MetaCon: Unsupervised Clustering of Metagenomic Contigs with Probabilistic k-mers Statistics and Coverage

Abstract:

Sequencing technologies allow the sequencing of microbial communities directly from the environment without prior culturing. Because assembly typically produces only genome fragments, also known as contigs, it is crucial to group them into operational taxonomic units (OTUs) for further taxonomic profiling and down-streaming functional analysis. Taxonomic analysis of microbial communities requires OTU clustering, a process referred to as binning, that is still one of the most challenging tasks when analyzing metagenomic data. The major problems are the lack of taxonomically related genomes in existing reference databases, the uneven abundance ratio of species, sequencing errors, and the limitations due to binning contig of different lengths.

Results:

In this context we present MetaCon a novel tool for unsupervised metagenomic contig binning based on probabilistic k-mers statistics and coverage. MetaCon uses a signature based on k-mers statistics that accounts for the different probability of appearance of a k-mer in different species, and also for the different length of contigs to be clustered. The effectiveness of MetaCon is demonstrated in both simulated and real datasets in comparison with state-of-art binning approaches such as CONCOCT, MaxBin and MetaBAT.

Licence

The software is freely available for academic use.

Reference

Please cite the following paper:

J. Qian, M. Comin
"MetaCon: unsupervised clustering of metagenomic contigs with probabilistic k-mers statistics and coverage"
BMC bioinformatics 2019, 20:367

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Welcome to use MetaCon (binning metagenomic contigs by composition matrix and coverage matrix)!
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Metacon is an easy, but effective method, it utilizes the composition and coverage matrices to compose the contigs feature matrix. In addition, it normalizes the two matrices independently and then feed them into the k-medoids in order to obtain well-generated clusters in the first-stage, and finally assign the short contigs to the closest cluster centroid during the second-stage.

Matlab (version R2017b)
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Description
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This package contains the following files.

	test_strain.m -> a demo on simulated 'strain' dataset
	permn.m -> generation of k-mers
	precision_recall_computing -> compute the precision and recall, defined in [1]
        data -> it contains the strain, species, sharon data set
        fastkmeans -> a fast method to compute k-means
        cal_number_cluster -> method to estimate the number of clusters


Please try to execute 'test_strain.m' to learn how to use this software.
***Note: this method is developed under MATLAB R2017b, and we used some built-in functions, if you have the different version, it may arouse some problems.

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Preprocessing
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The preprocessing steps aim to extract coverage profile and sequence composition profile as input to our program, which can be tackled by CONCOCT [2], you can find all the details there.



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Contacts and bug reports
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Please send bug reports, comments, or questions to 

Jia Qian: lainey.qian@gmail.com

Prof. Matteo Comin: comin@dei.unipd.it


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Copyright and License Information
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Copyright (C) 2018 University of Padova


This program is free software: you can redistribute it and/or modify it under
the terms of the GNU General Public License as published by the Free Software
Foundation, either version 3 of the License, or (at your option) any later
version.

This program is distributed in the hope that it will be useful, but WITHOUT
ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS
FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.


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References
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[1] Vinh, L.V. et al. A two-phase binning algorithm using l-mer frequency on groups of non-overlapping reads. Algorithms Mod. Biol.,10, 1-12 (2015)
[2] Alneberg, J., Bjarnason, B.S., et al. Binning metagenomic contigs by coverage and composition. Nature Methods 11(11), 1144-1146 (2014)
Last update: 29-Mar-2018