Sphingomonas sp. str. VL_57B
Jennifer Goff, Chris Hahn, Chanistha Tiyapun, Gray Waldschmidt, Iz Moxley, Julia Maresca, Rebecca Ingrassia
Generated May 5, 2024

Sphingomonas sp. str. VL_57B

Genome associated with "Genome sequences of four bacterial strains isolated from organofluorine compound enrichment cultures"

The publication by Jennifer L. Goff, Chris Hahn, Rebecca A. Ingrassia, Chanistha Tiyapun, Gray G. Waldschmidt, Emma R. Smith, Miranda N. Marini, Olga Shevchenko, Julia A. Maresca an be found here: [URL]

Manuscript Abstract

Microbial consortia can be applied to the bioremediation of organofluorine compounds, a class of highly persistent environmental contaminants. Here we report the genome sequences of four strains—Flavobacterium sp. str. WV_118_3, Nocardioides sp. str. WV_118_6, Sphingomonas sp. str. VL_57B (referred to in this narrative by an older strain name: PFAS_57B), and Ochrobactrum anthropi str. WV_118_8—isolated from two organofluorine enrichment cultures.

Introduction

Sphigomonas sp. are obligately aerobic, flagellated, rod-shaped bacteria. Their colonies are usually yellow in color. They are Gram-negative and can be found commonly in the environment(1).

Note that VL_57B = PFAS_57B

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: Chanistha Tiyapun

Background and Experimental Methods

Sample Collection

Samples were collected from the aeration basin in Van Lare Wastewater treatment plant in Rochester, NY (43.237° N/77.577° W) in April 2019 and inoculated into a minimal medium amended with 2 μM perfluorooctanoic acid (PFOA) (2).

Isolation

The samples were incubated aerobically with shaking (~150 rpm) at room temperature for 16 months, with periodic 1:100 transfers into fresh medium of the same composition until only one cell type was present. Prior to genome sequencing, this strain was revived from a 12% glycerol stock stored at -80°C on LB medium and incubated at room temperature until colonies appeared. A single colony was inoculated into 10 mL LB and incubated with shaking at room temperature until stationary phase. DNA was extracted using a phenol-chloroform extraction protocol (3). The 16S rRNA gene of each isolate was amplified and sequenced using primers 8F and 1492R (4). Strain 57B was given a preliminary designation of Sphingomonas.

Genome Sequencing

The PacBio SMRTbell Express template preparation kit version 2.0A was used to barcoded and prepared single-molecule real-time (SMRT) library. DNA fragments larger than 6 kb were size selected using BluePippin (Sage Science). Fragment analyzer (Advanced Analytical Technologies, Inc.) measured the avarage library fragment size as 12 kb. Sequencing was completed on a PacBio Sequel IIe single-molecule sequencer in one 1M version 3 LR SMRT Cell with a 30-h movie. PacBio SMRT Link version 11 was used to demultiplex the sample.

Import

The genome was imported to KBase by the third party application, Globus, and unzipped with Unpack a compressed File in Staging Area - V1.0.12 application

Unpack a Compressed File in Staging Area - v1.0.12
Unpack a compressed file in the staging area.
This app completed without errors in 59s.
Summary
Uploaded Files: 1 PFAS_Genomes/demultiplex.57B-bc2061.hifi_reads.fastq
PFAS_57B_reads
v1 - KBaseFile.SingleEndLibrary-2.1
The viewer for the data in this Cell is available at the original Narrative here: https://narrative.kbase.us/narrative/169830

