Hello, I am overlapping chipseq peaks with hichip peaks in RStudio using findOVerlaps() function. I performed the overlapping on each fragment of the hichip separately and extracted the query hits of each overlaps and then create empty columns in my chip peak file so I can fill it with the fragments that overlapped with it using the approach of queryHits and subjectHits. But I got kind of lost with query and subject hits. Does anyone have an idea about it plz.

hichip_gr <- GRangesList(frg1, frg2)
# Create GRanges for the first set of ranges
frg1 <- GRanges(seqnames = Rle(hichip$frg1),
               ranges = IRanges(start = hichip$start1, end = hichip$end1))

# Create GRanges for the second set of ranges
frg2 <- GRanges(seqnames = Rle(hichip$frg2),
               ranges = IRanges(start = hichip$start2, end = hichip$end2))

# Load my chip peak file   
S1 <- read.delim(file.path(path, "S1_Invi_fdr.bed") , header = FALSE)

# Convert it to Grange 
S1 <- GRanges(seqnames = Rle(S1$seqnames),
              ranges = IRanges(start = S1$start, end = S1$end))

# creating a data columns for hichip fragments
mcols(S1)$frg1 <- NA
mcols(S1)$start.1 <- NA
mcols(S1)$end.1 <- NA
mcols(S1)$frg2 <- NA
mcols(S1)$start.2 <- NA
mcols(S1)$end.2 <- NA

# find overlapping 
overlap1 <- findOverlaps(S1, frg1)

# extract queryHits from overlap1
query_hits <- overlap1$subjectHits
subset_frg1 <- frg1[query_hits]

# this is the result of overlap1
 overlap1
       queryHits subjectHits
    1:         1       35804
    2:         1       35805
    3:         5       44884
    4:         5       44885
    5:         5       44886
   ---                      
12398:     15969       43123
12399:     15970       25561
12400:     15971       55972
12401:     15971       55973
12402:     15971       55974

