SpidermiRquery: Searching networkSpidermiRdownload: Downloading network dataSpidermiRprepare: Preparing the dataSpidermiRanalyze: : Analyze data from network dataSpidermiRanalyze_direct_net: Searching by biomarkers of interest with direct interactionSpidermiRanalyze_direct_subnetwork: Network composed by only the nodes in a set of biomarkers of interestSpidermiRanalyze_subnetwork_neigh: Network composed by the nodes in the list of BI and all the edges among this brunch of nodes.SpidermiRanalyze_degree_centrality: Ranking degree centrality genesFeatures databases SpidermiR:Biological systems are composed of multiple layers of dynamic interaction networks. These networks can be decomposed, for example, into: co-expression, physical, co-localization, genetic, pathway, and shared protein domains.
GeneMania provides us with an enormous collection of data sets for interaction network studies (Warde-Farley D, Donaldson S, Comes O, Zuberi K, Badrawi R, and others 2010). The data can be accessed and downloaded from different database, using a web portal. But currently, there is not a R-package to query and download these data.
An important regulatory mechanism of these network data involves microRNAs (miRNAs). miRNAs are involved in various cellular functions, such as differentiation, proliferation, and tumourigenesis. However, our understanding of the processes regulated by miRNAs is currently limited and the integration of miRNA data in these networks provides a comprehensive genome-scale analysis of miRNA regulatory networks.Actually, GeneMania doesn’t integrate the information of miRNAs and their interactions in the network.
SpidermiR allows the user to query, prepare, download network data (e.g. from GeneMania), and to integrate this information with miRNA data
with the possibility to analyze
these downloaded data directly in one single R package.
This techincal report gives a short overview of the essential SpidermiR methods and their application.
To install use the code below.
SpidermiRquery: Searching networkYou can easily search GeneMania data using the SpidermiRquery function.
SpidermiRquery_species: Searching by speciesThe user can query the species supported by GeneMania, using the function SpidermiRquery_species:
The list of species is shown below:
| tabOrgd[, 2] | |
|---|---|
| 1 | Arabidopsis_thaliana |
| 2 | Caenorhabditis_elegans |
| 3 | Danio_rerio |
| 4 | Drosophila_melanogaster |
| 5 | Escherichia_coli |
| 6 | Homo_sapiens |
| 7 | Mus_musculus |
| 8 | Rattus_norvegicus |
| 9 | Saccharomyces_cerevisiae |
SpidermiRquery_networks_type: Searching by network categoriesThe user can query the network types supported by GeneMania for a specific specie, using the function SpidermiRquery_networks_type. The user can select a specific specie using an index obtained by the function SpidermiRquery_species (e.g. organismID=org[6,] is the input for Homo_sapiens,organismID=org[9,] is the input for Saccharomyces cerevisiae )
The list of network categories in Saccharomyces cerevisiae is shown below:
## [1] "Co-localization" "Genetic Interactions" "Physical Interactions"
## [4] "Predicted" "Co-expression" "Other"
## [7] "Shared protein domains"SpidermiRquery_spec_networks: Searching by species, and network categoriesYou can filter the search by species using organism ID (above reported), and the network category. The network category can be filtered using the following parameters:
The databases, which data are collected, are the output of this step. An example is shown below ( for Shared protein domains in Saccharomyces_cerevisiae data are collected in INTERPRO, and PFAM):
## [1] "http://genemania.org/data/current/Saccharomyces_cerevisiae/Shared_protein_domains.INTERPRO.txt"
## [2] "http://genemania.org/data/current/Saccharomyces_cerevisiae/Shared_protein_domains.PFAM.txt"SpidermiRdownload: Downloading network dataThe user in this step can download the data, as previously queried.
SpidermiRdownload_net: Download networkThe user can download the data (previously queried) with SpidermiRdownload_net.