QC, Assembly and Annotation

  1. The raw reads were filtered with Filter Reads with Filtlong - v0.2.1 application usig default parameters in KBase.
  2. The filtered reads were assembled in KBase with Assemble Long Reads with Flye -v2.9.2 application. The assembled genome has 2 contigs in 4.8 Mbp with 67.63% G+C content. Contig 1 is plasmid and contig 2 is circularized chromozome.
  3. geNomad (5) in the NMDC EDGE Bioinformatics platform (6) was used to identify the plasmid and prophages. The plasmid (contig 1) contains conjuagation genes. Contig 2 contains a provirus identified as Caudoviricetes
  4. The quality of the genome was then checked using KBase Assess Genome Quality with CheckM - v1.0.18 application.
  5. In KBase, the genome was annotated by Annotate Genome/Assembly with RASTtk - V1.073 apllication.
Filter Reads with Filtlong - v0.2.1
Filter long reads using Filtlong.
This app completed without errors in 4m 4s.
Objects
Created Object Name Type Description
PFAS_57B-filtlong.reads SingleEndLibrary Filtered reads
Summary
Running run_kb_filtlong with params: { "workspace_name": "jgoff:narrative_1707234069441", "workspace_id": 169830, "input_reads_library": "169830/10/1", "input_short_paired_library": null, "output_reads_name": "PFAS_57B-filtlong.reads", "min_read_length": 1000, "keep_percent": 90, "target_bases": 500000000 } Getting long reads (from reads library object). command: filtlong --min_length 1000 --keep_percent 90 --target_bases 500000000 /kb/module/work/tmp/d1f99f8f-800e-4cb2-90ea-583c68716e37.single.fastq > /kb/module/work/tmp/filtlong_output_4e0c7a2b-6a68-440a-a4d8-a838499a085d.fastq Scoring long reads 51 reads (488394 bp) 96 reads (978251 bp) 145 reads (1467481 bp) 192 reads (1953045 bp) 238 reads (2438576 bp) 282 reads (2925451 bp) 329 reads (3411446 bp) 374 reads (3901162 bp) 418 reads (4393836 bp) 463 reads (4881901 bp) 509 reads (5368240 bp) 558 reads (5857963 bp) 609 reads (6349164 bp) 658 reads (6838918 bp) 705 reads (7323118 bp) 754 reads (7811283 bp) 801 reads (8295080 bp) 848 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reads (1902280177 bp) 183256 reads (1902768765 bp) 183304 reads (1903259977 bp) 183351 reads (1903749443 bp) 183398 reads (1904233797 bp) 183446 reads (1904722984 bp) 183495 reads (1905208064 bp) 183542 reads (1905702147 bp) 183589 reads (1906186593 bp) 183632 reads (1906670748 bp) 183681 reads (1907154906 bp) 183727 reads (1907646420 bp) 183772 reads (1908135964 bp) 183817 reads (1908622406 bp) 183863 reads (1909107170 bp) 183911 reads (1909601692 bp) 183958 reads (1910094150 bp) 184005 reads (1910585111 bp) 184053 reads (1911069411 bp) 184102 reads (1911555428 bp) 184147 reads (1912047864 bp) 184194 reads (1912532284 bp) 184242 reads (1913018449 bp) 184290 reads (1913502450 bp) 184333 reads (1913989645 bp) 184379 reads (1914478050 bp) 184427 reads (1914967786 bp) 184475 reads (1915457270 bp) 184527 reads (1915946826 bp) 184573 reads (1916436890 bp) 184621 reads (1916930589 bp) 184663 reads (1917417087 bp) 184708 reads (1917901049 bp) 184753 reads (1918393237 bp) 184802 reads (1918877629 bp) 184848 reads (1919376876 bp) 184897 reads (1919864240 bp) 184943 reads (1920359690 bp) 184991 reads (1920850514 bp) 185039 reads (1921337314 bp) 185083 reads (1921834920 bp) 185132 reads (1922321356 bp) 185179 reads (1922808428 bp) 185224 reads (1923296896 bp) 185270 reads (1923786660 bp) 185317 reads (1924273326 bp) 185363 reads (1924774222 bp) 185409 reads (1925258414 bp) 185455 reads (1925746291 bp) 185502 reads (1926236433 bp) 185549 reads (1926720405 bp) 185596 reads (1927205020 bp) 185642 reads (1927696027 bp) 185684 reads (1928185174 bp) 185732 reads (1928670101 bp) 185779 reads (1929168625 bp) 185821 reads (1929664914 bp) 185867 reads (1930155261 bp) 185914 reads (1930639111 bp) 185960 reads (1931129439 bp) 186011 reads (1931614094 bp) 186057 reads (1932107116 bp) 186102 reads (1932596689 bp) 186146 reads (1933089270 bp) 186194 reads (1933575984 bp) 186239 reads (1934060780 bp) 186251 reads (1934190925 bp) Filtering long reads target: 500000000 bp keeping 500010788 bp Outputting passed long reads Uploading filtered reads: PFAS_57B-filtlong.reads Generating and saving report. Filtlong results saved.
Assemble Long Reads with Flye - v2.9.2
Assemble long reads using the Flye assembler.
This app completed without errors in 37m 49s.
Objects
Created Object Name Type Description
PFAS-57B-flye.contigs Assembly Assembled contigs
Report
Summary
Flye results saved to: jgoff:narrative_1707234069441//kb/module/work/tmp/flye_3b640dad-f853-470b-a4de-636b5951360b Assembly saved to: jgoff:narrative_1707234069441/PFAS-57B-flye.contigs Assembled into 2 contigs. Avg Length: 2426653.5 bp. Contig Length Distribution (# of contigs -- min to max basepairs): 1 -- 201522.0 to 646548.3 bp 0 -- 646548.3 to 1091574.6 bp 0 -- 1091574.6 to 1536600.9 bp 0 -- 1536600.9 to 1981627.2 bp 0 -- 1981627.2 to 2426653.5 bp 0 -- 2426653.5 to 2871679.8 bp 0 -- 2871679.8 to 3316706.1 bp 0 -- 3316706.1 to 3761732.4 bp 0 -- 3761732.4 to 4206758.699999999 bp 1 -- 4206758.699999999 to 4651785.0 bp
Links
Files
These are only available in the live Narrative: https://narrative.kbase.us/narrative/169830
  • flye_output.zip - Output file(s) generated by Flye
PFAS-57B-flye.contigs
v1 - KBaseGenomeAnnotations.Assembly-5.1
The viewer for the data in this Cell is available at the original Narrative here: https://narrative.kbase.us/narrative/169830
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 4m 59s.
Report
Links
Files
These are only available in the live Narrative: https://narrative.kbase.us/narrative/169830
  • CheckM_summary_table.tsv.zip - TSV Summary Table from CheckM
  • full_output.zip - Full output of CheckM
  • plots.zip - Output plots from CheckM
Annotate Genome/Assembly with RASTtk - v1.073
Annotate or re-annotate genome/assembly using RASTtk (Rapid Annotations using Subsystems Technology toolkit).
This app completed without errors in 17m 32s.
Objects
Created Object Name Type Description
Sphingomonas_PFAS57B Genome RAST re-annotated genome
Report
Summary
The RAST algorithm was applied to annotating a genome sequence comprised of 2 contigs containing 4853307 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, 4767 new features were called, of which 133 are non-coding. Output genome has the following feature types: Coding gene 4634 Non-coding repeat 78 Non-coding rna 55 The number of distinct functions can exceed the number of genes because some genes have multiple functions.
Links
Sphingomonas_PFAS57B
v1 - KBaseGenomes.Genome-11.1
The viewer for the data in this Cell is available at the original Narrative here: https://narrative.kbase.us/narrative/169830

