The gDNAx package

What is genomic DNA contamination in a RNA-seq experiment

RNA sequencing (RNA-seq) libraries may contain genomic DNA (gDNA) contamination due to an absent or inefficient gDNA digestion step (with DNase) during RNA extraction or library preparation. In fact, some protocols do not include a DNase treatment step, or they include it as optional.

While gDNA contamination is not a major issue in libraries built with poly(A) selected RNA molecules, it can remarkably affect gene expression quantification from libraries of total RNA. When present, gDNA contamination can lead to a misleading attribution of expression to unannotated regions of the genome. For this reason, it is important to check the levels of gDNA contamination during quality control before performing further analyses, specially when total RNA has been sequenced.

Diagnose gDNA contamination

Here we illustrate the use of the gDNAx package for producing different diagnostics and how do they reveal different gDNA contamination levels. We use a subset of the data in (Li et al. 2022), which consists of 9 paired-end samples of total RNA-seq with increasing levels of gDNA contamination: 0% (no contamination), 1% and 10%, with 3 replicates each. The data is available through the Bioconductor experiment data package gDNAinRNAseqData, which allows one to download 9 BAM files, containing about 100,000 alignments, sampled uniformly at random from the complete BAM files.

library(gDNAinRNAseqData)

# Retrieve BAM files
bamfiles <- LiYu22subsetBAMfiles()
bamfiles
[1] "/tmp/RtmpnlNV0F/s32gDNA0.bam"  "/tmp/RtmpnlNV0F/s33gDNA0.bam" 
[3] "/tmp/RtmpnlNV0F/s34gDNA0.bam"  "/tmp/RtmpnlNV0F/s26gDNA1.bam" 
[5] "/tmp/RtmpnlNV0F/s27gDNA1.bam"  "/tmp/RtmpnlNV0F/s28gDNA1.bam" 
[7] "/tmp/RtmpnlNV0F/s23gDNA10.bam" "/tmp/RtmpnlNV0F/s24gDNA10.bam"
[9] "/tmp/RtmpnlNV0F/s25gDNA10.bam"

# Retrieve information on the gDNA concentrations of each BAM file
pdat <- LiYu22phenoData(bamfiles)
pdat
          gDNA
s32gDNA0     0
s33gDNA0     0
s34gDNA0     0
s26gDNA1     1
s27gDNA1     1
s28gDNA1     1
s23gDNA10   10
s24gDNA10   10
s25gDNA10   10

Diagnosing the presence of gDNA contamination requires using an annotation of genes and transcripts. The gDNAx package expects that we provide such an annotation using a so-called TxDb package, either as a TxDb object, created once such a package is loaded into the R session, or by specifying the name of the package. The Bioconductor website provides a number of TxDb packages, but if the we do not find the one we are looking for, we can build a TxDb object using the function makeTxDbFromGFF() on a given GFF or GTF file, or any of the other makeTxDbFrom*() functions, available in the GenomicFeatures package. Here we load the TxDb package corresponding to the GENCODE annotation provided by the UCSC Genome Browser.

library(TxDb.Hsapiens.UCSC.hg38.knownGene)

txdb <- TxDb.Hsapiens.UCSC.hg38.knownGene
txdb
TxDb object:
# Db type: TxDb
# Supporting package: GenomicFeatures
# Data source: UCSC
# Genome: hg38
# Organism: Homo sapiens
# Taxonomy ID: 9606
# UCSC Table: knownGene
# UCSC Track: GENCODE V46
# Resource URL: https://genome.ucsc.edu/
# Type of Gene ID: Entrez Gene ID
# Full dataset: yes
# miRBase build ID: NA
# Nb of transcripts: 278220
# Db created by: txdbmaker package from Bioconductor
# Creation time: 2024-09-24 16:26:51 +0000 (Tue, 24 Sep 2024)
# txdbmaker version at creation time: 1.1.1
# RSQLite version at creation time: 2.3.7
# DBSCHEMAVERSION: 1.2

We can calculate diagnostics for gDNA contamination using the function gDNAdx() as follows.

