Flavobacterium sp. str. WV_118_3 Genome
Jennifer Goff, Chris Hahn, Julia Maresca
Generated May 5, 2024

Flavobacterium sp.str. WV_118_3 Genome

Abstract

Microbial consortia can be applied to the bioremediation of trifluoroacetate (TFA), a highly persistent environmental contaminant. Here, we report the genome sequence of Flavobacterium sp. str. WV_118_3 (referred to by a prior version name within this narrative: PFAS_118_3), to provide genomic insight into its strategies for TFA degredation.

Introduction

Flavobacterium, of the family Flavobacteriaceae, are omnipresent in soil, freshwater and marine environments(1). Three species within the genus are known fish pathogens, and several strains are known to degrade chitin, keratin, and alginate(2). Other species are widely used for the production of ß-carotene and within the health field for pharmaceutical purposes(2).

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 can be found here: [URL]

Note that PFAS_118_3 = WV_118_3

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: Chris Hahn

Background and Experimental Methods

Sample Collection

Samples were collected from the primary influent in the Wilmington Wastewater Treatment Plant (39.735°N,-75.518°W) in April 2019 and inoculated into a minimal medium amended with 2µM fluoroacetic acid(3).

Isolation

Over the course of 16 months, the sample was aerobically incubated at room temperature with shaking and periodic 1:100 transfers onto fresh medium. The cultures were archived in 12% glycerol in March 2020, and revived in July 2021 on the same medium. After one transfer of the cultures into minimal media amended with 200µM fluoroacetic acid and incubation for ~1 month, 20µL of the sample was plated on solid minimal media amended with 1% yeast extract. From the four visible colony types that appeared, two representatives from each type were grown in LB for ~two days before being harvested. DNA was extracted using phenol-chloroform extraction(4). The 16S rRNA gene of each isolate was amplified and sequenced using primers 8F and 1492R(5). From the 16S rRNA sequence, one isolate was identified as belonging to the genus Flavobacterium. This isolate was designated as PFAS_118_3, and its genome was sequenced.

Genome Sequencing

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

Import

The PFAS 118.3 gemone was imported to KBase via globus, a third party application, and unpacked using Unpack a Compressed File in Staging Area -V1.0.12.

Unpack a Compressed File in Staging Area - v1.0.12
Unpack a compressed file in the staging area.
This app completed without errors in 35s.
Summary
Uploaded Files: 1 PFAS_Genomes/demultiplex.118.3-bc2062.hifi_reads.fastq
PFAS_118_3_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/169829

QC, Assembly, and Annotation

Assembly

Filter Reads with Filtlong -v0.2.1 using default parameters was used tofilter the reads of PFAS_118_3. The genome was assembled with Assemble Long Reads with Flye -v2.9.2.

Quality Control

Quality control was completed with Assess Genome Quality with CheckM -v1.1.2 using default parameters.

Annotation

Annotation of the genome was conducted with Annotate Microbial Assembly with RASTtk -v1.073 using default parameters.

