Interpretable Convolution Methods for Learning Genomic Sequence Motifs

The first-layer filters employed in convolutional neural networks tend to learn, or extract, spatial features from the data. Within their application to genomic sequence data, these learned features are often visualized and interpreted by converting them to sequence logos; an information-based representation of the consensus nucleotide motif. The process to obtain such motifs, however, is done through post-training procedures which often discard the filter weights themselves and instead rely upon finding those sequences maximally correlated with the given filter. Moreover, the filters collectively learn motifs with high redundancy, often simply shifted representations of the same sequence. We propose a schema to learn sequence motifs directly through weight constraints and transformations such that the individual weights comprising the filter are directly interpretable as either position weight matrices (PWMs) or information gain matrices (IGMs). We additionally leverage regularization to encourage learning highly-representative motifs with low inter-filter redundancy. Through learning PWMs and IGMs directly we present preliminary results showcasing how our method is capable of incorporating previously-annotated database motifs along with learning motifs de novo and then outline a pipeline for how these tools may be used jointly in a data application.