Generated September 22, 2025
# Welcome to the Narrative
from IPython.display import IFrame
IFrame("https://www.kbase.us/narrative-welcome-cell/", width="100%", height="300px")
Out[1]:
from biokbase.narrative.jobs.appmanager import AppManager
AppManager().run_app_batch(
    [{
        "app_id": "kb_uploadmethods/import_fastq_noninterleaved_as_reads_from_staging",
        "tag": "release",
        "version": "5b9346463df88a422ff5d4f4cba421679f63c73f",
        "params": [{
            "fastq_fwd_staging_file_name": "Nos_PS.fastq",
            "fastq_rev_staging_file_name": None,
            "name": "NOSraw"
        }],
        "shared_params": {
            "sequencing_tech": "PacBio CLR",
            "single_genome": 1,
            "read_orientation_outward": 0,
            "insert_size_std_dev": None,
            "insert_size_mean": None
        }
    }],
    cell_id="1b4ecab4-dd52-48e1-9acc-4ac89f0b34fe",
    run_id="1f19584d-d6c0-47a2-9b9e-699da2d4689a"
)
Uses GOTTCHA2 to provide taxonomic classifications of shotgun metagenomic reads data.
This app completed without errors in 15m 29s.
Links
Files
These are only available in the live Narrative: https://narrative.kbase.us/narrative/216540
  • NOSraw_gottcha2.tsv
  • NOSraw_gottcha2.summary.tsv
  • NOSraw_gottcha2.gottcha_species.sam
  • NOSraw_gottcha2.datatable.html
  • NOSraw_gottcha2.out.list
  • NOSraw_gottcha2.full.tsv
  • NOSraw_gottcha2.krona.html
  • NOSraw_index.html
  • NOSraw_gottcha2.gottcha_species.log
  • NOSraw_gottcha2.lineage.tsv
  • NOSraw_gottcha2.tree.svg
  • NOSraw_gottcha2.out.tab_tree
  • NOSraw.zip
Allows users to perform taxonomic classification of shotgun metagenomic read data with Kaiju.
This app completed without errors in 3m 23s.
Links
Files
These are only available in the live Narrative: https://narrative.kbase.us/narrative/216540
  • kaiju_classifications.zip
  • kaiju_summaries.zip
  • krona_data.zip
  • stacked_bar_abundance_plots_PNG+PDF.zip
Assemble long reads using the Flye assembler.
This app completed without errors in 46m 56s.
Objects
Created Object Name Type Description
Nostoc_meta_flye.contigs Assembly Assembled contigs
Summary
Flye results saved to: nnp_552:narrative_1746738811614//kb/module/work/tmp/flye_27b8082b-aae7-4a65-aacc-9b81a8acbdba Assembly saved to: nnp_552:narrative_1746738811614/Nostoc_meta_flye.contigs Assembled into 14 contigs. Avg Length: 1072275.2857142857 bp. Contig Length Distribution (# of contigs -- min to max basepairs): 11 -- 45372.0 to 736105.2 bp 1 -- 736105.2 to 1426838.4 bp 0 -- 1426838.4 to 2117571.5999999996 bp 0 -- 2117571.5999999996 to 2808304.8 bp 1 -- 2808304.8 to 3499038.0 bp 0 -- 3499038.0 to 4189771.1999999997 bp 0 -- 4189771.1999999997 to 4880504.399999999 bp 0 -- 4880504.399999999 to 5571237.6 bp 0 -- 5571237.6 to 6261970.8 bp 1 -- 6261970.8 to 6952704.0 bp
Links
Files
These are only available in the live Narrative: https://narrative.kbase.us/narrative/216540
  • flye_output.zip - Output file(s) generated by Flye
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 2m 25s.
Links
Files
These are only available in the live Narrative: https://narrative.kbase.us/narrative/216540
  • CheckM_summary_table.tsv.zip - TSV Summary Table from CheckM
  • full_output.zip - Full output of CheckM
  • plots.zip - Output plots from CheckM
Group assembled metagenomic contigs into lineages (Bins) using depth-of-coverage, nucleotide composition, and marker genes.
This app completed without errors in 7h 58m 17s.
