Generated December 16, 2024
# 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": "Microcystis_S210_R1_001.fastq",
            "fastq_rev_staging_file_name": "Microcystis_S210_R2_001.fastq",
            "name": "1-Microcystis-Fastq-Import"
        }, {
            "fastq_fwd_staging_file_name": "Dolichospermum_S211_R1_001.fastq",
            "fastq_rev_staging_file_name": "Dolichospermum_S211_R2_001.fastq",
            "name": "1-Anabaena-Fastq-Import"
        }],
        "shared_params": {
            "sequencing_tech": "Illumina",
            "single_genome": 1,
            "read_orientation_outward": 0,
            "insert_size_std_dev": None,
            "insert_size_mean": None
        }
    }],
    cell_id="43d8f995-2969-49a1-9d51-8c07f2c1a65c",
    run_id="896c91af-1544-41ce-ad84-4d5285f541a1"
)
A quality control application for high throughput sequence data.
This app completed without errors in 4m 27s.
Links
Files
These are only available in the live Narrative: https://narrative.kbase.us/narrative/201678
  • 1-Microcystis-Fastq-Import_141595_4_1.rev_fastqc.zip - Zip file generated by fastqc that contains original images seen in the report
  • 1-Microcystis-Fastq-Import_141595_4_1.fwd_fastqc.zip - Zip file generated by fastqc that contains original images seen in the report
A quality control application for high throughput sequence data.
This app completed without errors in 4m 28s.
Links
Files
These are only available in the live Narrative: https://narrative.kbase.us/narrative/201678
  • 1-Anabaena-Fastq-Import_141595_2_1.rev_fastqc.zip - Zip file generated by fastqc that contains original images seen in the report
  • 1-Anabaena-Fastq-Import_141595_2_1.fwd_fastqc.zip - Zip file generated by fastqc that contains original images seen in the report
Runs the JGI reads data preprocessing pipeline
This app completed without errors in 1h 17m 53s.
Objects
Created Object Name Type Description
2-Microcystis-RQCFilter PairedEndLibrary Filtered reads library
Links
Files
These are only available in the live Narrative: https://narrative.kbase.us/narrative/201678
  • rqcfilter_report.zip - RQCFilter report files
Runs the JGI reads data preprocessing pipeline
This app completed without errors in 1h 12m 40s.
Objects
Created Object Name Type Description
2-Anabaena-RQCFilter PairedEndLibrary Filtered reads library
Links
Files
These are only available in the live Narrative: https://narrative.kbase.us/narrative/201678
  • rqcfilter_report.zip - RQCFilter report files
A quality control application for high throughput sequence data.
This app completed without errors in 4m 24s.
Links
Files
These are only available in the live Narrative: https://narrative.kbase.us/narrative/201678
  • 2-Microcystis-RQCFilter_141595_8_1.rev_fastqc.zip - Zip file generated by fastqc that contains original images seen in the report
  • 2-Microcystis-RQCFilter_141595_8_1.fwd_fastqc.zip - Zip file generated by fastqc that contains original images seen in the report
A quality control application for high throughput sequence data.
This app completed without errors in 4m 19s.
Links
Files
These are only available in the live Narrative: https://narrative.kbase.us/narrative/201678
  • 2-Anabaena-RQCFilter_141595_11_1.rev_fastqc.zip - Zip file generated by fastqc that contains original images seen in the report
  • 2-Anabaena-RQCFilter_141595_11_1.fwd_fastqc.zip - Zip file generated by fastqc that contains original images seen in the report
Trim paired- or single-end Illumina reads with Trimmomatic.
This app completed without errors in 8m 45s.
Objects
Created Object Name Type Description
3-Microcystis-Trimmomatic_paired PairedEndLibrary Trimmed Reads