Taxonomic Identification

  1. Taxonomic classification was peformed with Classify Microbes with GTDB-Tk - v1.7.0 application in KBase.
  2. The species tree was constructed in KBase using Insert Genome Into SpeciesTree - v2.2.0 application. PFAS-57B was identify as Sphingomonas sp.
Classify Microbes with GTDB-Tk - v1.7.0
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 36m 6s.
Report
Links
Insert Genome Into SpeciesTree - v2.2.0
Add one or more Genomes to a KBase SpeciesTree.
This app completed without errors in 3m 35s.
Report
Links
Files
These are only available in the live Narrative: https://narrative.kbase.us/narrative/169830
  • PFAS57B.newick
  • PFAS57B-labels.newick
  • PFAS57B.png
  • PFAS57B.pdf
PFAS57B
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/169830

Metabolic Modeling and Flux Balance Analysis

  1. Genome-scale metabolic models were built for this genome in KBase using THE MS2 - Build Prokaryotic Metabolic Models - v2.0.0 application.
  2. A minimal set of biochemical reactions was identified and added to draft model by Gapfill Metabolic Model - v2.2.1 application.
  3. Metabolite fluxes were predicted using the Run Flux Balance Analysis - v2.2.1 application.
MS2 - Build Prokaryotic Metabolic Models
Using ModelSEED2 pipeline, construct draft metabolic models based on input annotated genomes.
This app completed without errors in 14m 47s.
Objects
Created Object Name Type Description
Sphingomonas_PFAS57B.mdl FBAModel
Report
Links
Sphingomonas_PFAS57B.mdl
v1 - KBaseFBA.FBAModel-15.0
The viewer for the data in this Cell is available at the original Narrative here: https://narrative.kbase.us/narrative/169830
Gapfill Metabolic Model
Identify the minimal set of biochemical reactions to add to a draft metabolic model to enable it to produce biomass in a specified media.This app is now obsolete, replaced by the new ModelSEED2 app: MS2 - Improved Gapfill Metabolic Models.
This app completed without errors in 48s.
Objects
Created Object Name Type Description
Sphingomonas_PFAS57B-gapfill FBAModel FBAModel-15 Sphingomonas_PFAS57B-gapfill
Sphingomonas_PFAS57B-gapfill.gf.0 FBA FBA-13 Sphingomonas_PFAS57B-gapfill.gf.0
Report
Sphingomonas_PFAS57B-gapfill
v1 - KBaseFBA.FBAModel-15.0
The viewer for the data in this Cell is available at the original Narrative here: https://narrative.kbase.us/narrative/169830
Run Flux Balance Analysis
Predict metabolite fluxes in a metabolic model of an organism grown on a given media using flux balance analysis (FBA).
This app completed without errors in 32s.
Objects
Created Object Name Type Description
Sphingomonas_PFAS57B-FBA FBA FBA-13 Sphingomonas_PFAS57B-FBA
Report
Summary
A flux balance analysis (FBA) was performed on the metabolic model 169830/28/1 growing in Complete media.
Sphingomonas_PFAS57B-FBA
v1 - KBaseFBA.FBA-13.2
The viewer for the data in this Cell is available at the original Narrative here: https://narrative.kbase.us/narrative/169830

References

(1)Balkwill DL, Fredrickson JK, Romine MF. 2006. Sphingomonas and Related Genera. Prokaryotes 7:605–629

(2)Alexandrino DAM, Ribeiro I, Pinto LM, Cambra R, Oliveira RS, Pereira F, Carvalho MF. 2018. Biodegradation of mono-, di- and trifluoroacetate by microbial cultures with different origins. N Biotechnol 43:23-29.

(3)Kiledal E, Maresca JA. 2021. Chromosomal DNA extraction from Gram-positive bacteria. protocolsio.

(4)Frank JA, Reich CI, Sharma S, Weisbaum JS, Wilson BA, Olsen GJ. 2008. Critical evaluation of two primers commonly used for amplification of bacterial 16S rRNA genes. Applied and environmental microbiology 74, 2461-2470.

(5)Camargo AP, Roux S, Schulz F, Babinski M, Xu Y, Hu B, Chain PS, Nayfach S, Kyrpides NC. 2023. Identification of mobile genetic elements with geNomad. Nat Biotechnol:1-10.

(6)Eloe-Fadrosh EA, Ahmed F, Babinski M, Baumes J, Borkum M, Bramer L, Canon S, Christianson DS, Corilo YE, Davenport KW. 2022. The National Microbiome Data Collaborative Data Portal: an integrated multi-omics microbiome data resource. Nucleic Acids Res 50:D828-D836.

