![]() Users can either examine whether the input genes have been correctly enriched, or re-run the enrichment process with different gene synonyms and scoring methods (see Supplementary Fig. In this way, users are able to obtain the top-scored pathway list required for the network analysis. When the web-tool detects a gene list it automatically applies enrichment analysis, by means of (i) the EnrichR package required for enrichment analysis of human model organism ( Kuleshov et al., 2016), (ii) the “clusterProfiler” package ( Yu et al., 2012), required for enrichment analysis of non-human species employed in KEGG’s repository and (iii) the “ReactomePA” package ( Yu and He, 2016), required for the enrichment of non-human species included in Reactome’s repository. Users can provide two types of inputs, as shown in Figure 1A and B: (i) lists of genes as notated in either KEGG’s or Reactome’s repository, and (ii) lists of pathway IDs, that can be found in either KEGG’s or Reactome’s website. This network is regularly updated and works as the main pathway repository for the services/methods Pathwa圜onnector draws from. The output of a data mining process over the relationships that each database offers, provided us with all the links between pathways, which were used as edges to construct the undirected, unweighted pathway network. For this we focused on the connectivity information included in the selected database (KEGG: biochemical relationships, Reactome: relations of signaling and metabolic organized into biological pathways and processes). The derived pathway-pathway network acts as a reference network map for retrieving information about the functional interconnections between pathways of interest. 2 Implementationįor a number of species where data are available, an overall reference pathway-to-pathway network has been developed, that covers all the possible connections that exist between all the available pathways, as referenced in two major pathway databases: KEGG ( Kanehisa, 2002) and Reactome ( Fabregat et al., 2018). Pathwa圜onnector is able to provide with respect to a specific reference network: (i) direct connections between pathways of interest (first-neighbors approach), (ii) complementary networks that show the shortest paths between pathways of interest and the intermediate pathways involved between them and (iii) additional clustering approaches to highlight communities (clusters) of pathways. To this extend, the proposed Pathwa圜onnector is a web-tool that provides an easy way for rapidly relating pathways together, by creating complementary networks of pathways related to a specific biological status. ![]() Third, when dealing with large lists of pathways it is not always straightforward to relate them to a specific biological status building fully functional stories based on them. Second, when filtering the pathways with a specific threshold on their score, important yet not statistically significant pathways might be excluded whereas non-relevant yet statistically important pathways might be included. Firstly, it is not clear whether these pathways are functionally linked. Such tools may provide significant score-based information on how genes are involved into pathways and pathways to a disease, but also have several limitations that may fall in to the following criticisms. In classical pathway analysis gene lists, usually obtained from any experimental-computational method, can be further analyzed by relevant software tools, that allow enrichment analysis to be performed based on prior knowledge gene-set libraries and pathways connected to them ( Kuleshov et al., 2016). Although there are several available pathway analysis methods ( Jin et al., 2014), the way in which human pathways are functionally linked within an overall network of existing human pathways is largely unexplored. Pathway-based analysis allows for a comprehensive understanding of the molecular mechanisms related to complex diseases.
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