library(gDNAx)

gdnax <- gDNAdx(bamfiles, txdb)
class(gdnax)
[1] "gDNAx"
attr(,"package")
[1] "gDNAx"
gdnax
gDNAx object
# BAM files (9): s32gDNA0.bam, ..., s25gDNA10.bam
# Library layout: paired-end, 9 (2x50nt)
# Library protocol: unstranded (9 out of 9)
# Sequences: only standard chromosomes
# Annotation pkg: TxDb.Hsapiens.UCSC.hg38.knownGene
# Alignments employed: first 100000

The previous call will show progress through its calculations unless we set the argument verbose=FALSE, and return an object of class gDNAx once it has finished. We have let the gDNAdx() function figure out the library layout and protocol, but if we already knew those parameters from the data, we could set them through the arguments singleEnd and strandMode and speed up calculations. Another way to speed up calculations, which may be advantageous specially when analysing a high number of BAM files, is to use the BPPARAM argument to set a number of parallel threads of execution; see the help page of gDNAdx() for full details on how to specify non-default values to all these parameters.

Calling the plot() function with the resulting object gDNAx object as the first argument will plot several diagnostics. Here below, we also use a parameter called group to automatically color samples, in this case, by the gDNA contamination levels included in the experimental design of the data; see (Li et al. 2022) for full details on it.

par(mar=c(4, 5, 2, 1))
plot(gdnax, group=pdat$gDNA, pch=19)
Diagnostics. Default diagnostics obtained with the function `plot()` on a `gDNAx` object.

Diagnostics. Default diagnostics obtained with the function plot() on a gDNAx object.

The previous figure contains three diagnostic plots, each one showing the following values as a function of the percentage of read alignments fully contained in intergenic regions (IGC):

  • Percentage of Splice-compatible junction (SCJ) alignments. These are alignments compatible with a transcript in the given annotation, for which the aligned read, or at least one of the two aligned reads in the case of a paired-end layout, spans one or more exon-exon junctions over two or more exons of that transcript.
  • Percentage of splice compatible exonic (SCE) alignments. These are alignments compatible with a transcript in the given annotation, but which differently to SCJ alignments, do not include an exon-exon junction in the alignment.
  • Percentage of intronic (INT) alignments. These are alignments fully contained in intronic regions.

These data appear to come from an unstranded library, but if they would be stranded, a fourth diagnostic plot would appear showing an estimated value of the strandedness of each sample as function of the percentage of intergenic alignments. In stranded RNA-seq data, we should expect strandedness values close to 1, which imply that most reads align to the same strand than the annotated transcripts. Lower strandedness values can be indicative of gDNA contamination because reads sequenced from DNA are expected to align in equal proportions to both strands.

Because IGC alignments mainly originate from gDNA contamination, we may expect a negative correlation between the percentage of SCJ or SCE alignments and the percentage of IGC alignments. On the other hand, INT alignments may originate either from primary unprocessed transcripts in the nucleus, or from gDNA contamination as well. Therefore, we may also expect some positive correlation between the percentages of INT and IGC alignments, as it happens in this data.

Using the function getDx() on the gDNAx object, we obtain all the values used in the diagnostics.

dx <- getDx(gdnax)
dx
               IGC      INT      SCJ      SCE      JNC  IGCFLM  SCJFLM  SCEFLM
s32gDNA0  1.029500 11.76099 15.18463 40.02910 19.56452 174.272 161.078 160.939
s33gDNA0  1.118422 11.78807 15.21255 40.30533 19.55383 177.158 160.444 162.343
s34gDNA0  1.085408 12.26652 15.40036 40.18418 19.70788 163.860 154.493 151.493
s26gDNA1  1.394822 12.22450 14.78250 38.69502 18.73132 170.779 160.506 169.646
s27gDNA1  1.359395 12.44973 14.53507 38.20584 18.31765 168.934 160.994 164.855
s28gDNA1  1.491097 12.49686 14.09755 37.66753 17.96053 185.941 167.153 159.971
s23gDNA10 3.505813 13.17887 11.22446 30.99074 14.41110 169.345 164.557 168.161
s24gDNA10 3.756376 13.67709 10.84996 30.00253 13.91748 166.506 160.365 158.758
s25gDNA10 3.385574 13.53019 11.19650 30.63859 14.20691 166.726 166.015 158.318
           INTFLM STRAND
s32gDNA0  157.957     NA
s33gDNA0  158.708     NA
s34gDNA0  154.068     NA
s26gDNA1  156.734     NA
s27gDNA1  158.574     NA
s28gDNA1  156.877     NA
s23gDNA10 159.193     NA
s24gDNA10 159.978     NA
s25gDNA10 156.025     NA

