Introduction to SECOMO: A SEquence COntext MOdeler

This package provides functionality to automatically extract DNA sequence features from a provided set of sequences using a convolutional restricted Boltzmann machine, which was inspired by advances made in computer vision [1]. To that end, the cRBM learns redundant DNA sequnce features in terms of a set of weight matrices.

For a downstream analysis of the features that have been learned a number of utilities are provided:

1. Convert the cRBM features to Position frequency matrices, which are commonly used to represent transcription factor binding affinities [2].
2. Visualize the DNA features in terms of Sequence logos [2].
3. Visualize the positional enrichment of the features on a set of DNA sequences.
4. Visualize the relative enrichment of the features across a number of different datasets (e.g. sequences of different ChIP-seq experiments; treatment-control).
5. Visualize sequence-based clustering.

The tutorial illustrates the main functionality of the package on a toy example of Oct4 ChIP-seq peak regions obtained from embryonic stem cells.

Finally, if this tool helps for your analysis, please cite the package:

@Manual{,
    title = {SECOMO: A python package for automatically
            extracting DNA sequence features},
    author = {Roman Schulte-Sasse, Wolfgang Kopp},
    year = {2017},
}

References

[1]	Lee, Honglak (2009). Convolutional Deep Belief Networks for scalable unsupervised learning of hierarchical representations. Proceedings of the 26 th International Confefence on Machine Learning

[2]	(1, 2) Stormo, Gary D. (2000). DNA binding sites: representation and discovery. Bioinformatics.