GC bias affects genomic and metagenomic reconstructions, underrepresenting GC-poor organisms

Abstract

Background Metagenomic sequencing is a well-established tool in the modern biosciences. While it promises unparalleled insights into the genetic content of the biological samples studied, conclusions drawn are at risk from biases inherent to the DNA sequencing methods, including inaccurate abundance estimates as a function of genomic guanine-cytosine (GC) contents. Results We explored such GC biases across many commonly used platforms in experiments sequencing multiple genomes (with mean GC contents ranging from 28.9% to 62.4%) and metagenomes. GC bias profiles varied among different library preparation protocols and sequencing platforms. We found that our workflows using MiSeq and NextSeq were hindered by major GC biases, with problems becoming increasingly severe outside the 45–65% GC range, leading to a falsely low coverage in GC-rich and especially GC-poor sequences, where genomic windows with 30% GC content had >10-fold less coverage than windows close to 50% GC content. We also showed that GC content correlates tightly with coverage biases. The PacBio and HiSeq platforms also evidenced similar profiles of GC biases to each other, which were distinct from those seen in the MiSeq and NextSeq workflows. The Oxford Nanopore workflow was not afflicted by GC bias. Conclusions These findings indicate potential sources of difficulty, arising from GC biases, in genome sequencing that could be pre-emptively addressed with methodological optimizations provided that the GC biases inherent to the relevant workflow are understood. Furthermore, it is recommended that a more critical approach be taken in quantitative abundance estimates in metagenomic studies. In the future, metagenomic studies should take steps to account for the effects of GC bias before drawing conclusions, or they should use a demonstrably unbiased workflow.

Real-Time Culture-Independent Microbial Profiling Onboard the International Space Station Using Nanopore Sequencing

“…Specifically, the abundance of each species differs among the methods. In addition to the presence of unculturable bacteria in the sequencing data, these differences could result from several biases in DNA extraction, PCR efficiency, primer selection, GC content, and data analyses, all of which have been investigated among various sequencing platforms [ 36 , 45 , 46 , 47 , 48 ]. In addition, the impact of bioinformatic methods used to achieve final results is well characterized.…”

German-Wide Interlaboratory Study Compares Consistency, Accuracy and Reproducibility of Whole-Genome Short Read Sequencing

“…The species of the samples is indicated. Tyler et al, 2016;Grützke et al, 2019;Sato et al, 2019;Uelze et al, 2019;Browne et al, 2020) (also compare Supplementary File S7). This is a concern since a high number of contigs in a genome assembly may cause a fragmentation of genes at the contig borders, thereby affecting gene annotation and multilocus sequence typing.…”

“…Nevertheless, we found that all assemblies were overall very similar, with respect to assembly length, N50, GC and the number of CDSs, with a few notable exceptions. In particular, assemblies constructed from short read data of laboratories LC06 and LC08 (both using a MiSeq Illumina instrument) had much lower N50 values and a greater number of contigs, probably due to their use of the Nextera XT DNA Library Preparation Kit, which was shown to have a strong GC bias ( Lan et al, 2015 ; Tyler et al, 2016 ; Grützke et al, 2019 ; Sato et al, 2019 ; Uelze et al, 2019 ; Browne et al, 2020 ) (also compare Supplementary File S7 ). This is a concern since a high number of contigs in a genome assembly may cause a fragmentation of genes at the contig borders, thereby affecting gene annotation and multilocus sequence typing.…”

Bone biodeterioration—The effect of marine and terrestrial depositional environments on early diagenesis and bone bacterial community

“…As mentioned earlier, these observations could be interesting in terms of bone degradation, however, while amplicon metabarcoding has its clear advantages being both cost effective, fast and used in a variety of studies making the genetic databases available more exhaustive, the approach still has its disadvantages [ 32 ]. With the advancement of full shotgun metagenomic sequencing techniques, the sequence bias experienced with amplicon sequencing and issues of underrepresentation of uncultivable microorganisms in the databases, and therefore difficulties in characterisation, is reduced [ 70 ], although shotgun sequencing has recently been found to underrepresent low or high GC phyla with up to two logs [ 71 ]. A natural next step with this study would be to look into the functions of the different bacterial strains and examine how the bacterial communities are associated with bone tunneling using a shotgun metagenomics approach.…”