Statistical correction for functional metagenomic profiling of a microbial community with short NGS reads. Issue 14 (26th October 2018)

• Record Type: Journal Article
• Authors: Du, Ruofei; Fang, Zhide
• Is Part Of: Journal of applied statistics. Volume 45:Issue 14(2018)
• Page Start: 2521
• Page End: 2535
• Publication Date: 2018-10-26
• Subjects: Metagenomics -- functional profiling -- short reads -- length bias -- conservation bias; Primary: 62f10 -- Secondary: 62p10; Statistics -- Periodicals; 519.5
• DOI: 10.1080/02664763.2018.1426741
• Languages: English
• ISSNs: 0266-4763

Abstract

ABSTRACT: By sequence homology search, the list of all the functions found and the counts of reads being aligned to them present the functional profile of a metagenomic sample. However, a significant obstacle has been observed in this approach due to the short read length associated with many next-generation sequencing technologies. This includes artificial families, cross-annotations, length bias and conservation bias. The widely applied cut-off methods, such as BLAST E-value, are not able to solve the problems. Following the published successful procedures on the artificial families and the cross-annotation issue, we propose in this paper to use zero-truncated Poisson and Binomial (ZTP-Bin) hierarchical modelling to correct the length bias and the conservation bias. Goodness of fit of the modelling and cross-validation for the prediction using a bioinformatic simulated sample show the validity of this approach. Evaluated on an in vitro -simulated data set, the proposed modelling method outperforms other traditional methods. All three steps were then sequentially applied on real-life metagenomic samples to show that the proposed framework will lead to a more accurate functional profile of a short-read metagenomic sample.