Introduction

Transcription factors often bind to specific DNA nucleotide patterns referred to as sequence motifs. Although sequence motifs are well-characterized for many transcription factors, detecting the actual binding sites is not always a straightforward task. Chromatin immunoprecipitation (ChIP) followed by DNA sequencing (ChIP-seq) is the major technology to detect binding regions of transcription factors. However, the binding regions detected by ChIP-seq may contains several or no DNA sequence motifs. Motif2Site is a novel R package which uses ChIP-seq information and detect transcription factor binding sites from a user provided DNA sequence motifs set.

Motif2Site gets two different input, motif and ChIP-seq alignment information, to detect binding sites. First input is ChIP-seq alignment short reads in the bam or bed format. For each aligned short read the center of the alignment is calculated using GenomicAlignments (Lawrence et al. 2013) and Rsamtools (Morgan et al. 2020) packages. Motif information is the second input which is provided by user either as a bed file or a DNA string with a mismatch number. In the case of DNA string input, Biostrings (H. Pagès et al. 2019) and BSgenome (Hervé Pagès 2019) packages are used to find motif locations on the genome in GenomicRanges (Lawrence et al. 2013) format.

Negative binomial distribution is used to model count data by using edgeR package (Robinson, McCarthy, and Smyth 2010). mixtools (Benaglia et al. 2009) is used to deconvolve binding intensities of closely spaced binding sites. Motif2Site also combines binding sites across different experiments. It calls differential binding sites using TMM normalization and GLM test using edgeR package (Robinson, McCarthy, and Smyth 2010).

Major functions of Motif2Site

First install and load the libraries needed to run the examples of this document:

library(GenomicRanges)
library(Motif2Site)
library(BSgenome.Scerevisiae.UCSC.sacCer3)
library(BSgenome.Ecoli.NCBI.20080805)

The functions, implemented in Motif2Site, perform three tasks: 1. To assist users to select better sequence motif input. 2. To detect binding sites from sequence motifs and ChIP-seq datasets 3. To combine and compare binding sites across different experiments, conditions, or tissues. Each of these functions are explained in a separate section.

Selecting sequence motif

Motif2Site uses DNA motif information as one of its input. To facilitate choosing proper sequence motif, compareBedFiless2UserProvidedRegions and compareMotifs2UserProvidedRegions functions compare motif regions with a user provided confident regions in terms of precision and recall.

As the first example, an artificially generated “YeastSampleMotif.bed” bed file in yeast is considered as a confident binding regions set. compareMotifs2UserProvidedRegions function compares these regions with locations of ‘TGATTSCAGGANT’ 1-mismatch, ‘TGATTCCAGGANT’ 0-mismatch, ‘TGATWSCAGGANT’ 2-mismatches on the yeast genome in terms of precision and recall.

yeastExampleFile = 
  system.file("extdata", "YeastSampleMotif.bed", package="Motif2Site")
YeastRegionsChIPseq <- Bed2Granges(yeastExampleFile)
SequenceComparison <- 
  compareMotifs2UserProvidedRegions(
    givenRegion=YeastRegionsChIPseq,
    motifs = c("TGATTSCAGGANT", "TGATTCCAGGANT", "TGATWSCAGGANT"),
    mismatchNumbers = c(1,0,2),
    genome="Scerevisiae",
    genomeBuild="sacCer3"
    )

SequenceComparison

##                 motifName regionCoverage motifCoverage
## 1 TGATTSCAGGANT_1mismatch    0.350877193     0.6060606
## 2 TGATTCCAGGANT_0mismatch    0.005847953     1.0000000
## 3 TGATWSCAGGANT_2mismatch    1.000000000     0.0475132

In the following example, an artificially generated “YeastSampleMotif.bed” bed file in yeast is considered as a confident binding regions set. compareBedFiless2UserProvidedRegions function compares these regions with two bed files “YeastBedFile1.bed” and “YeastBedFile2.bed” in terms of precision and recall.

