scholarly journals MicroPattern: a web-based tool for microbe set enrichment analysis and disease similarity calculation based on a list of microbes

2017 ◽  
Vol 7 (1) ◽  
Author(s):  
Wei Ma ◽  
Chuanbo Huang ◽  
Yuan Zhou ◽  
Jianwei Li ◽  
Qinghua Cui
Keyword(s):  
Enrichment Analysis ◽  
Web Based ◽  
Disease Similarity ◽  
Similarity Calculation

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

2019 ◽  
Vol 35 (24) ◽  
pp. 5339-5340 ◽  
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.

scholarly journals Granatum: a graphical single-cell RNA-Seq analysis pipeline for genomics scientists

2017 ◽  
Author(s):  
Xun Zhu ◽  
Thomas Wolfgruber ◽  
Austin Tasato ◽  
David G. Garmire ◽  
Lana X Garmire
Keyword(s):  
Single Cell ◽  
Enrichment Analysis ◽  
Graphical Interface ◽  
Analysis Pipeline ◽  
Web Based ◽  
Cell Clustering ◽  
Gene Filtering ◽  
Study Heterogeneity ◽  
Differential Gene ◽  
Cell Series

AbstractBackgroundSingle-cell RNA sequencing (scRNA-Seq) is an increasingly popular platform to study heterogeneity at the single-cell level.Computational methods to process scRNA-Seq have limited accessibility to bench scientists as they require significant amounts of bioinformatics skills.ResultsWe have developed Granatum, a web-based scRNA-Seq analysis pipeline to make analysis more broadly accessible to researchers. Without a single line of programming code, users can click through the pipeline, setting parameters and visualizing results via the interactive graphical interface Granatum conveniently walks users through various steps of scRNA-Seq analysis. It has a comprehensive list of modules, including plate merging and batch-effect removal, outlier-sample removal, gene filtering, geneexpression normalization, cell clustering, differential gene expression analysis, pathway/ontology enrichment analysis, protein-networ interaction visualization, and pseudo-time cell series construction.ConclusionsGranatum enables broad adoption of scRNA-Seq technology by empowering the bench scientists with an easy-to-use graphical interface for scRNA-Seq data analysis. The package is freely available for research use athttp://garmiregroup.org/granatum/app

scholarly journals XSTREME: Comprehensive motif analysis of biological sequence datasets

2021 ◽  
Author(s):  
Charles E. Grant ◽  
Timothy L. Bailey
Keyword(s):  
Motif Discovery ◽  
De Novo ◽  
Positional Distribution ◽  
Enrichment Analysis ◽  
Biological Sequence ◽  
Motif Analysis ◽  
Web Based ◽  
Fully Integrated ◽  
Commercial Use ◽  
Motif Enrichment

AbstractXSTREME is a web-based tool for performing comprehensive motif discovery and analysis in DNA, RNA or protein sequences, as well as in sequences in user-defined alphabets. It is designed for both very large and very small datasets. XSTREME is similar to the MEME-ChIP tool, but expands upon its capabilities in several ways. Like MEME-ChIP, XSTREME performs two types of de novo motif discovery, and also performs motif enrichment analysis of the input sequences using databases of known motifs. Unlike MEME-ChIP, which ranks motifs based on their enrichment in the centers of the input sequences, XSTREME uses enrichment anywhere in the sequences for this purpose. Consequently, XSTREME is more appropriate for motif-based analysis of sequences regardless of how the motifs are distributed within the sequences. XSTREME uses the MEME and STREME algorithms for motif discovery, and the recently developed SEA algorithm for motif enrichment analysis. The interactive HTML output produced by XSTREME includes highly accurate motif significance estimates, plots of the positional distribution of each motif, and histograms of the number of motif matches in each sequences. XSTREME is easy to use via its web server at https://meme-suite.org, and is fully integrated with the widely-used MEME Suite of sequence analysis tools, which can be freely downloaded at the same web site for non-commercial use.