Objects
Created Object Name Type Description
MaxBin2_Nostoc_meta_flye BinnedContigs BinnedContigs from MaxBin2
Links
Files
These are only available in the live Narrative: https://narrative.kbase.us/narrative/216540
  • maxbin_result.zip - File(s) generated by MaxBin2 App
Output from Bin Contigs using MaxBin2 - v2.2.4
The viewer for the output created by this App is available at the original Narrative here: https://narrative.kbase.us/narrative/216540
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. Creates a new BinnedContigs object with High Quality bins that pass user-defined thresholds for Completeness and Contamination.
This app completed without errors in 8m 3s.
Objects
Created Object Name Type Description
CheckM_HQ_bins.BinnedContigs BinnedContigs HQ BinnedContigs CheckM_HQ_bins.BinnedContigs
Links
Files
These are only available in the live Narrative: https://narrative.kbase.us/narrative/216540
  • CheckM_summary_table.tsv.zip - TSV Summary Table from CheckM
  • full_output.zip - Full output of CheckM
  • plots.zip - Output plots from CheckM
Obtain objective taxonomic assignments for bacterial and archaeal genomes based on the Genome Taxonomy Database (GTDB)
This app completed without errors in 31m 3s.
Objects
Created Object Name Type Description
Bin.001.fasta_assembly Assembly Added GTDB lineage
Bin.002.fasta_assembly Assembly Added GTDB lineage
Bin.003.fasta_assembly Assembly Added GTDB lineage
extracted_bins.AssemblySet AssemblySet Added GTDB lineage
Links
Files
These are only available in the live Narrative: https://narrative.kbase.us/narrative/216540
  • gtdbtk.backbone.bac120.classify.tree - gtdbtk.backbone.bac120.classify.tree - whole tree GTDB formatted Newick
  • gtdbtk.backbone.bac120.classify-ITOL.tree - gtdbtk.backbone.bac120.classify-ITOL.tree - whole tree ITOL formatted Newick
  • gtdbtk.bac120.classify.tree.4.tree - gtdbtk.bac120.classify.tree.4.tree - whole tree GTDB formatted Newick
  • gtdbtk.bac120.classify.tree.4-ITOL.tree - gtdbtk.bac120.classify.tree.4-ITOL.tree - whole tree ITOL formatted Newick
  • gtdbtk.bac120.classify.tree.6.tree - gtdbtk.bac120.classify.tree.6.tree - whole tree GTDB formatted Newick
  • gtdbtk.bac120.classify.tree.6-ITOL.tree - gtdbtk.bac120.classify.tree.6-ITOL.tree - whole tree ITOL formatted Newick
  • gtdbtk.backbone.bac120.classify-proximals.tree - gtdbtk.backbone.bac120.classify-proximals.tree - Newick
  • gtdbtk.backbone.bac120.classify-trimmed.tree - gtdbtk.backbone.bac120.classify-trimmed.tree - Newick
  • gtdbtk.backbone.bac120.classify-lineages.map - gtdbtk.backbone.bac120.classify-lineages.map - GTDB lineage
  • gtdbtk.backbone.bac120.classify-trimmed.tree-rectangle.PNG - gtdbtk.backbone.bac120.classify-trimmed.tree - Image
  • gtdbtk.backbone.bac120.classify-trimmed.tree-rectangle.PDF - gtdbtk.backbone.bac120.classify-trimmed.tree - Image
  • gtdbtk.backbone.bac120.classify-trimmed.tree-circle.PNG - gtdbtk.backbone.bac120.classify-trimmed.tree - Image
  • gtdbtk.backbone.bac120.classify-trimmed.tree-circle.PDF - gtdbtk.backbone.bac120.classify-trimmed.tree - Image
  • gtdbtk.backbone.bac120.classify-trimmed.tree-circle-ultrametric.PNG - gtdbtk.backbone.bac120.classify-trimmed.tree - Image
