## 9.7 Motif discovery

The first analysis step downstream of peak calling is motif discovery. Motif discovery is a procedure of finding enriched sets of similar short sequences in a large sequence dataset. In our case the large sequence dataset are sequences around ChIP peaks, while the short sequence sets are the transcription factor binding sites.

There are two types of motif discovery tools: supervised and unsupervised. Supervised tools require explicit positive (we are certain that the motif is enriched), and negative sequence sets (we are certain that the motif is not enriched), and then search for relative enrichment of short motifs in the foreground versus the background. Unsupervised models, on the other hand, require only a set of positive sequences, and then compare motif abundance to a statistically constructed background set.

Due to the combinatorial nature of the procedure, motif discovery is computationally expensive. It is therefore often performed on a subset of the highest-quality peaks. In this tutorial we will use the rGADEM package for motif discovery. rGADEM is an unsupervised, stochastic motif discovery tools, which uses sampling with subsequent enrichment analysis to find over-represented sequence motifs.

We will firstly load our CTCF peaks, and convert them to a GRanges object. We will then select the top 500 peaks, and extract the DNA sequence, which will be used as input for the motif discovery. Nearby ChIP peaks can have overlapping coordinates. After selection, overlapping CTCF peaks have to be merged using the reduce() function from the GenomicRanges package. If we do not execute this step, we will include the same sequence multiple times in the sequence set, and artificially enrich DNA patterns.

# read the CTCF peaks created in the peak calling part of the tutorial

# convert the peaks into a GRanges object
ctcf_peaks = makeGRangesFromDataFrame(ctcf_peaks, keep.extra.columns = TRUE)

# order the peaks by qvalue, and take top 250 peaks
ctcf_peaks = ctcf_peaks[order(ctcf_peaks$qvalue)] ctcf_peaks = head(ctcf_peaks, n = 500) # merge nearby CTCF peaks ctcf_peaks = reduce(ctcf_peaks) Create a region of $$+/-$$ 50 bp around the center of the peaks, # expand the CTCF peaks ctcf_peaks_resized = resize(ctcf_peaks, width = 50, fix='center') and extract the genomic sequence. We are now ready to run the motif discovery. Firstly we load the rGADEM package: To run the motif discovery, we call the GADEM() function. with the extracted DNA sequences. In addition to the DNA sequences, we need to specify two parameters: 1. seed - the random number generator seed, which will make the analysis reproducible. 2. nmotifs - the number of motifs to look for. ## top 3 4, 5-mers: 12 40 52 ## top 3 4, 5-mers: 12 36 42 The rGADEM package contains a convenient plot() function for motif visualization. We will use the plot function to visualize the most enriched DNA motif: # visualize the resulting motif plot(novel_motifs[1]) The motif shown in Figure 9.22 corresponds to the previously visualized CTCF motif. Nevertheless, we will computationally annotate our motif by querying the JASPAR (Khan, Fornes, Stigliani, et al. 2018) database in the next section. ### 9.7.1 Motif comparison We will now compare our unknown motif with the JASPAR2018 (Khan, Fornes, Stigliani, et al. 2018) database, to figure out to which transcription factor it corresponds. Firstly we convert the frequency matrix into a PWMatrix object, and then use this object to query the database. # load the TFBSTools library library(TFBSTools) # extract the motif of interest from the GADEM object unknown_motif = getPWM(novel_motifs)[[1]] # convert the motif to a PWM matrix unknown_pwm = PWMatrix( ID = 'unknown', profileMatrix = unknown_motif ) Using the getMatrixSet() function we extract all motifs which correspond to known human transcription factors. The opts parameter defines which PWM database to use for comparison. # load the JASPAR motif database library(JASPAR2018) # extract motifs corresponding to human transcription factors pwm_library = getMatrixSet( JASPAR2018, opts=list( collection = 'CORE', species = 'Homo sapiens', matrixtype = 'PWM' )) The PWMSimilarity() function calculates the Pearson correlation between the database, and our discovered motif via rGADEM. # find the most similar motif to our motif pwm_sim = PWMSimilarity( # JASPAR library pwm_library, # out motif unknown_pwm, # measure for comparison method = 'Pearson') We extract the motif names from the PWM library. For each motif in the library we append the Pearson correlation with our unknown motif, and look at the topmost candidates. # extract the motif names from the pwm library pwm_library_list = lapply(pwm_library, function(x){ data.frame(ID = ID(x), name = name(x)) }) # combine the list into one data frame pwm_library_dt = dplyr::bind_rows(pwm_library_list) # fetch the similarity of each motif to our unknown motif pwm_library_dt$similarity = pwm_sim[pwm_library_dt$ID] # find the most similar motif in the library pwm_library_dt = pwm_library_dt[order(-pwm_library_dt$similarity),]
head(pwm_library_dt)
##           ID  name similarity
## 24  MA0139.1  CTCF  0.7033789
## 370 MA1100.1 ASCL1  0.4769023
## 281 MA0807.1  TBX5  0.4762250
## 101 MA0033.2 FOXL1  0.4605249
## 302 MA0825.1   MNT  0.4370585
## 277 MA0803.1 TBX15  0.4317270

As expected, the topmost candidate is CTCF.

### References

Khan, Fornes, Stigliani, et al. 2018. “JASPAR 2018: Update of the Open-Access Database of Transcription Factor Binding Profiles and Its Web Framework.” Nucleic Acids Res 46 (D1): D260–D266. https://doi.org/10.1093/nar/gkx1126.