Decontaminating eukaryotic genome assemblies with machine learning.

Background: High-throughput sequencing has made it theoretically possible to obtain high-quality de novo assembled genome sequences but in practice DNA extracts are often contaminated with sequences from other organisms. Currently, there are few existing methods for rigorously decontaminating eukaryotic assemblies. Those that do exist filter sequences based on nucleotide similarity to contaminants and risk eliminating sequences from the target organism.

Metagenome-Assembled Draft Genome Sequence of a Novel Microbial Strain Isolated from Tissue.

is a Gram-negative aerobic bacterium and emerging nosocomial pathogen. Here, we present a draft genome sequence for an strain assembled from a metagenomic DNA extract isolated from a laboratory stock of the nematode worm .