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Question: Mapping genome regions to gene symbols
1
gravatar for NS
2.6 years ago by
NS10
United States
NS10 wrote:

I have just downloaded CNV level 3 files from TCGA database.

As you know, in these files there are three columns: chromosome, start, and end which presents the coordinates of genes.

Now, I would like to map them to gene symbols, but I don't know.

I appreciate any help.

Thanks

ADD COMMENTlink modified 2.6 years ago by pshannon80 • written 2.6 years ago by NS10
8
gravatar for Martin Morgan
2.6 years ago by
Martin Morgan ♦♦ 20k
United States
Martin Morgan ♦♦ 20k wrote:

I wrote two helper functions, explained below

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
}

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)
}

Load the GenomicRanges package

library(GenomicRanges)

Get the CNV regions into a GRanges instance. For instance, if the data is in a BED file, perhaps using the rtracklayer package

cnv = rtracklayer::import("my.bed")

or if in a tab-delimited file

df = read.delim("my.tsv")
cnv = makeGRangesFromDataFrame(df)

(if you're a fan of dplyr / magrittr style pipes then cnv = read.delim("my.tsv") %>% makeGRangesFromDataFrame() works, too). Here I make a couple of genomic ranges 'by hand'

> cnv = GRanges(c("chr1", "chr2"), IRanges(94312388,244006886))
> cnv
GRanges object with 2 ranges and 0 metadata columns:
      seqnames                ranges strand
         <Rle>             <IRanges>  <Rle>
  [1]     chr1 [94312388, 244006886]      *
  [2]     chr2 [94312388, 244006886]      *
  -------
  seqinfo: 2 sequences from an unspecified genome; no seqlengths

Load the Homo.sapiens package

library(Homo.sapiens)

Find the genomic coordinates for each (entrez) gene, as well as the gene symbol

> gns = geneRanges(Homo.sapiens, column="SYMBOL")
> gns
GRanges object with 23056 ranges and 1 metadata column:
        seqnames                 ranges strand   |      SYMBOL
           <Rle>              <IRanges>  <Rle>   | <character>
      1    chr19 [ 58858172,  58874214]      -   |        A1BG
     10     chr8 [ 18248755,  18258723]      +   |        NAT2
    100    chr20 [ 43248163,  43280376]      -   |         ADA
   1000    chr18 [ 25530930,  25757445]      -   |        CDH2
  10000     chr1 [243651535, 244006886]      -   |        AKT3
    ...      ...                    ...    ... ...         ...
   9991     chr9 [114979995, 115095944]      -   |       PTBP3
   9992    chr21 [ 35736323,  35743440]      +   |       KCNE2
   9993    chr22 [ 19023795,  19109967]      -   |       DGCR2
   9994     chr6 [ 90539619,  90584155]      +   |    CASP8AP2
   9997    chr22 [ 50961997,  50964905]      -   |        SCO2
  -------
  seqinfo: 93 sequences (1 circular) from hg19 genome

Find which genes overlap which copy number regions

> symInCnv = splitByOverlap(gns, cnv, "SYMBOL")
> symInCnv
CharacterList of length 2
[["1"]] AKT3 MIR942 MIR190B MIR760 MIR921 ... FAM20B LPGAT1 KIF14 RBM8A NR1I3
[["2"]] TANK SNORD11B MIR933 LOC100129175 CCNT2-AS1 ... ZEB2 FARP2 TLK1 CD302

The result is a CharacterList (list of character vectors) where each element contains the genes overlapping the corresponding CNV region.

A CharacterList is very convenient to work with, but use as.vector(symInCnv) to work as a plain list-of-characters, or unstrsplit(symInCnv, sep=", ") to paste the symbols in each CNV together.

Here's the first helper function, disected

geneRanges <- 
    function(db, column="ENTREZID")

'db' is a so-called OrganismDb, containing information about genes (e.g., ENTREZ or SYMBOL gene ids) as well as gene models (e.g., TXSTART, TXEND). The Homo.sapiens OrangismDb is based on the org.Hs.eg.db gene annotation package, and the TxDb.Hsapiens.UCSC.hg19.knownGene package; it is relatively easy to make a custom package if you have different annotations.