3-Microcystis-Trimmomatic_unpaired_fwd SingleEndLibrary Trimmed Unpaired Forward Reads
3-Microcystis-Trimmomatic_unpaired_rev SingleEndLibrary Trimmed Unpaired Reverse Reads
Trim paired- or single-end Illumina reads with Trimmomatic.
This app completed without errors in 9m 0s.
Objects
Created Object Name Type Description
3-Anabaena-Trimmomatic_paired PairedEndLibrary Trimmed Reads
3-Anabaena-Trimmomatic_unpaired_fwd SingleEndLibrary Trimmed Unpaired Forward Reads
3-Anabaena-Trimmomatic_unpaired_rev SingleEndLibrary Trimmed Unpaired Reverse Reads
Allows users to perform taxonomic classification of shotgun metagenomic read data with Kaiju.
This app completed without errors in 12m 36s.
Links
Files
These are only available in the live Narrative: https://narrative.kbase.us/narrative/201678
  • kaiju_classifications.zip
  • kaiju_summaries.zip
  • krona_data.zip
  • stacked_bar_abundance_plots_PNG+PDF.zip
Allows users to perform taxonomic classification of shotgun metagenomic read data with Kaiju.
This app completed without errors in 12m 31s.
Links
Files
These are only available in the live Narrative: https://narrative.kbase.us/narrative/201678
  • kaiju_classifications.zip
  • kaiju_summaries.zip
  • krona_data.zip
  • stacked_bar_abundance_plots_PNG+PDF.zip
Align sequencing reads to long reference prokaryotic genome sequences using Bowtie2.
This app completed without errors in 1h 9m 39s.
No output found.
Assemble metagenomic reads using the SPAdes assembler.
This app completed without errors in 3h 30m 0s.
Objects
Created Object Name Type Description
4-Microcystis-metaSPAdes.Assembly Assembly Assembled contigs
Summary
Assembly saved to: shatara:narrative_1679504252385/4-Microcystis-metaSPAdes.Assembly Assembled into 3146 contigs. Avg Length: 12430.58200890019 bp. Contig Length Distribution (# of contigs -- min to max basepairs): 3035 -- 2000.0 to 80379.1 bp 67 -- 80379.1 to 158758.2 bp 18 -- 158758.2 to 237137.30000000002 bp 16 -- 237137.30000000002 to 315516.4 bp 4 -- 315516.4 to 393895.5 bp 2 -- 393895.5 to 472274.60000000003 bp 2 -- 472274.60000000003 to 550653.7000000001 bp 0 -- 550653.7000000001 to 629032.8 bp 0 -- 629032.8 to 707411.9 bp 2 -- 707411.9 to 785791.0 bp
Links
Assemble metagenomic reads using the SPAdes assembler.
This app completed without errors in 53m 41s.
Objects
Created Object Name Type Description
4-Anabaena-metaSPAdes.Assembly Assembly Assembled contigs
Summary
Assembly saved to: shatara:narrative_1679504252385/4-Anabaena-metaSPAdes.Assembly Assembled into 4345 contigs. Avg Length: 12085.423475258918 bp. Contig Length Distribution (# of contigs -- min to max basepairs): 4318 -- 2000.0 to 243227.1 bp 21 -- 243227.1 to 484454.2 bp 4 -- 484454.2 to 725681.3 bp 1 -- 725681.3 to 966908.4 bp 0 -- 966908.4 to 1208135.5 bp 0 -- 1208135.5 to 1449362.6 bp 0 -- 1449362.6 to 1690589.7 bp 0 -- 1690589.7 to 1931816.8 bp 0 -- 1931816.8 to 2173043.9 bp 1 -- 2173043.9 to 2414271.0 bp
Links
Group assembled metagenomic contigs into lineages (Bins) using depth-of-coverage, nucleotide composition, and marker genes.
This app completed without errors in 11m 19s.
Objects
Created Object Name Type Description
5-Anabaena-MaxBin2Contigs BinnedContigs BinnedContigs from MaxBin2
Links
Files
These are only available in the live Narrative: https://narrative.kbase.us/narrative/201678
  • maxbin_result.zip - File(s) generated by MaxBin2 App
Group assembled metagenomic contigs into lineages (Bins) using depth-of-coverage, nucleotide composition, and marker genes.
This app completed without errors in 10m 14s.
Objects
Created Object Name Type Description
