Some prominent binning algorithms for metagenomic datasets obtained through shotgun sequencing include TETRA, MEGAN, Phylopythia, SOrt-ITEMS, and DiScRIBinATE, among others.
Phylopythia is one supervised classifier developed by researchers at IBM labs, and is basically a support vector machine trained with DNA k-mers from known sequences.
[5] SOrt-ITEMS[13] is an alignment-based binning algorithm developed by Innovations Labs of Tata Consultancy Services (TCS) Ltd., India.
The method uses a range of BLAST alignment parameter thresholds to first identify an appropriate taxonomic level (or rank) where the read can be assigned.
Other alignment-based binning algorithms developed by the Innovation Labs of Tata Consultancy Services (TCS) include DiScRIBinATE,[14] ProViDE [15] and SPHINX.
DiScRIBinATE [14] is an alignment-based binning algorithm developed by the Innovations Labs of Tata Consultancy Services (TCS) Ltd., India.
Incorporating this alternate strategy was observed to reduce the binning time by half without any significant loss in the accuracy and specificity of assignments.
ProViDE [15] is an alignment-based binning approach developed by the Innovation Labs of Tata Consultancy Services (TCS) Ltd. for the estimation of viral diversity in metagenomic samples.
ProViDE adopts the reverse orthology based approach similar to SOrt-ITEMS for the taxonomic classification of metagenomic sequences obtained from virome datasets.
In addition, the binning efficiency (in terms of accuracy and specificity of assignments) of SPHINX was observed to be comparable with results obtained using alignment-based algorithms.
These algorithms utilize a range of oligonucleotide compositional (as well as statistical) parameters to improve binning time while maintaining the accuracy and specificity of taxonomic assignments.