The column JNC contains the percentage of alignments that include one or more junctions, irrespective of whether those aligments are compatible with an spliced transcript in the given annotation. The columns with the suffix FLM contain an estimation of the fragment length mean in the alignments originating in the corresponding region, and the column STRAND stores the strandedness values, which in this case are NA because this dataset is not strand-specific.

We can directly plot the estimated fragments length distributions with the function plotFrgLength().

plotFrgLength(gdnax)
Fragments length distributions. Density and location of the estimated fragments length distribution, by the origin of the alignments.

Fragments length distributions. Density and location of the estimated fragments length distribution, by the origin of the alignments.

Another way to represent some of diagnostic measurements is to examine the origin of the alignments per sample in percentages. Fluctuations of these proportions across samples can help quantifying the amount of gDNA contamination per sample.

plotAlnOrigins(gdnax, group=pdat$gDNA)
Alignment origins.

Alignment origins.

If we are interested in knowing exactly which annotations of intergenic and intronic regions have been used to compute these diagnostics, we can easily retrieve them using the functions getIgc() and getInt() on the output gDNAx object, respectively.

igcann <- getIgc(gdnax)
igcann
GRanges object with 910526 ranges and 0 metadata columns:
           seqnames      ranges strand
              <Rle>   <IRanges>  <Rle>
       [1]     chr1     1-10000      *
       [2]     chr1 14410-14695      *
       [3]     chr1 24887-26355      *
       [4]     chr1 26413-26582      *
       [5]     chr1 27138-27268      *
       ...      ...         ...    ...
  [910522]     chrM   2746-3229      *
  [910523]     chrM   3308-4328      *
  [910524]     chrM   4401-7447      *
  [910525]     chrM  7515-16204      *
  [910526]     chrM 16250-16569      *
  -------
  seqinfo: 25 sequences (1 circular) from hg38 genome
intann <- getInt(gdnax)
intann
GRanges object with 1493398 ranges and 0 metadata columns:
            seqnames            ranges strand
               <Rle>         <IRanges>  <Rle>
        [1]     chr1       12228-12612      *
        [2]     chr1       12722-12974      *
        [3]     chr1       13053-13220      *
        [4]     chr1       14830-14969      *
        [5]     chr1       15039-15264      *
        ...      ...               ...    ...
  [1493394]     chrY 57207675-57208210      *
  [1493395]     chrY 57208313-57208518      *
  [1493396]     chrY 57213358-57213525      *
  [1493397]     chrY 57213603-57213879      *
  [1493398]     chrY 57213965-57214349      *
  -------
  seqinfo: 25 sequences (1 circular) from hg38 genome

Strandedness estimation

Since we have let the gDNAdx() function to estimate strandedness, we can examine those estimated values using the getter function strandedness() on the gDNAx object.

strandedness(gdnax)
          strandMode1 strandMode2      ambig Nalignments
s32gDNA0    0.4804169   0.4783929 0.04119024       66205
s33gDNA0    0.4801948   0.4780386 0.04176656       66321
s34gDNA0    0.4838895   0.4747806 0.04132993       66199
s26gDNA1    0.4845803   0.4756344 0.03978530       63717
s27gDNA1    0.4862653   0.4727614 0.04097329       62797
s28gDNA1    0.4800895   0.4789145 0.04099601       62128
s23gDNA10   0.4816028   0.4763408 0.04205635       50361
s24gDNA10   0.4778240   0.4817199 0.04045603       48769
s25gDNA10   0.4795262   0.4782635 0.04221033       49893

Using the function classifyStrandMode() we can obtain a classification of the most likely strand mode for each BAM file, given some default cutoff values.

