De novo transcriptome assembly and annotations for wheat curl mite (Aceria tosichella)

To study the impact of wheat streak mosaic virus on global gene expression in wheat curl mite, we generated a de novo transcriptome assembly using 50 x 50 paired end reads from the Illumina HiSeq 2500. Reads were assembled using Trinity (version 2.0.6) and contigs greater than 200 nt were retained. All assembled transcripts were annotated using the Trinotate pipeline using blastp searches against the Swiss-prot/Uni-Prot database, blastx searches against the Swiss-prot/Uni-Prot databases, HMM searches against the Pfam-A database, blastp searches against the non-redundant protein database, and signalP and tmHMM predictions. To reduce noise from low abundance transcripts not well supported by the data, we filtered the assembly to retain only those transcripts with TPM values >=0.5.

Genomics and Genetics

Annotations of Unigenes Assembled from Schizaphis graminum and Sipha flava

Transcriptomes were assembled de novo from pools of adult aphids that were feeding on sorghum and switchgrass. Reads from all replicates were pooled, normalized in silico to 25X coverage, and assembled using Trinity. Only the most abundant isoform for each unigene was retained for annotation and unigenes with transcripts per million mapped reads (TPM) less than 0.5 were removed from the dataset. The remaining unigenes were annotated using Trinotate with BLASTP comparisons against the Swiss-Prot/UniProt database. In addition, Pfam-A assignments were computed using hmmer, signal peptide predictions were performed using SignalP, and transmembrane domain predictions were performed using tmHMM. Gene ontology (GO assignments) were retrieved from Trinotate using the highest scoring BLASTp matches as queries.

Genomics and Genetics

Rapid Carbon Assessment (RaCA)

The Rapid Carbon Assessment (RaCA) was initiated by the USDA-NRCS Soil Science Division in 2010 with the following objectives:

  • To develop statistically reliable quantitative estimates of amounts and distribution of carbon stocks for U.S. soils under various land covers and to the extent possible, differing agricultural management.
  • To provide data to support model simulations of soil carbon change related to land use change, agricultural management, conservation practices, and climate change.
  • To provide a scientifically and statistically defensible inventory of soil carbon stocks for the U.S.
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