Logomaker: beautiful sequence logos in Python

1Sequence logos provide evocative graphical representations of the functional properties of DNA, RNA and protein sequences. Logos consist of characters stacked upon one another at a series of integer-valued positions, with the height of each character conveying some type of information about its biological importance. This graphical representation was introduced by for illustrating statistical properties of multiple-sequence alignments. Although the specific representation they advocated is still widely used, sequence logos have since evolved into a general data visualization strategy that can be used to illustrate many different kinds of biological information. For example, logos can be used to illustrate base-pair-specific contributions to protein–DNA binding energy, the effects of mutations in massively parallel selection experiments, and attribution method visualizations of deep neural networks.

2A substantial number of software tools for generating sequence logos have been described. However, each of these tools substantially limits the kinds of logos that one can make and the ways in which those logos can be styled. For example, WebLogo was one of the first logo-generating tools to be described and is still perhaps the most widely used. WebLogo allows users to create two standard types of sequence logos (information logos and probability logos) from a list of input sequences. However, it does not allow one to generate logos from arbitrary matrices of character heights. This capability is needed for illustrating the ΔΔG values of energy matrix models (Fig. 1B), the log-enrichment values obtained in high-throughput selection experiments (Fig. 1E) or importance scores that describe the predictions of deep neural networks (Fig. 1F). Moreover, although WebLogo is available as a Python package, the graphics it generates are written directly to file. This prevents logos from being customized using the matplotlib routines familiar to most Python users, or automatically incorporated into multi-panel figures.

3In contrast to WebLogo and the other tools described above, ggseqlogo enables the creation of sequence logos within the R programming environment from arbitrary user-provided data. Importantly, ggseqlogo renders logos using native vector graphics, which facilitates post-hoc styling and the incorporation of logos into multi-panel figures. However, similar software is not yet available in Python. Because many biological data analysis pipelines are written in Python, there is a clear need for such logo-generating capabilities. Here we describe Logomaker, a Python package that addresses this need.

4Logomaker is a flexible Python API for creating sequence logos. Logomaker takes a pandas DataFrame as input, one in which columns represent characters, rows represent positions and values represent character heights (Fig. 1A). This enables the creation of logos for any type of data that are amenable to such a representation. The resulting logo is drawn using vector graphics embedded within a standard matplotlib Axes object, thus facilitating a high level of customization as well as incorporation into complex figures. Indeed, the logos in Figure 1 were generated as part of a single multi-panel matplotlib figure. Logomaker provides a variety of options for styling the characters within a logo, including the choice of font, color scheme, vertical and horizontal padding, etc. Logomaker also enables the highlighting of specific sequences within a logo (Fig. 1E), as well as the use of value-specific transparency in logos that illustrate probabilities (Fig. 1C). If desired, users can further customize individual characters within any rendered logo.

5Because sequence logos are still commonly used to represent the statistics of multiple-sequence alignments, Logomaker provides methods for processing such alignments into matrices that can then be used to generate logos. Multiple types of matrices can be generated in this way, including matrices that represent probabilities (Fig. 1C), log odds ratios (Fig. 1E) or the information values described by (Fig. 1D). Methods for transforming between these types of matrices are also provided. Finally, Logomaker supports the creation of masked matrices and logos that, e.g., represent deep neural network importance scores, as in Figure 1F.

6Logomaker thus fills a major need in the Python community for flexible logo-generating software. Indeed, Logomaker has already been used to generate logos for multiple preprints and publications. Logomaker is thoroughly tested, has minimal dependencies and can be installed from PyPI by executing ‘pip install logomaker’ at the command line. A step-by-step tutorial on how to use Logomaker, as well as comprehensive documentation, is available at http://logomaker.readthedocs.io.