## [1] "Downloading: http://genemania.org/data/current/Saccharomyces_cerevisiae/Shared_protein_domains.INTERPRO.txt ... reference n. 1 of 2"
## [1] "Downloading: http://genemania.org/data/current/Saccharomyces_cerevisiae/Shared_protein_domains.PFAM.txt ... reference n. 2 of 2"The list of SpidermiRdownload_net is shown below:
## List of 2
## $ :'data.frame': 58612 obs. of 3 variables:
## ..$ Gene_A: chr [1:58612] "Q0050" "Q0050" "Q0055" "Q0050" ...
## ..$ Gene_B: chr [1:58612] "Q0055" "Q0060" "Q0060" "Q0065" ...
## ..$ Weight: num [1:58612] 0.27 0.048 0.12 0.048 0.12 0.17 0.048 0.12 0.17 0.17 ...
## $ :'data.frame': 25587 obs. of 3 variables:
## ..$ Gene_A: chr [1:25587] "Q0050" "Q0060" "Q0060" "Q0065" ...
## ..$ Gene_B: chr [1:25587] "Q0055" "Q0065" "Q0070" "Q0070" ...
## ..$ Weight: num [1:25587] 1 0.21 0.21 0.21 0.11 0.11 0.11 0.081 0.081 0.081 ...SpidermiRdownload_miRNAprediction: Downloading miRNA predicted data targetThe user can download the predicted miRNA-gene from 4 databases:DIANA, Miranda, PicTar and TargetScan using miRNAtap (Pajak M, Simpson TI 2019).
SpidermiRdownload_miRNAextra_cir:Download Extracellular Circulating microRNAsThe user can download extracellular circulating miRNAs from miRandola database
SpidermiRprepare: Preparing the dataSpidermiRprepare_NET: Prepare matrix of gene network with Ensembl Gene ID, and gene symbolsSpidermiRprepare_NET reads network data from SpidermiRdownload_net and enables user to prepare them for downstream analysis. In particular, it prepares matrix of gene network mapping Ensembl Gene ID to gene symbols. Gene symbols are needed to integrate miRNAdata.
## [1] "Preprocessing of the network n. 1 of 2"
## [1] "Preprocessing of the network n. 2 of 2"The network with gene symbols ID is shown below:
| Gene_A | Gene_B | Weight | gene_symbolA | gene_symbolB |
|---|---|---|---|---|
| Q0050 | Q0055 | 0.27 | NP_009310.1 | NP_009309.1 |
| Q0050 | Q0060 | 0.05 | NP_009310.1 | NP_009308.2 |
| Q0055 | Q0060 | 0.12 | NP_009309.1 | NP_009308.2 |
| Q0050 | Q0065 | 0.05 | NP_009310.1 | NP_009307.2 |
| Q0055 | Q0065 | 0.12 | NP_009309.1 | NP_009307.2 |
SpidermiRanalyze: : Analyze data from network dataSpidermiRanalyze_direct_net: Searching by biomarkers of interest with direct interactionStarting from a set of biomarkers of interest (BI), genes, miRNA or both, given by the user, this function finds sub-networks including all direct interactions involving at least one of the BI.
biomark_of_interest<-c("hsa-let-7a","CDC34","hsa-miR-27a","PEX7","EPT1","FOX","hsa-miR-5a")
miRNA_NET <-data.frame(V1=c('hsa-let-7a','CASP3','BRCA','hsa-miR-7a','hsa-miR-5a','SMAD','SOX'),V2=c('CASP3','TAMOXIFEN','MYC','PTEN','FOX','HIF1','P53'),stringsAsFactors=FALSE)
GIdirect_net<-SpidermiRanalyze_direct_net(data=miRNA_NET,BI=biomark_of_interest)## [1] "CDC34 is not in the network or please check the correct name"
## [1] "hsa-miR-27a is not in the network or please check the correct name"
## [1] "PEX7 is not in the network or please check the correct name"
## [1] "EPT1 is not in the network or please check the correct name"The data frame of SpidermiRanalyze_direct_net, GIdirect_net, is shown below:
## 'data.frame': 2 obs. of 2 variables:
## $ V1: chr "hsa-let-7a" "hsa-miR-5a"
## $ V2: chr "CASP3" "FOX"SpidermiRanalyze_direct_subnetwork: Network composed by only the nodes in a set of biomarkers of interestStarting from BI, this function finds sub-networks including all direct interactions involving only BI.
