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International Conference on Research in Computational Molecular Biology

RECOMB 2017: Research in Computational Molecular Biology pp 18–33Cite as

A Concurrent Subtractive Assembly Approach for Identification of Disease Associated Sub-metagenomes

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Part of the book series: Lecture Notes in Computer Science ((LNBI,volume 10229))

Abstract

Comparative analysis of metagenomes can be used to detect sub-metagenomes (species or gene sets) that are associated with specific phenotypes (e.g., host status). The typical workflow is to assemble and annotate metagenomic datasets individually or as a whole, followed by statistical tests to identify differentially abundant species/genes. We previously developed subtractive assembly (SA), a de novo assembly approach for comparative metagenomics that first detects differential reads that distinguish between two groups of metagenomes and then only assembles these reads. Application of SA to type 2 diabetes (T2D) microbiomes revealed new microbial genes associated with T2D. Here we further developed a Concurrent Subtractive Assembly (CoSA) approach, which uses a Wilcoxon rank-sum (WRS) test to detect k-mers that are differentially abundant between two groups of microbiomes (by contrast, SA only checks ratios of k-mer counts in one pooled sample versus the other). It then uses identified differential k-mers to extract reads that are likely sequenced from the sub-metagenome with consistent abundance differences between the groups of microbiomes. Further, CoSA attempts to reduce the redundancy of reads (from abundant common species) by excluding reads containing abundant k-mers. Using simulated microbiome datasets and T2D datasets, we show that CoSA achieves strikingly better performance in detecting consistent changes than SA does, and it enables the detection and assembly of genomes and genes with minor abundance difference. A SVM classifier built upon the microbial genes detected by CoSA from the T2D datasets can accurately discriminates patients from healthy controls, with an AUC of 0.94 (10-fold cross-validation), and therefore these differential genes (207 genes) may serve as potential microbial marker genes for T2D.

W. Han and M. Wang—These authors contributed equally to this work.

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Acknowledgement

This work was supported by the NIH grant 1R01AI108888 to Ye.

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  1. Indiana University, Bloomington, Indiana, USA

    Wontack Han, Mingjie Wang & Yuzhen Ye

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  1. Wontack Han
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Correspondence to Yuzhen Ye .

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  1. Indiana University Bloomington, Bloomington, Indiana, USA

    S. Cenk Sahinalp

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Han, W., Wang, M., Ye, Y. (2017). A Concurrent Subtractive Assembly Approach for Identification of Disease Associated Sub-metagenomes. In: Sahinalp, S. (eds) Research in Computational Molecular Biology. RECOMB 2017. Lecture Notes in Computer Science(), vol 10229. Springer, Cham. https://doi.org/10.1007/978-3-319-56970-3_2

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  • DOI: https://doi.org/10.1007/978-3-319-56970-3_2

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