation (DIRAC), in an open source R package, gene set regulation (GSReg). EVA inferred similar pathways as DIRAC at reduced computational costs. Moreover, EVA also inferred different dysregulated pathways than those identified by enrichment analysis.

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TFEA.ChIP: a tool kit for transcription factor binding site enrichment analysis capitalizing on ChIP-seq datasets

Bioinformatics ◽

10.1093/bioinformatics/btz573 ◽

2019 ◽

Vol 35 (24) ◽

pp. 5339-5340 ◽

Cited By ~ 8

Author(s):

Laura Puente-Santamaria ◽

Wyeth W Wasserman ◽

Luis del Peso

Keyword(s):

Genomic Analysis ◽

Enrichment Analysis ◽

R Package ◽

Supplementary Information ◽

Web Based ◽

Factor Binding Site ◽

Gene Sets ◽

Transcription Regulators ◽

Computational Identification ◽

On Chip

Abstract Summary The computational identification of the transcription factors (TFs) [more generally, transcription regulators, (TR)] responsible for the co-regulation of a specific set of genes is a common problem found in genomic analysis. Herein, we describe TFEA.ChIP, a tool that makes use of ChIP-seq datasets to estimate and visualize TR enrichment in gene lists representing transcriptional profiles. We validated TFEA.ChIP using a wide variety of gene sets representing signatures of genetic and chemical perturbations as input and found that the relevant TR was correctly identified in 126 of a total of 174 analyzed. Comparison with other TR enrichment tools demonstrates that TFEA.ChIP is an highly customizable package with an outstanding performance. Availability and implementation TFEA.ChIP is implemented as an R package available at Bioconductor https://www.bioconductor.org/packages/devel/bioc/html/TFEA.ChIP.html and github https://github.com/LauraPS1/TFEA.ChIP_downloads. A web-based GUI to the package is also available at https://www.iib.uam.es/TFEA.ChIP/ Supplementary information Supplementary data are available at Bioinformatics online.

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PhenoExam: an R package and Web application for the examination of phenotypes linked to genes and gene sets

10.1101/2021.06.29.450324 ◽

2021 ◽

Author(s):

Alejandro Cisterna García ◽

Aurora González-Vidal ◽

Daniel Ruiz Villa ◽

Jordi Ortiz Murillo ◽

Alicia Gómez-Pascual ◽

...

Keyword(s):

Web Application ◽

Enrichment Analysis ◽

R Package ◽

Web Interface ◽

Gene Set ◽

New Genes ◽

Gene Sets ◽

Phenotype Analysis ◽

New Gene ◽

Early Onset Parkinson’S Disease

Gene set based phenotype enrichment analysis (detecting phenotypic terms that emerge as significant in a set of genes) can improve the rate of genetic diagnoses amongst other research purposes. To facilitate diverse phenotype analysis, we developed PhenoExam, a freely available R package for tool developers and a web interface for users, which performs: (1) phenotype and disease enrichment analysis on a gene set; (2) measures statistically significant phenotype similarities between gene sets and (3) detects significant differential phenotypes or disease terms across different databases. PhenoExam achieves these tasks by integrating databases or resources such as the HPO, MGD, CRISPRbrain, CTD, ClinGen, CGI, OrphaNET, UniProt, PsyGeNET, and Genomics England Panel App. PhenoExam accepts both human and mouse genes as input. We developed PhenoExam to assist a variety of users, including clinicians, computational biologists and geneticists. It can be used to support the validation of new gene-to-disease discoveries, and in the detection of differential phenotypes between two gene sets (a phenotype linked to one of the gene set but no to the other) that are useful for differential diagnosis and to improve genetic panels. We validated PhenoExam performance through simulations and its application to real cases. We demonstrate that PhenoExam is effective in distinguishing gene sets or Mendelian diseases with very similar phenotypes through projecting the disease-causing genes into their annotation-based phenotypic spaces. We also tested the tool with early onset Parkinson's disease and dystonia genes, to show phenotype-level similarities but also potentially interesting differences. More specifically, we used PhenoExam to validate computationally predicted new genes potentially associated with epilepsy. Therefore, PhenoExam effectively discovers links between phenotypic terms across annotation databases through effective integration. The R package is available at https://github.com/alexcis95/PhenoExam and the Web tool is accessible at https://snca.atica.um.es/PhenoExamWeb/.

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GMrepo: a database of curated and consistently annotated human gut metagenomes

Nucleic Acids Research ◽

10.1093/nar/gkz764 ◽

2019 ◽

Vol 48 (D1) ◽

pp. D545-D553 ◽

Cited By ~ 12

Author(s):

Sicheng Wu ◽

Chuqing Sun ◽

Yanze Li ◽

Teng Wang ◽

Longhao Jia ◽

...

