hello, I'm trying to extract the hichip fragments that interacts with my chip peak, So I did an overlapping between my chip peak and hichip(annotated hichip). The hichip are composed from 2 fragments as they identify the interactions between 2 long genomic ranges. Now the overlapping gives me in subjectHits the fragments that overlaps with my chip peak, that could be either frg1 or frg2, and so I want to see if its frg1 what's the frg2 that it interacts with it and if its frg2 what is the frg1 that it interacts with it , this is what I did so far, but im having difficulties to get these interactions. Does anyone have any idea? I thought about getting the Ids of fragments and find it in frg1 and frg2 object but it didn't help.

load("genomic_objects_hg38 (1).RData")
# Load HiChIP data
hichip_file <- ".../GSE222206_HiChIP_Mock.bedpe.gz"
hichip <- read.delim(hichip_file, header = FALSE)
colnames(hichip)[1:6] <- c("frg1", "start1", "end1", "frg2", "start2", "end2")

# Convert to GRanges
frg1 <- GRanges(seqnames = Rle(hichip$frg1), ranges = IRanges(start = hichip$start1, end = hichip$end1))
frg2 <- GRanges(seqnames = Rle(hichip$frg2), ranges = IRanges(start = hichip$start2, end = hichip$end2))
hichip <- c(frg1, frg2)
hichip <- unique(hichip)

# hichip annotation 
# Initialize columns for multiple annotations
mcols(hichip)$all_genes <- character(length(hichip))
mcols(hichip)$TSS <- character(length(hichip))
mcols(hichip)$promoter <- character(length(hichip))
mcols(hichip)$all_genes_status <- character(length(hichip))
mcols(hichip)$TSS_status <- character(length(hichip))
mcols(hichip)$promoter_status <- character(length(hichip))
mcols(hichip)$genomic_location <- character(length(hichip))

# Overlap with all genes
ovl1 <- findOverlaps(hichip, all.genes)
ovl1_df <- as.data.frame(ovl1)
all_genes_by_fragment <- tapply(all.genes$SYMBOL[ovl1_df$subjectHits], ovl1_df$queryHits, paste, collapse = ", ")
all_genes_status_by_fragment <- tapply(all.genes$in.microarray[ovl1_df$subjectHits], ovl1_df$queryHits, paste, collapse = ", ")
mcols(hichip)$all_genes[as.numeric(names(all_genes_by_fragment))] <- all_genes_by_fragment # ou  ovl_1_df$queryhits 
mcols(hichip)$all_genes_status[as.numeric(names(all_genes_status_by_fragment))] <- all_genes_status_by_fragment

# Overlap with TSS
ovl2 <- findOverlaps(hichip, TSS)
ovl2_df <- as.data.frame(ovl2)
tss_by_fragment <- tapply(TSS$SYMBOL[ovl2_df$subjectHits], ovl2_df$queryHits, paste, collapse = ", ")
tss_status_by_fragment <- tapply(TSS$in.microarray[ovl2_df$subjectHits], ovl2_df$queryHits, paste, collapse = ", ")
mcols(hichip)$TSS[as.numeric(names(tss_by_fragment))] <- tss_by_fragment
mcols(hichip)$TSS_status[as.numeric(names(tss_status_by_fragment))] <- tss_status_by_fragment

# Overlap with promoters
ovl3 <- findOverlaps(hichip, prom)
ovl3_df <- as.data.frame(ovl3)
promoters_by_fragment <- tapply(prom$SYMBOL[ovl3_df$subjectHits], ovl3_df$queryHits, paste, collapse = ", ")
promoters_status_by_fragment <- tapply(prom$in.microarray[ovl3_df$subjectHits], ovl3_df$queryHits, paste, collapse = ", ")
mcols(hichip)$promoter[as.numeric(names(promoters_by_fragment))] <- promoters_by_fragment
mcols(hichip)$promoter_status[as.numeric(names(promoters_status_by_fragment))] <- promoters_status_by_fragment

# Summary column
mcols(hichip)$summary <- ifelse(
  mcols(hichip)$TSS != "", 
  mcols(hichip)$TSS,  
  ifelse(
    mcols(hichip)$promoter != "", 
    mcols(hichip)$promoter,  
    ifelse(
      mcols(hichip)$all_genes != "",
      mcols(hichip)$all_genes, 
      "intergenic" 
    )
  )
)

