BinChill: A Metagenomic Binning Ensemble Method

The goal of metagenomic binning is to reconstruct genomes from a mixture DNA sequences into genomic bins, which can be considered clustering task. Multiple methods have been proposed for this task, such as distance-based metrics, machine learning, and ensemble approaches. We propose BinChill, method, based on the generic co-occurrence ensembler ACE. BinChill incorporates domain information in form Single-Copy Genes (SCG) with strategy. This strategy combines multiple partitions according how often two items co-occur same cluster. was able more or equally many high- medium quality while having an equal faster runtime than other metagenomics-specific smaller simulated dataset. On larger datasets, both real-world, outperformed reconstructing high-quality at cost increased processing time when compared algorithms. due domain-specific steps that our method implements. Our results show strengths combined generate partition higher quality.