The publication by Rosie Maloney, Spencer Leinbach, and Cole Milholen can be found here: https://docs.google.com/document/d/18KPwR29_H5EUOLV4kd-9gq9hCv5dsWLJlIWGek7gv6Y/edit?usp=sharing
Microbes found by researchers Dr. Jason Whitham and Dr. Amy Grunden (NC State University Plant and Microbial Biology Department) were being studied. These microbes were isolated from their environmental source, pokeweed and acid mine drainage, by performing serial dilutions. This process allowed the microbes of interest to be separated from soil particles. Glycerol was added to the isolates for stabilization, allowing them to be stored at -80 °C. Samples were then plated on Tryptic Soy Broth (TSB) plates at room temperature by Brenna Bilodeau (an undergraduate working under Dr. Claire Gordy in the NCSU Department of Biological Sciences). First, the mixed culture was plated, and then individual pure cultures were streaked on separate plates. Bilodeau and Dr. Gordy used the isolated microbe cultures and cultured individual microbes by putting them in liquid cultures with TSA and putting them in a shaking incubator at 25°C. These samples were grown with the intention to perform cell lysis (cell death) in order to isolate strands of DNA. After the DNA was collected, it was tested for purity and quantity using a Nanodrop and a Tapestation. The isolated DNA was then run through the NanoPore MinION sequencer to get the whole genomic sequence. Taking the data from the sequencer, Dr. Goller then used the GLC Genomics Workbench to assemble the genomic data. Dr. Carlos Goller is a part of North Carolina State’s Biotechnology program. He prepared the DNA sample of our microbial genus Beijerinckia, and then used the NanoPore MinIon sequencer to sequence its entire genome. In order for this data to be useful, Dr. Goller then had to use CLC Genomic Workbench to assemble the data previously produced. By examining the genome, we will identify potential genes with applicability to the e-waste problem.
from biokbase.narrative.jobs.appmanager import AppManager
AppManager().run_app_bulk(
[{
"app_id": "kb_uploadmethods/import_fasta_as_assembly_from_staging",
"tag": "release",
"version": "1dbd08a56befada8f204b4d1db5a872796cd45a5",
"params": [{
"staging_file_subdir_path": "Barcode05.fasta",
"assembly_name": "Barcode05.fasta_assembly",
"type": "draft isolate",
"min_contig_length": 10000
}]
}],
cell_id="61e87ea3-aab8-4209-86fa-5356051cf780",
run_id="d9da3558-3ae8-402c-970b-dca0dcfcebc2"
)
There is no "best" assembler, and assembly tools should be evaluated on a case-by-case basis. You are welcome and encouraged to use multiple apps and compare the results.
Annotation can be performed using Prokka or RAST. Again, comparison of multiple tools is encouraged. For downstream steps such as metabolic modeling, however, RAST-annotated genomes perform better with KBase modeling tools.