Additional file 4. The list of relevant enriched pathways(p value \(\le 0.005\) ≤ 0.005 ) obtained by pathDip using eachcancer gene list and the related intersection between the two listsof enriched pathways referring to the same cancer type.

Supplemental data for the manuscript

"Systematic assessment of pathway databases, based on a diverse collection of user-submitted experiments".

Content

• functional_annotations.tar.gz
  • functional annotations for 10 different functional annotation systems, for 5090 species
• genome_info_and_statistics.tar.gz
  • basic genome info, annotation system statistics, user query statistics
• example_user_queries.tar.gz
  • three example files for user query inputs used in the analysis
• README.txt
  • details about the files and file formats

Cancer type enrichment results from GeneSCF for CLL associated differentially expressed genes (Case study 1) predicted using NCG as reference database. (XLS 17 kb)

Supplemental data for the manuscript

"Systematic assessment of pathway databases, based on a diverse collection of user-submitted experiments".

Content

• functional_annotations.tar.gz
  • functional annotations for 11 different functional annotations, for 5090 species
• genome_info_and_statistics.tar.gz
  • basic genome info, annotation system statistics, user query statistics
• README.txt
  • details about the files and file formats

Sample datasets for testing CellEnrich package.

The list of organisms supported by GeneSCF for KEGG pathway database. (XLS 542 kb)

The proteins directly interacting with the proteins encoded by NAFLD-candidate genes were identified from the liver-specific protein-protein interaction network (PPIN). The interacting partners were significantly enriched within biological pathways related to cancers and NAFLD pathogenesis. The eQTLs regulating the interacting partners were enriched for GWAS traits including those that were previously linked to or multimorbid with NAFLD.

Data analysis service to predict the function of your favorite genes and gene sets. Indexing 1,421 association networks containing 266,984,699 interactions mapped to 155,238 genes from 7 organisms. GeneMANIA interaction networks are available for download in plain text format. GeneMANIA finds other genes that are related to a set of input genes, using a very large set of functional association data. Association data include protein and genetic interactions, pathways, co-expression, co-localization and protein domain similarity. You can use GeneMANIA to find new members of a pathway or complex, find additional genes you may have missed in your screen or find new genes with a specific function, such as protein kinases. Your question is defined by the set of genes you input. If members of your gene list make up a protein complex, GeneMANIA will return more potential members of the protein complex. If you enter a gene list, GeneMANIA will return connections between your genes, within the selected datasets. GeneMANIA suggests annotations for genes based on Gene Ontology term enrichment of highly interacting genes with the gene of interest. GeneMANIA is also a gene recommendation system. GeneMANIA is also accessible via a Cytoscape plugin, designed for power users. Platform: Online tool, Windows compatible, Mac OS X compatible, Linux compatible, Unix compatible

Pathway analysis is often the first choice for studying the mechanisms underlying a phenotype. However, conventional methods for pathway analysis do not take into account complex protein-protein interaction information, resulting in incomplete conclusions. Previously, numerous approaches that utilize protein-protein interaction information to enhance pathway analysis yielded superior results compared to conventional methods. Hereby, we present pathfindR, another approach exploiting protein-protein interaction information and the first R package for active-subnetwork-oriented pathway enrichment analyses for class comparison omics experiments. Using the list of genes obtained from an omics experiment comparing two groups of samples, pathfindR identifies active subnetworks in a protein-protein interaction network. It then performs pathway enrichment analyses on these identified subnetworks. To further reduce the complexity, it provides functionality for clustering the resulting pathways. Moreover, through a scoring function, the overall activity of each pathway in each sample can be estimated. We illustrate the capabilities of our pathway analysis method on three gene expression datasets and compare our results with those obtained from three popular pathway analysis tools. The results demonstrate that literature-supported disease-related pathways ranked higher in our approach compared to the others. Moreover, pathfindR identified additional pathways relevant to the conditions that were not identified by other tools, including pathways named after the conditions.

1The filtered gene list from Golden Retrievers with hemangiosarcoma vs. non-Golden Retrievers with hemangiosarcoma were compared using the GSEA software. ES (Enrichment Score) is a value that represents how well the gene set is enriched within the selected gene list. NES (normalized enrichment score) corrects the ES for differences in gene set size and can be used to compare across gene sets. A high ES or NES indicates that gene set is highly enriched within our gene list. FDR represents the probability that the NES for a gene set gives a false positive finding. The highest FDR shown here is 0.005 indicating that there is a 0.005% chance that the gene set indicates a false positive finding. The lists shown are those gene sets with an NES higher than 2.10.

