Generated April 25, 2022

Genome Sequence of Ensifer sp., Isolated from pokeweed and acid mine drainage


A novel Ensifer species, closely related to Ensifer adhaerens, was collected from pokeweed and acid mine drainage.

The publication by Rosie Maloney, Spencer Leinbach, and Cole Milholen can be found here:

Table of Contents

  1. Background and Experimental Methods
  2. Import and annotation
  3. QC, Assembly, and Annotation
  4. Taxonomic Classification
  5. Metabolic Modeling and Flux Balance Analysis
  6. References
Narrative created by: Rosie Maloney and BIT 295 Group 3

Background and Experimental Methods

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.


Note to authors

Reads or assemblies should be imported through the staging area. See the upload and download guide on the KBase documentation site for details instructions.

Upload a data file (which may be compressed) from a web URL to your staging area.
This app produced errors.
No output found.
Upload a data file (which may be compressed) from a web URL to your staging area.
This app produced errors.
No output found.
from import AppManager
        "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
Annotate your assembly with DRAM. Annotations will then be distilled to create an interactive functional summary per assembly.
This app completed without errors in 51m 38s.
Created Object Name Type Description
Barcode05.fasta_assembly_DRAM Genome Annotated Genome
Ensifer GenomeSet Ensifer genome?
Here are the results from your DRAM run.
These are only available in the live Narrative:
  • annotations.tsv - DRAM annotations in a tab separate table format
  • genes.fna - Genes as nucleotides predicted by DRAM with brief annotations
  • genes.faa - Genes as amino acids predicted by DRAM with brief annotations
  • genes.gff - GFF file of all DRAM annotations
  • rrnas.tsv - Tab separated table of rRNAs as detected by barrnap
  • trnas.tsv - Tab separated table of tRNAs as detected by tRNAscan-SE
  • genbank.tar.gz - Compressed folder of output genbank files
  • product.tsv - DRAM product in tabular format
  • metabolism_summary.xlsx - DRAM metabolism summary tables
  • genome_stats.tsv - DRAM genome statistics table

QC, Assembly, and Annotation

Author Checklist

Note to authors

The recommended app for quality control is FastQC. There are multiple apps for assembly.

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.

Taxonomic Identification

Note to Authors

One common way is to use the GTDB-Tk classify app. We recommend adding a phylogenetic tree using the Insert Genome into SpeciesTree app.

Obtain objective taxonomic assignments for bacterial and archaeal genomes based on the Genome Taxonomy Database (GTDB) ver R06-RS202
This app completed without errors in 32m 43s.
Created Object Name Type Description
Barcode05.fasta_assembly_DRAM Genome Taxonomy and taxon_assignment updated with GTDB
Ensifer GenomeSet Taxonomy and taxon_assignment updated with GTDB
Annotate or re-annotate genome/assembly using RASTtk (Rapid Annotations using Subsystems Technology toolkit).
This app completed without errors in 26m 39s.
Created Object Name Type Description
RAST-annotation Genome RAST re-annotated genome
The RAST algorithm was applied to annotating a genome sequence comprised of 4 contigs containing 7215895 nucleotides. No initial gene calls were provided. Standard gene features were called using: prodigal; glimmer3. A scan was conducted for the following additional feature types: rRNA; tRNA; selenoproteins; pyrrolysoproteins; repeat regions; crispr. The genome features were functionally annotated using the following algorithm(s): Kmers V2; Kmers V1; protein similarity. In addition to the remaining original 0 coding features and 0 non-coding features, 8537 new features were called, of which 129 are non-coding. Output genome has the following feature types: Coding gene 8408 Non-coding prophage 5 Non-coding repeat 50 Non-coding rna 74 Overall, the genes have 3519 distinct functions The genes include 3046 genes with a SEED annotation ontology across 1528 distinct SEED functions. The number of distinct functions can exceed the number of genes because some genes have multiple functions.
Annotate Assembly and Re-annotate Genomes with Prokka annotation pipeline.
This app completed without errors in 7m 16s.
Created Object Name Type Description
Prokka-ann Genome Annotated Genome
Annotated Genome saved to: cgoller:narrative_1649170660833/Prokka-ann Number of genes predicted: 7978 Number of protein coding genes: 7898 Number of genes with non-hypothetical function: 3898 Number of genes with EC-number: 1730 Number of genes with Seed Subsystem Ontology: 0 Average protein length: 255 aa.
Upload a data file (which may be compressed) from a web URL to your staging area.
This app completed without errors in 2m 27s.
Uploaded Files: 1 /barcode05.fastq
Output from Annotate Assembly and Re-annotate Genomes with Prokka - v1.14.5
The viewer for the output created by this App is available at the original Narrative here:
Add one or more Genomes to a KBase SpeciesTree.
This app completed without errors in 6m 52s.
These are only available in the live Narrative:
  • EnsiferTree.newick
  • EnsiferTree-labels.newick
  • EnsiferTree.png
  • EnsiferTree.pdf

