scholarly journals Enrichment Map – a Cytoscape app to visualize and explore OMICs pathway enrichment results

F1000Research ◽  
2014 ◽  
Vol 3 ◽  
pp. 141 ◽  
Author(s):  
Ruth Isserlin ◽  
Daniele Merico ◽  
Veronique Voisin ◽  
Gary D. Bader
Keyword(s):  
High Throughput ◽  
Enrichment Analysis ◽  
Biological Entity ◽  
Command Line ◽  
Pathway Enrichment ◽  
Data Formats

High-throughput OMICs experiments generate signals for millions of entities (i.e. genes, proteins, metabolites or any measurable biological entity) in the cell. In an effort to summarize and explore these signals, expression results are examined in the context of known pathways and processes, through enrichment analysis to generate a set of pathways and processes that is significantly enriched. Due to the high redundancy in annotation resources this often results in hundreds of sets. To facilitate the analysis of these results, we have developed the Enrichment Map app to visualize enrichments as a network. We have updated Enrichment Map to support Cytoscape 3, and have added additional features including new data formats and command line access.

scholarly journals FAN-C: A Feature-rich Framework for the Analysis and Visualisation of C data

2020 ◽  
Author(s):  
Kai Kruse ◽  
Clemens B. Hug ◽  
Juan M. Vaquerizas
Keyword(s):  
High Throughput ◽  
Matrix Analysis ◽  
Set Covering ◽  
Command Line ◽  
Chromosome Conformation ◽  
C Storage ◽  
Data Formats ◽  
Analysis Tools ◽  
Command Line Tool ◽  
Broad Feature

Chromosome conformation capture data, particularly from high-throughput approaches such as Hi-C and its derivatives, are typically very complex to analyse. Existing analysis tools are often single-purpose, or limited in compatibility to a small number of data formats, frequently making Hi-C analyses tedious and time-consuming. Here, we present FAN-C, an easy-to-use command-line tool and powerful Python API with a broad feature set covering matrix generation, analysis, and visualisation for C-like data (https://github.com/vaquerizaslab/fanc). Due to its comprehensiveness and compatibility with the most prevalent Hi-C storage formats, FAN-C can be used in combination with a large number of existing analysis tools, thus greatly simplifying Hi-C matrix analysis.

scholarly journals Comparative transcriptomic analysis of heterophylly of the aquatic plant Potamogeton octandrus (Potamogetonaceae)

2017 ◽  
Author(s):  
Dingxuan He ◽  
Pin Guo ◽  
Paul F Gugger ◽  
Youhao Guo ◽  
Xing Liu ◽  
...  
Keyword(s):  
Differentially Expressed Genes ◽  
High Throughput ◽  
Leaf Development ◽  
Aquatic Plant ◽  
Kegg Pathway ◽  
Enrichment Analysis ◽  
Pathway Enrichment Analysis ◽  
Pathway Enrichment ◽  
Functional Studies ◽  
Floating Leaves

Many plant species exhibit heterophylly, displaying different leaves upon a single plant. The molecular mechanisms regulating this phenomenon, however, have remained elusive. In this study, the transcriptomes of submerged and floating leaves of an aquatic heterophyllous plant, Potamogeton octandrus Poir, were sequenced using a high-throughput sequencing technique (RNA-Seq), which aims to assist with the gene discovery and functional studies of genes involved in heterophyllous leaf development. A total of 81,103 unigenes were identified from the submerged and floating leaves, and a total of 6,822 differentially expressed genes (DEGs) were identified by comparing the samples from each developmental stage. KEGG pathway enrichment analysis categorized these unigenes into 128 pathways (p-value < 10-5). A total of 24,025 differentially expressed genes were involved in the carbon metabolic pathway, biosynthesis of amino acids, ribosomes, and plant-pathogen interaction. KEGG pathway enrichment analysis categorized a total of 70 DEGs into plant hormone signal transduction pathways. This study describes the initial results of the high-throughput transcriptome sequencing of heterophylly. Understanding the transcriptomes of floating and submerged leaves of the aquatic plant P. octandrus will assist with gene cloning and functional studies of genes involved in leaf development. This is especially the case with those involved in heterophyllous leaf development.

scholarly journals Comparative transcriptomic analysis of heterophylly of the aquatic plant Potamogeton octandrus (Potamogetonaceae)

2017 ◽  
Author(s):  
Dingxuan He ◽  
Pin Guo ◽  
Paul F Gugger ◽  
Youhao Guo ◽  
Xing Liu ◽  
...  
Keyword(s):  
Differentially Expressed Genes ◽  
High Throughput ◽  
Leaf Development ◽  
Aquatic Plant ◽  
Kegg Pathway ◽  
Enrichment Analysis ◽  
Pathway Enrichment Analysis ◽  
Pathway Enrichment ◽  
Functional Studies ◽  
Floating Leaves

