Effect of LSU and ITS genetic markers and reference databases on analyses of fungal communities

Understudied, underrepresented, and unknown: methodological biases that limit detection of early diverging fungi from environmental samples

“…Primer comparison -Comparisons between metabarcoding datasets from different studies are challenging due to variable methods of sample collection, PCR amplification, sequencing, and bioinformatics. However, our results using data from >127 environmental samples support the broad pattern of fungal community congruence between ITS and LSU markers as evidenced by significant Mantel tests (TABLE 3) and found by others (Amend et al, 2010;Benucci et al, 2019;Bonito et al, 2014;Brown et al, 2014;Johansen et al, 2016;Mota-Gutierrez et al, 2019;Nelson & Shaw, 2019;Skelton et al, 2019;Xue et al, 2019). Our NMDS plots (FIG 4;SUPP FIG 7) demonstrate that the ITS1F, LROR, and LR22F datasets recovered similar fungal communities and detected mostly the same OTU-rich lineages (SUPP FIG 6).…”

Primer and Database Choice Affect Fungal Functional but Not Biological Diversity Findings in a National Soil Survey

“…Unlike Tedersoo et al (2015) we observed considerable differences in the proportions of fungal classes between the ITS1 and 18S data sets. We suspect that such differences stem from the need to use appropriate databases to assign taxonomy to OTUs to each dataset (Xue et al, 2019). Perhaps only 30-35% of Glomeromycetes are present in 18S and ITS databases, respectively (Hart et al, 2015), and although sequences are continuously being uploaded to such repositories, it is likely the majority of AMF are not identifiable from environmental samples (but see Öpik et al, 2014).…”

“…Within this, the internal transcribed spacer (ITS) region has been accepted as a universal barcode for fungi (Schoch et al, 2012). Recent development of ITS-based databases such as UNITE (Kõljalg et al, 2013) and Warcup (Deshpande et al, 2016) have overcome limitations in collecting and assigning taxonomic identities to unknown sequences, though database selection may introduce bias into results (Tedersoo et al, 2015;Xue et al, 2019). Yet ITS barcodes exhibit some limitations when dealing with unknown or environmental samples.…”

“…Despite the widespread use of ITS barcodes, other markers may better capture the diversity of some fungal taxa. Primers targeting the small and large subunits as well as the ITS regions of the rRNA gene have all been applied to fungi (Tedersoo et al, 2015;Xue et al, 2019). For example, early diverging lineages such as Chytridiomycota (Schoch et al, 2012;Tedersoo et al, 2015) and Glomeromycetes (Tedersoo et al, 2015) are poorly represented in ITS sequencing.…”

Characterization of maize root microbiome in two different soils by minimizing plant DNA contamination in metabarcoding analysis

“…ITS reads were not trimmed to avoid losing relevant information. ITS databases like UNITE provide a good coverage of fungal taxa although many of the sequences may not be identified to specific taxa in comparison to the LSU (large subunit) marker gene which in turn may affect alpha diversity (Xue et al 2019). After de-noising sequencing errors using predicted and observed error rates, the forward and reverse reads were merged to infer sequence variants.…”