KS-TmG.2.0 (Metagenome, KS)

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Please cite as: Anderson L.N., R. Wu, W.C. Nelson, J.E. McDermott, K.S. Hofmockel, and J.K. Jansson. 2021. KS-TmG.2.0 (Metagenome, KS). [Data Set] PNNL DataHub. https://doi.org/10.25584/KSTmG2/1770332

Soil samples were collected in triplicate in the fall of 2017 across the three grassland locations having differences in historical annual precipitation field site locations (WA, IA, KS). For each location, one deeply sequenced composite metagenome was obtained to detect viral contigs using a bioinformatics workflow approach. In addition, each of the three field soil samples were individually sequenced (0.5 Tb each) to provide three replicate metagenomes for a statistical comparison of the impact of historical annual precipitation on viral types, relative abundances, diversity, and AMGs.


Metagenomics sequencing of material recovered from environmental soil samples is to provide insight into the biodiversity and function of associated environmental data. Terabase soil metagenome samples were collected from Konza Prairie Biological Station (KPBS) field site. Soil samples were collected from the same independent soil site collection tubes as the first release (KS-TmG.1.0), however were not pooled prior to sequencing as previously reported (10.1128/MRA.00718-20). The versions described in this paper are the second version (2.0), for which dataset downloads contain raw read sequencing files, assembly files, functional annotations, MIMS.me.soil.5.0 metadata information, and a dataset download “Read Me” file. For reference field site collection plot map, please see the KPBS KS-TmG.1.0 data download plot map listed below under the data dictionary.


Data Package Resource Files:

Data Available at Download Button:

  1. KS-TmG.2.0 Raw Reads [356 GB; 6 items]

Data Pending:

  1. KS-TmG.2.0 Functional Annotations
  2. KS-TmG.2.0 Assembly


Linked Experimental Data

KS-TmG.1.0 | 10.25584/KSTmG1/1635004


Data Dictionary

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Lindsey Anderson’s research has been dedicated to the identification and characterization of novel, targeted and non-targeted, functional metabolic interactions using a high-throughput systems biology and computational biology approach. Her expertise in functional metabolism and multidisciplinary...

Dr. Jason McDermott, senior research scientist, has extensive research experience in molecular and structural virology and data resource design, data integration and prediction of biological networks, bridging experimental and computational biology. Currently, his research interests include data...

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