Binning unassembled short reads based on k-mer abundance covariance using sparse coding.

ABSTRACT:

Background

Sequence-binning techniques enable the recovery of an increasing number of genomes from complex microbial metagenomes and typically require prior metagenome assembly, incurring the computational cost and drawbacks of the latter, e.g., biases against low-abundance genomes and inability to conveniently assemble multi-terabyte datasets.

Results

We present here a scalable pre-assembly binning scheme (i.e., operating on unassembled short reads) enabling latent genome recovery by leveraging sparse dictionary learning and elastic-net regularization, and its use to recover hundreds of metagenome-assembled genomes, including very low-abundance genomes, from a joint analysis of microbiomes from the LifeLines DEEP population cohort (n = 1,135, >1010 reads).

Conclusion

SUBMITTER: Kyrgyzov O 

PROVIDER: S-EPMC7099633 | biostudies-literature | 2020 Apr

REPOSITORIES: biostudies-literature