  • gtdbtk.backbone.bac120.classify-trimmed.tree-circle-ultrametric.PDF - gtdbtk.backbone.bac120.classify-trimmed.tree - Image
  • gtdbtk.bac120.classify.tree.4-proximals.tree - gtdbtk.bac120.classify.tree.4-proximals.tree - Newick
  • gtdbtk.bac120.classify.tree.4-trimmed.tree - gtdbtk.bac120.classify.tree.4-trimmed.tree - Newick
  • gtdbtk.bac120.classify.tree.4-lineages.map - gtdbtk.bac120.classify.tree.4-lineages.map - GTDB lineage
  • gtdbtk.bac120.classify.tree.4-trimmed.tree-rectangle.PNG - gtdbtk.bac120.classify.tree.4-trimmed.tree - Image
  • gtdbtk.bac120.classify.tree.4-trimmed.tree-rectangle.PDF - gtdbtk.bac120.classify.tree.4-trimmed.tree - Image
  • gtdbtk.bac120.classify.tree.4-trimmed.tree-circle.PNG - gtdbtk.bac120.classify.tree.4-trimmed.tree - Image
  • gtdbtk.bac120.classify.tree.4-trimmed.tree-circle.PDF - gtdbtk.bac120.classify.tree.4-trimmed.tree - Image
  • gtdbtk.bac120.classify.tree.4-trimmed.tree-circle-ultrametric.PNG - gtdbtk.bac120.classify.tree.4-trimmed.tree - Image
  • gtdbtk.bac120.classify.tree.4-trimmed.tree-circle-ultrametric.PDF - gtdbtk.bac120.classify.tree.4-trimmed.tree - Image
  • gtdbtk.bac120.classify.tree.6-proximals.tree - gtdbtk.bac120.classify.tree.6-proximals.tree - Newick
  • gtdbtk.bac120.classify.tree.6-trimmed.tree - gtdbtk.bac120.classify.tree.6-trimmed.tree - Newick
  • gtdbtk.bac120.classify.tree.6-lineages.map - gtdbtk.bac120.classify.tree.6-lineages.map - GTDB lineage
  • gtdbtk.bac120.classify.tree.6-trimmed.tree-rectangle.PNG - gtdbtk.bac120.classify.tree.6-trimmed.tree - Image
  • gtdbtk.bac120.classify.tree.6-trimmed.tree-rectangle.PDF - gtdbtk.bac120.classify.tree.6-trimmed.tree - Image
  • gtdbtk.bac120.classify.tree.6-trimmed.tree-circle.PNG - gtdbtk.bac120.classify.tree.6-trimmed.tree - Image
  • gtdbtk.bac120.classify.tree.6-trimmed.tree-circle.PDF - gtdbtk.bac120.classify.tree.6-trimmed.tree - Image
  • gtdbtk.bac120.classify.tree.6-trimmed.tree-circle-ultrametric.PNG - gtdbtk.bac120.classify.tree.6-trimmed.tree - Image
  • gtdbtk.bac120.classify.tree.6-trimmed.tree-circle-ultrametric.PDF - gtdbtk.bac120.classify.tree.6-trimmed.tree - Image
  • GTDB-Tk_classify_wf.zip - GTDB-Tk Classify WF output
Extract a bin as an Assembly from a BinnedContig dataset
This app completed without errors in 1m 20s.
Objects
Created Object Name Type Description
extracted_bins.AssemblySet AssemblySet Assembly set of extracted assemblies
Bin.001.fasta_assembly Assembly Assembly object of extracted contigs
Bin.002.fasta_assembly Assembly Assembly object of extracted contigs
Bin.003.fasta_assembly Assembly Assembly object of extracted contigs
Summary
Job Finished Generated Assembly Reference: 216540/20/1, 216540/21/1, 216540/22/1 Generated Assembly Set: 216540/23/1
Annotate Assembly and Re-annotate Genomes with Prokka annotation pipeline.
This app completed without errors in 4m 5s.
Objects
Created Object Name Type Description
bin1_anno Genome Annotated Genome
Summary
Annotated Genome saved to: nnp_552:narrative_1746738811614/bin1_anno Number of genes predicted: 5988 Number of protein coding genes: 5912 Number of genes with non-hypothetical function: 2672 Number of genes with EC-number: 1247 Number of genes with Seed Subsystem Ontology: 0 Average protein length: 325 aa.