# Summary status column
mcols(hichip)$summary_status <- ifelse(
  mcols(hichip)$TSS != "", 
  mcols(hichip)$TSS_status,  
  ifelse(
    mcols(hichip)$promoter != "", 
    mcols(hichip)$promoter_status,  
    ifelse(
      mcols(hichip)$all_genes != "",
      mcols(hichip)$all_genes_status,  
      NA  
    )
  )
)

mcols(hichip)$genomic_location <- ifelse(
  !is.na(mcols(hichip)$TSS) & mcols(hichip)$TSS != "", 
  "TSS",
  ifelse(
    !is.na(mcols(hichip)$promoter) & mcols(hichip)$promoter != "", 
    "promoter",
    ifelse(
      !is.na(mcols(hichip)$all_genes) & mcols(hichip)$all_genes != "",
      "gene_body",
      "intergenic"
    )
  )
)

chipseq_file <- ".../S1_Invi_fdr.bed"
S1 <- read.delim(chipseq_file, header = FALSE)
colnames(S1)[1:3] <- c("seqnames", "start", "end")
S1 <- GRanges(seqnames = Rle(S1$seqnames), ranges = IRanges(start = S1$start, end = S1$end))
ovl_s1 <- findOverlaps(S1, hichip)
ovl_s1_df <- as.data.frame(ovl_s1)

#IDs from overlaps
query_hits <- ovl_s1_df$queryHits
subject_hits <- ovl_s1_df$subjectHits

fragments <- hichip[subject_hits]
peaks <- hichip[query_hits]

sessionInfo()
R version 4.1.1 (2021-08-10)
Platform: x86_64-conda-linux-gnu (64-bit)
Running under: Ubuntu 20.04.6 LTS

Matrix products: default
BLAS/LAPACK: /shared/ifbstor1/software/miniconda/envs/r-4.1.1/lib/libopenblasp-r0.3.18.so

locale:
 [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C               LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8     LC_MONETARY=en_US.UTF-8   
 [6] LC_MESSAGES=en_US.UTF-8    LC_PAPER=en_US.UTF-8       LC_NAME=C                  LC_ADDRESS=C               LC_TELEPHONE=C            
[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       

attached base packages:
[1] grid      stats4    stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
 [1] data.table_1.14.2                        reshape2_1.4.4                           TxDb.Hsapiens.UCSC.hg19.knownGene_3.2.2 
 [4] ChIPseeker_1.30.3                        ChIPpeakAnno_3.28.0                      msigdbr_7.4.1                           
 [7] ggupset_0.3.0                            DOSE_3.20.1                              RColorBrewer_1.1-3                      
[10] fgsea_1.20.0                             org.Hs.eg.db_3.14.0                      enrichplot_1.14.2                       
[13] clusterProfiler_4.2.2                    pheatmap_1.0.12                          VennDiagram_1.7.3                       
[16] futile.logger_1.4.3                      enrichR_3.2                              rtracklayer_1.54.0                      
[19] TxDb.Hsapiens.UCSC.hg38.knownGene_3.14.0 GenomicFeatures_1.46.1                   AnnotationDbi_1.56.1                    
[22] DiffBind_3.4.11                          BiocParallel_1.28.3                      BiocManager_1.30.23                     
[25] openxlsx_4.2.6.1                         readxl_1.4.3                             EnhancedVolcano_1.13.2                  
[28] ggrepel_0.9.5                            magrittr_2.0.3                           lubridate_1.9.3                         
[31] forcats_1.0.0                            stringr_1.5.1                            dplyr_1.1.4                             
[34] purrr_1.0.2                              readr_2.1.5                              tidyr_1.3.1                             
[37] tibble_3.2.1                             tidyverse_2.0.0                          DESeq2_1.34.0                           
[40] SummarizedExperiment_1.24.0              MatrixGenerics_1.6.0                     matrixStats_0.62.0                      
[43] GenomicRanges_1.46.1                     GenomeInfoDb_1.30.1                      IRanges_2.28.0                          
[46] S4Vectors_0.32.4                         Biobase_2.54.0                           BiocGenerics_0.40.0                     
[49] limma_3.50.3                             ggplot2_3.5.1                           