SpidermiRanalyze_subnetwork_neigh: Network composed by the nodes in the list of BI and all the edges among this brunch of nodes.Starting from BI, this function finds sub-networks including all direct and indirect interactions involving at least one of BI.
SpidermiRanalyze_degree_centrality: Ranking degree centrality genesThis function finds the number of direct neighbours of a node in a network and allows the selection of those nodes with a number of direct neighbours higher than a selected cut-off.
Features databases SpidermiR:Features of databases integrated in SpidermiR are:
Session Information ******
## R version 4.1.0 (2021-05-18)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 20.04.2 LTS
##
## Matrix products: default
## BLAS: /home/biocbuild/bbs-3.13-bioc/R/lib/libRblas.so
## LAPACK: /home/biocbuild/bbs-3.13-bioc/R/lib/libRlapack.so
##
## locale:
## [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
## [3] LC_TIME=en_GB LC_COLLATE=C
## [5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
## [7] LC_PAPER=en_US.UTF-8 LC_NAME=C
## [9] LC_ADDRESS=C LC_TELEPHONE=C
## [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
##
## attached base packages:
## [1] parallel stats4 stats graphics grDevices utils datasets
## [8] methods base
##
## other attached packages:
## [1] SpidermiR_1.22.1 miRNAtap_1.26.0 AnnotationDbi_1.54.1
## [4] IRanges_2.26.0 S4Vectors_0.30.0 Biobase_2.52.0
## [7] BiocGenerics_0.38.0 BiocStyle_2.20.2
##
## loaded via a namespace (and not attached):
## [1] shadowtext_0.0.8 fastmatch_1.1-0
## [3] BiocFileCache_2.0.0 plyr_1.8.6
## [5] igraph_1.2.6 lazyeval_0.2.2
## [7] splines_4.1.0 BiocParallel_1.26.0
## [9] usethis_2.0.1 GenomeInfoDb_1.28.0
## [11] ggplot2_3.3.4 digest_0.6.27
## [13] htmltools_0.5.1.1 GOSemSim_2.18.0
## [15] viridis_0.6.1 GO.db_3.13.0
## [17] gdata_2.18.0 fansi_0.5.0
## [19] magrittr_2.0.1 memoise_2.0.0
## [21] remotes_2.4.0 graphlayouts_0.7.1
## [23] Biostrings_2.60.1 readr_1.4.0
## [25] matrixStats_0.59.0 R.utils_2.10.1
## [27] enrichplot_1.12.1 prettyunits_1.1.1
## [29] jpeg_0.1-8.1 colorspace_2.0-1
## [31] blob_1.2.1 rvest_1.0.0
## [33] rappdirs_0.3.3 ggrepel_0.9.1
## [35] xfun_0.24 dplyr_1.0.6
## [37] callr_3.7.0 crayon_1.4.1
## [39] RCurl_1.98-1.3 jsonlite_1.7.2
## [41] scatterpie_0.1.6 ape_5.5
## [43] miRNAtap.db_0.99.10 glue_1.4.2
## [45] polyclip_1.10-0 gtable_0.3.0
## [47] zlibbioc_1.38.0 XVector_0.32.0
## [49] DelayedArray_0.18.0 pkgbuild_1.2.0
## [51] scales_1.1.1 DOSE_3.18.0
## [53] DBI_1.1.1 Rcpp_1.0.6
## [55] viridisLite_0.4.0 progress_1.2.2