ID correspondence table and information on the SRA Runs. The correspondences among Entrez Gene ID, Kegg Gene ID, and Ensemble Gene ID are shown in Table 1. Information (Run ID, Experiment ID, Study ID, sample tissue and cultivar) on the SRA Runs used to construct the gene expression matrix is shown in Table 2. (XLSX 1015 kb)

Enrichment analysis results for list-B genes from GeneSCF and DAVID 6.7 using KEGG as a reference database. (XLS 112 kb)

Pathway enrichment results from GeneSCF for p53 bound genes at 0Â h (Case study 2) predicted by GeneSCF using KEGG as a reference database. (XLS 32 kb)

Pathway enrichment analysis represents a key technique for analyzing high-throughput omic data, and it can help to link individual genes or proteins found to be differentially expressed under specific conditions to well-understood biological pathways. We present here a computational tool, SEAS, for pathway enrichment analysis over a given set of genes in a specified organism against the pathways (or subsystems) in the SEED database, a popular pathway database for bacteria. SEAS maps a given set of genes of a bacterium to pathway genes covered by SEED through gene ID and/or orthology mapping, and then calculates the statistical significance of the enrichment of each relevant SEED pathway by the mapped genes. Our evaluation of SEAS indicates that the program provides highly reliable pathway mapping results and identifies more organism-specific pathways than similar existing programs. SEAS is publicly released under the GPL license agreement and freely available at http://csbl.bmb.uga.edu/~xizeng/research/seas/.

Functional enrichment results from GeneSCF for p53 bound genes at 12Â h (Case study 2) predicted by GeneSCF using Gene ontology, Molecular Function (GO_MF) as a reference database. (XLS 97 kb)

pathways affected by degs

The top ten enrichment terms are listed and were sorted based on statistical significance.

Functional analysis of gene sets derived from experiments is typically done by pathway annotation. Although many algorithms exist for analyzing the association between a gene set and a pathway, an issue which is generally ignored is that gene sets often represent multiple pathways. In such cases an association to a pathway is weakened by the presence of genes associated with other pathways. A way to counteract this is to cluster the gene set into more homogenous parts before performing pathway analysis on each module. We explored whether network-based pre-clustering of a query gene set can improve pathway analysis. The methods MCL, Infomap, and MGclus were used to cluster the gene set projected onto the FunCoup network. We characterized how well these methods are able to detect individual pathways in multi-pathway gene sets, and applied each of the clustering methods in combination with four pathway analysis methods: Gene Enrichment Analysis, BinoX, NEAT, and ANUBIX. Using benchmarks constructed from the KEGG pathway database we found that clustering can be beneficial by increasing the sensitivity of pathway analysis methods and by providing deeper insights of biological mechanisms related to the phenotype under study. However, keeping a high specificity is a challenge. For ANUBIX, clustering caused a minor loss of specificity, while for BinoX and NEAT it caused an unacceptable loss of specificity. GEA had very low sensitivity both before and after clustering. The choice of clustering method only had a minor effect on the results. We show examples of this approach and conclude that clustering can improve overall pathway annotation performance, but should only be used if the used enrichment method has a low false positive rate.

Differential co-expression-based pathway analysis is still limited and not widely used. In most current methods, the pathways were considered as gene sets, but the gene regulation relationships were not considered, and the computational speed was slow. In this article, we proposed a novel Dysregulated Pathway Identification Analysis (DysPIA) method to overcome these shortcomings. We adopted the idea of Correlation by Individual Level Product into analysis and performed a fast enrichment analysis. We constructed a combined gene-pair background which was much more sufficient than the background used in Edge Set Enrichment Analysis. In simulation study, DysPIA was able to identify the causal pathways with high AUC (0.9584 to 0.9896). In p53 mutation data, DysPIA obtained better performance than other methods. It obtained more potential dysregulated pathways that could be literature verified, and it ran much faster (∼1,700–8,000 times faster than other methods when 10,000 permutations). DysPIA was also applied to breast cancer relapse dataset and breast cancer subtype dataset. The results show that DysPIA is effective and has a great biological significance. R packages “DysPIA” and “DysPIAData” are constructed and freely available on R CRAN (https://cran.r-project.org/web/packages/DysPIA/index.html and https://cran.r-project.org/web/packages/DysPIAData/index.html), and on GitHub (https://github.com/lemonwang2020).

Pathway rankings based on adjusted p-values. Those pathways with positive mean differences show that the gene-gene pairs on average have a higher correlation in ER-positive patient samples and a lower correlation in ER-negative patient samples for that pathway. (Full pathway ranking in Supplemental Data). *: Benjamini correction.