Hi,

I'm trying to use getGeneLengthAndGCContent to normalize some RNASeq data. My data was aligned to hg38 and I used featureCounts to aggregate by Ensembl gene ID (GRCh38 v. 87). I used the following call:

> hsa.len.gc <- getGeneLengthAndGCContent(id=rownames(counts.no.sex), org="hsa", mode=c("biomart")) 

I received the following error:

NAs produced by integer overflowError in .Call2("new_XString_from_CHARACTER", classname, x, start(solved_SEW),  : 
  zero or more than one input sequence

Oddly, when I ran it a second time, the error changed a bit, but the same result:

Error in .Call2("new_XString_from_CHARACTER", classname, x, start(solved_SEW),  : 
  zero or more than one input sequence
In addition: Warning message:
In nchar(str, "bytes") * 4L : NAs produced by integer overflow

I then switched to org.db mode with the following call to see if it could map Ensembl IDs:

> hsa.len.gc <- getGeneLengthAndGCContent(id=rownames(counts.no.sex), org="hg38", mode=c("org.db")) 

This completed without errors, but most of the genes came back as NA:

> summary(hsa.len.gc)
     length             gc       
 Min.   :    41   Min.   :0.20   
 1st Qu.:  1800   1st Qu.:0.45   
 Median :  3582   Median :0.51   
 Mean   :  4566   Mean   :0.51   
 3rd Qu.:  6144   3rd Qu.:0.57   
 Max.   :156366   Max.   :0.93   
 NA's   :33901    NA's   :33901

Seems I need to get the biomart version working. I suspect the issue is related to many:1 mappings. Does anyone know how to fix this?

Really appreciate your help. 

Reproducible example set

Here is a link to the smallest subset of Ensembl IDs I could get to fail: https://www.dropbox.com/s/gthmo1rb5lcrbvr/gene_ids.txt?dl=0

> tmp<-read.delim("gene_ids.txt", header=FALSE)
> head(tmp)
               V1
1 ENSG00000243477
2 ENSG00000114378
3 ENSG00000068001
4 ENSG00000114383
5 ENSG00000068028
6 ENSG00000281358
> hsa.len.gc <- getGeneLengthAndGCContent(id=tmp$V1, org="hsa", mode=c("biomart"))
Connecting to BioMart ...
Downloading sequences ...
This may take a few minutes ...
Error in .Call2("new_XString_from_CHARACTER", classname, x, start(solved_SEW),  : 
  zero or more than one input sequence

Hi Mark,

I've just tested your code and it works on my machine.

Here's what I did:

library(EDASeq)
tmp <- read.delim("gene_ids.txt", header=FALSE)
hsa.len.gc <- getGeneLengthAndGCContent(id=tmp$V1, org="hsa", mode=c("biomart"))

and the resulting object

> summary(hsa.len.gc)
     length            gc        
 Min.   :   23   Min.   :0.1633  
 1st Qu.:  406   1st Qu.:0.3942  
 Median :  897   Median :0.4347  
 Mean   : 2341   Mean   :0.4477  
 3rd Qu.: 3089   3rd Qu.:0.4910  
 Max.   :42646   Max.   :0.8636  
 NA's   :40      NA's   :40

Are you using the latest versions of EDASeq and biomaRt? I'm using EDASeq 2.12.0 and biomaRt 2.34.0.

R version 3.4.2 (2017-09-28)
Platform: x86_64-apple-darwin15.6.0 (64-bit)
Running under: macOS Sierra 10.12.6

Matrix products: default
BLAS: /Library/Frameworks/R.framework/Versions/3.4/Resources/lib/libRblas.0.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/3.4/Resources/lib/libRlapack.dylib

locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8

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

other attached packages:
 [1] EDASeq_2.12.0              ShortRead_1.36.0          
 [3] GenomicAlignments_1.14.1   SummarizedExperiment_1.8.0
 [5] DelayedArray_0.4.1         matrixStats_0.52.2        
 [7] Rsamtools_1.30.0           GenomicRanges_1.30.0      
 [9] GenomeInfoDb_1.14.0        Biostrings_2.46.0         
[11] XVector_0.18.0             IRanges_2.12.0            
[13] S4Vectors_0.16.0           BiocParallel_1.12.0       
[15] Biobase_2.38.0             BiocGenerics_0.24.0       

loaded via a namespace (and not attached):
 [1] genefilter_1.60.0       progress_1.1.2          splines_3.4.2          
 [4] lattice_0.20-35         rtracklayer_1.38.0      GenomicFeatures_1.30.0 
 [7] blob_1.1.0              XML_3.98-1.9            survival_2.41-3        
[10] rlang_0.1.4             R.oo_1.21.0             DBI_0.7                
[13] R.utils_2.6.0           bit64_0.9-7             aroma.light_3.8.0      
[16] RColorBrewer_1.1-2      GenomeInfoDbData_0.99.1 stringr_1.2.0          
[19] zlibbioc_1.24.0         hwriter_1.3.2           R.methodsS3_1.7.1      
[22] memoise_1.1.0           latticeExtra_0.6-28     geneplotter_1.56.0     
[25] biomaRt_2.34.0          AnnotationDbi_1.40.0    Rcpp_0.12.14           
[28] xtable_1.8-2            annotate_1.56.1         bit_1.1-12             
[31] RMySQL_0.10.13          digest_0.6.12           stringi_1.1.6          
[34] DESeq_1.30.0            grid_3.4.2              tools_3.4.2            
[37] bitops_1.0-6            magrittr_1.5            RCurl_1.95-4.8         
[40] RSQLite_2.0             tibble_1.3.4            Matrix_1.2-12          
[43] prettyunits_1.0.2       assertthat_0.2.0        R6_2.2.2               
[46] compiler_3.4.2