Many plant species exhibit heterophylly, displaying different leaves upon a single plant. The molecular mechanisms regulating this phenomenon, however, have remained elusive. In this study, the transcriptomes of submerged and floating leaves of an aquatic heterophyllous plant, Potamogeton octandrus Poir, were sequenced using a high-throughput sequencing technique (RNA-Seq), which aims to assist with the gene discovery and functional studies of genes involved in heterophyllous leaf development. A total of 81,103 unigenes were identified from the submerged and floating leaves, and a total of 6,822 differentially expressed genes (DEGs) were identified by comparing the samples from each developmental stage. KEGG pathway enrichment analysis categorized these unigenes into 128 pathways (p-value < 10-5). A total of 24,025 differentially expressed genes were involved in the carbon metabolic pathway, biosynthesis of amino acids, ribosomes, and plant-pathogen interaction. KEGG pathway enrichment analysis categorized a total of 70 DEGs into plant hormone signal transduction pathways. This study describes the initial results of the high-throughput transcriptome sequencing of heterophylly. Understanding the transcriptomes of floating and submerged leaves of the aquatic plant P. octandrus will assist with gene cloning and functional studies of genes involved in leaf development. This is especially the case with those involved in heterophyllous leaf development.

scholarly journals LIPEA: Lipid Pathway Enrichment Analysis

2018 ◽  
Author(s):  
Aldo Acevedo ◽  
Claudio Durán ◽  
Sara Ciucci ◽  
Mathias Gerl ◽  
Carlo Vittorio Cannistraci
Keyword(s):  
High Throughput ◽  
Metabolic Diseases ◽  
Enrichment Analysis ◽  
Absolute Quantification ◽  
Gene Set Enrichment Analysis ◽  
Pathway Enrichment Analysis ◽  
Web Tool ◽  
Gene Set Enrichment ◽  
Pathway Enrichment ◽  
Web Platform

AbstractMotivationAnalyzing associations among multiple omic variables to infer mechanisms that meaningfully link them is a crucial step in systems biology. Gene Set Enrichment Analysis (GSEA) was conceived to pursue this aim in computational genomics, unveiling significant pathways associated to certain gene signatures under investigation. Lipidomics is a rapidly growing omic field, and absolute quantification of lipid abundance by shotgun mass spectrometry is generating high-throughput datasets that depict lipid metabolism in a plethora of conditions and organisms. In addition, high-throughput lipidomics represents a new important ally to develop personalized medicine approaches, investigate the causes and predict effective biomarkers in metabolic diseases, and not only.ResultsHere, we present Lipid Pathway Enrichment Analysis (LIPEA), a web-tool for over-representation analysis of lipid signatures and detection of the biological pathways in which they are enriched. LIPEA is a new valid resource for biologists and physicians to mine pathways significantly associated to a set of lipids, helping them to discover whether common and collective mechanisms are hidden behind those lipids. LIPEA was extensively tested and we provide two examples where our system gave successfully results related with Major Depression Disease (MDD) and insulin re-sistance.AvailabilityThe tool is available as web platform at https://lipea.biotec.tu-dresden.de.

scholarly journals Using BioPAX-Parser (BiP) to enrich lists of genes or proteins with pathway data

2021 ◽  
Vol 22 (S13) ◽  
Author(s):  
Giuseppe Agapito ◽  
Mario Cannataro
Keyword(s):  
Impact Analysis ◽  
Enrichment Analysis ◽  
Gene Expression Omnibus ◽  
The Cancer Genome Atlas ◽  
Biological Entity ◽  
Pathway Enrichment Analysis ◽  
Cancer Type ◽  
Pathway Enrichment ◽  
Analysis Tools ◽  
The Stability

Abstract Background Pathway enrichment analysis (PEA) is a well-established methodology for interpreting a list of genes and proteins of interest related to a condition under investigation. This paper aims to extend our previous work in which we introduced a preliminary comparative analysis of pathway enrichment analysis tools. We extended the earlier work by providing more case studies, comparing BiP enrichment performance with other well-known PEA software tools. Methods PEA uses pathway information to discover connections between a list of genes and proteins as well as biological mechanisms, helping researchers to overcome the problem of explaining biological entity lists of interest disconnected from the biological context. Results We compared the results of BiP with some existing pathway enrichment analysis tools comprising Centrality-based Pathway Enrichment, pathDIP, and Signaling Pathway Impact Analysis, considering three cancer types (colorectal, endometrial, and thyroid), for a total of six datasets (that is, two datasets per cancer type) obtained from the The Cancer Genome Atlas and Gene Expression Omnibus databases. We measured the similarities between the overlap of the enrichment results obtained using each couple of cancer datasets related to the same cancer. Conclusion As a result, BiP identified some well-known pathways related to the investigated cancer type, validated by the available literature. We also used the Jaccard and meet-min indices to evaluate the stability and the similarity between the enrichment results obtained from each couple of cancer datasets. The obtained results show that BiP provides more stable enrichment results than other tools.

