PlotPWM is a package for plotting position weight matrices (PWMs), which is commonly used to characterize and visualize motifs — the binding sites where proteins interact with DNA or RNA.

• Installation
• Usage
  • Plot your typical PWMs
    • Save the PWMs
  • Plot your PWMs with crosslinking tendencies
    • Multiplexed crosslinking tendencies
  • Plot your PWM with highlighted regions

To install PlotPWM use Julia's package manager:

pkg> add PlotPWM

using PlotPWM

# Given a position frequency matrix (PFM), where each column sums to 1

pfm =  [0.02  1.0  0.98  0.0   0.0   0.0   0.98  0.0   0.18  1.0
        0.98  0.0  0.02  0.19  0.0   0.96  0.01  0.89  0.03  0.0
        0.0   0.0  0.0   0.77  0.01  0.0   0.0   0.0   0.56  0.0
        0.0   0.0  0.0   0.04  0.99  0.04  0.01  0.11  0.23  0.0]

# Define the background probabilities for (A, C, G, T)

background = [0.25, 0.25, 0.25, 0.25]

logoplot(pfm, background)

will give

The function logoplot(pfm, background) produces a plot where:

• The x-axis shows the positions in the PWM.
• The y-axis shows the information content (bits) for each position.

The background is an array representing the background probabilities for A, C, G, and T. These should sum to 1. In this example, a uniform background of [0.25, 0.25, 0.25, 0.25] is used, assuming equal probabilities for each base.

You can also call:

logoplot(pfm)

to get the same results as above, where the background is set to [0.25, 0.25, 0.25, 0.25] by default.

To save your plot, use save_logoplot(pfm, background, save_name). For example:

save_logoplot(pfm, background, "tmp/logo.png")

Or simply:

save_logoplot(pfm, "tmp/logo.png")

where a uniform background of [0.25, 0.25, 0.25, 0.25] is used implicitly.

Cross-linked PWMs not only display the PWM but also account for crosslinking tendencies, which are particularly relevant for the binding sites of RNA-binding proteins (RBPs) from CLIP-Seq.

To plot these, you'll need to estimate the crosslinking tendencies along with the PFM. For a PFM with $L$ columns, provide a $K \times L$ matrix $C$, where $\sum_{k,\ell}C_{k\ell} \leq 1$.

For example, when $K=1$:

C = [0.01  0.04  0.05  0.0  0.74  0.05  0.03  0.05  0.03  0.0] 

and the background:

background = [0.25, 0.25, 0.25, 0.25]

You can then plot the cross-linked PWM using:

logoplotwithcrosslink(pfm, background, C; rna=true)

This will generate:

Setting the tag rna=true will change the logo from using thymine T to uracil U.

Alternatively, you can use:

logoplotwithcrosslink(pfm, C; rna=true)

which will automatically assume a uniform background of [0.25, 0.25, 0.25, 0.25].

Multiplexed crosslinking tendencies occur when multiple crosslinking signatures are present in the dataset. Each signature can be applied to each sequence before performing motif discovery tasks. This situation corresponds to cases where the crosslink matrix $C$ has more than one row, i.e., $K > 1$."

Suppose we have

C2 = [0.01  0.01  0.03  0.0   0.37  0.03  0.02  0.03  0.01  0.0
     0.01  0.0   0.11  0.01  0.26  0.0   0.03  0.01  0.02  0.01]

Now, using

logoplotwithcrosslink(pfm, background, C; rna=true)

You'd get

Here, different colors indicate different crosslinking signatures, and their height is proportional to the crosslinking tendency at each position in the PWM.

Sometimes you may have columns that you want to highlight, for example, when you have transcription factors binding sites embedded in a (long) transposable element (e.g. see figure 4 in this paper). Then, what you can do is to provide a vector of UnitRange{Int} to highlight the regions of interest, e.g.

highlighted_regions1=[4:8]

and do

logoplot_with_highlight(pfm, background, highlighted_regions1)

to get

You can do it for crosslinked version as well:

highlighted_regions2=[1:5]
logoplot_with_highlight_crosslink(pfm, background, C2, highlighted_regions2)

This code repo modifies some of the code using the work from https://github.com/BenjaminDoran/LogoPlots.jl.