GenomicInteractionNodes 1.2.0
The profiles of genome interactions can be categorized into three levels: loops, domains and compartments. Cis-regulatory elements (cREs) regulate the distal target gene expression through three-dimensional chromatin looping. Most of these loops occur within the boundaries of topologically associating domains (TADs). The TAD boundaries are enriched for insulator binding proteins. HiC analysis revealed the TADs can be segregated into an active (A) and inactive (B) compartment. TADs are megabase-sized chromosomal regions where the loop sizes may very from thousand bases to mega bases. We know that not all the loops within one TADs are involved in single binding platform. There are hundreds or even thousands binding platforms even in a single TAD. Batut et al. report the ‘tethering element’ help to establish long-range enhancer-promoter interactions. Tethering elements are the one kind of the binding platform which bring together enhancers and promoters for rapid gene activation. For each binding platform, multiple loops may work together to work as a single function, such as repression or promotion one or more distal target gene expression. That means multiple promoters and enhancers may bind together to perform as super enhancer or regulatory elements.
We defined such kind of binding platform as genomic interaction node or interaction hot spot. The interaction node can be a tethering element working as node, or a super-enhancer (or super regulatory element region). It is a level of genome organization higher than loops but smaller than TADs. It is a kind of tethering element with multiple interaction loops.
The interaction node has two attributes: 1. it must contain multiple interaction loops, 2. it regulates one or more target genes.
To help users to define the interaction nodes, we developed the
Bioconductor package: GenomicInteractionNodes.
The GenomicInteractionNodes package will define the interaction node
by testing the involved loops within one connected component
by Poisson distribution. The annotated loops will be used for
enrichment analysis.
You can install the package via devtools::install_github from github.
library(devtools)
install_github("jianhong/GenomicInteractionNodes")You can also try to install it via BiocManager::install when it is ready in Bioconductor.
library(BiocManager)
install("GenomicInteractionNodes")Here is an example to use GenomicInteractionNodes to define interaction nodes
and do downstream enrichment analysis.
There are three steps,
detectNodes, define the nodes.
annotateNodes, annotate the nodes for downstream analysis.
enrichmentAnalysis, Gene Ontology enrichment analysis.
## load library
library(GenomicInteractionNodes)
library(rtracklayer)
library(TxDb.Hsapiens.UCSC.hg19.knownGene) ## for human hg19
library(org.Hs.eg.db) ## used to convert gene_id to gene_symbol
library(GO.db) ## used for enrichment analysis
## import the interactions from bedpe file
p <- system.file("extdata", "WT.2.bedpe",
package = "GenomicInteractionNodes")
#### please try to replace the connection to your file path
interactions <- import(con=p, format="bedpe")
## define the nodes
nodes <- detectNodes(interactions)
names(nodes)## [1] "node_connection" "nodes" "node_regions"lapply(nodes, head, n=2)## $node_connection
## Pairs object with 2 pairs and 3 metadata columns:
## first second | name score
## <GRanges> <GRanges> | <character> <numeric>
## [1] chr22:20003029-20003427 chr22:51213576-51213946 | * 7.38906
## [2] chr22:40149276-40149674 chr22:51213576-51213946 | * 7.38906
## comp_id
## <character>
## [1] 24
## [2] 24
##
## $nodes
## GRanges object with 2 ranges and 1 metadata column:
## seqnames ranges strand | comp_id
## <Rle> <IRanges> <Rle> | <integer>
## [1] chr22 26964172-26964589 * | 106
## [2] chr22 27077493-27077713 * | 111
## -------
## seqinfo: 1 sequence from an unspecified genome; no seqlengths
##
## $node_regions
## GRanges object with 2 ranges and 1 metadata column:
## seqnames ranges strand | comp_id
## <Rle> <IRanges> <Rle> | <character>
## [1] chr22 20003029-20003427 * | 24
## [2] chr22 40149276-40149674 * | 24
## -------
## seqinfo: 1 sequence from an unspecified genome; no seqlengths## annotate the nodes by gene promoters
node_regions <- nodes$node_regions
node_regions <- annotateNodes(node_regions,
txdb=TxDb.Hsapiens.UCSC.hg19.knownGene,
orgDb=org.Hs.eg.db,
upstream=2000, downstream=500)
head(node_regions, n=2)## GRanges object with 2 ranges and 3 metadata columns:
## seqnames ranges strand | comp_id gene_id symbols
## <Rle> <IRanges> <Rle> | <character> <List> <List>
## [1] chr22 20003029-20003427 * | 24 128989 TANGO2
## [2] chr22 40149276-40149674 * | 24 <NA> <NA>
## -------
## seqinfo: 1 sequence from an unspecified genome; no seqlengths## Gene Ontology enrichment analysis
enrich <- enrichmentAnalysis(node_regions, orgDb=org.Hs.eg.db)
names(enrich$enriched)## [1] "BP" "CC" "MF"names(enrich$enriched_in_compound)## [1] "BP" "CC" "MF"res <- enrich$enriched$BP
res <- res[order(res$fdr), ]
head(res, n=2)## GO term
## GO:0000470 GO:0000470 maturation of LSU-rRNA
## GO:0002387 GO:0002387 immune response in gut-associated lymphoid tissue
## definition
## GO:0000470 Any process involved in the maturation of a precursor Large SubUnit (LSU) ribosomal RNA (rRNA) molecule into a mature LSU-rRNA molecule.
