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

摘要

Background: Metagenomic sequencing is a well-established tool in the modern biosciences. While it promises unparalleledinsights into the genetic content of the biological samples studied, conclusions drawn are at risk from biases inherent tothe 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 multiplegenomes (with mean GC contents ranging from 28.9% to 62.4%) and metagenomes. GC bias profiles varied among differentlibrary preparation protocols and sequencing platforms. We found that our workflows using MiSeq and NextSeq werehindered by major GC biases, with problems becoming increasingly severe outside the 45–65% GC range, leading to a falselylow coverage in GC-rich and especially GC-poor sequences, where genomic windows with 30% GC content had 10-fold lesscoverage 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 thoseseen in the MiSeq and NextSeq workflows. The Oxford Nanopore workflow was not afflicted by GC bias. Conclusions: Thesefindings indicate potential sources of difficulty, arising from GC biases, in genome sequencing that could be pre-emptivelyaddressed with methodological optimizations provided that the GC biases inherent to the relevant workflow areunderstood. 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 beforedrawing conclusions, or they should use a demonstrably unbiased workflow.