# Yeast artificial dataset for comparison bed files

yeastExampleFile = 
  system.file("extdata", "YeastSampleMotif.bed", package="Motif2Site")
YeastRegionsChIPseq <- Bed2Granges(yeastExampleFile)
bed1 <- system.file("extdata", "YeastBedFile1.bed", package="Motif2Site")
bed2 <- system.file("extdata", "YeastBedFile2.bed", package="Motif2Site")
BedFilesVector <- c(bed1, bed2)
SequenceComparison <- 
  compareBedFiless2UserProvidedRegions(
    givenRegion=YeastRegionsChIPseq,
    bedfiles = BedFilesVector,
    motifnames = c("YeastBed1", "YeastBed2")
    )

SequenceComparison

##   motifName regionCoverage motifCoverage
## 1 YeastBed1      0.3099415      1.000000
## 2 YeastBed2      0.4853801      0.209068

Detecting binding sites

DetectBindingSitesMotif function detects binding sites from provided sequence motif information. Here, Artificial ChIP-seq data for FUR transcription factor in Escherichia coli was generated in two conditions fe2+ and dpd inspired by (Seo et al. 2014). In the following examples, artificial sequence motif locations have been and provided as a bed file called ‘FurMotifs.bed’. The alignment of ChIP-seq short reads is the other input of this function. The alignment files can be passed to the function as bam or bed files. In the following examples both IP and background alignment files have been passed as single-end bed files to this function.

# FUR candidate motifs in NC_000913 E. coli
FurMotifs = system.file("extdata", "FurMotifs.bed", package="Motif2Site")

# ChIP-seq FUR fe datasets binding sites from user provided bed file 
# ChIP-seq datasets in bed single end format

IPFe <- c(system.file("extdata", "FUR_fe1.bed", package="Motif2Site"),
          system.file("extdata", "FUR_fe2.bed", package="Motif2Site"))
Inputs <- c(system.file("extdata", "Input1.bed", package="Motif2Site"),
            system.file("extdata", "Input2.bed", package="Motif2Site"))
FURfeBedInputStats <- 
  DetectBindingSitesBed(BedFile=FurMotifs,
                        IPfiles=IPFe, 
                        BackgroundFiles=Inputs, 
                        genome="Ecoli",
                        genomeBuild="20080805",
                        DB="NCBI",
                        expName="FUR_Fe_BedInput",
                        format="BEDSE"
                        )
FURfeBedInputStats

## $FRiP
## [1] 0.3668289 0.1997165
## 
## $sequencingStatitic
##   nonSequenced underBinding overBinding
## 1   0.02293399            0 0.005022041
## 
## $motifStatistics
##   skewnessTestRejected decompositionRejected accepted
## 1                   98                    52      199

# ChIP-seq FUR dpd datasets binding sites from user provided bed file 
# ChIP-seq datasets in bed single end format

IPDpd <- c(system.file("extdata", "FUR_dpd1.bed", package="Motif2Site"),
           system.file("extdata", "FUR_dpd2.bed", package="Motif2Site"))
FURdpdBedInputStats <- 
  DetectBindingSitesBed(BedFile=FurMotifs,
                        IPfiles=IPDpd, 
                        BackgroundFiles=Inputs, 
                        genome="Ecoli",
                        genomeBuild="20080805",
                        DB="NCBI",
                        expName="FUR_Dpd_BedInput",
                        format="BEDSE"
                        )
FURdpdBedInputStats

## $FRiP
## [1] 0.2826046 0.2823270
## 
## $sequencingStatitic
##   nonSequenced underBinding overBinding
## 1   0.02254338            0 0.005022041
## 
## $motifStatistics
##   skewnessTestRejected decompositionRejected accepted
## 1                   63                    62      189

DetectBindingSitesMotif function also works with DNA string motifs. In the following example, FUR binding sites in fe2+ condition are detected from ‘GWWTGAGAA’ with 1-mismatch motif. The dataset is generated only for ‘NC_000913’ build. Therefore, in this example the coordinates of this regions are provided as a ‘GivenRegion’ field. Providing this field ensures that binding sites are only detected in the given regions. This will accelerate the peak calling, and also improve the prediction accuracy.

# Granages region for motif search           
NC_000913_Coordiante <- GRanges(seqnames=Rle("NC_000913"),
                                ranges = IRanges(1, 4639675))           

# ChIP-seq FUR fe datasets binding sites from user provided string motif
# ChIP-seq datasets in bed single end format
           
FURfeStringInputStats <- 
  DetectBindingSitesMotif(motif = "GWWTGAGAA",
                          mismatchNumber = 1,
                          IPfiles=IPFe, 
                          BackgroundFiles=Inputs, 
                          genome="Ecoli",
                          genomeBuild="20080805",
                          DB= "NCBI",
                          expName="FUR_Fe_StringInput",
                          format="BEDSE",
                          GivenRegion = NC_000913_Coordiante 
                          )
FURfeStringInputStats

## $FRiP
## [1] 0.07620002 0.08057296
## 
## $sequencingStatitic
##   nonSequenced underBinding overBinding
## 1   0.02464332            0 0.003891051
## 
## $motifStatistics
##   skewnessTestRejected decompositionRejected accepted
## 1                    3                     2       36

Combining binding sites across experiments

recenterBindingSitesAcrossExperiments function combines the binding sites of different tissues or conditions into a single count matrix. In the FUR example, at the first step it combines fe2+ and dpd binding sites. At the next step, it recalculates the p-adjusted values. To ensure the high quality of the combined binding site set, an stringent cross-experiment FDR cutoff is applied (default 0.001). The accepted binding sites should fullfill this cutoff at least in one experiment. Another FDR cutoff value (default 0.05) is used to assign binding or non-binding labels to each binding site for each experiment.

