Genomics technologies, such as ATAC-seq, ChIP-seq, and DNase-seq, have revolutionised molecular biology, generating a complete genome’s worth of signal in a single assay. The challengeis no longer data generation, it's effectively and reproducibly extracting biological meaning from such massively complex datasets. While other tools approach this problem with simple statistical tests, our novel machine learning model uses a convolutional neural network, local genomic enrichments measurements, and Poisson-based significance testing from multiple viewpoints, all integrated using a multilayer perceptron to give a probability of being a true biological signal. We hand-labelled 499Mb of genomic data, built 5,000 models, and tested with over 100 unique users from labs around the world. And because it’s built on the powerful MLV visualisation software, results can easily be visualised and shared with collaborators or reviewers. The culmination of these efforts is a peak caller that can extract more from your data, create interactive charts, and improve interpretability - all while simplifying the analysis process.
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Watch a video demonstrating the basic functionality of LanceOtron