5-Microcystis-MaxBin2Contigs BinnedContigs BinnedContigs from MaxBin2
Links
Files
These are only available in the live Narrative: https://narrative.kbase.us/narrative/201678
  • maxbin_result.zip - File(s) generated by MaxBin2 App
Bin metagenomic contigs
This app completed without errors in 9m 51s.
Links
Files
These are only available in the live Narrative: https://narrative.kbase.us/narrative/201678
  • metabat_result.zip - Files generated by MetaBAT2 App
Bin metagenomic contigs
This app completed without errors in 9m 46s.
Links
Files
These are only available in the live Narrative: https://narrative.kbase.us/narrative/201678
  • metabat_result.zip - Files generated by MetaBAT2 App
Group assembled metagenomic contigs into lineages (Bins) using depth-of-coverage and nucleotide composition
This app completed without errors in 12m 60s.
Links
Files
These are only available in the live Narrative: https://narrative.kbase.us/narrative/201678
  • concoct_result.zip - Files generated by CONCOCT App
Group assembled metagenomic contigs into lineages (Bins) using depth-of-coverage and nucleotide composition
This app completed without errors in 12m 52s.
Links
Files
These are only available in the live Narrative: https://narrative.kbase.us/narrative/201678
  • concoct_result.zip - Files generated by CONCOCT App
Optimize bacterial or archaeal genome bins using a dereplication, aggregation and scoring strategy
This app completed without errors in 4m 42s.
Links
Files
These are only available in the live Narrative: https://narrative.kbase.us/narrative/201678
  • das_tool_result.zip - Files generated by kb_das_tool App
Optimize bacterial or archaeal genome bins using a dereplication, aggregation and scoring strategy
This app completed without errors in 4m 12s.
Links
Files
These are only available in the live Narrative: https://narrative.kbase.us/narrative/201678
  • das_tool_result.zip - Files generated by kb_das_tool App
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 19m 49s.
Links
Files
These are only available in the live Narrative: https://narrative.kbase.us/narrative/201678
  • CheckM_summary_table.tsv.zip - TSV Summary Table from CheckM
  • full_output.zip - Full output of CheckM
  • plots.zip - Output plots from CheckM
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 16m 34s.
Links
Files
These are only available in the live Narrative: https://narrative.kbase.us/narrative/201678
  • CheckM_summary_table.tsv.zip - TSV Summary Table from CheckM
  • full_output.zip - Full output of CheckM
  • plots.zip - Output plots from CheckM
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 17m 17s.
Objects
Created Object Name Type Description
6-AnabaenaCheckM_HQ_bins.BinnedContigs BinnedContigs HQ BinnedContigs 6-AnabaenaCheckM_HQ_bins.BinnedContigs
Links
Files
These are only available in the live Narrative: https://narrative.kbase.us/narrative/201678
  • CheckM_summary_table.tsv.zip - TSV Summary Table from CheckM
  • full_output.zip - Full output of CheckM
  • plots.zip - Output plots from CheckM
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 14m 50s.
Objects
Created Object Name Type Description
6-MicrocystisCheckM_HQ_bins.BinnedContigs BinnedContigs HQ BinnedContigs 6-MicrocystisCheckM_HQ_bins.BinnedContigs
Links
Files
These are only available in the live Narrative: https://narrative.kbase.us/narrative/201678
  • CheckM_summary_table.tsv.zip - TSV Summary Table from CheckM
  • full_output.zip - Full output of CheckM
  • plots.zip - Output plots from CheckM
Extract a bin as an Assembly from a BinnedContig dataset
This app completed without errors in 4m 0s.
Objects
Created Object Name Type Description