scholarly journals Investigating the molecular basis for heterophylly in the aquatic plant Potamogeton octandrus (Potamogetonaceae) with comparative transcriptomics

PeerJ ◽  
2018 ◽  
Vol 6 ◽  
pp. e4448 ◽  
Author(s):  
Dingxuan He ◽  
Pin Guo ◽  
Paul F. Gugger ◽  
Youhao Guo ◽  
Xing Liu ◽  
...  
Keyword(s):  
Differentially Expressed Genes ◽  
High Throughput ◽  
Molecular Mechanisms ◽  
High Throughput Sequencing ◽  
Kegg Pathway ◽  
Enrichment Analysis ◽  
Differentially Expressed ◽  
Pathway Enrichment Analysis ◽  
Pathway Enrichment ◽  
Floating Leaves

Many plant species exhibit different leaf morphologies within a single plant, or heterophylly. The molecular mechanisms regulating this phenomenon, however, have remained elusive. In this study, the transcriptomes of submerged and floating leaves of an aquatic heterophyllous plant, Potamogeton octandrus Poir, at different stages of development, were sequenced using high-throughput sequencing (RNA-Seq), in order to aid gene discovery and functional studies of genes involved in heterophylly. A total of 81,103 unigenes were identified in submerged and floating leaves and 6,822 differentially expressed genes (DEGs) were identified by comparing samples at differing time points of development. KEGG pathway enrichment analysis categorized these unigenes into 128 pathways. A total of 24,025 differentially expressed genes were involved in carbon metabolic pathways, biosynthesis of amino acids, ribosomal processes, and plant-pathogen interactions. In particular, KEGG pathway enrichment analysis categorized a total of 70 DEGs into plant hormone signal transduction pathways. The high-throughput transcriptomic results presented here highlight the potential for understanding the molecular mechanisms underlying heterophylly, which is still poorly understood. Further, these data provide a framework to better understand heterophyllous leaf development in P. octandrus via targeted studies utilizing gene cloning and functional analyses.

A Method of Pathway Enrichment Analysis Based Gene Expression Variability

2013 ◽  
Vol 40 (12) ◽  
pp. 1256
Author(s):  
XiaoDong JIA ◽  
XiuJie CHEN ◽  
Xin WU ◽  
JianKai XU ◽  
FuJian TAN ◽  
...  
Keyword(s):  
Gene Expression ◽  
Enrichment Analysis ◽  
Pathway Enrichment Analysis ◽  
Expression Variability ◽  
Pathway Enrichment

Systematic Investigation of Quercetin for Treating Cardiovascular Disease Based on Network Pharmacology

2019 ◽  
Vol 22 (6) ◽  
pp. 411-420 ◽  
Author(s):  
Xian-Jun Wu ◽  
Xin-Bin Zhou ◽  
Chen Chen ◽  
Wei Mao
Keyword(s):  
Cardiovascular Disease ◽  
Cardiovascular Diseases ◽  
Signaling Pathway ◽  
Kegg Pathway ◽  
Enrichment Analysis ◽  
Network Pharmacology ◽  
Pathway Enrichment Analysis ◽  
Therapeutic Effects ◽  
Pathway Enrichment ◽  
Compound Target

Aim and Objective: Cardiovascular disease is a serious threat to human health because of its high mortality and morbidity rates. At present, there is no effective treatment. In Southeast Asia, traditional Chinese medicine is widely used in the treatment of cardiovascular diseases. Quercetin is a flavonoid extract of Ginkgo biloba leaves. Basic experiments and clinical studies have shown that quercetin has a significant effect on the treatment of cardiovascular diseases. However, its precise mechanism is still unclear. Therefore, it is necessary to exploit the network pharmacological potential effects of quercetin on cardiovascular disease. Materials and Methods: In the present study, a novel network pharmacology strategy based on pharmacokinetic filtering, target fishing, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis, compound-target-pathway network structured was performed to explore the anti- cardiovascular disease mechanism of quercetin. Results:: The outcomes showed that quercetin possesses favorable pharmacokinetic profiles, which have interactions with 47 cardiovascular disease-related targets and 12 KEGG signaling pathways to provide potential synergistic therapeutic effects. Following the construction of Compound-Target-Pathway (C-T-P) network, and the network topological feature calculation, we obtained top 10 core genes in this network which were AKT1, IL1B, TNF, IL6, JUN, CCL2, FOS, VEGFA, CXCL8, and ICAM1. KEGG pathway enrichment analysis. These indicated that quercetin produced the therapeutic effects against cardiovascular disease by systemically and holistically regulating many signaling pathways, including Fluid shear stress and atherosclerosis, AGE-RAGE signaling pathway in diabetic complications, TNF signaling pathway, MAPK signaling pathway, IL-17 signaling pathway and PI3K-Akt signaling pathway.

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