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| Item Type: | Article |
|---|---|
| Title: | Deep learning for genomics using Janggu |
| Creators Name: | Kopp, W., Monti, R., Tamburrini, A., Ohler, U. and Akalin, A. |
| Abstract: | In recent years, numerous applications have demonstrated the potential of deep learning for an improved understanding of biological processes. However, most deep learning tools developed so far are designed to address a specific question on a fixed dataset and/or by a fixed model architecture. Here we present Janggu, a python library facilitates deep learning for genomics applications, aiming to ease data acquisition and model evaluation. Among its key features are special dataset objects, which form a unified and flexible data acquisition and pre-processing framework for genomics data that enables streamlining of future research applications through reusable components. Through a numpy-like interface, these dataset objects are directly compatible with popular deep learning libraries, including keras or pytorch. Janggu offers the possibility to visualize predictions as genomic tracks or by exporting them to the bigWig format as well as utilities for keras-based models. We illustrate the functionality of Janggu on several deep learning genomics applications. First, we evaluate different model topologies for the task of predicting binding sites for the transcription factor JunD. Second, we demonstrate the framework on published models for predicting chromatin effects. Third, we show that promoter usage measured by CAGE can be predicted using DNase hypersensitivity, histone modifications and DNA sequence features. We improve the performance of these models due to a novel feature in Janggu that allows us to include high-order sequence features. We believe that Janggu will help to significantly reduce repetitive programming overhead for deep learning applications in genomics, and will enable computational biologists to rapidly assess biological hypotheses. |
| Keywords: | Computational Biology, Electronic Data Processing, Genomics, Animals |
| Source: | Nature Communications |
| ISSN: | 2041-1723 |
| Publisher: | Nature Publishing Group |
| Volume: | 11 |
| Number: | 1 |
| Page Range: | 3488 |
| Date: | 13 July 2020 |
| Official Publication: | https://doi.org/10.1038/s41467-020-17155-y |
| PubMed: | View item in PubMed |
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