Released Apps

  1. Annotate Genome/Assembly with RASTtk - v1.073
    • [1] 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
    • [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] 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. doi:10.1038/srep08365
    • [4] Kent WJ. BLAT The BLAST-Like Alignment Tool. Genome Res. 2002;12: 656 664. doi:10.1101/gr.229202
    • [5] Altschul SF, Madden TL, Schaffer AA, Zhang J, Zhang Z, Miller W, Lipman DJ. Gapped BLAST and PSI-BLAST: a new generation of protein database search programs. Nucleic Acids Res. 1997;25: 3389-3402. doi:10.1093/nar/25.17.3389
    • [6] Lowe TM, Eddy SR. tRNAscan-SE: a program for improved detection of transfer RNA genes in genomic sequence. Nucleic Acids Res. 1997;25: 955 964.
    • [7] Cobucci-Ponzano B, Rossi M, Moracci M. Translational recoding in archaea. Extremophiles. 2012;16: 793 803. doi:10.1007/s00792-012-0482-8
    • [8] Meyer F, Overbeek R, Rodriguez A. FIGfams: yet another set of protein families. Nucleic Acids Res. 2009;37 6643-54. doi:10.1093/nar/gkp698.
    • [9] van Belkum A, Sluijuter M, de Groot R, Verbrugh H, Hermans PW. Novel BOX repeat PCR assay for high-resolution typing of Streptococcus pneumoniae strains. J Clin Microbiol. 1996;34: 1176 1179.
    • [10] Croucher NJ, Vernikos GS, Parkhill J, Bentley SD. Identification, variation and transcription of pneumococcal repeat sequences. BMC Genomics. 2011;12: 120. doi:10.1186/1471-2164-12-120
    • [11] Hyatt D, Chen G-L, Locascio PF, Land ML, Larimer FW, Hauser LJ. Prodigal: prokaryotic gene recognition and translation initiation site identification. BMC Bioinformatics. 2010;11: 119. doi:10.1186/1471-2105-11-119
    • [12] Delcher AL, Bratke KA, Powers EC, Salzberg SL. Identifying bacterial genes and endosymbiont DNA with Glimmer. Bioinformatics. 2007;23: 673 679. doi:10.1093/bioinformatics/btm009
    • [13] Akhter S, Aziz RK, Edwards RA. PhiSpy: a novel algorithm for finding prophages in bacterial genomes that combines similarity- and composition-based strategies. Nucleic Acids Res. 2012;40: e126. doi:10.1093/nar/gks406
  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. Gapfill 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] Henry CS, Jankowski MD, Broadbelt LJ, Hatzimanikatis V. Genome-Scale Thermodynamic Analysis of Escherichia coli Metabolism. Biophysical Journal. 2006;90: 1453 1461. doi:10.1529/biophysj.105.071720
    • [3] Jankowski MD, Henry CS, Broadbelt LJ, Hatzimanikatis V. Group Contribution Method for Thermodynamic Analysis of Complex Metabolic Networks. Biophysical Journal. 2008;95: 1487 1499. doi:10.1529/biophysj.107.124784
    • [4] Henry CS, Zinner JF, Cohoon MP, Stevens RL. iBsu1103: a new genome-scale metabolic model of Bacillus subtilisbased on SEED annotations. Genome Biology. 2009;10: R69. doi:10.1186/gb-2009-10-6-r69
    • [5] Orth JD, Thiele I, Palsson B . What is flux balance analysis? Nature Biotechnology. 2010;28: 245 248. doi:10.1038/nbt.1614
    • [6] Latendresse M. Efficiently gap-filling reaction networks. BMC Bioinformatics. 2014;15: 225. doi:10.1186/1471-2105-15-225
    • [7] 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
  4. 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
  5. MS2 - Build Prokaryotic Metabolic Models with OMEGGA
    • [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.
  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
  7. Unpack a Compressed File in Staging Area - 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

Apps in Beta

  1. Assemble Long Reads with Flye - v2.9.2
    • [1] Mikhail Kolmogorov, Jeffrey Yuan, Yu Lin and Pavel Pevzner, "Assembly of Long Error-Prone Reads Using Repeat Graphs", Nature Biotechnology, 2019 doi:10.1038/s41587-019-0072-8
  2. 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: https://doi.org/10.1093/bioinformatics/btz848
    • 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: https://doi.org/10.1038/nbt.4229
    • 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: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2835736/
    • Eddy SR. Accelerated Profile HMM Searches. PLoS Comput Biol. 2011;7(10):e1002195. DOI:10.1371/journal.pcbi.1002195
  3. Filter Reads with Filtlong - v0.2.1
    • [1] Wick RR, 2017. Filtlong.