16Stimator: statistical estimation of ribosomal gene copy numbers from draft genome assemblies

Abstract

The 16S rRNA gene (16S) is an accepted marker of bacterial taxonomic diversity, even though differences in copy number obscure the relationship between amplicon and organismal abundances. Ancestral state reconstruction methods can predict 16S copy numbers through comparisons with closely related reference genomes; however, the database of closed genomes is limited. Here, we extend the reference database of 16S copy numbers to de novo assembled draft genomes by developing 16Stimator, a method to estimate 16S copy numbers when these repetitive regions collapse during assembly. Using a read depth approach, we estimate 16S copy numbers for 12 endophytic isolates from Arabidopsis thaliana and confirm estimates by qPCR. We further apply this approach to draft genomes deposited in NCBI and demonstrate accurate copy number estimation regardless of sequencing platform, with an overall median deviation of 14%. The expanded database of isolates with 16S copy number estimates increases the power of phylogenetic correction methods for determining organismal abundances from 16S amplicon surveys.

Genome‐based estimates of fungal rDNA copy number variation across phylogenetic scales and ecological lifestyles

“…The consequences of rDNA CNV have received considerable attention in the context of DNA damage response (Ide, Miyazaki, Maki, & Kobayashi, ), DNA replication stress (Salim et al, ) and the expression of nonribosomal genes (Paredes, Branco, Hartl, Maggert, & Lemos, ). Similarly, the ecological importance of rDNA CNV has also been well characterized, with rDNA copy number being linked to ecosystem stoichiometry (Elser et al, ), growth rate and competitive ability (Klappenbach, Dunbar, & Schmidt, ; Nemergut et al, ) as well as bias in estimates of organismal abundance in high‐throughput amplicon sequencing (Kembel et al, , Perisin, Vetter, Gilbert, & Bergelson, ).…”

“…One solution to this problem is comparing the abundance of raw reads aligned to both single and multi‐copy regions of DNA, an approach commonly known as relative read depth. Analysis of CNV using read depth was first developed to analyse repeat variation in tumour genomes (Chiang et al, ), and later used to account for anomalies in 16S read abundance in bacteria (Perisin et al, ). Here, we apply this approach to estimate rDNA copy number across a phylogenetically and ecologically diverse suite of fungi (Figure ).…”

“…Analysis of CNV using read depth was first developed to analyse repeat variation in tumour genomes (Chiang et al, 2009), and later used to account for anomalies in 16S read abundance in bacteria (Perisin et al, 2016). Here, we apply this approach to estimate rDNA copy number across a phylogenetically and ecologically diverse suite of fungi ( Figure 1).…”

Genomics and metagenomics technologies to recover ribosomal DNA and single-copy genes from old fruit-body and ectomycorrhiza specimens

“…The range of sequence coverage ratio of nuclear rDNA to SCGs (1.6 to 43.4) is somewhat lower than the previously reported rDNA copy numbers based on qPCR (range, 20 to 200; reviewed in Baldrian et al 2013). Our indirect estimates should be viewed with caution, because the coverage ratio is based on only 2-3 SCGs and does not account for the AT/GC bias (Perisin et al 2016). The relative amount of mitochondrial DNA certainly depends on the metabolic activity of a fungus, potentially varying between living cultures, fruit-bodies, EcM root tips and natural mycelium.…”

riboSeed: leveraging prokaryotic genomic architecture to assemble across ribosomal regions

“…The riboSeed algorithm proceeds from two observations: first, that although repeated rRNA coding sequences within a single genome are nearly identical, their flanking regions (that is, the neighboring locations within the genome) are distinct in that genome, and second, that the genomic contexts of equivalent rDNA sequences are also conserved within a taxonomic grouping ( Figure S4). riboSeed uses only reads that map to rDNA regions from a reference genome, and 35 is not affected by chromosomal rearrangements that occur outside the flanking regions immediately adjacent to each rRNA.…”