Metabolic Modeling and Flux Balance Analysis (optional)

Note to Authors

Metabolic modeling is not listed on the MRA checklist; however, many MRA authors using KBase have included a metaboic model in their Narrative. If you are new to metabolic modeling, we have tutorials and webinars on the docs site and on YouTube.
If you do not use this section, delete this Markdown cell, and remove it from the table of contents at the top of the Narrative.


Note to authors

The KBase static Narrative service automatically generates a list of citations for apps used. If you provide any citations for literature or outside tools within the markdown cells, those should be included here.


  1. Annotate and Distill Assemblies with DRAM
    • DRAM source code
    • DRAM documentation
    • DRAM publication
  2. Annotate Assembly and Re-annotate Genomes with Prokka - v1.14.5
    • Seemann T. Prokka: rapid prokaryotic genome annotation. Bioinformatics. 2014;30: 2068 2069. doi:10.1093/bioinformatics/btu153
  3. Annotate Genome/Assembly with RASTtk - v1.073
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  4. Classify Microbes with GTDB-Tk - v1.7.0
    • Pierre-Alain Chaumeil, Aaron J Mussig, Philip Hugenholtz, Donovan H Parks, GTDB-Tk: a toolkit to classify genomes with the Genome Taxonomy Database, Bioinformatics, Volume 36, Issue 6, 15 March 2020, Pages 1925 1927. DOI:
    • Parks, D., Chuvochina, M., Waite, D. et al. A standardized bacterial taxonomy based on genome phylogeny substantially revises the tree of life. Nat Biotechnol 36, 996 1004 (2018). DOI:
    • Parks DH, Chuvochina M, Chaumeil PA, Rinke C, Mussig AJ, Hugenholtz P. A complete domain-to-species taxonomy for Bacteria and Archaea. Nat Biotechnol. 2020;10.1038/s41587-020-0501-8. DOI:10.1038/s41587-020-0501-8
    • Rinke C, Chuvochina M, Mussig AJ, Chaumeil PA, Dav n AA, Waite DW, Whitman WB, Parks DH, and Hugenholtz P. A standardized archaeal taxonomy for the Genome Taxonomy Database. Nat Microbiol. 2021 Jul;6(7):946-959. DOI:10.1038/s41564-021-00918-8
    • Matsen FA, Kodner RB, Armbrust EV. pplacer: linear time maximum-likelihood and Bayesian phylogenetic placement of sequences onto a fixed reference tree. BMC Bioinformatics. 2010;11:538. Published 2010 Oct 30. doi:10.1186/1471-2105-11-538
    • Jain C, Rodriguez-R LM, Phillippy AM, Konstantinidis KT, Aluru S. High throughput ANI analysis of 90K prokaryotic genomes reveals clear species boundaries. Nat Commun. 2018;9(1):5114. Published 2018 Nov 30. DOI:10.1038/s41467-018-07641-9
    • Hyatt D, Chen GL, Locascio PF, Land ML, Larimer FW, Hauser LJ. Prodigal: prokaryotic gene recognition and translation initiation site identification. BMC Bioinformatics. 2010;11:119. Published 2010 Mar 8. DOI:10.1186/1471-2105-11-119
    • Price MN, Dehal PS, Arkin AP. FastTree 2--approximately maximum-likelihood trees for large alignments. PLoS One. 2010;5(3):e9490. Published 2010 Mar 10. DOI:10.1371/journal.pone.0009490 link:
    • Eddy SR. Accelerated Profile HMM Searches. PLoS Comput Biol. 2011;7(10):e1002195. DOI:10.1371/journal.pcbi.1002195
  5. Insert Genome Into SpeciesTree - v2.2.0
    • Price MN, Dehal PS, Arkin AP. FastTree 2 Approximately Maximum-Likelihood Trees for Large Alignments. PLoS One. 2010;5. doi:10.1371/journal.pone.0009490
  6. Upload File to Staging from Web - v1.0.12
    • Arkin AP, Cottingham RW, Henry CS, Harris NL, Stevens RL, Maslov S, et al. KBase: The United States Department of Energy Systems Biology Knowledgebase. Nature Biotechnology. 2018;36: 566. doi: 10.1038/nbt.4163