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: https://narrative.kbase.us/narrative/216540
Annotate Assembly and Re-annotate Genomes with Prokka annotation pipeline.
This app completed without errors in 2m 26s.
Objects
Created Object Name Type Description
bin2_anno Genome Annotated Genome
Summary
Annotated Genome saved to: nnp_552:narrative_1746738811614/bin2_anno Number of genes predicted: 3237 Number of protein coding genes: 3186 Number of genes with non-hypothetical function: 1681 Number of genes with EC-number: 859 Number of genes with Seed Subsystem Ontology: 0 Average protein length: 321 aa.
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: https://narrative.kbase.us/narrative/216540
Annotate Assembly and Re-annotate Genomes with Prokka annotation pipeline.
This app completed without errors in 2m 40s.
Objects
Created Object Name Type Description
bin3_anno Genome Annotated Genome
Summary
Annotated Genome saved to: nnp_552:narrative_1746738811614/bin3_anno Number of genes predicted: 4475 Number of protein coding genes: 4413 Number of genes with non-hypothetical function: 2423 Number of genes with EC-number: 1076 Number of genes with Seed Subsystem Ontology: 0 Average protein length: 308 aa.
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: https://narrative.kbase.us/narrative/216540
Add one or more Genomes to a KBase SpeciesTree.
This app completed without errors in 4m 54s.
Files
These are only available in the live Narrative: https://narrative.kbase.us/narrative/216540
  • AllBinSPTree.newick
  • AllBinSPTree-labels.newick
  • AllBinSPTree.png
  • AllBinSPTree.pdf
Allows users to create a GenomeSet object.
This app completed without errors in 20s.
Objects
Created Object Name Type Description
bin1_Gset GenomeSet KButil_Build_GenomeSet
Summary
genomes in output set bin1_Gset: 1
Allows users to create a GenomeSet object.
This app completed without errors in 19s.
Objects
Created Object Name Type Description
bin2_Gset GenomeSet KButil_Build_GenomeSet
Summary
genomes in output set bin2_Gset: 1
Allows users to create a GenomeSet object.
This app completed without errors in 16s.
Objects
Created Object Name Type Description
bin3_Gset GenomeSet KButil_Build_GenomeSet
Summary
genomes in output set bin3_Gset: 1
Obtain objective taxonomic assignments for bacterial and archaeal genomes based on the Genome Taxonomy Database (GTDB)
This app completed without errors in 19m 49s.
Objects
Created Object Name Type Description
bin1_anno Genome Taxonomy and taxon_assignment updated with GTDB
bin1_Gset GenomeSet Taxonomy and taxon_assignment updated with GTDB
Links
Files
These are only available in the live Narrative: https://narrative.kbase.us/narrative/216540
  • gtdbtk.backbone.bac120.classify.tree - gtdbtk.backbone.bac120.classify.tree - whole tree GTDB formatted Newick
  • gtdbtk.backbone.bac120.classify-ITOL.tree - gtdbtk.backbone.bac120.classify-ITOL.tree - whole tree ITOL formatted Newick
  • gtdbtk.bac120.classify.tree.6.tree - gtdbtk.bac120.classify.tree.6.tree - whole tree GTDB formatted Newick
  • gtdbtk.bac120.classify.tree.6-ITOL.tree - gtdbtk.bac120.classify.tree.6-ITOL.tree - whole tree ITOL formatted Newick