'column' is the type of gene identifier you are interested in mapping to. See columns(Homo.sapiens) for some ideas.

{
    g <- genes(db, columns=column)

This line extracts the coordinates (min and max) of each gene, as well as the column (e.g., SYMBOL associated with that gene.

The TxDb is organized around a primary key (the ENTREZID), and there may be several gene SYMBOLS per ENTREZID. The next few lines makes each SYMBOL map to a single genomic location.

    col <- mcols(g)[[column]]
    genes <- granges(g)[rep(seq_along(g), elementLengths(col))]
    mcols(genes)[[column]] <- as.character(unlist(col))
    genes
}

The end result is a GRanges instance, with a metadata column corresponding to the type of gene identifier of interest.

The basic idea behind splitByOverlap is that we can find which gene coordinates overlap which copy number variant coordinates, and then split the column of gene identifiers into lists corresponding to the regions of overlap

splitByOverlap <-
    function(x, f, column="ENTREZID", ...)

'query' is the gene coordinates, 'subject' the copy number coordinates. 'column' needs to match 'column' in the geneRanges() function.

{
    olaps <- findOverlaps(query, subject, ...)

findOverlaps() is a very powerful function. It returns a 'Hits' object that has two parallel vectors. The vectors can be extracted with queryHits() and subjectHits(). queryHits() are the indexes of the queries (the gene coordinates) that overlap the corresponding subjectHits(), i.e., the indexes of the subjects, the copy number regions. 

The next lines line up the query column identifier (e.g., gene SYMBOL) that overlaps each subject.

    f1 <- factor(subjectHits(olaps), levels=seq_len(subjectLength(olaps)))
    splitAsList(mcols(x)[[column]][queryHits(olaps)], f1)

the use of factor() with exactly as many levels as there are subjects ensures that the splitAsList() command returns a 1:1 mapping between the subjects (copy number regions) and the genes in the corresponding CharacterList.

}

 

 

 

ADD COMMENTlink modified 2.6 years ago • written 2.6 years ago by Martin Morgan ♦♦ 20k

@Martin Thank you so much.

It was really helpful, I learned many thing!

ADD REPLYlink written 2.6 years ago by NS10

Thanks a lot for this post. This is really helpful. I was trying to annotate cnv regions using the code snippets you provided.

For some of the regions the annotation appears to be incorrect or I am doing something wrong.

Example:

chr10 80763309 81342462   1 0.5693    ZEB2-AS1,NXPH2,TEX41,LRP1B,ARHGAP15,YY1P2,PABPC1P2,GTDC1,KYNU,ACVR2A,ZEB2

The genes returned are not in chr 10 . Could you please guide me to understand what I am doing wrong?

What I have:

>cnv

GRanges object with 15663 ranges and 0 metadata columns:
          seqnames                 ranges strand
             <Rle>              <IRanges>  <Rle>
      [1]     chr1 [152275770, 152286617]      *
      [2]     chr1 [152327008, 152328775]      *
      [3]     chr1 [225688735, 225695788]      *
      [4]     chr1 [226923138, 226925166]      *
      [5]     chr1 [228466864, 228481334]      *
      ...      ...                    ...    ...
  [15659]     chr7 [155187653, 155251762]      *
  [15660]     chr8 [145486495, 145488470]      *
  [15661]     chr8 [ 37707064,  37732844]      *
  [15662]     chr8 [145486495, 145488470]      *
  [15663]     chrY [  3447306,   4905323]      *
  -------
  seqinfo: 24 sequences from an unspecified genome; no seqlengths

