Biases in genome reconstruction from metagenomic data

PeerJ

article

William C. Nelson¹  Benjamin J. Tully²  Jennifer M. Mobberley⁴ 

[1] Biological Sciences Division, Pacific Northwest National Laboratory;Department of Biological Sciences, Marine Environmental Biology Section, University of Southern California;Center for Dark Energy Biosphere Investigations, University of Southern California;Chemical and Biological Signature Science Group, Pacific Northwest National Laboratory

关键词: Binning;    Metagenomics;    Metagenome assembled genome;   

DOI  :  10.7717/peerj.10119

学科分类：社会科学、人文和艺术（综合）

来源: Inra

【 摘 要 】

Background Advances in sequencing, assembly, and assortment of contigs into species-specific bins has enabled the reconstruction of genomes from metagenomic data (MAGs). Though a powerful technique, it is difficult to determine whether assembly and binning techniques are accurate when applied to environmental metagenomes due to a lack of complete reference genome sequences against which to check the resulting MAGs. Methods We compared MAGs derived from an enrichment culture containing ~20 organisms to complete genome sequences of 10 organisms isolated from the enrichment culture. Factors commonly considered in binning software—nucleotide composition and sequence repetitiveness—were calculated for both the correctly binned and not-binned regions. This direct comparison revealed biases in sequence characteristics and gene content in the not-binned regions. Additionally, the composition of three public data sets representing MAGs reconstructed from the Tara Oceans metagenomic data was compared to a set of representative genomes available through NCBI RefSeq to verify that the biases identified were observable in more complex data sets and using three contemporary binning software packages. Results 90% complete are likely to effectively represent organismal function; however, population-level genotypic heterogeneity in natural populations, such as uneven distribution of plasmids, can lead to incorrect inferences.

【 授权许可】

CC BY