  • gtdbtk.backbone.bac120.classify-proximals.tree - gtdbtk.backbone.bac120.classify-proximals.tree - Newick
  • gtdbtk.backbone.bac120.classify-trimmed.tree - gtdbtk.backbone.bac120.classify-trimmed.tree - Newick
  • gtdbtk.backbone.bac120.classify-lineages.map - gtdbtk.backbone.bac120.classify-lineages.map - GTDB lineage
  • gtdbtk.backbone.bac120.classify-trimmed.tree-rectangle.PNG - gtdbtk.backbone.bac120.classify-trimmed.tree - Image
  • gtdbtk.backbone.bac120.classify-trimmed.tree-rectangle.PDF - gtdbtk.backbone.bac120.classify-trimmed.tree - Image
  • gtdbtk.backbone.bac120.classify-trimmed.tree-circle.PNG - gtdbtk.backbone.bac120.classify-trimmed.tree - Image
  • gtdbtk.backbone.bac120.classify-trimmed.tree-circle.PDF - gtdbtk.backbone.bac120.classify-trimmed.tree - Image
  • gtdbtk.backbone.bac120.classify-trimmed.tree-circle-ultrametric.PNG - gtdbtk.backbone.bac120.classify-trimmed.tree - Image
  • gtdbtk.backbone.bac120.classify-trimmed.tree-circle-ultrametric.PDF - gtdbtk.backbone.bac120.classify-trimmed.tree - Image
  • gtdbtk.bac120.classify.tree.6-proximals.tree - gtdbtk.bac120.classify.tree.6-proximals.tree - Newick
  • gtdbtk.bac120.classify.tree.6-trimmed.tree - gtdbtk.bac120.classify.tree.6-trimmed.tree - Newick
  • gtdbtk.bac120.classify.tree.6-lineages.map - gtdbtk.bac120.classify.tree.6-lineages.map - GTDB lineage
  • gtdbtk.bac120.classify.tree.6-trimmed.tree-rectangle.PNG - gtdbtk.bac120.classify.tree.6-trimmed.tree - Image
  • gtdbtk.bac120.classify.tree.6-trimmed.tree-rectangle.PDF - gtdbtk.bac120.classify.tree.6-trimmed.tree - Image
  • gtdbtk.bac120.classify.tree.6-trimmed.tree-circle.PNG - gtdbtk.bac120.classify.tree.6-trimmed.tree - Image
  • gtdbtk.bac120.classify.tree.6-trimmed.tree-circle.PDF - gtdbtk.bac120.classify.tree.6-trimmed.tree - Image
  • gtdbtk.bac120.classify.tree.6-trimmed.tree-circle-ultrametric.PNG - gtdbtk.bac120.classify.tree.6-trimmed.tree - Image
  • gtdbtk.bac120.classify.tree.6-trimmed.tree-circle-ultrametric.PDF - gtdbtk.bac120.classify.tree.6-trimmed.tree - Image
  • GTDB-Tk_classify_wf.zip - GTDB-Tk Classify WF output
Obtain objective taxonomic assignments for bacterial and archaeal genomes based on the Genome Taxonomy Database (GTDB)
This app completed without errors in 20m 22s.
Objects
Created Object Name Type Description
bin2_anno Genome Taxonomy and taxon_assignment updated with GTDB
bin2_Gset GenomeSet Taxonomy and taxon_assignment updated with GTDB
Links
Files
These are only available in the live Narrative: https://narrative.kbase.us/narrative/216540
  • gtdbtk.backbone.bac120.classify.tree - gtdbtk.backbone.bac120.classify.tree - whole tree GTDB formatted Newick
  • gtdbtk.backbone.bac120.classify-ITOL.tree - gtdbtk.backbone.bac120.classify-ITOL.tree - whole tree ITOL formatted Newick
  • gtdbtk.bac120.classify.tree.4.tree - gtdbtk.bac120.classify.tree.4.tree - whole tree GTDB formatted Newick
  • gtdbtk.bac120.classify.tree.4-ITOL.tree - gtdbtk.bac120.classify.tree.4-ITOL.tree - whole tree ITOL formatted Newick