>gns

cnv
GRanges object with 15663 ranges and 0 metadata columns:
          seqnames                 ranges strand
             <Rle>              <IRanges>  <Rle>
      [1]     chr1 [152275770, 152286617]      *
      [2]     chr1 [152327008, 152328775]      *
      [3]     chr1 [225688735, 225695788]      *
      [4]     chr1 [226923138, 226925166]      *
      [5]     chr1 [228466864, 228481334]      *
      ...      ...                    ...    ...
  [15659]     chr7 [155187653, 155251762]      *
  [15660]     chr8 [145486495, 145488470]      *
  [15661]     chr8 [ 37707064,  37732844]      *
  [15662]     chr8 [145486495, 145488470]      *
  [15663]     chrY [  3447306,   4905323]      *
  -------
  seqinfo: 24 sequences from an unspecified genome; no seqlengths

symInCnv = splitColumnByOverlap(gns, cnv, "SYMBOL")

 

 

ADD REPLYlink written 16 months ago by mkaushal0
1

By way of reproducible example, I loaded the packages and created the range that you indicate is problematic

library(GenomicRanges)
library(Homo.sapiens)
cnv = GRanges("chr10:80763309-81342462")

I then copy and pasted the two functions above (updated here) into my R session, and calculated the gene ranges and overlaps of my test range

gns = geneRanges(Homo.sapiens, column="SYMBOL")
xx = splitColumnByOverlap(gns, cnv, "SYMBOL")

I get

> gns[gns$SYMBOL %in% xx[[1]]]
GRanges object with 6 ranges and 1 metadata column:
         seqnames               ranges strand |      SYMBOL
            <Rle>            <IRanges>  <Rle> | <character>
   10105    chr10 [81107220, 81115089]      + |        PPIF
  143244    chr10 [81272357, 81276192]      + |     EIF5AL1
  219654    chr10 [81142083, 81205383]      - |     ZCCHC24
  283050    chr10 [80703083, 80828536]      - |   ZMIZ1-AS1
   57178    chr10 [80828792, 81076285]      + |       ZMIZ1
  729238    chr10 [81315608, 81320163]      - |      SFTPA2
  -------
  seqinfo: 93 sequences (1 circular) from hg19 genome

The versions of the various packages in use are

> sessionInfo()
R version 3.3.1 Patched (2016-07-20 r70947)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Ubuntu 16.04.1 LTS

locale:
 [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
 [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    
 [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    parallel  stats     graphics  grDevices utils     datasets 
[8] methods   base     

other attached packages:
 [1] Homo.sapiens_1.3.1                     
 [2] TxDb.Hsapiens.UCSC.hg19.knownGene_3.2.2
 [3] org.Hs.eg.db_3.3.0                     
 [4] GO.db_3.3.0                            
 [5] OrganismDbi_1.14.1                     
 [6] GenomicFeatures_1.24.4                 
 [7] AnnotationDbi_1.34.4                   
 [8] Biobase_2.32.0                         
 [9] GenomicRanges_1.24.2                   
[10] GenomeInfoDb_1.8.3                     
[11] IRanges_2.6.1                          
[12] S4Vectors_0.10.2                       
[13] BiocGenerics_0.18.0                    
[14] BiocInstaller_1.22.3                   

loaded via a namespace (and not attached):
 [1] graph_1.50.0               XVector_0.12.0            
 [3] zlibbioc_1.18.0            GenomicAlignments_1.8.4   
 [5] BiocParallel_1.6.2         tools_3.3.1               
 [7] SummarizedExperiment_1.2.3 DBI_0.4-1                 
 [9] RBGL_1.48.1                rtracklayer_1.32.1        
[11] bitops_1.0-6               RCurl_1.95-4.8            
[13] biomaRt_2.28.0             RSQLite_1.0.0             
[15] Biostrings_2.40.2          Rsamtools_1.24.0          
[17] XML_3.98-1.4              

What do you get, what is your sessionInfo(), and which gene(s) are not on chr10?

ADD REPLYlink modified 16 months ago • written 16 months ago by Martin Morgan ♦♦ 20k

It worked this time around. Not very sure what mistake I was doing last time. LRP1B was listed as gene on chr 10, which is not correct. It worked perfectly fine this time. Thanks a lot for your help.