# Combine FUR binding sites from bed input into one table 
corMAT <- recenterBindingSitesAcrossExperiments(
  expLocations=c("FUR_Fe_BedInput","FUR_Dpd_BedInput"),
  experimentNames=c("FUR_Fe","FUR_Dpd"),
  expName="combinedFUR"
  )
corMAT

##             FUR_Fe     FUR_Fe    FUR_Dpd    FUR_Dpd
## FUR_Fe   1.0000000  0.4172015 -0.4277362 -0.3712685
## FUR_Fe   0.4172015  1.0000000 -0.4812927 -0.4519734
## FUR_Dpd -0.4277362 -0.4812927  1.0000000  0.7274417
## FUR_Dpd -0.3712685 -0.4519734  0.7274417  1.0000000

FurTable <- 
  read.table(file.path("combinedFUR","CombinedMatrix"), 
             header = TRUE,
              check.names = FALSE
             )
FurBindingTotal <- 
  GRanges(seqnames=Rle(FurTable[,1]), 
          ranges = IRanges(FurTable[,2], FurTable[,3])
          )
FurFe <- FurBindingTotal[which((FurTable$FUR_Fe_binding =="Binding")==TRUE)]
FurDpd <- FurBindingTotal[which((FurTable$FUR_Dpd_binding =="Binding")==TRUE)]
findOverlaps(FurFe,FurDpd)

## Hits object with 107 hits and 0 metadata columns:
##         queryHits subjectHits
##         <integer>   <integer>
##     [1]         1           1
##     [2]         7           5
##     [3]        10           7
##     [4]        11           8
##     [5]        17          10
##     ...       ...         ...
##   [103]       207         187
##   [104]       208         188
##   [105]       209         189
##   [106]       210         190
##   [107]       212         192
##   -------
##   queryLength: 216 / subjectLength: 193

pairwisDifferential function uses edgeR TMM normalization and GLM test to detect differential binding sites across two experiments. In the following example it takes combined FUR count matrix and detect differential binding sites across fe2+ and dpd.

# Differential binding sites across FUR conditions fe vs dpd
diffFUR <- pairwisDifferential(tableOfCountsDir="combinedFUR",
                              exp1="FUR_Fe",
                              exp2="FUR_Dpd",
                              FDRcutoff = 0.05,
                              logFCcuttoff = 1
                              )
FeUp <- diffFUR[[1]]
DpdUp <- diffFUR[[2]]
TotalComparison <- diffFUR[[3]]
head(TotalComparison)

##    seqnames  start    end      logFC   logCPM         LR      PValue
## 1 NC_000913   8346   8355  0.3894958 11.71413 0.05503008 0.814530921
## 2 NC_000913  58862  58874  7.7288074 11.52722 9.39574933 0.002174891
## 3 NC_000913  70522  70531  5.4750865 11.81084 6.76519659 0.009295268
## 4 NC_000913 111527 111535  5.0360670 11.44461 5.09591510 0.023982257
## 5 NC_000913 146492 146501 -6.8326623 10.77875 4.92341789 0.026495052
## 6 NC_000913 167150 167158 -4.4346309 11.37027 3.96157917 0.046550014