Microcystis_extracted_bins.AssemblySet AssemblySet Assembly set of extracted assemblies
bin.003.fastaMicrocystis_assembly Assembly Assembly object of extracted contigs
bin.005.fastaMicrocystis_assembly Assembly Assembly object of extracted contigs
bin.001.fastaMicrocystis_assembly Assembly Assembly object of extracted contigs
bin.002.fastaMicrocystis_assembly Assembly Assembly object of extracted contigs
bin.006.fastaMicrocystis_assembly Assembly Assembly object of extracted contigs
bin.008.fastaMicrocystis_assembly Assembly Assembly object of extracted contigs
bin.004.fastaMicrocystis_assembly Assembly Assembly object of extracted contigs
bin.007.fastaMicrocystis_assembly Assembly Assembly object of extracted contigs
Summary
Job Finished Generated Assembly Reference: 141595/70/1, 141595/72/1, 141595/74/1, 141595/76/1, 141595/78/1, 141595/79/1, 141595/81/1, 141595/84/1 Generated Assembly Set: 141595/85/1
Extract a bin as an Assembly from a BinnedContig dataset
This app completed without errors in 5m 34s.
Objects
Created Object Name Type Description
Anabaena_extracted_bins.AssemblySet AssemblySet Assembly set of extracted assemblies
bin.004.fastaAnabaena_assembly Assembly Assembly object of extracted contigs
bin.003.fastaAnabaena_assembly Assembly Assembly object of extracted contigs
bin.007.fastaAnabaena_assembly Assembly Assembly object of extracted contigs
bin.011.fastaAnabaena_assembly Assembly Assembly object of extracted contigs
bin.008.fastaAnabaena_assembly Assembly Assembly object of extracted contigs
bin.009.fastaAnabaena_assembly Assembly Assembly object of extracted contigs
bin.002.fastaAnabaena_assembly Assembly Assembly object of extracted contigs
bin.006.fastaAnabaena_assembly Assembly Assembly object of extracted contigs
bin.005.fastaAnabaena_assembly Assembly Assembly object of extracted contigs
bin.010.fastaAnabaena_assembly Assembly Assembly object of extracted contigs
bin.001.fastaAnabaena_assembly Assembly Assembly object of extracted contigs
Summary
Job Finished Generated Assembly Reference: 141595/69/1, 141595/71/1, 141595/73/1, 141595/75/1, 141595/77/1, 141595/80/1, 141595/82/1, 141595/83/1, 141595/87/1, 141595/88/1, 141595/89/1 Generated Assembly Set: 141595/90/1
Annotate bacterial or archaeal assemblies and/or assembly sets using RASTtk (Rapid Annotations using Subsystems Technology toolkit).
This app completed without errors in 47m 5s.
Objects
Created Object Name Type Description
bin.005.fastaAnabaena_assembly.RAST Genome RAST annotation
bin.011.fastaAnabaena_assembly.RAST Genome RAST annotation
bin.001.fastaAnabaena_assembly.RAST Genome RAST annotation
bin.008.fastaAnabaena_assembly.RAST Genome RAST annotation
bin.010.fastaAnabaena_assembly.RAST Genome RAST annotation
bin.003.fastaAnabaena_assembly.RAST Genome RAST annotation
bin.006.fastaAnabaena_assembly.RAST Genome RAST annotation
bin.002.fastaAnabaena_assembly.RAST Genome RAST annotation
bin.007.fastaAnabaena_assembly.RAST Genome RAST annotation
bin.009.fastaAnabaena_assembly.RAST Genome RAST annotation
bin.004.fastaAnabaena_assembly.RAST Genome RAST annotation
7.AnabaenaCommAnnotation GenomeSet Genome Set
Summary
The RAST algorithm was applied to annotating a genome sequence comprised of 91 contigs containing 3313966 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, 3169 new features were called, of which 113 are non-coding.
Output genome has the following feature types:
	Coding gene                     3056 
	Non-coding repeat                 82 
	Non-coding rna                    31 
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.
bin.005.fastaAnabaena_assembly succeeded!

