title: Fungi isolated from Intermediate wheatgrass monoculture plots at 10 - 30 cm depth in block 7.
design: Soil samples (1.7 x 30 cm) were taken in June 2015.  Three soil cores were taken from each planting treatment in three blocks (n= 3 cores per treatment).  Each core was separated by depth into a zero to 10 cm sample and 10 cm to 30 cm sample.   The three cores taken from each treatment were combined and homogenized into one aggregate sample and stored at -20 C until processing. DNA was extracted from 0.25 g of each molecular subsample using MoBio PowerSoil Kits (MoBio, Carlsbad, USA). Extracted DNA was quantified using a Qubit 2.0 (LifeTechnologies, Carlsbad, USA) to normalize template DNA for PCR. The ITS2 region of rDNA was amplified from 5 ng of template DNA with fungal-specific primers ITS4 and fITS7 using the PCR parameters described in Ihrmark et al. (2012) [43] and Q5 proof-reading polymerase (New England Biosystems, Ipswich, USA). PCR products were cleaned using Agencourt AMPure XP magnetic beads (Beckman Coulter, Indianapolis, USA), then unique Nexterra indices (Illumina, San Diego, USA) were ligated to each sample. Finally, a second bead-purification step was used. Final libraries for each sample were pooled to a single library; concentration and size were verified using a TapeStation 2200 (Agilent, Santa Clara, USA). Fungal sequences were generated using an Illumina Mi-seq (Illumina, San Diego, USA) at the Kansas State Integrated Genomics Center. Qiime v.1.9.0 was used for bioinformatic processing, largely following the protocol of Caporaso et al. (2010) [44]. Quality and barcode filtering resulted in 2,281,565 reads with an average phred score ?30 and median length of 270 bp. Open-reference OTU picking and chimera checking using Usearch 6.1 and the UNITE fungal ITS reference database [45] was used to cluster OTUs (97%). All OTUs with <5 reads were removed to eliminate potential PCR/sequencing artifacts. Taxonomic classification was assigned to OTUs using the RDP classifier.  DESeq2 normalization in R was used to account for uneven sampling depth across samples [46].  FUNguild [47] was used to assign functional classification to OTUs.