Towards quantitative metagenomics of wild viruses and other ultra-low concentration DNA samples: a rigorous assessment and optimization of the linker amplification method.
Metagenomics generates and tests hypotheses about dynamics and mechanistic drivers in wild populations, yet commonly suffers from insufficient (< 1 ng) starting genomic material for sequencing. Current solutions for amplifying sufficient DNA for metagenomics analyses include linear amplification for deep sequencing (LADS), which requires more DNA than is normally available, linker-amplified shotgun libraries (LASLs), which is prohibitively low throughput, and whole-genome amplification, which is significantly biased and thus non-quantitative. Here, we adapt the LASL approach to next generation sequencing by offering an alternate polymerase for challenging samples, developing a more efficient sizing step, integrating a 'reconditioning PCR' step to increase yield and minimize late-cycle PCR artefacts, and empirically documenting the quantitative capability of the optimized method with both laboratory isolate and wild community viral DNA. Our optimized linker amplification method requires as little as 1 pg of DNA and is the most precise and accurate available, with G + C content amplification biases less than 1.5-fold, even for complex samples as diverse as a wild virus community. While optimized here for 454 sequencing, this linker amplification method can be used to prepare metagenomics libraries for sequencing with next-generation platforms, including Illumina and Ion Torrent, the first of which we tested and present data for here.
Department of Ecology and Evolutionary Biology, University of Arizona, Tucson, AZ, USA.
Melissa B
MB
Duhaime
Li
L
Deng
Bonnie T
BT
Poulos
Matthew B
MB
Sullivan
D001482:Base Composition; D004267:DNA Viruses / Q000145:classification / Q000235:genetics*; D004783:Environmental Microbiology; D056186:Metagenomics / Q000379:methods*; D021141:Nucleic Acid Amplification Techniques / Q000592:standards*; D012680:Sensitivity and Specificity