The RAST algorithm was applied to annotating a genome sequence comprised of 50 contigs containing 4751264 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, 4449 new features were called, of which 249 are non-coding.
Output genome has the following feature types:
	Coding gene                     4200 
	Non-coding crispr_array            2 
	Non-coding crispr_repeat          91 
	Non-coding crispr_spacer          89 
	Non-coding repeat                 20 
	Non-coding rna                    47 
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.
bin.011.fastaAnabaena_assembly succeeded!

The RAST algorithm was applied to annotating a genome sequence comprised of 23 contigs containing 4068854 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, 3918 new features were called, of which 51 are non-coding.
Output genome has the following feature types:
	Coding gene                     3867 
	Non-coding repeat                  8 
	Non-coding rna                    43 
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.
bin.001.fastaAnabaena_assembly succeeded!

The RAST algorithm was applied to annotating a genome sequence comprised of 1358 contigs containing 4828739 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, 6029 new features were called, of which 31 are non-coding.
Output genome has the following feature types:
	Coding gene                     5998 
	Non-coding repeat                  2 
	Non-coding rna                    29 
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.
bin.008.fastaAnabaena_assembly succeeded!

The RAST algorithm was applied to annotating a genome sequence comprised of 56 contigs containing 2697984 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, 2519 new features were called, of which 47 are non-coding.
Output genome has the following feature types:
	Coding gene                     2472 
	Non-coding repeat                  4 
	Non-coding rna                    43 
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.
bin.010.fastaAnabaena_assembly succeeded!

The RAST algorithm was applied to annotating a genome sequence comprised of 697 contigs containing 3844699 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, 4480 new features were called, of which 40 are non-coding.
Output genome has the following feature types:
	Coding gene                     4440 
	Non-coding repeat                  9 
	Non-coding rna                    31 
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.
bin.003.fastaAnabaena_assembly succeeded!

The RAST algorithm was applied to annotating a genome sequence comprised of 49 contigs containing 6263306 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, 6146 new features were called, of which 526 are non-coding.
Output genome has the following feature types:
	Coding gene                     5620 
	Non-coding crispr_array            7 
	Non-coding crispr_repeat         148 
	Non-coding crispr_spacer         141 
	Non-coding repeat                190 
	Non-coding rna                    40 
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.
bin.006.fastaAnabaena_assembly succeeded!

The RAST algorithm was applied to annotating a genome sequence comprised of 611 contigs containing 4053288 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, 4520 new features were called, of which 49 are non-coding.
Output genome has the following feature types:
	Coding gene                     4471 
	Non-coding repeat                 15 
	Non-coding rna                    34 
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.
bin.002.fastaAnabaena_assembly succeeded!

The RAST algorithm was applied to annotating a genome sequence comprised of 353 contigs containing 5531808 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, 5551 new features were called, of which 88 are non-coding.
Output genome has the following feature types:
	Coding gene                     5463 
	Non-coding repeat                 48 
	Non-coding rna                    40 
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.
bin.007.fastaAnabaena_assembly succeeded!

The RAST algorithm was applied to annotating a genome sequence comprised of 4 contigs containing 4029898 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, 3864 new features were called, of which 42 are non-coding.
Output genome has the following feature types:
	Coding gene                     3822 
	Non-coding repeat                  2 
	Non-coding rna                    40 
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.
bin.009.fastaAnabaena_assembly succeeded!

The RAST algorithm was applied to annotating a genome sequence comprised of 84 contigs containing 4182746 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, 4212 new features were called, of which 64 are non-coding.
Output genome has the following feature types:
	Coding gene                     4148 
	Non-coding repeat                 20 
	Non-coding rna                    44 
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.
bin.004.fastaAnabaena_assembly succeeded!

