Mapping genome regions to gene symbols in two ways get weird results

Question

[Na Sed]

I have some genome regions which I am gonna map them to gene symbols.

To do that there are two methods which have different result. I have mentioned one example below:

1) Using 'Homo.sapiens' package

library(Homo.sapiens)
geneRanges <- function(db, column="ENTREZID")
{
  g <- genes(db, columns=column)
  col <- mcols(g)[[column]]
  genes <- granges(g)[rep(seq_along(g), elementLengths(col))]
  mcols(genes)[[column]] <- as.character(unlist(col))
  genes
}

gns <- geneRanges(Homo.sapiens, column="SYMBOL")
splitColumnByOverlap <-  function(query, subject, column="ENTREZID", ...)
{
  olaps <- findOverlaps(query, subject, ...)
  f1 <- factor(subjectHits(olaps), levels=seq_len(subjectLength(olaps)))
  splitAsList(mcols(query)[[column]][queryHits(olaps)], f1)
}

library(GenomicRanges)
cnv <- makeGRangesFromDataFrame(data.frame(chr="chr2", start=109993208, end=109993264))
gene.Symbol <- splitColumnByOverlap(gns, cnv, "SYMBOL")

# The obtained gene symbol is 'SH3RF3'.

2) using 'biomaRt' package

library(GenomicRanges)
library(biomaRt)

mart <- useMart(biomart="ensembl", dataset="hsapiens_gene_ensembl")
region <- "2:109993208:109993264"
gene.Symbol <- getBM(attributes = c("hgnc_symbol", "chromosome_name", "start_position","end_position"),
            filters = c("chromosomal_region"), values=region, mart=mart)

The obtained result is:

hgnc_symbol chromosome_name start_position end_position
1   LINC01123               2      109987063    109996140

Anyone knows why the results are different from each other?

Thanks

Answer 1

[Devon Ryan]

You're using two different genome versions. Biomart is giving you overlaps on GRCh38. The Homo.Sapiens package is giving you overlaps on GRCh37.

[Na Sed]

Oh, I didn't notice to it.

Do you know which of them is suitable for hg19? How can I find that?

[Devon Ryan]

hg19 is GRCh37.

[Na Sed]

How you know Homo.sapiens is GRCh37?

[Devon Ryan]

I looked at the genes overlapping the region you specified in GRCh37 and GRCh38. The results from those matched perfectly what you described. It's unfortunate that that R package doesn't mention its genome version.

[Na Sed]

Thank you so much.