Larkinella sp. BK230
Dale Pelletier, Zach Crockett
Generated August 10, 2020

Draft Genome Sequence of Larkinella sp. Strain BK230, Isolated from Populus deltoides Roots

Introduction

This narrative was used for a draft genome of a Larkinella species. This genus contains 9 identified species, of which only 5 have genome sequence data. This specimen was collected from a root sample of an eastern cottonwood (Populus deltoides) from a fied site in Bellville, Georgia.

The publication by Dale A. Pelletier, Leah H. Burdick, Mircea Podar, Christopher W. Schadt, Udaya C. Kalluri can be found here: https://mra.asm.org/content/9/12/e00159-20.abstract

Table of Contents

  1. Prior Methods
  2. Import, annotation, QC, and Classification
  3. Metabolic Modeling and Flux Balance Analysis
  4. References

Narrative created by Dale Pelletier, edited by Zachary Crockett

Sample Collection, Isolation, and Sequencing

Sample Collection

Fine root samples were harvested from Populus deltoides WV94 growing in a nursery in Bellville, Georgia in September 2017.

Washed root tissue was ground, diluted, and plated on Reasoner's 2A agar and incubated for 7 days at 25°C.

Isolation

Colonies that developed were analyzed using small-subunit rRNA gene amplicon sequencing1. Based on 16 sequences, a ClustalW alignment and neighbor-joining phylogenetic tree generated by Geneious2 indicated that the clostest relatives of the isolated strain were Larkinella insperata and Larkinella aboricola with 96.2% and 95.5% indentities, respectively.

For genome sequencing, cells were grown in R2A medium overnight at 25°C.

Genome Sequencing

DNA was prepared using Qiagen DNeasy kit and a drag genome was generated at the DOE Joint Genome Institute using the Illumina NovaSeq platform3.

An Illumina standard shotgun library was constructed, consisting of 12,544,362 reads totaling 1,894,198,662 bp.

The reads were quality filtered using BBTools4 and then assembled using SPAdes5.

The final draft assembly contained 16 contigs totaling 7,274,818 bp based on 1,497,902,613 bp of Illumina data with 203.4x coverage.

A metabolic model was constructed in KBase, shown below.

Import, Annotation, QC, and Classification

  1. The previously mentioned assembly was imported using default parameters through the Import FASTA File as Assembly from Staging Area.
  2. The assembly was annotated using the KBase Annotate Microbial Assembly App, based on the RASTtk, with default parameters.
  3. The genome objected generated from the RAST annotation was check for quality using Assess Genome Quality with CheckM with default parameters.
  4. The resulting Insert Genome Into Species Tree App was used to generate a relatives list.
Import FASTA File as Assembly from Staging Area
Import a FASTA file from your staging area into your Narrative as an Assembly data object
This app completed without errors in 48s.
Objects
Created Object Name Type Description
2802428837.fna_assembly Assembly Imported Assembly
Report
Links
  • report.html - HTML summary report for Imported Assembly
2802428837.fna_assembly
v1 - KBaseGenomeAnnotations.Assembly-5.0
The viewer for the data in this Cell is available at the original Narrative here: https://narrative.kbase.us/narrative/54100
Annotate Microbial Assembly
Annotate a bacterial or archaeal assembly using components from the RAST (Rapid Annotations using Subsystems Technology) toolkit (RASTtk).
This app completed without errors in 6m 28s.
Objects
Created Object Name Type Description
LarkinellaBK230 Genome Annotated genome
Summary 
The RAST algorithm was applied to annotating a genome sequence comprised of 15 contigs containing 7274818 nucleotides. 
No initial gene calls were provided.
Standard features were called using: glimmer3; prodigal.
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, 6463 new features were called, of which 191 are non-coding.
Output genome has the following feature types:
	Coding gene                     6272 
	Non-coding crispr_array            1 
	Non-coding crispr_repeat          66 
	Non-coding crispr_spacer          65 
	Non-coding repeat                 12 
	Non-coding rna                    47 
Overall, the genes have 2244 distinct functions. 
The genes include 2883 genes with a SEED annotation ontology across 999 distinct SEED functions.
The number of distinct functions can exceed the number of genes because some genes have multiple functions.
Output from Annotate Microbial Assembly
The viewer for the output created by this App is available at the original Narrative here: https://narrative.kbase.us/narrative/54100
Assess Genome Quality with CheckM - v1.0.18
Runs the CheckM lineage workflow to assess the genome quality of isolates, single cells, or genome bins from metagenome assemblies through comparison to an existing database of genomes.
This app completed without errors in 10m 52s.
Report
Links
Files
These are only available in the live Narrative: https://narrative.kbase.us/narrative/54100
  • CheckM_summary_table.tsv.zip - TSV Summary Table from CheckM
  • full_output.zip - Full output of CheckM
  • plots.zip - Output plots from CheckM
Insert Genome Into Species Tree 2.1.10
Add one or more genomes to a KBase species tree.
This app completed without errors in 3m 10s.
Report
Links
Files
These are only available in the live Narrative: https://narrative.kbase.us/narrative/54100
  • BK230tree.newick
  • BK230tree-labels.newick
  • BK230tree.png
  • BK230tree.pdf

