CLIOR: Metagenomic CLassification Improvement based on Overlapping Reads

CLIOR is a tool for improving metagenomic classification of state-of-the art classifiers. It was developed with the support of the Italian Ministry of Education, University and Reasearch Research within the Project of National Interest PRIN 20122F87B2 ``Compositional Approaches for the Analysis and Mining of Omics Data", PI Cinzia Pizzi

CLIOR is a metagenomic classification approach that exploits the information captured by the reads overlap graph of the input dataset in order to improve recall and f-measure. In fact, with CLIOR is possible to boost the performances of a state-of-the-art metagenomic classifiers by inferring and/or correcting the assignment of reads with missing or erroneous labeling.
Results on simulated, and synthetic metagenomes show that CLIOR can improve the recall rate substantially, sometime doubling the recall, and also increases the precision on average by 8%. Experiments on real metagenomes confirm that CLIOR is able to assign many more reads and that the abundance ratios are in line with previous studies.


CLIOR was implemented by Samuele Girotto and it is freely available for academic us at CLIOR bitbucket repository.
Contact address: Cinzia Pizzi


If you use CLIOR, please cite:
S.Girotto, M.Comin, C.Pizzi: Higher Recall in Metagenomic Sequence Classification Exploiting Overlapping Reads
Presented at ICCABS 2016, under submission to an international journal