RT Journal Article SR Electronic T1 fastISM: Performant in-silico saturation mutagenesis for convolutional neural networks JF bioRxiv FD Cold Spring Harbor Laboratory SP 2020.10.13.337147 DO 10.1101/2020.10.13.337147 A1 Surag Nair A1 Avanti Shrikumar A1 Anshul Kundaje YR 2020 UL http://biorxiv.org/content/early/2020/10/13/2020.10.13.337147.abstract AB Deep learning models such as convolutional neural networks are able to accurately map biological sequences to associated functional readouts and properties by learning predictive de novo representations. In-silico saturation mutagenesis (ISM) is a popular feature attribution technique for inferring contributions of all characters in an input sequence to the model’s predicted output. The main drawback of ISM is its runtime, as it involves multiple forward propagations of all possible mutations of each character in the input sequence through the trained model to predict the effects on the output. We present fastISM, an algorithm that speeds up ISM by a factor of over 10x for commonly used convolutional neural network architectures. fastISM is based on the observations that the majority of computation in ISM is spent in convolutional layers, and a single mutation only disrupts a limited region of intermediate layers, rendering most computation redundant. fastISM reduces the gap between backpropagation-based feature attribution methods and ISM. It far surpasses the runtime of backpropagation-based methods on multi-output architectures, making it feasible to run ISM on a large number of sequences. An easy-to-use Keras/TensorFlow 2 implementation of fastISM is available at https://github.com/kundajelab/fastISM, and a hands-on tutorial at https://colab.research.google.com/github/kundajelab/fastISM/blob/master/notebooks/colab/DeepSEA.ipynb.Competing Interest StatementThe authors have declared no competing interest.