Files
These are only available in the live Narrative: https://narrative.kbase.us/narrative/201678
  • annotation_report.7.AnabaenaCommAnnotation - Microbial Annotation Report
Annotate bacterial or archaeal assemblies and/or assembly sets using RASTtk (Rapid Annotations using Subsystems Technology toolkit).
This app completed without errors in 32m 32s.
Objects
Created Object Name Type Description
bin.002.fastaMicrocystis_assembly.RAST Genome RAST annotation
bin.006.fastaMicrocystis_assembly.RAST Genome RAST annotation
bin.005.fastaMicrocystis_assembly.RAST Genome RAST annotation
bin.001.fastaMicrocystis_assembly.RAST Genome RAST annotation
bin.007.fastaMicrocystis_assembly.RAST Genome RAST annotation
bin.004.fastaMicrocystis_assembly.RAST Genome RAST annotation
bin.003.fastaMicrocystis_assembly.RAST Genome RAST annotation
bin.008.fastaMicrocystis_assembly.RAST Genome RAST annotation
7.MicrocystisCommAnnotation GenomeSet Genome Set
Summary
The RAST algorithm was applied to annotating a genome sequence comprised of 80 contigs containing 4174534 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, 4185 new features were called, of which 66 are non-coding.
Output genome has the following feature types:
	Coding gene                     4119 
	Non-coding repeat                 19 
	Non-coding rna                    47 
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.
bin.002.fastaMicrocystis_assembly succeeded!

The RAST algorithm was applied to annotating a genome sequence comprised of 462 contigs containing 2168266 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, 2469 new features were called, of which 28 are non-coding.
Output genome has the following feature types:
	Coding gene                     2441 
	Non-coding repeat                  7 
	Non-coding rna                    21 
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.
bin.006.fastaMicrocystis_assembly succeeded!

The RAST algorithm was applied to annotating a genome sequence comprised of 218 contigs containing 4517780 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, 5990 new features were called, of which 1048 are non-coding.
Output genome has the following feature types:
	Coding gene                     4942 
	Non-coding crispr_array            5 
	Non-coding crispr_repeat         182 
	Non-coding crispr_spacer         177 
	Non-coding repeat                645 
	Non-coding rna                    39 
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.
bin.005.fastaMicrocystis_assembly succeeded!

The RAST algorithm was applied to annotating a genome sequence comprised of 47 contigs containing 4860791 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, 4810 new features were called, of which 70 are non-coding.
Output genome has the following feature types:
	Coding gene                     4740 
	Non-coding repeat                 23 
	Non-coding rna                    47 
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.
bin.001.fastaMicrocystis_assembly succeeded!

The RAST algorithm was applied to annotating a genome sequence comprised of 22 contigs containing 4031587 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, 3860 new features were called, of which 46 are non-coding.
Output genome has the following feature types:
	Coding gene                     3814 
	Non-coding repeat                  6 
	Non-coding rna                    40 
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.
bin.007.fastaMicrocystis_assembly succeeded!

The RAST algorithm was applied to annotating a genome sequence comprised of 66 contigs containing 3780807 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, 3829 new features were called, of which 64 are non-coding.
Output genome has the following feature types:
	Coding gene                     3765 
	Non-coding repeat                 14 
	Non-coding rna                    50 
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.
bin.004.fastaMicrocystis_assembly succeeded!

The RAST algorithm was applied to annotating a genome sequence comprised of 31 contigs containing 3189321 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, 2915 new features were called, of which 49 are non-coding.
Output genome has the following feature types:
	Coding gene                     2866 
	Non-coding repeat                  6 
	Non-coding rna                    43 
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.
bin.003.fastaMicrocystis_assembly succeeded!

The RAST algorithm was applied to annotating a genome sequence comprised of 42 contigs containing 4448024 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, 4156 new features were called, of which 244 are non-coding.
Output genome has the following feature types:
	Coding gene                     3912 
	Non-coding crispr_array            1 
	Non-coding crispr_repeat          91 
	Non-coding crispr_spacer          90 
	Non-coding repeat                 15 
	Non-coding rna                    47 
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.
bin.008.fastaMicrocystis_assembly succeeded!