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       
 [4] LC_COLLATE=en_US.UTF-8     LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   
 [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                  LC_ADDRESS=C              
[10] LC_TELEPHONE=C             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                          
 [3] TxDb.Hsapiens.UCSC.hg19.knownGene_3.2.2  ChIPseeker_1.30.3                       
 [5] ChIPpeakAnno_3.28.0                      msigdbr_7.4.1                           
 [7] ggupset_0.3.0                            DOSE_3.20.1                             
 [9] RColorBrewer_1.1-3                       fgsea_1.20.0                            
[11] org.Hs.eg.db_3.14.0                      enrichplot_1.14.2                       
[13] clusterProfiler_4.2.2                    pheatmap_1.0.12                         
[15] VennDiagram_1.7.3                        futile.logger_1.4.3                     
[17] enrichR_3.2                              rtracklayer_1.54.0                      
[19] TxDb.Hsapiens.UCSC.hg38.knownGene_3.14.0 GenomicFeatures_1.46.1                  
[21] AnnotationDbi_1.56.1                     DiffBind_3.4.11                         
[23] BiocParallel_1.28.3                      BiocManager_1.30.23                     
[25] openxlsx_4.2.5.2                         readxl_1.4.3                            
[27] EnhancedVolcano_1.13.2                   ggrepel_0.9.5                           
[29] magrittr_2.0.3                           lubridate_1.9.3                         
[31] forcats_1.0.0                            stringr_1.5.1                           
[33] dplyr_1.1.4                              purrr_1.0.2                             
[35] readr_2.1.5                              tidyr_1.3.1                             
[37] tibble_3.2.1                             tidyverse_2.0.0                         
[39] DESeq2_1.34.0                            SummarizedExperiment_1.24.0             
[41] MatrixGenerics_1.6.0                     matrixStats_0.62.0                      
[43] GenomicRanges_1.46.1                     GenomeInfoDb_1.30.1                     
[45] IRanges_2.28.0                           S4Vectors_0.32.4                        
[47] 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       
  [5] scatterpie_0.1.7         munsell_0.5.0            systemPipeR_2.0.4        withr_2.5.0             
  [9] colorspace_2.0-3         GOSemSim_2.20.0          filelock_1.0.2           rstudioapi_0.16.0       
 [13] bbmle_1.0.24             GenomeInfoDbData_1.2.7   mixsqp_0.3-43            hwriter_1.3.2           
 [17] 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          
 [25] lambda.r_1.2.4           timechange_0.3.0         BiocFileCache_2.2.0      regioneR_1.26.0         
 [29] R6_2.5.1                 apeglm_1.16.0            graphlayouts_0.8.0       invgamma_1.1            
 [33] locfit_1.5-9.4           AnnotationFilter_1.18.0  bitops_1.0-7             cachem_1.0.6            
 [37] 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        
 [45] tidygraph_1.2.0          rlang_1.1.4              genefilter_1.76.0        splines_4.1.1           
 [49] lazyeval_0.2.2           yaml_2.3.6               qvalue_2.26.0            RBGL_1.70.0             
 [53] tools_4.1.1              ggplotify_0.1.0          gplots_3.1.3.1           Rcpp_1.0.9              
 [57] 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    
 [65] DO.db_2.9                truncnorm_1.0-8          mvtnorm_1.1-3            SQUAREM_2021.1          
 [69] amap_0.8-19              ProtGenerics_1.26.0      patchwork_1.1.1          hms_1.1.3               
 [73] xtable_1.8-4             XML_3.99-0.9             emdbook_1.3.12           jpeg_0.1-9              
 [77] 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         
 [85] ggfun_0.0.4              tzdb_0.2.0               geneplotter_1.72.0       aplot_0.1.1             
 [89] DBI_1.1.2                tweenr_1.0.2             formatR_1.11             WriteXLS_6.6.0          
 [93] dbplyr_2.1.1             MASS_7.3-54              rappdirs_0.3.3           boot_1.3-28             
 [97] 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
[105] numDeriv_2016.8-1.1      InteractionSet_1.22.0    xml2_1.3.3               ggtree_3.2.1            
[109] annotate_1.72.0          multtest_2.50.0          XVector_0.34.0           yulab.utils_0.0.4       
[113] digest_0.6.30            graph_1.72.0             Biostrings_2.62.0        cellranger_1.1.0        
[117] 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            
[125] jsonlite_1.8.8           nlme_3.1-153             lifecycle_1.0.3          viridisLite_0.4.1       
[129] BSgenome_1.62.0          fansi_1.0.3              pillar_1.9.0             lattice_0.20-45         
[133] plotrix_3.8-2            KEGGREST_1.34.0          fastmap_1.1.0            httr_1.4.4              
[137] 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           
[145] blob_1.2.2               latticeExtra_0.6-29      caTools_1.18.2           memoise_2.0.1           
[149] ape_5.5                  irlba_2.3.5.1

The queryHits are the positions in the query that the subjectHits overlap. As an example,

> gr1 <- GRanges(rep("chr1", 5), IRanges(c(5,80, 329, 1111, 2345), c(35, 99, 450, 1211, 3456)))
> gr1
GRanges object with 5 ranges and 0 metadata columns:
      seqnames    ranges strand
         <Rle> <IRanges>  <Rle>
  [1]     chr1      5-35      *
  [2]     chr1     80-99      *
  [3]     chr1   329-450      *
  [4]     chr1 1111-1211      *
  [5]     chr1 2345-3456      *
  -------
  seqinfo: 1 sequence from an unspecified genome; no seqlengths
> gr2 <- rev(gr1)
> findOverlaps(gr1, gr2)
Hits object with 5 hits and 0 metadata columns:
      queryHits subjectHits
      <integer>   <integer>
  [1]         1           5
  [2]         2           4
  [3]         3           3
  [4]         4           2
  [5]         5           1
  -------
  queryLength: 5 / subjectLength: 5

In this contrived example, the first range in gr1 overlaps the last range in gr2 (because it's the same thing, only reversed), and 2 overlaps 4, etc.