Hello,

I'm trying to find the nearest CpG islands around each miRNA of interest(gene) and also return distance between them. For example for hsa-mir-137 the nearest CpG islands are 73 and 91 where CpG 73 overlaps with this miRNA gene and CpG 91 is ~7500 bp away. What is more is it possible to filter only miRNAs where CpG overlaps it or where distance between them is less than 1000 bp?

Here is what I got:

hub <- AnnotationHub()
query(hub, c("cpg","hg19"))
cpgs <- hub[["AH5086"]] ## the granges of all CpG's (hg19)

gffRangedData<-import.gff("allmirna.gff3")
mirna_granges<-as(gffRangedData, "GRanges") ## I downloaded all annotated miRNAs from miRBase and I converted them to granges object

sample_mirnas <- read.table("sample_data.txt", header = TRUE, sep ='\t') #here are my 3 miRNAs of interest (hsa-mir-106b,hsa-mir-150,hsa-mir-107)
final_mirnas <- mirna_granges[mirna_granges$Name %in% sample_mirnas$mirna] # now I wanted to have granges only for my miRNA so I subset data from miRBase

So as a result I have:

1) CpG's granges

> cpgs
GRanges object with 28691 ranges and 1 metadata column:
                       seqnames           ranges strand |        name
                          <Rle>        <IRanges>  <Rle> | <character>
      [1]                  chr1 [ 28736,  29810]      * |    CpG:_116
      [2]                  chr1 [135125, 135563]      * |     CpG:_30
      [3]                  chr1 [327791, 328229]      * |     CpG:_29
      [4]                  chr1 [437152, 438164]      * |     CpG:_84
      [5]                  chr1 [449274, 450544]      * |     CpG:_99
      ...                   ...              ...    ... .         ...
  [28687]  chr9_gl000201_random [ 15651,  15909]      * |     CpG:_30
  [28688]  chr9_gl000201_random [ 26397,  26873]      * |     CpG:_43
  [28689] chr11_gl000202_random [ 16284,  16540]      * |     CpG:_23
  [28690] chr17_gl000204_random [ 54686,  57368]      * |    CpG:_228
  [28691] chr17_gl000205_random [117501, 117801]      * |     CpG:_23
  -------
  seqinfo: 93 sequences (1 circular) from hg19 genome

2) Granges of miRNA's of interest

> final_mirnas
GRanges object with 3 ranges and 8 metadata columns:
      seqnames                 ranges strand |   source                     type     score
         <Rle>              <IRanges>  <Rle> | <factor>                 <factor> <numeric>
  [1]    chr10 [ 89592747,  89592827]      - |     <NA> miRNA_primary_transcript      <NA>
  [2]    chr19 [ 49500785,  49500868]      - |     <NA> miRNA_primary_transcript      <NA>
  [3]     chr7 [100093993, 100094074]      - |     <NA> miRNA_primary_transcript      <NA>
          phase          ID           Alias         Name Derives_from
      <integer> <character> <CharacterList>  <character>  <character>
  [1]      <NA>   MI0000114       MI0000114  hsa-mir-107         <NA>
  [2]      <NA>   MI0000479       MI0000479  hsa-mir-150         <NA>
  [3]      <NA>   MI0000734       MI0000734 hsa-mir-106b         <NA>
  -------
  seqinfo: 24 sequences from an unspecified genome; no seqlengths

Now, how to combine this data togesher to obtain something like (results are fake, made them up for question):

miRNA           CpG_upstream    CpG_downstream       CpG_up_distance          CpG_down_distance
hsa-mir-106b    57                  23                          852                         15
hsa-mir-150     NA                  77                          NA                          987
hsa-mir-107     12                  NA                          -12                         NA

I put NAIf distance between miRNA and CpG is greater than 1000bp but it can be actual value, what is more i put negative number when CpG and miRNA gene overlap.

I tried to use distance() and follow() functions but there are only bare numbers, can you help me how to figure it out?