  • gtdbtk.backbone.bac120.classify-proximals.tree - gtdbtk.backbone.bac120.classify-proximals.tree - Newick
  • gtdbtk.backbone.bac120.classify-trimmed.tree - gtdbtk.backbone.bac120.classify-trimmed.tree - Newick
  • gtdbtk.backbone.bac120.classify-lineages.map - gtdbtk.backbone.bac120.classify-lineages.map - GTDB lineage
  • gtdbtk.backbone.bac120.classify-trimmed.tree-rectangle.PNG - gtdbtk.backbone.bac120.classify-trimmed.tree - Image
  • gtdbtk.backbone.bac120.classify-trimmed.tree-rectangle.PDF - gtdbtk.backbone.bac120.classify-trimmed.tree - Image
  • gtdbtk.backbone.bac120.classify-trimmed.tree-circle.PNG - gtdbtk.backbone.bac120.classify-trimmed.tree - Image
  • gtdbtk.backbone.bac120.classify-trimmed.tree-circle.PDF - gtdbtk.backbone.bac120.classify-trimmed.tree - Image
  • gtdbtk.backbone.bac120.classify-trimmed.tree-circle-ultrametric.PNG - gtdbtk.backbone.bac120.classify-trimmed.tree - Image
  • gtdbtk.backbone.bac120.classify-trimmed.tree-circle-ultrametric.PDF - gtdbtk.backbone.bac120.classify-trimmed.tree - Image
  • gtdbtk.bac120.classify.tree.4-proximals.tree - gtdbtk.bac120.classify.tree.4-proximals.tree - Newick
  • gtdbtk.bac120.classify.tree.4-trimmed.tree - gtdbtk.bac120.classify.tree.4-trimmed.tree - Newick
  • gtdbtk.bac120.classify.tree.4-lineages.map - gtdbtk.bac120.classify.tree.4-lineages.map - GTDB lineage
  • gtdbtk.bac120.classify.tree.4-trimmed.tree-rectangle.PNG - gtdbtk.bac120.classify.tree.4-trimmed.tree - Image
  • gtdbtk.bac120.classify.tree.4-trimmed.tree-rectangle.PDF - gtdbtk.bac120.classify.tree.4-trimmed.tree - Image
  • gtdbtk.bac120.classify.tree.4-trimmed.tree-circle.PNG - gtdbtk.bac120.classify.tree.4-trimmed.tree - Image
  • gtdbtk.bac120.classify.tree.4-trimmed.tree-circle.PDF - gtdbtk.bac120.classify.tree.4-trimmed.tree - Image
  • gtdbtk.bac120.classify.tree.4-trimmed.tree-circle-ultrametric.PNG - gtdbtk.bac120.classify.tree.4-trimmed.tree - Image
  • gtdbtk.bac120.classify.tree.4-trimmed.tree-circle-ultrametric.PDF - gtdbtk.bac120.classify.tree.4-trimmed.tree - Image
  • GTDB-Tk_classify_wf.zip - GTDB-Tk Classify WF output
Obtain objective taxonomic assignments for bacterial and archaeal genomes based on the Genome Taxonomy Database (GTDB)
This app completed without errors in 19m 53s.
Objects
Created Object Name Type Description
bin3_anno Genome Taxonomy and taxon_assignment updated with GTDB
bin3_Gset GenomeSet Taxonomy and taxon_assignment updated with GTDB
Links
Files
These are only available in the live Narrative: https://narrative.kbase.us/narrative/216540
  • gtdbtk.backbone.bac120.classify.tree - gtdbtk.backbone.bac120.classify.tree - whole tree GTDB formatted Newick
  • gtdbtk.backbone.bac120.classify-ITOL.tree - gtdbtk.backbone.bac120.classify-ITOL.tree - whole tree ITOL formatted Newick
  • gtdbtk.bac120.classify.tree.4.tree - gtdbtk.bac120.classify.tree.4.tree - whole tree GTDB formatted Newick
  • gtdbtk.bac120.classify.tree.4-ITOL.tree - gtdbtk.bac120.classify.tree.4-ITOL.tree - whole tree ITOL formatted Newick
  • gtdbtk.backbone.bac120.classify-proximals.tree - gtdbtk.backbone.bac120.classify-proximals.tree - Newick