## GO:0002387 Immune response taking place in the gut-associated lymphoid tissue (GALT). GALT includes Peyer's patches, appendix, and solitary lymph nodules.
## pvalue fdr countInDataset countInGenome
## GO:0000470 0.000356213 0.1311529 2 28
## GO:0002387 0.002009306 0.1311529 1 2
## totalGeneInDataset totalGeneInGenome geneInDataset
## GO:0000470 19 18903 PES1;SNU13
## GO:0002387 19 18903 TTLL1sessionInfo()## R version 4.2.1 (2022-06-23)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 20.04.5 LTS
##
## Matrix products: default
## BLAS: /home/biocbuild/bbs-3.16-bioc/R/lib/libRblas.so
## LAPACK: /home/biocbuild/bbs-3.16-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] stats4 stats graphics grDevices utils datasets methods
## [8] base
##
## other attached packages:
## [1] GO.db_3.16.0
## [2] org.Hs.eg.db_3.16.0
## [3] TxDb.Hsapiens.UCSC.hg19.knownGene_3.2.2
## [4] GenomicFeatures_1.50.0
## [5] AnnotationDbi_1.60.0
## [6] Biobase_2.58.0
## [7] rtracklayer_1.58.0
## [8] GenomicRanges_1.50.0
## [9] GenomeInfoDb_1.34.0
## [10] IRanges_2.32.0
## [11] S4Vectors_0.36.0
## [12] BiocGenerics_0.44.0
## [13] GenomicInteractionNodes_1.2.0
## [14] BiocStyle_2.26.0
##
## loaded via a namespace (and not attached):
## [1] MatrixGenerics_1.10.0 httr_1.4.4
## [3] sass_0.4.2 bit64_4.0.5
## [5] jsonlite_1.8.3 bslib_0.4.0
## [7] assertthat_0.2.1 BiocManager_1.30.19
## [9] BiocFileCache_2.6.0 RBGL_1.74.0
## [11] blob_1.2.3 GenomeInfoDbData_1.2.9
## [13] Rsamtools_2.14.0 yaml_2.3.6
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## [21] XVector_0.38.0 Matrix_1.5-1
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## [27] bookdown_0.29 zlibbioc_1.44.0
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## [31] KEGGREST_1.38.0 generics_0.1.3
## [33] ellipsis_0.3.2 SummarizedExperiment_1.28.0
## [35] cachem_1.0.6 cli_3.4.1
## [37] magrittr_2.0.3 crayon_1.5.2
## [39] memoise_2.0.1 evaluate_0.17
## [41] fansi_1.0.3 xml2_1.3.3
## [43] graph_1.76.0 tools_4.2.1
## [45] prettyunits_1.1.1 hms_1.1.2
## [47] matrixStats_0.62.0 BiocIO_1.8.0
## [49] lifecycle_1.0.3 stringr_1.4.1
## [51] DelayedArray_0.24.0 Biostrings_2.66.0
## [53] compiler_4.2.1 jquerylib_0.1.4
## [55] rlang_1.0.6 grid_4.2.1
## [57] RCurl_1.98-1.9 rjson_0.2.21
## [59] rappdirs_0.3.3 bitops_1.0-7
## [61] rmarkdown_2.17 codetools_0.2-18
## [63] restfulr_0.0.15 DBI_1.1.3
## [65] curl_4.3.3 R6_2.5.1
## [67] GenomicAlignments_1.34.0 knitr_1.40
## [69] dplyr_1.0.10 fastmap_1.1.0
## [71] bit_4.0.4 utf8_1.2.2
## [73] filelock_1.0.2 stringi_1.7.8
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## [81] xfun_0.34