Filter Reads with Filtlong - v0.2.1
Filter long reads using Filtlong.
This app completed without errors in 2m 42s.
Objects
Created Object Name Type Description
PFAS_118_3_filtlong.reads SingleEndLibrary Filtered reads
Summary
Running run_kb_filtlong with params: { "workspace_name": "jgoff:narrative_1707234058505", "workspace_id": 169829, "input_reads_library": "169829/8/1", "input_short_paired_library": null, "output_reads_name": "PFAS_118_3_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/44e29cc8-8e85-4909-9d04-30a04fce1e0f.single.fastq > /kb/module/work/tmp/filtlong_output_f404e71a-aa12-43c8-9033-ea832a5427c4.fastq Scoring long reads 46 reads (493213 bp) 97 reads (985001 bp) 151 reads (1470851 bp) 202 reads (1955054 bp) 254 reads (2445951 bp) 304 reads (2943454 bp) 355 reads (3428218 bp) 408 reads (3919437 bp) 460 reads (4407118 bp) 509 reads (4900171 bp) 561 reads (5389466 bp) 613 reads (5880791 bp) 664 reads (6373621 bp) 717 reads (6862159 bp) 767 reads (7346225 bp) 817 reads (7831269 bp) 865 reads (8317488 bp) 914 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(1025414644 bp) 105945 reads (1025908720 bp) 105996 reads (1026396960 bp) 106049 reads (1026882984 bp) 106102 reads (1027372581 bp) 106155 reads (1027862271 bp) 106204 reads (1028347683 bp) 106253 reads (1028833234 bp) 106305 reads (1029319802 bp) 106354 reads (1029813835 bp) 106407 reads (1030304132 bp) 106458 reads (1030794491 bp) 106512 reads (1031285031 bp) 106562 reads (1031770359 bp) 106609 reads (1032254274 bp) 106661 reads (1032743925 bp) 106710 reads (1033230693 bp) 106756 reads (1033716894 bp) 106804 reads (1034210847 bp) 106858 reads (1034697622 bp) 106909 reads (1035189274 bp) 106962 reads (1035676849 bp) 107011 reads (1036172200 bp) 107060 reads (1036663198 bp) 107114 reads (1037153902 bp) 107167 reads (1037641571 bp) 107216 reads (1038125363 bp) 107264 reads (1038618763 bp) 107311 reads (1039110830 bp) 107360 reads (1039603208 bp) 107415 reads (1040095731 bp) 107464 reads (1040584755 bp) 107519 reads (1041081071 bp) 107565 reads (1041571618 bp) 107618 reads (1042059187 bp) 107675 reads (1042549280 bp) 107726 reads (1043034877 bp) 107782 reads (1043520982 bp) 107835 reads (1044008254 bp) 107885 reads (1044494408 bp) 107937 reads (1044978869 bp) 107985 reads (1045464511 bp) 108034 reads (1045953861 bp) 108083 reads (1046442072 bp) 108134 reads (1046936361 bp) 108184 reads (1047420340 bp) 108233 reads (1047908653 bp) 108285 reads (1048410917 bp) 108338 reads (1048895232 bp) 108391 reads (1049386101 bp) 108445 reads (1049869741 bp) 108497 reads (1050359081 bp) 108553 reads (1050855625 bp) 108600 reads (1051339354 bp) 108648 reads (1051829019 bp) 108701 reads (1052315542 bp) 108750 reads (1052810031 bp) 108796 reads (1053293878 bp) 108847 reads (1053785633 bp) 108899 reads (1054283697 bp) 108950 reads (1054782338 bp) 109002 reads (1055267905 bp) 109053 reads (1055758352 bp) 109102 reads (1056248207 bp) 109157 reads (1056734747 bp) 109209 reads (1057228608 bp) 109263 reads (1057718648 bp) 109316 reads (1058214039 bp) 109362 reads (1058702306 bp) 109416 reads (1059190990 bp) 109466 reads (1059676887 bp) 109520 reads (1060163055 bp) 109573 reads (1060650894 bp) 109626 reads (1061143224 bp) 109680 reads (1061633240 bp) 109726 reads (1062121424 bp) 109777 reads (1062610280 bp) 109830 reads (1063098221 bp) 109884 reads (1063589168 bp) 109933 reads (1064073849 bp) 109984 reads (1064564182 bp) 110034 reads (1065049879 bp) 110084 reads (1065535648 bp) 110129 reads (1066024216 bp) 110179 reads (1066516032 bp) 110231 reads (1066999715 bp) 110279 reads (1067492958 bp) 110327 reads (1067985438 bp) 110380 reads (1068471903 bp) 110431 reads (1068960726 bp) 110480 reads (1069450001 bp) 110529 reads (1069940425 bp) 110580 reads (1070430917 bp) 110634 reads (1070921550 bp) 110685 reads (1071425660 bp) 110735 reads (1071913563 bp) 110781 reads (1072397265 bp) 110826 reads (1072887079 bp) 110874 reads (1073376057 bp) 110923 reads (1073870184 bp) 110970 reads (1074354806 bp) 111022 reads (1074844086 bp) 111070 reads (1075328873 bp) 111124 reads (1075815094 bp) 111173 reads (1076298924 bp) 111222 reads (1076785473 bp) 111270 reads (1077269998 bp) 111317 reads (1077758226 bp) 111368 reads (1078246758 bp) 111419 reads (1078736630 bp) 111470 reads (1079228563 bp) 111520 reads (1079712607 bp) 111572 reads (1080204313 bp) 111623 reads (1080692142 bp) 111670 reads (1081176036 bp) 111722 reads (1081663228 bp) 111773 reads (1082160415 bp) 111824 reads (1082651853 bp) 111876 reads (1083142508 bp) 111924 reads (1083629267 bp) 111975 reads (1084121567 bp) 112025 reads (1084611543 bp) 112073 reads (1085096496 bp) 112120 reads (1085580359 bp) 112169 reads (1086070247 bp) 112219 reads (1086560797 bp) 112270 reads (1087053272 bp) 112320 reads (1087542242 bp) 112374 reads (1088028536 bp) 112428 reads (1088518066 bp) 112481 reads (1089002854 bp) 112530 reads (1089487994 bp) 112582 reads (1089977018 bp) 112634 reads (1090462815 bp) 112686 reads (1090949238 bp) 112737 reads (1091439401 bp) 112789 reads (1091930969 bp) 112834 reads (1092417946 bp) 112885 reads (1092905200 bp) 112935 reads (1093394175 bp) 112988 reads (1093885249 bp) 113039 reads (1094373395 bp) 113089 reads (1094858853 bp) 113137 reads (1095344303 bp) 113188 reads (1095834887 bp) 113236 reads (1096323282 bp) 113290 reads (1096807675 bp) 113342 reads (1097295711 bp) 113392 reads (1097785306 bp) 113440 reads (1098272392 bp) 113490 reads (1098765781 bp) 113537 reads (1099260769 bp) 113590 reads (1099745688 bp) 113639 reads (1100241195 bp) 113691 reads (1100731723 bp) 113739 reads (1101222426 bp) 113790 reads (1101709236 bp) 113839 reads (1102200223 bp) 113888 reads (1102685094 bp) 113936 reads (1103176817 bp) 113990 reads (1103660810 bp) 114041 reads (1104150763 bp) 114094 reads (1104641904 bp) 114146 reads (1105132081 bp) 114195 reads (1105627341 bp) 114247 reads (1106112409 bp) 114297 reads (1106597164 bp) 114347 reads (1107090022 bp) 114397 reads (1107576512 bp) 114446 reads (1108066861 bp) 114493 reads (1108556878 bp) 114544 reads (1109044894 bp) 114594 reads (1109538763 bp) 114646 reads (1110031924 bp) 114696 reads (1110515781 bp) 114750 reads (1111009827 bp) 114801 reads (1111496338 bp) 114852 reads (1111989295 bp) 114902 reads (1112478566 bp) 114952 reads (1112970878 bp) 115001 reads (1113456137 bp) 115053 reads (1113942861 bp) 115108 reads (1114433016 bp) 115161 reads (1114921426 bp) 115210 reads (1115410242 bp) 115257 reads (1115897108 bp) 115309 reads (1116394692 bp) 115355 reads (1116879290 bp) 115407 reads (1117373058 bp) 115457 reads (1117868472 bp) 115503 reads (1118355298 bp) 115548 reads (1118847347 bp) 115599 reads (1119334660 bp) 115648 reads (1119818334 bp) 115701 reads (1120307425 bp) 115745 reads (1120802971 bp) 115796 reads (1121300139 bp) 115845 reads (1121785699 bp) 115896 reads (1122272528 bp) 115946 reads (1122757797 bp) 115998 reads (1123242846 bp) 116046 reads (1123729336 bp) 116093 reads (1124182724 bp) Filtering long reads target: 500000000 bp keeping 500000616 bp Outputting passed long reads Uploading filtered reads: PFAS_118_3_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 38s.
Objects
Created Object Name Type Description
PFAS_118_3_flye.contigs Assembly Assembled contigs
Report
Summary
Flye results saved to: jgoff:narrative_1707234058505//kb/module/work/tmp/flye_58f18eae-7d03-47f4-b39b-114fa09cc7ad Assembly saved to: jgoff:narrative_1707234058505/PFAS_118_3_flye.contigs Assembled into 1 contigs. Avg Length: 3853966.0 bp. Contig Length Distribution (# of contigs -- min to max basepairs): 0 -- 3853965.5 to 3853965.6 bp 0 -- 3853965.6 to 3853965.7 bp 0 -- 3853965.7 to 3853965.8 bp 0 -- 3853965.8 to 3853965.9 bp 0 -- 3853965.9 to 3853966.0 bp 1 -- 3853966.0 to 3853966.1 bp 0 -- 3853966.1 to 3853966.2 bp 0 -- 3853966.2 to 3853966.3 bp 0 -- 3853966.3 to 3853966.4 bp 0 -- 3853966.4 to 3853966.5 bp
Links
Files
These are only available in the live Narrative: https://narrative.kbase.us/narrative/169829
  • flye_output.zip - Output file(s) generated by Flye
PFAS_118_3_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/169829
Assess Genome Quality with CheckM - v1.1.2
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 5m 25s.
Report
Links
Files
These are only available in the live Narrative: https://narrative.kbase.us/narrative/169829
  • CheckM_summary_table.tsv.zip - TSV Summary Table from CheckM
  • full_output.zip - Full output of CheckM
  • plots.zip - Output plots from CheckM
Annotate Microbial Assembly with RASTtk - v1.073
Annotate a bacterial or archaeal assembly using RASTtk (Rapid Annotations using Subsystems Technology toolkit).
This app completed without errors in 6m 46s.
Objects
Created Object Name Type Description
Flavobacterium_Assembly Genome RAST annotation
Summary 
The RAST algorithm was applied to annotating a genome sequence comprised of 1 contigs containing 3853966 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, 3773 new features were called, of which 165 are non-coding.
Output genome has the following feature types:
	Coding gene                     3608 
	Non-coding repeat                 87 
	Non-coding rna                    78 
Overall, the genes have 0 distinct functions. 
The genes include 0 genes with a SEED annotation ontology across 0 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 with RASTtk - v1.073
The viewer for the output created by this App is available at the original Narrative here: https://narrative.kbase.us/narrative/169829

