Methodologies for probing the metatranscriptome of grassland soil

dc.contributor.author Garoutte, Aaron
dc.contributor.author Cardenas, Erick
dc.contributor.author Howe, Adina
dc.contributor.author Tiedje, James
dc.contributor.author Howe, Adina
dc.contributor.department Agricultural and Biosystems Engineering
dc.date 2018-02-18T04:32:18.000
dc.date.accessioned 2020-06-29T22:42:37Z
dc.date.available 2020-06-29T22:42:37Z
dc.date.copyright Fri Jan 01 00:00:00 UTC 2016
dc.date.embargo 2017-12-01
dc.date.issued 2016-12-01
dc.description.abstract <p>Metatranscriptomics provides an opportunity to identify active microbes and expressed genes in complex soil communities in response to particular conditions. Currently, there are a limited number of soil metatranscriptome studies to provide guidance for using this approach in this challenging matrix. Hence, we evaluated the technical challenges of applying soil metatranscriptomics to a highly diverse, low activity natural system. We used a non-targeted rRNA removal approach, duplex nuclease specific (DSN) normalization, to generate a metatranscriptomic library from field collected soil supporting a perennial grass, <em>Miscanthus x giganteus</em> (a biofuel crop), and evaluated its ability to provide insight into its active community members and their expressed protein-coding genes. We also evaluated various bioinformatics approaches for analyzing our soil metatranscriptome, including annotation of unassembled transcripts, <em>de novo</em> assembly, and aligning reads to known genomes. Further, we evaluated various databases for their ability to provide annotations for our metatranscriptome. Overall, our results emphasize that low activity, highly genetically diverse and relatively stable microbiomes, like soil, requires very deep sequencing to sample the transcriptome beyond the common core functions. We identified several key areas that metatranscriptomic analyses will benefit from including increased rRNA removal, assembly of short read transcripts, and more relevant reference bases while providing a priority set of expressed genes for functional assessment.</p>
dc.description.comments <p>This is the accepted manuscript of an article published in <em>Journal of Microbiological Methods</em>, 131 (December 2016): 122-129, <a href="http://dx.doi.org/10.1016/j.mimet.2016.10.018">http://dx.doi.org/10.1016/j.mimet.2016.10.018</a>. </p>
dc.format.mimetype application/pdf
dc.identifier archive/lib.dr.iastate.edu/abe_eng_pubs/784/
dc.identifier.articleid 2067
dc.identifier.contextkey 9684508
dc.identifier.s3bucket isulib-bepress-aws-west
dc.identifier.submissionpath abe_eng_pubs/784
dc.identifier.uri https://dr.lib.iastate.edu/handle/20.500.12876/1584
dc.language.iso en
dc.source.bitstream archive/lib.dr.iastate.edu/abe_eng_pubs/784/2016_Garoutte_MethodologiesProbing.docx|||Sat Jan 15 01:54:50 UTC 2022
dc.source.bitstream archive/lib.dr.iastate.edu/abe_eng_pubs/784/auto_convert.pdf|||Sat Jan 15 01:54:52 UTC 2022
dc.source.uri 10.1016/j.mimet.2016.10.018.
dc.subject.disciplines Agriculture
dc.subject.disciplines Bioresource and Agricultural Engineering
dc.subject.disciplines Environmental Microbiology and Microbial Ecology
dc.subject.keywords Metatranscriptome
dc.subject.keywords Metagenome
dc.subject.keywords Switchgrass
dc.subject.keywords Short read assembly
dc.title Methodologies for probing the metatranscriptome of grassland soil
dc.type article
dc.type.genre article
dspace.entity.type Publication
relation.isAuthorOfPublication e2017bbe-ba62-4969-946e-aaf072d8bb4f
relation.isOrgUnitOfPublication 8eb24241-0d92-4baf-ae75-08f716d30801
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