Keyword(s):

Relevant Information ◽

Metagenomic Data ◽

Data Repository ◽

Healthy Controls ◽

Healthy Individuals ◽

Human Gut ◽

Meta Data ◽

Biologically Relevant ◽

Manual Curation ◽

State Of Art

Abstract GMrepo (data repository for Gut Microbiota) is a database of curated and consistently annotated human gut metagenomes. Its main purpose is to facilitate the reusability and accessibility of the rapidly growing human metagenomic data. This is achieved by consistently annotating the microbial contents of collected samples using state-of-art toolsets and by manual curation of the meta-data of the corresponding human hosts. GMrepo organizes the collected samples according to their associated phenotypes and includes all possible related meta-data such as age, sex, country, body-mass-index (BMI) and recent antibiotics usage. To make relevant information easier to access, GMrepo is equipped with a graphical query builder, enabling users to make customized, complex and biologically relevant queries. For example, to find (1) samples from healthy individuals of 18 to 25 years old with BMIs between 18.5 and 24.9, or (2) projects that are related to colorectal neoplasms, with each containing >100 samples and both patients and healthy controls. Precomputed species/genus relative abundances, prevalence within and across phenotypes, and pairwise co-occurrence information are all available at the website and accessible through programmable interfaces. So far, GMrepo contains 58 903 human gut samples/runs (including 17 618 metagenomes and 41 285 amplicons) from 253 projects concerning 92 phenotypes. GMrepo is freely available at: https://gmrepo.humangut.info.

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mCSEA: Detecting subtle differentially methylated regions

10.1101/293381 ◽

2018 ◽

Cited By ~ 2

Author(s):

Jordi Martorell-Marugán ◽

Víctor González-Rumayor ◽

Pedro Carmona-Sáez

Keyword(s):

Gene Expression ◽

Expression Patterns ◽

Enrichment Analysis ◽

R Package ◽

Gene Set Enrichment Analysis ◽

Differentially Methylated Regions ◽

Gene Set Enrichment ◽

Sibling Pairs ◽

Complex Disorders ◽

Obesity And Diabetes

AbstractMotivationThe identification of differentially methylated regions (DMRs) among phenotypes is one of the main goals of epigenetic analysis. Although there are several methods developed to detect DMRs, most of them are focused on detecting relatively large differences in methylation levels and fail to detect moderate, but consistent, methylation changes that might be associated to complex disorders.ResultsWe present mCSEA, an R package that implements a Gene Set Enrichment Analysis method to identify differentially methylated regions from Illumina 450K and EPIC array data. It is especially useful for detecting subtle, but consistent, methylation differences in complex phenotypes. mCSEA also implements functions to integrate gene expression data and to detect genes with significant correlations among methylation and gene expression patterns. Using simulated datasets, we show that mCSEA outperforms other tools in detecting DMRs. In addition, we applied mCSEA to a previously published dataset of sibling pairs discordant for intrauterine hyperglycemia exposure. We found several differentially methylated promoters in genes related to metabolic disorders like obesity and diabetes, demonstrating the potential of mCSEA to identify differentially methylated regions not detected by other methods.AvailabilitymCSEA is freely available from the Bioconductor [email protected]

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GSEA-InContext: Identifying novel and common patterns in expression experiments

10.1101/259440 ◽

2018 ◽

Author(s):

Rani K. Powers ◽

Andrew Goodspeed ◽

Harrison Pielke-Lombardo ◽

Aik-Choon Tan ◽

James C. Costello

Keyword(s):

Expression Patterns ◽

Null Distribution ◽

Enrichment Analysis ◽

Gene Set Enrichment Analysis ◽

Enrichment Score ◽

Specific Gene ◽

Gene Set Enrichment ◽

Single Experiment ◽

Gene Set ◽

Gene Sets

AbstractMotivationGene Set Enrichment Analysis (GSEA) is routinely used to analyze and interpret coordinate changes in transcriptomics experiments. For an experiment where less than seven samples per condition are compared, GSEA employs a competitive null hypothesis to test significance. A gene set enrichment score is tested against a null distribution of enrichment scores generated from permuted gene sets, where genes are randomly selected from the input experiment. Looking across a variety of biological conditions, however, genes are not randomly distributed with many showing consistent patterns of up- or down-regulation. As a result, common patterns of positively and negatively enriched gene sets are observed across experiments. Placing a single experiment into the context of a relevant set of background experiments allows us to identify both the common and experiment-specific patterns of gene set enrichment.ResultsWe compiled a compendium of 442 small molecule transcriptomic experiments and used GSEA to characterize common patterns of positively and negatively enriched gene sets. To identify experiment-specific gene set enrichment, we developed the GSEA-InContext method that accounts for gene expression patterns within a user-defined background set of experiments to identify statistically significantly enriched gene sets. We evaluated GSEA-InContext on experiments using small molecules with known targets and show that it successfully prioritizes gene sets that are specific to each experiment, thus providing valuable insights that complement standard GSEA analysis.Availability and ImplementationGSEA-InContext is implemented in Python. Code, the background expression compendium, and results are available at: https://github.com/CostelloLab/GSEA-InContext

