DOI: 10.5061/dryad.t8g16
title: Data from: High-throughput amplicon sequencing of rRNA genes requires a copy number correction to accurately reflect the effects of management practices on soil nematode community structure

description: Nematodes are abundant consumers in grassland soils, but more sensitive
 and specific methods of enumeration are needed to improve our
 understanding of how different nematode species affect, and are affected
 by, ecosystem processes. High-throughput amplicon sequencing is used to
 enumerate microbial and invertebrate communities at a high level of
 taxonomic resolution, but the method requires validation against
 traditional specimen-based morphological identifications. To investigate
 the consistency between these approaches, we enumerated nematodes from a
 25-year field experiment using both morphological and molecular
 identification techniques in order to determine the long-term effects of
 annual burning and nitrogen enrichment on soil nematode communities.
 Family-level frequencies based on amplicon sequencing were not initially
 consistent with specimen-based counts, but correction for differences in
 rRNA gene copy number using a genetic algorithm improved quantitative
 accuracy. Multivariate analysis of corrected sequence-based abundances of
 nematode families was consistent with, but not identical to, analysis of
 specimen-based counts. In both cases, herbivores, fungivores and
 predator/omnivores generally were more abundant in burned than nonburned
 plots, while bacterivores generally were more abundant in nonburned or
 nitrogen-enriched plots. Discriminate analysis of sequence-based
 abundances identified putative indicator species representing each trophic
 group. We conclude that high-throughput amplicon sequencing can be a
 valuable method for characterizing nematode communities at high taxonomic
 resolution as long as rRNA gene copy number variation is accounted for and
 accurate sequence databases are available.
description: Sequence-based
 dataThis comma-delimited
 file contains the raw number of sequences for each of 131
 "species" (Genbank accessions) for 64 field samples (16 plots x
 2 seasons x 2 reps) and two "test" samples. Field samples are
 coded as with (1) or without (0) burning or nitrogen
 enrichment.sequences.csvSpecimen-based dataThis comma-delimited file contains the raw abundance (individuals per 100g) for each of 28 families for 64 field samples (16 plots x 2 seasons x 2 reps). Field samples are coded as with (1) or without (0) burning or nitrogen enrichment.specimens.csvRegion 1 (subsamples "A") raw 454 sequencing readsText files with all raw 454 amplicon sequencing reads from region 1, containing all "A" subsamples, in FastA format. This is one of the datafiles (along with "region 2") used for pre-processing with the python script "amPy30.py"TCA.454Reads.zipAmplicon sequencing pipelineBASH script used to call sequence processing programs. Internal dependencies: amPy30.py, blastAnalysis30.py. External dependencies: Requires cutadapt 1.0, USEARCH 5.1, ssaha2.AmpSeqpipeline30.shamPy30Python script used to pre-process raw sequencing reads. Calls on three data files: 2 raw sequencing read files in FastA format ("X.TCA.454Reads.fna"), and "barcodeIDs.txt"barcodeIDsTXT file mapping the multiplex identifier barcodes from each read to their respective sample and subsample ID. Treatment information (season, burning, and nitrogen) are also indicated in the file.eukSSUEukaryotic SSU databased used in this study. Called for by AmpSeqPpipeline30.sh.blastAnalysis30Python script used to process results of matching non-redundant sequencing reads to their database match. Called for by "AmpSeqpipeline30.sh"