Taxonomic Identification

Taxonomic identification was performed with Classify Microbes with GTDB-Tk -v1.7.0 using default parameters. A phylogenetic tree was constructed using the Insert Genome into SpeciesTree -v2.2.0 application. PFAS 118_3 was identified as a species belonging to the genus Flavobacterium.

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 38m 60s.
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 30s.
Report
Links
Files
These are only available in the live Narrative: https://narrative.kbase.us/narrative/169829
  • Flavobacterium_Tree.newick
  • Flavobacterium_Tree-labels.newick
  • Flavobacterium_Tree.png
  • Flavobacterium_Tree.pdf

Metabolic Modeling and Flux Balance Analysis

A metabolic model was constructed using MS2-Build Prokaryotic Metabolic Models. Gapfill Metabolic Model was used to identify the minimal set of biochemical reactions and add them to the metabolic model. Metabolite fluxes in the metabolic model were predicted using the application Run Flux Balance Analysis.

MS2 - Build Prokaryotic Metabolic Models
Using ModelSEED2 pipeline, construct draft metabolic models based on input annotated genomes.
This app completed without errors in 10m 5s.
Objects
Created Object Name Type Description
Flavobacterium_Assembly.mdl FBAModel
Report
Links
Build Metabolic Model
Construct a draft metabolic model based on an annotated genome.This app is now obsolete, replaced by the new ModelSEED2 app: MS2 - Build Prokaryotic Metabolic Models.
This app is new, and hasn't been started.
No output found.
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 1m 6s.
Objects
Created Object Name Type Description
Gapfill_188_3 FBAModel FBAModel-15 Gapfill_188_3
Gapfill_188_3.gf.0 FBA FBA-13 Gapfill_188_3.gf.0
Report
Output from Gapfill Metabolic Model
The viewer for the output created by this App is available at the original Narrative here: https://narrative.kbase.us/narrative/169829
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 30s.
Objects
Created Object Name Type Description
118_3_FBA FBA FBA-13 118_3_FBA
Report
Summary
A flux balance analysis (FBA) was performed on the metabolic model 169829/30/1 growing in Complete 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/169829

References

(1)Whitman, William B., ed. Bergey’s Manual of Systematics of Archaea and Bacteria. 1st ed. Wiley, 2015. https://doi.org/10.1002/9781118960608.

(2)Enisoglu-Atalay, Vildan, Belkis Atasever-Arslan, Bugra Yaman, Rumeysa Cebecioglu, Aykut Kul, Selma Ozilhan, Fatih Ozen, and Tunc Catal. “Chemical and Molecular Characterization of Metabolites from Flavobacterium Sp.” Edited by Gabriel Agbor. PLOS ONE 13, no. 10 (October 17, 2018): e0205817. https://doi.org/10.1371/journal.pone.0205817.

(3)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.

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

(5)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.

Released Apps

  1. Annotate Microbial 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.vThe 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. 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.
  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. Assess Genome Quality with CheckM - v1.1.2
    • 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. 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
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  4. Filter Reads with Filtlong - v0.2.1
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