classifyStrandMode(strandedness(gdnax))
 s32gDNA0  s33gDNA0  s34gDNA0  s26gDNA1  s27gDNA1  s28gDNA1 s23gDNA10 s24gDNA10 
       NA        NA        NA        NA        NA        NA        NA        NA 
s25gDNA10 
       NA

Li et al. (2022) report in their publication that “sequencing libraries were generated using a TruSeq Stranded Total RNA Library Prep Kit”. However, we can see that the proportion of alignments overlapping transcripts in the column strandMode1 is very similar to the one in the column strandMode2, which is compatible with an unstranded library and the reason why we obtain NA values in the output of classifyStrandMode(). We reach the same conclusion if we use the RSeQC tool infer_experiment.py (Wang, Wang, and Li 2012) and by visual inspection of the alignment data in the Integrative Genomics Viewer (IGV) (Robinson et al. 2011).

Following the recommendations made by Signal and Kahlke (2022), gDNAx attempts to use at least 200,000 alignments overlapping exonic regions to estimate strandedness. In the subset of data used in this vignette, the number of alignments used for that estimation is close to 60,000, which is the total number of exonic alignments present in the BAM files.

If we are only interested in the estimation of strandedness values, we can can also directly call strandedness() with a character string vector of BAM filenames and a TxDb annotation object; see the help page of strandedness().

Remove gDNA contamination

We can attempt removing read alignments from putative gDNA origin using the function gDNAtx(), which should be called with the gDNAx object returned by gDNAdx() and a path in the filesystem where to stored the filtered BAM files. By default, these filtered BAM files include splice-compatible read alignments (SCJ and SCE) that are found in a genomic window enriched for stranded alignments. For further fine tuning of this filtering strategy please use the function filterBAMtx().

## fbf <- filterBAMtxFlag(isSpliceCompatibleJunction=TRUE,
##                        isSpliceCompatibleExonic=TRUE)
## fstats <- filterBAMtx(gdnax, path=tmpdir, txflag=fbf)
## fstats
tmpdir <- tempdir()
fstats <- gDNAtx(gdnax, path=tmpdir)
fstats
           NALN NIGC NINT  NSCJ  NSCE  NSTW NNCH
s32gDNA0  99660   NA   NA 15134 39905 46336  340
s33gDNA0  99694   NA   NA 15170 40189 46823  306
s34gDNA0  99686   NA   NA 15352 40068 46529  314
s26gDNA1  99726   NA   NA 14743 38586 45061  274
s27gDNA1  99456   NA   NA 14466 38011 45083  544
s28gDNA1  99457   NA   NA 14023 37470 43892  543
s23gDNA10 99007   NA   NA 11115 30690 36424  993
s24gDNA10 99005   NA   NA 10746 29716 35032  995
s25gDNA10 99156   NA   NA 11103 30394 36039  844

The first column NALN corresponds to the total number of read alignments processed. Columns NIGC to NSCE contain the number of selected alignments from each corresponding origin, where NA indicates that that type of alignment was not selected for filtering. The column NSTW corresponds to selected alignments occurring in stranded windows, and therefore this number will be always equal or smaller than the number of the previous columns. The column NNCH corresponds to discarded read alignments ocurring in non-standard chromosomes.

Session information

sessionInfo()
R version 4.4.2 (2024-10-31)
Platform: x86_64-pc-linux-gnu
Running under: Ubuntu 24.04.1 LTS

Matrix products: default
BLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 
LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.26.so;  LAPACK version 3.12.0

locale:
 [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
 [3] LC_TIME=en_US.UTF-8        LC_COLLATE=C              
 [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       

time zone: Etc/UTC
tzcode source: system (glibc)