Metabolic Modeling and Flux Balance Analysis

  1. The Build Metabolic Model App was used to generate a draft metabolic model with gapfilling on and other parameters set to default.
  2. The Run Flux Balance Analysis App was used to perform flux balance analysis of the model on C-D-Glucose media, maximizing for total biomass (option bio1).
BK230tree
v1 - KBaseTrees.Tree-1.0
The viewer for the data in this Cell is available at the original Narrative here: https://narrative.kbase.us/narrative/54100
Build Metabolic Model
Generate a draft metabolic model based on an annotated genome.
This app completed without errors in 2m 38s.
Objects
Created Object Name Type Description
Larkinella FBAModel FBAModel-11 Larkinella
Larkinella.gf.0 FBA FBA-13 Larkinella.gf.0
Report
Larkinella
v1 - KBaseFBA.FBAModel-11.0
The viewer for the data in this Cell is available at the original Narrative here: https://narrative.kbase.us/narrative/54100
Output from Build Metabolic Model
The viewer for the output created by this App is available at the original Narrative here: https://narrative.kbase.us/narrative/54100
Run Flux Balance Analysis
Use flux balance analysis to predict metabolic fluxes in a metabolic model of an organism grown on a given media.
This app completed without errors in 1m 25s.
Objects
Created Object Name Type Description
Lark-glu FBA FBA-13 Lark-glu
Report
Summary
A flux balance analysis (FBA) was performed on the metabolic model 54100/10/1 growing in 54100/13/1 media.
Output from Run Flux Balance Analysis
The viewer for the output created by this App is available at the original Narrative here: https://narrative.kbase.us/narrative/54100
Larkinella
v1 - KBaseFBA.FBAModel-11.0
The viewer for the data in this Cell is available at the original Narrative here: https://narrative.kbase.us/narrative/54100

References

  1. Utturkar SM, Cude WN, Robeson MS, Jr, Yang ZK, Klingeman DM, Land ML, Allman SL, Lu TY, Brown SD, Schadt CW, Podar M, Doktycz MJ, Pelletier DA. 2016. Enrichment of root endophytic bacteria from Populus deltoides and single-cell-genomics analysis. Appl Environ Microbiol 82:5698 –5708. https://doi.org/10.1128/AEM.01285-16.
  2. Kearse M, Moir R, Wilson A, Stones-Havas S, Cheung M, Sturrock S, Buxton S, Cooper A, Markowitz S, Duran C, Thierer T, Ashton B, Meintjes P, Drummond A. 2012. Geneious Basic: an integrated and extendable desktop software platform for the organization and analysis of sequence data. Bioinformatics 28:1647–1649. https://doi.org/10.1093/bioinformatics/bts19.
  3. Bennett S. 2004. Solexa Ltd. Pharmacogenomics 5:433– 438. https://doi.org/10.1517/14622416.5.4.433.
  4. Bushnell B. 2014. BBTools software package. http://bbtools.jgi.doe.gov
  5. Bankevich A, Nurk S, Antipov D, Gurevich AA, Dvorkin M, Kulikov AS, Lesin VM, Nikolenko SI, Pham S, Prjibelski AD, Pyshkin AV, Sirotkin AV, Vyahhi N, Tesler G, Alekseyev MA, Pevzner PA. 2012. SPAdes: a new genome assembly algorithm and its applications to single-cell sequencing. J Comput Biol 19:455– 477. https://doi.org/10.1089/cmb.2012.0021.

