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.
Download
CLIOR was implemented by Samuele Girotto and it is freely available for academic us at
CLIOR bitbucket repository.
Contact address: Cinzia Pizzi
Reference
If you use CLIOR, please cite:
S.Girotto, M.Comin, C.Pizzi: Higher Recall in Metagenomic Sequence Classification Exploiting Overlapping Reads
In proc. of ICCABS 2016, 6th IEEE International Conference on Computational Advances in Bio- and medical Science - ICCABS 2016, Atlanta 13-15 Oct. 2016 online
S.Girotto, M.Comin, C.Pizzi
Higher Recall in Metagenomic Sequence Classification Exploiting Overlapping Reads
BMC Genomics,18(10):971, 6th December 2017 open access