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GSOAP: a tool for visualization of gene set over-representation analysis

Bioinformatics ◽

10.1093/bioinformatics/btaa001 ◽

2020 ◽

Vol 36 (9) ◽

pp. 2923-2925 ◽

Cited By ~ 1

Author(s):

Tomas Tokar ◽

Chiara Pastrello ◽

Igor Jurisica

Keyword(s):

Enrichment Analysis ◽

R Package ◽

Closeness Centrality ◽

Visual Exploration ◽

Gene Set ◽

Gene Sets ◽

Common Technique

Abstract Motivation Gene sets over-representation analysis (GSOA) is a common technique of enrichment analysis that measures the overlap between a gene set and selected instances (e.g. pathways). Despite its popularity, there is currently no established standard for visualization of GSOA results. Results Here, we propose a visual exploration of the GSOA results by showing the relationships among the enriched instances, while highlighting important instance attributes, such as significance, closeness (centrality) and clustering. Availability and implementation GSOAP is implemented as an R package and is available at https://github.com/tomastokar/gsoap.

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snpGeneSets: An R Package for Genome-Wide Study Annotation

G3 Genes|Genome|Genetics ◽

10.1534/g3.116.034694 ◽

2016 ◽

Vol 6 (12) ◽

pp. 4087-4095 ◽

Cited By ~ 4

Author(s):

Hao Mei ◽

Lianna Li ◽

Fan Jiang ◽

Jeannette Simino ◽

Michael Griswold ◽

...

Keyword(s):

Genome Wide Association Study ◽

Enrichment Analysis ◽

R Package ◽

Knowledge Bases ◽

Gene Expressions ◽

Next Generation Sequencing Technology ◽

Gene Sets ◽

Genome Wide ◽

A Genome ◽

Downstream Analysis

Abstract Genome-wide studies (GWS) of SNP associations and differential gene expressions have generated abundant results; next-generation sequencing technology has further boosted the number of variants and genes identified. Effective interpretation requires massive annotation and downstream analysis of these genome-wide results, a computationally challenging task. We developed the snpGeneSets package to simplify annotation and analysis of GWS results. Our package integrates local copies of knowledge bases for SNPs, genes, and gene sets, and implements wrapper functions in the R language to enable transparent access to low-level databases for efficient annotation of large genomic data. The package contains functions that execute three types of annotations: (1) genomic mapping annotation for SNPs and genes and functional annotation for gene sets; (2) bidirectional mapping between SNPs and genes, and genes and gene sets; and (3) calculation of gene effect measures from SNP associations and performance of gene set enrichment analyses to identify functional pathways. We applied snpGeneSets to type 2 diabetes (T2D) results from the NHGRI genome-wide association study (GWAS) catalog, a Finnish GWAS, and a genome-wide expression study (GWES). These studies demonstrate the usefulness of snpGeneSets for annotating and performing enrichment analysis of GWS results. The package is open-source, free, and can be downloaded at: https://www.umc.edu/biostats_software/.

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pathfindR: An R Package for Pathway Enrichment Analysis Utilizing Active Subnetworks

10.1101/272450 ◽

2018 ◽

Cited By ~ 5

Author(s):

Ege Ulgen ◽

Ozan Ozisik ◽

Osman Ugur Sezerman

Keyword(s):

Interaction Network ◽

Enrichment Analysis ◽

R Package ◽

Pathway Enrichment Analysis ◽

Pathway Enrichment ◽

Protein Protein Interaction ◽

Gene Sets ◽

User Data ◽

Pathway Diagrams ◽

Protein Protein Interaction Network

AbstractSummaryPathfindR is a tool for pathway enrichment analysis utilizing active subnetworks. It identifies gene sets that form active subnetworks in a protein-protein interaction network using a list of genes provided by the user. It then performs pathway enrichment analyses on the identified gene sets. Further, using the R package pathview, it maps the user data on the enriched pathways and renders pathway diagrams with the mapped genes. Because many of the enriched pathways are usually biologically related, pathfindR also offers functionality to cluster these pathways and identify representative pathways in the clusters. PathfindR is built as a stand-alone package but it can easily be integrated with other tools, such as differential expression/methylation analysis tools, for building fully automated pipelines. In this article, an overview of pathfindR is provided and an example application on a rheumatoid arthritis dataset is presented and discussed.AvailabilityThe package is freely available under MIT license at: https://github.com/egeulgen/pathfindR

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