ADD REPLYlink written 15 months ago by mkaushal0

It works fine @Martin, thank you very much. But I need to export the GRanges object with the SYMBOL column to a data frame, or to print it in a text file, but I can`t, it is possible? thank you very much in advance.
 

ADD REPLYlink written 7 months ago by julrodr8020

Convert a GRanges gr to data.frame with as.data.frame(gr). Paste the elements of a CharacterList x  into a character vector with paste(x, collapse=", "). Think twice about whether either of these is something you really want to do.

ADD REPLYlink written 7 months ago by Martin Morgan ♦♦ 20k

Thank you very much @MartinMorgan, but if I do that, the name of the genes is substituted by numbers, why? How can I solve that?

Thank you very much and sorry the annoyances

ADD REPLYlink written 6 months ago by julrodr8020

Maybe the relevant column is a factor() not a character() vector.

ADD REPLYlink written 6 months ago by Martin Morgan ♦♦ 20k

Yes, is a factor... I`ve already solve the problem. Thanks!!

ADD REPLYlink modified 6 months ago • written 6 months ago by julrodr8020

@Martin thank you so much

I need to trasnform

"symInCnv" into a data frame with two columns, exactly like the print
["1"]] AKT3 MIR942 MIR190B MIR760 MIR921 ... FAM20B LPGAT1 KIF14 RBM8A NR1I3
[["2"]] TANK SNORD11B MIR933 LOC100129175 CCNT2-AS1 ... ZEB2 FARP2 TLK1 CD302

 

because I need the genes for each cnv to joint as a column to other data frame (where I have my original cnvs with their chromosomal ranges)

Thank you very much

 

ADD REPLYlink written 7 months ago by julrodr8020

It would make more sense to have the CNV regions as GRanges objects, and simply add the copy number columns

cnv = GRanges(c("chr1:100-200", "chr10:200-300"))
​cnv$genes = symInCnv
ADD REPLYlink written 7 months ago by Martin Morgan ♦♦ 20k

ok, thanks. It works perfect. Thank you very much!!

Just one thing, you should put the update version of the helper functions (updated here) in your first post, because is confusing... ;)

thank you very much!!

regards

ADD REPLYlink modified 7 months ago • written 7 months ago by julrodr8020

@Martin I'm trying to map CNV to genes, I had the following error when I applied your code

symInCnv = splitByOverlap(gns, cnv, "SYMBOL")
Error in findOverlaps(query, subject, ...) : object 'query' not found

what should I do also I have 24 chromosomes , I would like to apply the code to the whole CNV file.

ADD REPLYlink modified 4 months ago by Martin Morgan ♦♦ 20k • written 4 months ago by mms1401300

Use the function definitions at the top of the response -- splitColumnsByOverlap() -- the code and explanation seem to have drifted a bit.

ADD REPLYlink written 4 months ago by Martin Morgan ♦♦ 20k

@Martin Thanks for your answer, I got the following error

 xx = splitColumnByOverlap(gns, cnv, "SYMBOL")
Warning message:
In .Seqinfo.mergexy(x, y) :
  The 2 combined objects have no sequence levels in common. (Use
  suppressWarnings() to suppress this warning.)

my cnv is the copy number variation file for tumor tissue only

 GRanges object with 284458 ranges and 0 metadata columns:
           seqnames                 ranges strand
              <Rle>              <IRanges>  <Rle>
       [1]        1 [  3218610,  95674710]      *
       [2]        1 [ 95676511,  95676518]      *
       [3]        1 [ 95680124, 167057183]      *
       [4]        1 [167057495, 167059336]      *
       [5]        1 [167059760, 181602002]      *
       ...      ...                    ...    ...
  [284454]       19  [  284018,  58878226]      *
  [284455]       20  [  455764,  62219837]      *
  [284456]       21  [15347621,  47678774]      *
  [284457]       22  [17423930,  49331012]      *
  [284458]       23  [ 3157107, 154905589]      *
  -------
  seqinfo: 23 sequences from an unspecified genome; no seqlengths

my data file look like the following(part of it)