Session Info

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] BSgenome.Ecoli.NCBI.20080805_1.3.1000  
##  [2] BSgenome.Scerevisiae.UCSC.sacCer3_1.4.0
##  [3] BSgenome_1.75.0                        
##  [4] rtracklayer_1.67.0                     
##  [5] BiocIO_1.17.1                          
##  [6] Biostrings_2.75.1                      
##  [7] XVector_0.47.0                         
##  [8] Motif2Site_1.11.0                      
##  [9] GenomicRanges_1.59.1                   
## [10] GenomeInfoDb_1.43.1                    
## [11] IRanges_2.41.1                         
## [12] S4Vectors_0.45.2                       
## [13] BiocGenerics_0.53.3                    
## [14] generics_0.1.3                         
## [15] BiocStyle_2.35.0                       
## 
## loaded via a namespace (and not attached):
##  [1] tidyselect_1.2.1            viridisLite_0.4.2          
##  [3] dplyr_1.1.4                 mixtools_2.0.0             
##  [5] bitops_1.0-9                lazyeval_0.2.2             
##  [7] fastmap_1.2.0               RCurl_1.98-1.16            
##  [9] GenomicAlignments_1.43.0    XML_3.99-0.17              
## [11] digest_0.6.37               lifecycle_1.0.4            
## [13] survival_3.7-0              statmod_1.5.0              
## [15] kernlab_0.9-33              magrittr_2.0.3             
## [17] compiler_4.4.2              rlang_1.1.4                
## [19] sass_0.4.9                  tools_4.4.2                
## [21] utf8_1.2.4                  yaml_2.3.10                
## [23] data.table_1.16.2           knitr_1.49                 
## [25] S4Arrays_1.7.1              htmlwidgets_1.6.4          
## [27] curl_6.0.1                  DelayedArray_0.33.2        
## [29] abind_1.4-8                 BiocParallel_1.41.0        
## [31] purrr_1.0.2                 sys_3.4.3                  
## [33] grid_4.4.2                  fansi_1.0.6                
## [35] colorspace_2.1-1            edgeR_4.5.0                
## [37] ggplot2_3.5.1               scales_1.3.0               
## [39] MASS_7.3-61                 SummarizedExperiment_1.37.0
## [41] cli_3.6.3                   rmarkdown_2.29             
## [43] crayon_1.5.3                httr_1.4.7                 
## [45] rjson_0.2.23                cachem_1.1.0               
## [47] splines_4.4.2               zlibbioc_1.52.0            
## [49] parallel_4.4.2              BiocManager_1.30.25        
## [51] restfulr_0.0.15             matrixStats_1.4.1          
## [53] vctrs_0.6.5                 Matrix_1.7-1               
## [55] jsonlite_1.8.9              maketools_1.3.1            
## [57] locfit_1.5-9.10             limma_3.63.2               
## [59] plotly_4.10.4               tidyr_1.3.1                
## [61] jquerylib_0.1.4             glue_1.8.0                 
## [63] codetools_0.2-20            gtable_0.3.6               
## [65] UCSC.utils_1.3.0            munsell_0.5.1              
## [67] tibble_3.2.1                pillar_1.9.0               
## [69] htmltools_0.5.8.1           GenomeInfoDbData_1.2.13    
## [71] R6_2.5.1                    evaluate_1.0.1             
## [73] lattice_0.22-6              Biobase_2.67.0             
## [75] Rsamtools_2.23.0            segmented_2.1-3            
## [77] bslib_0.8.0                 nlme_3.1-166               
## [79] SparseArray_1.7.2           xfun_0.49                  
## [81] MatrixGenerics_1.19.0       buildtools_1.0.0           
## [83] pkgconfig_2.0.3

References

Benaglia, T., D. Chauveau, D. R. Hunter, and D. Young. 2009. “mixtools: An R Package for Analyzing Finite Mixture Models.” Journal of Statistical Software 32 (6): 1–29. http://www.jstatsoft.org/v32/i06/.

Lawrence, M., W. Huber, H. Pagès, P. Aboyoun, M. Carlson, R. Gentleman, M. Morgan, and V. Carey. 2013. “Software for Computing and Annotating Genomic Ranges.” PLoS Computational Biology 9. https://doi.org/10.1371/journal.pcbi.1003118.

Morgan, M., H. Pagès, V. Obenchain, and N. Hayden. 2020. Rsamtools: Binary Alignment (BAM), FASTA, Variant Call (BCF), and Tabix File Import. http://bioconductor.org/packages/Rsamtools.

Pagès, H., P. Aboyoun, R. Gentleman, and S. DebRoy. 2019. Biostrings: Efficient Manipulation of Biological Strings.

Pagès, Hervé. 2019. BSgenome: Software Infrastructure for Efficient Representation of Full Genomes and Their SNPs.

Robinson, M. D., D. J. McCarthy, and G. K. Smyth. 2010. “edgeR: A Bioconductor Package for Differential Expression Analysis of Digital Gene Expression Data.” Bioinformatics 26 (1): 139–40. https://doi.org/10.1093/bioinformatics/btp616.

Seo, S. W., D. Kim, H. Latif, et al. 2014. “Deciphering Fur Transcriptional Regulatory Network Highlights Its Complex Role Beyond Iron Metabolism in Escherichia Coli.” Nature Communications 5. https://doi.org/10.1038/ncomms5910.