  • gtdbtk.backbone.bac120.classify-trimmed.tree - gtdbtk.backbone.bac120.classify-trimmed.tree - Newick
  • gtdbtk.backbone.bac120.classify-lineages.map - gtdbtk.backbone.bac120.classify-lineages.map - GTDB lineage
  • gtdbtk.backbone.bac120.classify-trimmed.tree-rectangle.PNG - gtdbtk.backbone.bac120.classify-trimmed.tree - Image
  • gtdbtk.backbone.bac120.classify-trimmed.tree-rectangle.PDF - gtdbtk.backbone.bac120.classify-trimmed.tree - Image
  • gtdbtk.backbone.bac120.classify-trimmed.tree-circle.PNG - gtdbtk.backbone.bac120.classify-trimmed.tree - Image
  • gtdbtk.backbone.bac120.classify-trimmed.tree-circle.PDF - gtdbtk.backbone.bac120.classify-trimmed.tree - Image
  • gtdbtk.backbone.bac120.classify-trimmed.tree-circle-ultrametric.PNG - gtdbtk.backbone.bac120.classify-trimmed.tree - Image
  • gtdbtk.backbone.bac120.classify-trimmed.tree-circle-ultrametric.PDF - gtdbtk.backbone.bac120.classify-trimmed.tree - Image
  • gtdbtk.bac120.classify.tree.4-proximals.tree - gtdbtk.bac120.classify.tree.4-proximals.tree - Newick
  • gtdbtk.bac120.classify.tree.4-trimmed.tree - gtdbtk.bac120.classify.tree.4-trimmed.tree - Newick
  • gtdbtk.bac120.classify.tree.4-lineages.map - gtdbtk.bac120.classify.tree.4-lineages.map - GTDB lineage
  • gtdbtk.bac120.classify.tree.4-trimmed.tree-rectangle.PNG - gtdbtk.bac120.classify.tree.4-trimmed.tree - Image
  • gtdbtk.bac120.classify.tree.4-trimmed.tree-rectangle.PDF - gtdbtk.bac120.classify.tree.4-trimmed.tree - Image
  • gtdbtk.bac120.classify.tree.4-trimmed.tree-circle.PNG - gtdbtk.bac120.classify.tree.4-trimmed.tree - Image
  • gtdbtk.bac120.classify.tree.4-trimmed.tree-circle.PDF - gtdbtk.bac120.classify.tree.4-trimmed.tree - Image
  • gtdbtk.bac120.classify.tree.4-trimmed.tree-circle-ultrametric.PNG - gtdbtk.bac120.classify.tree.4-trimmed.tree - Image
  • gtdbtk.bac120.classify.tree.4-trimmed.tree-circle-ultrametric.PDF - gtdbtk.bac120.classify.tree.4-trimmed.tree - Image
  • GTDB-Tk_classify_wf.zip - GTDB-Tk Classify WF output
Add one or more Genomes to a KBase SpeciesTree.
This app completed without errors in 4m 7s.
Files
These are only available in the live Narrative: https://narrative.kbase.us/narrative/216540
  • bin3tree.newick
  • bin3tree-labels.newick
  • bin3tree.png
  • bin3tree.pdf
Annotate or re-annotate genome/assembly using RASTtk (Rapid Annotations using Subsystems Technology toolkit).
This app completed without errors in 11m 48s.
Objects
Created Object Name Type Description
Bin3_anno_RASTtk Genome RAST re-annotated genome
Summary
The RAST algorithm was applied to annotating a genome sequence comprised of 10 contigs containing 4577652 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, 4637 new features were called, of which 108 are non-coding. Output genome has the following feature types: Coding gene 4529 Non-coding crispr_array 1 Non-coding crispr_repeat 10 Non-coding crispr_spacer 9 Non-coding repeat 31 Non-coding rna 57 The number of distinct functions can exceed the number of genes because some genes have multiple functions.
Links
Annotate Metagenome Assembly with Prokka annotation pipeline.
This app produced errors.
No output found.