Sample Chromosome Start End Num_Probes Segment_Mean
TCGA-3C-AAAU-01A-11D-A41E-01 1 3218610 63469503 33369 0.1791
TCGA-3C-AAAU-01A-11D-A41E-01 1 63471492 63472103 3 -0.8257
TCGA-3C-AAAU-01A-11D-A41E-01 1 63472868 85632596 13663 0.2994
TCGA-3C-AAAU-01A-11D-A41E-01 1 85635413 85878136 146 0.6498
TCGA-3C-AAAU-01A-11D-A41E-01 1 85881278 149890533 20618 0.2781
ADD REPLYlink modified 4 months ago • written 4 months ago by mms1401300

compare seqlevels(gns) and seqlevels(cnv). see ?"seqlevelsStyle<-" to coerce between 'styles' (e.g,. NCBI, UCSC), but make sure it is sensible to do so (e.g., gns and cnv based on the same annotation). You're really asking a different (i.e., new) question, so start a new question rather than extending this comment.

ADD REPLYlink written 4 months ago by Martin Morgan ♦♦ 20k
3
gravatar for pshannon
2.6 years ago by
pshannon80
United States
pshannon80 wrote:

Another approach uses Steffen Durink's fine biomaRt package, a  contribution to Bioconductor of many year's standing.  In its simplest form just two lines of code gets you all the genes in one copy number region:

library(biomaRt)
mart <- useMart(biomart="ensembl", dataset="hsapiens_gene_ensembl")
results <- getBM(attributes = c("hgnc_symbol", "chromosome_name", 
                                 "start_position", "end_position"),
                 filters = c("chromosome_name", "start", "end"), 
                 values=list(1, 94312388, 96000000),
                 mart=mart)
dim(results)  # 34 hits, only 12 with gene symbols

If you are reading your data from a file (the TCGA level 3 data you mentioned), and if you wish to filter the regions, this approach may be useful:

library(GenomicRanges)
filename <- "TRIBE_p_TCGAaffx_B1_2_GBM_Nsp_GenomeWideSNP_6_A01_155716.nocnv_hg19.seg.txt"
tbl <- read.table(filename, sep="\t", as.is=TRUE, header=TRUE);
gr <- makeGRangesFromDataFrame(tbl)

With the data in a standard Bioconductor GRanges object, filtering the data becomes easy.  Let's submit just very short copy number regions to biomaRt:

gr.short <- subset(gr, width < 100)
length(gr) # 117 regions
length(gr.short) # just 2 regions
gr.short
GRanges object with 2 ranges and 0 metadata columns:
      seqnames                 ranges strand
         <Rle>              <IRanges>  <Rle>
  [1]        2 [109993208, 109993264]      *
  [2]       10 [114452689, 114452764]      *

 

We need a different biomaRt filter in order to retrieve gene symbols from multiple regions with one call, and we have to specify the copy number the regions in a different format:

regions <- paste(seqnames(gr.short), start(gr.short), end(gr.short), sep=":")
regions
[1] "2:109993208:109993264"  "10:114452689:114452764"

results <- getBM(attributes = c("hgnc_symbol", "chromosome_name", 
                                "start_position","end_position"),
                 filters = c("chromosomal_region"),
                 values=regions,
                 mart=mart)

biomaRt returns two genes, one each from the two short regions:

hgnc_symbol chromosome_name start_position end_position
1      ABLIM1              10      114431113    114685003
2   LINC01123               2      109987063    109996140

You may wish to create a GRanges object from this data.frame in order to simplify subsequent analyses.  In general we recommend this: the standard Bioc data types promote interoperability between different packages.  In this particular case -- gene symbols mapped to copy number regions -- you may wish to preserve the original TCGA-reported regions in a GRanges object, and then represent the mapped genes as GRanges metadata.  However (and as Herve' helpfully pointed out to me) this relationship could be one-to-many, or many-to-one, and the data structure can get a little complicated - though still very useful.   I will followup with more detail on this later.

 

 

 
 

 

ADD COMMENTlink modified 2.6 years ago • written 2.6 years ago by pshannon80
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