loaded via a namespace (and not attached):
  [1] utf8_1.2.2               tidyselect_1.2.1         RSQLite_2.2.8            htmlwidgets_1.5.4        scatterpie_0.1.7        
  [6] munsell_0.5.0            systemPipeR_2.0.4        withr_2.5.0              colorspace_2.0-3         GOSemSim_2.20.0         
 [11] filelock_1.0.2           rstudioapi_0.16.0        bbmle_1.0.24             GenomeInfoDbData_1.2.7   mixsqp_0.3-43           
 [16] hwriter_1.3.2            polyclip_1.10-0          bit64_4.0.5              farver_2.1.1             downloader_0.4          
 [21] treeio_1.18.1            coda_0.19-4              vctrs_0.6.5              generics_0.1.3           lambda.r_1.2.4          
 [26] timechange_0.3.0         BiocFileCache_2.2.0      regioneR_1.26.0          R6_2.5.1                 apeglm_1.16.0           
 [31] graphlayouts_0.8.0       invgamma_1.1             locfit_1.5-9.4           AnnotationFilter_1.18.0  bitops_1.0-7            
 [36] cachem_1.0.6             gridGraphics_0.5-1       DelayedArray_0.20.0      assertthat_0.2.1         BiocIO_1.4.0            
 [41] scales_1.3.0             ggraph_2.0.5             gtable_0.3.1             ensembldb_2.18.2         tidygraph_1.2.0         
 [46] rlang_1.1.4              genefilter_1.76.0        splines_4.1.1            lazyeval_0.2.2           yaml_2.3.6              
 [51] qvalue_2.26.0            RBGL_1.70.0              tools_4.1.1              ggplotify_0.1.0          gplots_3.1.3.1          
 [56] Rcpp_1.0.9               plyr_1.8.7               progress_1.2.2           zlibbioc_1.40.0          RCurl_1.98-1.9          
 [61] prettyunits_1.1.1        viridis_0.6.2            ashr_2.2-47              futile.options_1.0.1     DO.db_2.9               
 [66] truncnorm_1.0-8          mvtnorm_1.1-3            SQUAREM_2021.1           amap_0.8-19              ProtGenerics_1.26.0     
 [71] patchwork_1.1.1          hms_1.1.3                xtable_1.8-4             XML_3.99-0.9             emdbook_1.3.12          
 [76] jpeg_0.1-9               gridExtra_2.3            compiler_4.1.1           biomaRt_2.50.0           bdsmatrix_1.3-7         
 [81] shadowtext_0.0.9         KernSmooth_2.23-20       crayon_1.5.2             htmltools_0.5.2          ggfun_0.0.4             
 [86] tzdb_0.2.0               geneplotter_1.72.0       aplot_0.1.1              DBI_1.1.2                tweenr_1.0.2            
 [91] formatR_1.11             WriteXLS_6.7.0           dbplyr_2.1.1             MASS_7.3-54              rappdirs_0.3.3          
 [96] boot_1.3-28              babelgene_21.4           ShortRead_1.52.0         Matrix_1.3-4             cli_3.6.3               
[101] parallel_4.1.1           igraph_2.0.3             pkgconfig_2.0.3          GenomicAlignments_1.30.0 numDeriv_2016.8-1.1     
[106] InteractionSet_1.22.0    xml2_1.3.3               ggtree_3.2.1             annotate_1.72.0          multtest_2.50.0         
[111] XVector_0.34.0           yulab.utils_0.0.4        digest_0.6.30            graph_1.72.0             Biostrings_2.62.0       
[116] cellranger_1.1.0         fastmatch_1.1-3          tidytree_0.3.6           restfulr_0.0.13          GreyListChIP_1.26.0     
[121] curl_4.3.3               Rsamtools_2.10.0         gtools_3.9.3             rjson_0.2.20             jsonlite_1.8.8          
[126] nlme_3.1-153             lifecycle_1.0.3          viridisLite_0.4.1        BSgenome_1.62.0          fansi_1.0.3             
[131] pillar_1.9.0             lattice_0.20-45          plotrix_3.8-2            KEGGREST_1.34.0          fastmap_1.1.0           
[136] httr_1.4.4               survival_3.2-13          GO.db_3.14.0             glue_1.6.2               zip_2.2.0               
[141] png_0.1-7                bit_4.0.4                ggforce_0.3.3            stringi_1.7.8            blob_1.2.2              
[146] latticeExtra_0.6-29      caTools_1.18.2           memoise_2.0.1            ape_5.5                  irlba_2.3.5.1