WormExp: a web-based application for a Caenorhabditis elegans-specific gene expression enrichment analysis

2015 ◽  
Vol 32 (6) ◽  
pp. 943-945 ◽  
Author(s):  
Wentao Yang ◽  
Katja Dierking ◽  
Hinrich Schulenburg
Keyword(s):  
Gene Expression ◽  
Caenorhabditis Elegans ◽  
Enrichment Analysis ◽  
Supplementary Information ◽  
Specific Gene ◽  
Data Sets ◽  
Complete Collection ◽  
Web Based ◽  
Expression Of Genes ◽  
Gene Sets

Abstract Motivation: A particular challenge of the current omics age is to make sense of the inferred differential expression of genes and proteins. The most common approach is to perform a gene ontology (GO) enrichment analysis, thereby relying on a database that has been extracted from a variety of organisms and that can therefore only yield reliable information on evolutionary conserved functions. Results: We here present a web-based application for a taxon-specific gene set exploration and enrichment analysis, which is expected to yield novel functional insights into newly determined gene sets. The approach is based on the complete collection of curated high-throughput gene expression data sets for the model nematode Caenorhabditis elegans, including 1786 gene sets from more than 350 studies. Availability and implementation: WormExp is available at http://wormexp.zoologie.uni-kiel.de. Contacts: [email protected] Supplementary information: Supplementary data are available at Bioinformatics online.

scholarly journals A novel dysregulated pathway-identification analysis based on global influence of within-pathway effects and crosstalk between pathways

2015 ◽  
Vol 12 (102) ◽  
pp. 20140937 ◽  
Author(s):  
Junwei Han ◽  
Chunquan Li ◽  
Haixiu Yang ◽  
Yanjun Xu ◽  
Chunlong Zhang ◽  
...  
Keyword(s):  
Enrichment Analysis ◽  
Distribution Functions ◽  
Gene Set Enrichment Analysis ◽  
Cumulative Distribution ◽  
Global Network ◽  
Web Based ◽  
Novel Method ◽  
Microarray Datasets ◽  
Identification Analysis ◽  
Global Influence

Identifying dysregulated pathways from high-throughput experimental data in order to infer underlying biological insights is an important task. Current pathway-identification methods focus on single pathways in isolation; however, consideration of crosstalk between pathways could improve our understanding of alterations in biological states. We propose a novel method of pathway analysis based on global influence (PAGI) to identify dysregulated pathways, by considering both within-pathway effects and crosstalk between pathways. We constructed a global gene–gene network based on the relationships among genes extracted from a pathway database. We then evaluated the extent of differential expression for each gene, and mapped them to the global network. The random walk with restart algorithm was used to calculate the extent of genes affected by global influence. Finally, we used cumulative distribution functions to determine the significance values of the dysregulated pathways. We applied the PAGI method to five cancer microarray datasets, and compared our results with gene set enrichment analysis and five other methods. Based on these analyses, we demonstrated that PAGI can effectively identify dysregulated pathways associated with cancer, with strong reproducibility and robustness. We implemented PAGI using the freely available R-based and Web-based tools ( http://bioinfo.hrbmu.edu.cn/PAGI ).

scholarly journals MonaGO: a novel Gene Ontology enrichment analysis visualisation system

2020 ◽  
Author(s):  
Ziyin Xin ◽  
Yujun Cai ◽  
Louis T. Dang ◽  
Hannah M.S. Burke ◽  
Jerico Revote ◽  
...  
Keyword(s):  
Gene Ontology ◽  
Enrichment Analysis ◽  
Web Browser ◽  
Web Based ◽  
Single Output ◽  
Go Enrichment ◽  
Unbiased Manner ◽  
High Resolution Images ◽  
Interactive Visualisation ◽  
Go Terms

AbstractMonaGO is a novel web-based visualisation system that provides an intuitive, interactive and responsive interface for performing gene ontology (GO) enrichment analysis and visualising the results. MonaGO combines dynamic clustering and interactive visualisation as well as customisation options to assist biologists in obtaining meaningful representation of overrepresented GO terms, producing simplified outputs in an unbiased manner. MonaGO supports gene lists as well as GO terms as inputs. Visualisation results can be exported as high-resolution images or restored in new sessions, allowing reproducibility of the analysis. An extensive comparison between MonaGO and 11 state-of-the-art GO enrichment visualisation tools based on 9 features revealed that MonaGO is the only platform that simultaneously allows interactive visualisation within one single output page, directly accessible through a web browser with customisable display options. In summary, MonaGO will facilitate the interpretation of GO analysis and will assist the biologists into the representation of the results.

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