Revised 16S rRNA V4 hypervariable region targeting primers enhance detection of Patescibacteria and other lineages across diverse environments
Hu, H.; Karwautz, C.; Duszka, K.; Karner, T.; Wagner, I.; Grander, C.; Grander, W.; Steinwidder, L.; Boito, L.; Velde, V. V. d.; Bauters, M.; Boeckx, P.; Seki, D.; Glasl, B.; Thiele, S.; Schmidt, H.; Seneca, J.; Wagner, M.; Pjevac, P.
Show abstract
Primer bias in 16S rRNA gene amplicon sequencing can distort microbial diversity estimates by underrepresenting key taxa. We introduce a modified primer pair (V4-EXT) targeting the hypervariable V4 region of bacterial and archaeal 16S rRNA genes, with improved in silico taxonomic inclusivity. To benchmark performance, we analyzed 938 samples from terrestrial, aquatic, and host-associated habitats, comparing microbial community profiles derived with V4-EXT and the currently most widely used V4-targeted primers. V4-EXT substantially improved the detection of Patescibacteria and other underrepresented lineages, such as Chloroflexota and Iainarchaeota, while enhancing recovery of novel amplicon sequence variants across sample types. Overall, V4-EXT provides broader taxonomic coverage and more inclusive microbial community profiles, particularly in high-diversity ecosystems such as groundwater and soils. We propose V4-EXT as a robust successor for comprehensive microbial community analysis across diverse habitats.
Matching journals
The top 6 journals account for 50% of the predicted probability mass.
Similar papers in this journal
Similar papers in this journal
- The economical lifestyle of CPR bacteria in groundwater allows little preference for environmental drivers 96%
- Competition-cooperation in the chemoautotrophic ecosystem of Movile Cave - first metagenomic approach on sediments 96%
- Microbiome of the Black Sea water column analyzed by genome centric metagenomics 95%
Similar papers in this journal
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.