## [57] tidytree_0.3.4 bit_4.0.4
## [59] sqldf_0.4-11 htmlwidgets_1.5.3
## [61] httr_1.4.2 fgsea_1.18.0
## [63] gplots_3.1.1 RColorBrewer_1.1-2
## [65] ellipsis_0.3.2 farver_2.1.0
## [67] pkgconfig_2.0.3 XML_3.99-0.6
## [69] R.methodsS3_1.8.1 sass_0.4.0
## [71] dbplyr_2.1.1 utf8_1.2.1
## [73] reshape2_1.4.4 tidyselect_1.1.1
## [75] rlang_0.4.11 munsell_0.5.0
## [77] tools_4.1.0 visNetwork_2.0.9
## [79] cachem_1.0.5 downloader_0.4
## [81] cli_2.5.0 gsubfn_0.7
## [83] generics_0.1.0 RSQLite_2.2.7
## [85] devtools_2.4.2 evaluate_0.14
## [87] stringr_1.4.0 fastmap_1.1.0
## [89] yaml_2.2.1 ggtree_3.0.2
## [91] processx_3.5.2 org.Hs.eg.db_3.13.0
## [93] knitr_1.33 bit64_4.0.5
## [95] fs_1.5.0 tidygraph_1.2.0
## [97] caTools_1.18.2 purrr_0.3.4
## [99] ggraph_2.0.5 KEGGREST_1.32.0
## [101] TCGAbiolinks_2.20.0 nlme_3.1-152
## [103] R.oo_1.24.0 aplot_0.0.6
## [105] DO.db_2.9 xml2_1.3.2
## [107] biomaRt_2.48.1 MAGeCKFlute_1.12.0
## [109] compiler_4.1.0 rstudioapi_0.13
## [111] filelock_1.0.2 curl_4.3.1
## [113] png_0.1-7 testthat_3.0.3
## [115] treeio_1.16.1 tweenr_1.0.2
## [117] tibble_3.1.2 bslib_0.2.5.1
## [119] stringi_1.6.2 highr_0.9
## [121] ps_1.6.0 TCGAbiolinksGUI.data_1.12.0
## [123] desc_1.3.0 lattice_0.20-44
## [125] Matrix_1.3-4 vctrs_0.3.8
## [127] networkD3_0.4 pillar_1.6.1
## [129] lifecycle_1.0.0 BiocManager_1.30.16
## [131] jquerylib_0.1.4 cowplot_1.1.1
## [133] data.table_1.14.0 bitops_1.0-7
## [135] patchwork_1.1.1 qvalue_2.24.0
## [137] GenomicRanges_1.44.0 R6_2.5.0
## [139] latticeExtra_0.6-29 bookdown_0.22
## [141] KernSmooth_2.23-20 gridExtra_2.3
## [143] sessioninfo_1.1.1 MASS_7.3-54
## [145] gtools_3.9.2 assertthat_0.2.1
## [147] pkgload_1.2.1 chron_2.3-56
## [149] SummarizedExperiment_1.22.0 proto_1.0.0
## [151] rprojroot_2.0.2 withr_2.4.2
## [153] GenomeInfoDbData_1.2.6 hms_1.1.0
## [155] clusterProfiler_4.0.0 grid_4.1.0
## [157] tidyr_1.1.3 rvcheck_0.1.8
## [159] rmarkdown_2.9 MatrixGenerics_1.4.0
## [161] ggforce_0.3.3Dweep H, Sticht C, Pandey P, Gretz N. 2011. “miRWalk - Database Prediction of Possible miRNA Binding Sites by ‘Walking’ the Genes of 3 Genomes.”
Jiang Q, Wang Y, Hao Y, Juan L, Teng M, Zhang X, Li M, Wang G, Liu Y. 2009. “miR2Disease a Manually Curated Database for microRNA Deregulation in Human Disease.”
Pajak M, Simpson TI. 2019. “miRNAtap microRNA Targets - Aggregated Predictions.”
Warde-Farley D, Donaldson S, Comes O, Zuberi K, Badrawi R, and others. 2010. “The Gene Mania Prediction Server Biological Network Integration for Gene Prioritization and Predicting Gene Function.”