AutoBinner: A Metagenomic Binner combining Feature Learning and HDBSCAN

Hansen, Mathias Lundhede ; Vive, Lasse Erritsø ; Linnebjerg, Simon Nagel

4. term

Software, Master

2020

2020-06-11

65

This report documents the development of the metagenomic binner AutoBinner. Autobinner combines feature learning and clustering by using a stacked undercomplete autoencoder and the clustering algorithm HDBSCAN. The autoencoder learns a feature embedding, given abundance and composition features before the embedding is clustered. To provide the full picture of AutoBinner the report also provides an explanation of the workings of neural networks in the context of autoencoders and feature learning. The performance of AutoBinner is evaluated with a comparison with state of the art binners, MetaBAT2 [Kang et al., 2019], CONCOCT [Alneberg et al., 2013] and VAMB [Nissen et al., 2018], on three different datasets CAMI Medium, CAMI High, and CAMI Airways. The results indicate that further refinement of AutoBinner is needed, yet we see potential of using autoencoders and HDBSCAN for metagenomic binning.

Binner ; Autoencoder ; Binning ; Deep learning ; Feature Learning ; Tensorflow ; Keras ; HDBSCAN ; Clustering

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