Identifying complex sequence patterns with a variable-convolutional layer effectively and efficiently

Motif identification is among the most canonical and essential computational tasks for bioinformatics and genomics. Here we proposed a simple and scalable novel convolution-based layer, Variable Convolutional neural layer (vConv), for effective motif identification in high-throughput omics data by learning kernel length on-the-fly. Empirical evaluations on DNA-protein binding and DNase footprinting cases well demonstrated that vConv-based networks have superior performance to their convolutional counterparts regardless of model complexity. All source codes are freely available on GitHub (https://github.com/gao-lab/vConv) for academic usage.