Skip to content

Metagenomic Classification

BugSeq outputs metagenomic classification from up to three different algorithms, depending on input data. Below, we detail the output files and link to each


BugSplit performs especially well on large datasets that can be assembled into contiguous genomes, but also performs well on fragmented assemblies.


Chandrakumar et al. BugSplit enables genome-resolved metagenomics through highly accurate taxonomic binning of metagenomic assemblies. Communications Biology (2022).

Original BugSeq

Our original algorithm for nanopore metagenomic data, the Original BugSeq algorithm aligns reads to a reference database and refines alignment using a Bayesian statistical framework. The Original BugSeq algorithm performs especially well when there are fewer reads in the input data such that they cannot be assembled.


Fan, Huang & Chorlton. BugSeq: a highly accurate cloud platform for long-read metagenomic analyses. BMC Bioinformatics (2021).

BugSeq 16S

This algorithm is designed for noisy nanopore 16S sequencing data. Reads are clustered, sequencing errors corrected, and amplicon sequencing variants classified with QIIME2’s feature classifier.


Jung & Chorlton. BugSeq 16S: NanoCLUST with Improved Consensus Sequence Classification. bioRxiv (2021).

Last update: March 1, 2022