Files
These are only available in the live Narrative: https://narrative.kbase.us/narrative/201678
  • annotation_report.7.MicrocystisCommAnnotation - Microbial Annotation Report
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 42m 34s.
Links
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 1h 35m 12s.
Links
Add one or more Genomes to a KBase SpeciesTree.
This app completed without errors in 7m 51s.
Files
These are only available in the live Narrative: https://narrative.kbase.us/narrative/201678
  • 9-All-SpeciesTree.newick
  • 9-All-SpeciesTree-labels.newick
  • 9-All-SpeciesTree.png
  • 9-All-SpeciesTree.pdf
Add a user-provided GenomeSet to a KBase SpeciesTree.
This app completed without errors in 6m 0s.
Files
These are only available in the live Narrative: https://narrative.kbase.us/narrative/201678
  • 9-Microcystis-SpeciesTree.newick
  • 9-Microcystis-SpeciesTree-labels.newick
  • 9-Microcystis-SpeciesTree.png
  • 9-Microcystis-SpeciesTree.pdf
Add a user-provided GenomeSet to a KBase SpeciesTree.
This app completed without errors in 6m 36s.
Files
These are only available in the live Narrative: https://narrative.kbase.us/narrative/201678
  • 9-Anabaena-SpeciesTree.newick
  • 9-Anabaena-SpeciesTree-labels.newick
  • 9-Anabaena-SpeciesTree.png
  • 9-Anabaena-SpeciesTree.pdf
Annotate domains in every Genome within a GenomeSet using protein domains from widely used domain libraries.
This app completed without errors in 1d 13h 32m 25s.
Summary
Search Domains output: Getting DomainModelSet from storage. Getting Genome from storage. Running domain search against library 2959/1/7 Running domain search against library 2959/6/6 Running domain search against library 2959/7/6 Running domain search against library 2959/4/6 Running domain search against library 2959/5/7 Search Domains output: Getting DomainModelSet from storage. Getting Genome from storage. Running domain search against library 2959/1/7 Running domain search against library 2959/6/6 Running domain search against library 2959/7/6 Running domain search against library 2959/4/6 Running domain search against library 2959/5/7 Search Domains output: Getting DomainModelSet from storage. Getting Genome from storage. Running domain search against library 2959/1/7 Running domain search against library 2959/6/6 Running domain search against library 2959/7/6 Running domain search against library 2959/4/6 Running domain search against library 2959/5/7 Search Domains output: Getting DomainModelSet from storage. Getting Genome from storage. Running domain search against library 2959/1/7 Running domain search against library 2959/6/6 Running domain search against library 2959/7/6 Running domain search against library 2959/4/6 Running domain search against library 2959/5/7 Search Domains output: Getting DomainModelSet from storage. Getting Genome from storage. Running domain search against library 2959/1/7 Running domain search against library 2959/6/6 Running domain search against library 2959/7/6 Running domain search against library 2959/4/6 Running domain search against library 2959/5/7 Search Domains output: Getting DomainModelSet from storage. Getting Genome from storage. Running domain search against library 2959/1/7 Running domain search against library 2959/6/6 Running domain search against library 2959/7/6 Running domain search against library 2959/4/6 Running domain search against library 2959/5/7 Search Domains output: Getting DomainModelSet from storage. Getting Genome from storage. Running domain search against library 2959/1/7 Running domain search against library 2959/6/6 Running domain search against library 2959/7/6 Running domain search against library 2959/4/6 Running domain search against library 2959/5/7 Search Domains output: Getting DomainModelSet from storage. Getting Genome from storage. Running domain search against library 2959/1/7 Running domain search against library 2959/6/6 Running domain search against library 2959/7/6 Running domain search against library 2959/4/6 Running domain search against library 2959/5/7 Search Domains output: Getting DomainModelSet from storage. Getting Genome from storage. Running domain search against library 2959/1/7 Running domain search against library 2959/6/6 Running domain search against library 2959/7/6 Running domain search against library 2959/4/6 Running domain search against library 2959/5/7 Search Domains output: Getting DomainModelSet from storage. Getting Genome from storage. Running domain search against library 2959/1/7 Running domain search against library 2959/6/6 Running domain search against library 2959/7/6 Running domain search against library 2959/4/6 Running domain search against library 2959/5/7 Search Domains output: Getting DomainModelSet from storage. Getting Genome from storage. Running domain search against library 2959/1/7 Running domain search against library 2959/6/6 Running domain search