Apps

  1. 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
  2. Annotate Genome/Assembly with RASTtk - v1.073
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  3. Annotate Metagenome Assembly with Prokka - v1.14.5
    • Seemann T. Prokka: rapid prokaryotic genome annotation. Bioinformatics. 2014;30: 2068 2069. doi:10.1093/bioinformatics/btu153
  4. Assemble Long Reads with Flye - v2.9.4
    • [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
  5. 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:
  6. Bin Contigs using MaxBin2 - v2.2.4
    • Wu Y-W, Simmons BA, Singer SW. MaxBin 2.0: an automated binning algorithm to recover genomes from multiple metagenomic datasets. Bioinformatics. 2016;32: 605 607. doi:10.1093/bioinformatics/btv638 (2) 1. Wu Y-W, Tang Y-H, Tringe SG, Simmons BA, Singer SW. MaxBin: an automated binning method to recover individual genomes from metagenomes using an expectation-maximization algorithm. Microbiome. 2014;2: 26. doi:10.1186/2049-2618-2-26
    • Wu Y-W, Tang Y-H, Tringe SG, Simmons BA, Singer SW. MaxBin: an automated binning method to recover individual genomes from metagenomes using an expectation-maximization algorithm. Microbiome. 2014;2: 26. doi:10.1186/2049-2618-2-26
    • Maxbin2 source:
    • Maxbin source:
  7. Build GenomeSet - v1.7.6
    • Chivian D, Jungbluth SP, Dehal PS, Wood-Charlson EM, Canon RS, Allen BH, Clark MM, Gu T, Land ML, Price GA, Riehl WJ, Sneddon MW, Sutormin R, Zhang Q, Cottingham RW, Henry CS, Arkin AP. Metagenome-assembled genome extraction and analysis from microbiomes using KBase. Nat Protoc. 2023 Jan;18(1):208-238. doi: 10.1038/s41596-022-00747-x
    • 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
  8. Classify Microbes with GTDB-Tk - v2.3.2
    • Pierre-Alain Chaumeil, Aaron J Mussig, Philip Hugenholtz, Donovan H Parks. GTDB-Tk v2: memory friendly classification with the genome taxonomy database. Bioinformatics, Volume 38, Issue 23, 1 December 2022, Pages 5315 5316. DOI: https://doi.org/10.1093/bioinformatics/btac672
    • 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
    • Donovan H Parks, Maria Chuvochina, Christian Rinke, Aaron J Mussig, Pierre-Alain Chaumeil, Philip Hugenholtz. GTDB: an ongoing census of bacterial and archaeal diversity through a phylogenetically consistent, rank normalized and complete genome-based taxonomy. Nucleic Acids Research, Volume 50, Issue D1, 7 January 2022, Pages D785 D794. DOI: https://doi.org/10.1093/nar/gkab776
    • 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
    • Chivian D, Jungbluth SP, Dehal PS, Wood-Charlson EM, Canon RS, Allen BH, Clark MM, Gu T, Land ML, Price GA, Riehl WJ, Sneddon MW, Sutormin R, Zhang Q, Cottingham RW, Henry CS, Arkin AP. Metagenome-assembled genome extraction and analysis from microbiomes using KBase. Nat Protoc. 2023 Jan;18(1):208-238. doi: 10.1038/s41596-022-00747-x
    • 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
    • Ondov BD, Treangen TJ, Melsted P, Mallonee AB, Bergman NH, Koren S, Phillippy AM. Mash: fast genome and metagenome distance estimation using MinHash. Genome Biol. 2016 Jun 20;17(1):132. DOI: 10.1186/s13059-016-0997-x
  9. Classify Taxonomy of Metagenomic Reads with GOTTCHA2 - v2.1.7
    • Tracey Allen K. Freitas, Po-E Li, Matthew B. Scholz and Patrick S. G. Chain (2015) Accurate read-based metagenome characterization using a hierarchical suite of unique signatures, Nucleic Acids Research (DOI: 10.1093/nar/gkv180)
    • GOTTCHA2 DBs from:
    • Krona homepage:
    • Github for Krona:
  10. Classify Taxonomy of Metagenomic Reads with Kaiju - v1.9.0
    • Chivian D, et al. Metagenome-assembled genome extraction and analysis from microbiomes using KBase. Nat Protoc. 2023 Jan;18(1):208-238. doi: 10.1038/s41596-022-00747-x
    • Menzel P, Ng KL, Krogh A. Fast and sensitive taxonomic classification for metagenomics with Kaiju. Nat Commun. 2016;7: 11257. doi:10.1038/ncomms11257
    • Ondov BD, Bergman NH, Phillippy AM. Interactive metagenomic visualization in a Web browser. BMC Bioinformatics. 2011;12: 385. doi:10.1186/1471-2105-12-385
    • Kaiju Homepage:
    • Kaiju DBs from:
    • Github for Kaiju:
    • Krona homepage:
    • Github for Krona:
  11. Extract Bins as Assemblies from BinnedContigs - v1.0.2
    • 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
  12. Filter Bins by 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:
  13. 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