Apps

  1. Annotate Microbial Assembly
    • Aziz RK, Bartels D, Best AA, DeJongh M, Disz T, Edwards RA, et al. The RAST Server: Rapid Annotations using Subsystems Technology. BMC Genomics. 2008;9: 75. doi:10.1186/1471-2164-9-75
    • Brettin T, Davis JJ, Disz T, Edwards RA, Gerdes S, Olsen GJ, et al. RASTtk: a modular and extensible implementation of the RAST algorithm for building custom annotation pipelines and annotating batches of genomes. Sci Rep. 2015;5: 8365. doi:10.1038/srep08365
    • Overbeek R, Olson R, Pusch GD, Olsen GJ, Davis JJ, Disz T, et al. The SEED and the Rapid Annotation of microbial genomes using Subsystems Technology (RAST). Nucleic Acids Res. 2014;42: D206-214. doi:10.1093/nar/gkt1226
  2. Assess Genome Quality with CheckM - v1.0.18
    • Parks DH, Imelfort M, Skennerton CT, Hugenholtz P, Tyson GW. CheckM: assessing the quality of microbial genomes recovered from isolates, single cells, and metagenomes. Genome Res. 2015;25: 1043 1055. doi:10.1101/gr.186072.114
    • CheckM source:
    • Additional info:
  3. Build Metabolic Model
    • [1] Henry CS, DeJongh M, Best AA, Frybarger PM, Linsay B, Stevens RL. High-throughput generation, optimization and analysis of genome-scale metabolic models. Nat Biotechnol. 2010;28: 977 982. doi:10.1038/nbt.1672
    • [2] Overbeek R, Olson R, Pusch GD, Olsen GJ, Davis JJ, Disz T, et al. The SEED and the Rapid Annotation of microbial genomes using Subsystems Technology (RAST). Nucleic Acids Res. 2014;42: D206 D214. doi:10.1093/nar/gkt1226
    • [3] Latendresse M. Efficiently gap-filling reaction networks. BMC Bioinformatics. 2014;15: 225. doi:10.1186/1471-2105-15-225
    • [4] Dreyfuss JM, Zucker JD, Hood HM, Ocasio LR, Sachs MS, Galagan JE. Reconstruction and Validation of a Genome-Scale Metabolic Model for the Filamentous Fungus Neurospora crassa Using FARM. PLOS Computational Biology. 2013;9: e1003126. doi:10.1371/journal.pcbi.1003126
    • [5] Mahadevan R, Schilling CH. The effects of alternate optimal solutions in constraint-based genome-scale metabolic models. Metab Eng. 2003;5: 264 276.
  4. Import FASTA File as Assembly from Staging Area
    no citations
  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. Run Flux Balance Analysis
    • Henry CS, DeJongh M, Best AA, Frybarger PM, Linsay B, Stevens RL. High-throughput generation, optimization and analysis of genome-scale metabolic models. Nat Biotechnol. 2010;28: 977 982. doi:10.1038/nbt.1672
    • Orth JD, Thiele I, Palsson B . What is flux balance analysis? Nature Biotechnology. 2010;28: 245 248. doi:10.1038/nbt.1614