Oct 2, 2009 · We derive an unsupervised, maximum-likelihood formalism for clustering short sequences by their taxonomic origin on the basis of their k-mer ...
Oct 2, 2009 · An unsupervised binning method based on statistical signatures of short environmental sequences is a viable stand-alone binning method for low complexity ...
An unsupervised, maximum-likelihood formalism for clustering short sequences by their taxonomic origin on the basis of their k-mer distributions is derived ...
Abstract: The development of effective environmental shotgun sequence binning methods remains an ongoing challenge in algorithmic analysis of metagenomic data.
Dive into the research topics of 'Unsupervised statistical clustering of environmental shotgun sequences'. Together they form a unique fingerprint. Sort by ...
LikelyBin is an unsupervised metagenomic binner developed at the Weitz group at Georgia Tech Department of Biology.
Table 3 Summary of algorithm performance on JGI FAMeS data.
bmcbioinformatics.biomedcentral.com › ...
The development of effective environmental shotgun sequence binning methods remains an ongoing challenge in algorithmic analysis of metagenomic data.
In essence, the proposed method provides a general statistical framework for associating each read with its species of origin, based on its genome signatures.
Unsupervised statistical clustering of environmental shotgun sequences. *Cluster Analysis. chromosome. Arthrobacter aurescens. Direct Link · View Detail. BMC ...
As environmental shotgun sequencing has become more prevalent, computational gene prediction approaches have adapted to the particular challenges of these data.