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

other attached packages:
 [1] gDNAx_1.5.0                             
 [2] TxDb.Hsapiens.UCSC.hg38.knownGene_3.20.0
 [3] GenomicFeatures_1.59.1                  
 [4] AnnotationDbi_1.69.0                    
 [5] Biobase_2.67.0                          
 [6] Rsamtools_2.23.1                        
 [7] Biostrings_2.75.3                       
 [8] XVector_0.47.1                          
 [9] GenomicRanges_1.59.1                    
[10] GenomeInfoDb_1.43.2                     
[11] IRanges_2.41.2                          
[12] S4Vectors_0.45.2                        
[13] BiocGenerics_0.53.3                     
[14] generics_0.1.3                          
[15] gDNAinRNAseqData_1.6.0                  
[16] knitr_1.49                              
[17] BiocStyle_2.35.0                        

loaded via a namespace (and not attached):
 [1] tidyselect_1.2.1            dplyr_1.1.4                
 [3] blob_1.2.4                  filelock_1.0.3             
 [5] bitops_1.0-9                fastmap_1.2.0              
 [7] RCurl_1.98-1.16             BiocFileCache_2.15.0       
 [9] VariantAnnotation_1.53.0    GenomicAlignments_1.43.0   
[11] XML_3.99-0.17               digest_0.6.37              
[13] mime_0.12                   lifecycle_1.0.4            
[15] KEGGREST_1.47.0             RSQLite_2.3.9              
[17] magrittr_2.0.3              compiler_4.4.2             
[19] rlang_1.1.4                 sass_0.4.9                 
[21] tools_4.4.2                 plotrix_3.8-4              
[23] yaml_2.3.10                 rtracklayer_1.67.0         
[25] S4Arrays_1.7.1              bit_4.5.0.1                
[27] curl_6.0.1                  DelayedArray_0.33.3        
[29] RColorBrewer_1.1-3          abind_1.4-8                
[31] BiocParallel_1.41.0         withr_3.0.2                
[33] purrr_1.0.2                 sys_3.4.3                  
[35] grid_4.4.2                  ExperimentHub_2.15.0       
[37] SummarizedExperiment_1.37.0 cli_3.6.3                  
[39] rmarkdown_2.29              crayon_1.5.3               
[41] httr_1.4.7                  rjson_0.2.23               
[43] DBI_1.2.3                   cachem_1.1.0               
[45] zlibbioc_1.52.0             parallel_4.4.2             
[47] BiocManager_1.30.25         restfulr_0.0.15            
[49] matrixStats_1.4.1           vctrs_0.6.5                
[51] Matrix_1.7-1                jsonlite_1.8.9             
[53] bit64_4.5.2                 GenomicFiles_1.43.0        
[55] maketools_1.3.1             jquerylib_0.1.4            
[57] glue_1.8.0                  codetools_0.2-20           
[59] BiocVersion_3.21.1          BiocIO_1.17.1              
[61] UCSC.utils_1.3.0            tibble_3.2.1               
[63] pillar_1.10.0               rappdirs_0.3.3             
[65] htmltools_0.5.8.1           BSgenome_1.75.0            
[67] GenomeInfoDbData_1.2.13     R6_2.5.1                   
[69] dbplyr_2.5.0                lattice_0.22-6             
[71] evaluate_1.0.1              AnnotationHub_3.15.0       
[73] png_0.1-8                   memoise_2.0.1              
[75] bslib_0.8.0                 SparseArray_1.7.2          
[77] xfun_0.49                   MatrixGenerics_1.19.0      
[79] buildtools_1.0.0            pkgconfig_2.0.3

References

Li, Xiangnan, Peipei Zhang, Haijian Wang, and Ying Yu. 2022. “Genes Expressed at Low Levels Raise False Discovery Rates in RNA Samples Contaminated with Genomic DNA.” BMC Genomics 23 (1): 554.
Robinson, James T, Helga Thorvaldsdóttir, Wendy Winckler, Mitchell Guttman, Eric S Lander, Gad Getz, and Jill P Mesirov. 2011. “Integrative Genomics Viewer.” Nature Biotechnology 29 (1): 24–26.
Signal, Brandon, and Tim Kahlke. 2022. “How_are_we_stranded_here: Quick Determination of RNA-Seq Strandedness.” BMC Bioinformatics 23 (1): 1–9.
Wang, Liguo, Shengqin Wang, and Wei Li. 2012. “RSeQC: Quality Control of RNA-Seq Experiments.” Bioinformatics 28 (16): 2184–85.