against library 2959/7/6 Running domain search against library 2959/4/6 Running domain search against library 2959/5/7 Search Domains output: Getting DomainModelSet from storage. Getting Genome from storage. Running domain search against library 2959/1/7 Running domain search against library 2959/6/6 Running domain search against library 2959/7/6 Running domain search against library 2959/4/6 Running domain search against library 2959/5/7 Search Domains output: Getting DomainModelSet from storage. Getting Genome from storage. Running domain search against library 2959/1/7 Running domain search against library 2959/6/6 Running domain search against library 2959/7/6 Running domain search against library 2959/4/6 Running domain search against library 2959/5/7 Search Domains output: Getting DomainModelSet from storage. Getting Genome from storage. Running domain search against library 2959/1/7 Running domain search against library 2959/6/6 Running domain search against library 2959/7/6 Running domain search against library 2959/4/6 Running domain search against library 2959/5/7 Search Domains output: Getting DomainModelSet from storage. Getting Genome from storage. Running domain search against library 2959/1/7 Running domain search against library 2959/6/6 Running domain search against library 2959/7/6 Running domain search against library 2959/4/6 Running domain search against library 2959/5/7 Search Domains output: Getting DomainModelSet from storage. Getting Genome from storage. Running domain search against library 2959/1/7 Running domain search against library 2959/6/6 Running domain search against library 2959/7/6 Running domain search against library 2959/4/6 Running domain search against library 2959/5/7 Search Domains output: Getting DomainModelSet from storage. Getting Genome from storage. Running domain search against library 2959/1/7 Running domain search against library 2959/6/6 Running domain search against library 2959/7/6 Running domain search against library 2959/4/6 Running domain search against library 2959/5/7 Search Domains output: Getting DomainModelSet from storage. Getting Genome from storage. Running domain search against library 2959/1/7 Running domain search against library 2959/6/6 Running domain search against library 2959/7/6 Running domain search against library 2959/4/6 Running domain search against library 2959/5/7 Search Domains output: Getting DomainModelSet from storage. Getting Genome from storage. Running domain search against library 2959/1/7 Running domain search against library 2959/6/6 Running domain search against library 2959/7/6 Running domain search against library 2959/4/6 Running domain search against library 2959/5/7 Search Domains output: Getting DomainModelSet from storage. Getting Genome from storage. Running domain search against library 2959/1/7 Running domain search against library 2959/6/6 Running domain search against library 2959/7/6 Running domain search against library 2959/4/6 Running domain search against library 2959/5/7 Search Domains output: Getting DomainModelSet from storage. Getting Genome from storage. Running domain search against library 2959/1/7 Running domain search against library 2959/6/6 Running domain search against library 2959/7/6 Running domain search against library 2959/4/6 Running domain search against library 2959/5/7 Search Domains output: Getting DomainModelSet from storage. Getting Genome from storage. Running domain search against library 2959/1/7 Running domain search against library 2959/6/6 Running domain search against library 2959/7/6 Running domain search against library 2959/4/6 Running domain search against library 2959/5/7 Search Domains output: Getting DomainModelSet from storage. Getting Genome from storage. Running domain search against library 2959/1/7 Running domain search against library 2959/6/6 Running domain search against library 2959/7/6 Running domain search against library 2959/4/6 Running domain search against library 2959/5/7 Search Domains output: Getting DomainModelSet from storage. Getting Genome from storage. Running domain search against library 2959/1/7 Running domain search against library 2959/6/6 Running domain search against library 2959/7/6 Running domain search against library 2959/4/6 Running domain search against library 2959/5/7 Search Domains output: Getting DomainModelSet from storage. Getting Genome from storage. Running domain search against library 2959/1/7 Running domain search against library 2959/6/6 Running domain search against library 2959/7/6 Running domain search against library 2959/4/6 Running domain search against library 2959/5/7 Search Domains output: Getting DomainModelSet from storage. Getting Genome from storage. Running domain search against library 2959/1/7 Running domain search against library 2959/6/6 Running domain search against library 2959/7/6 Running domain search against library 2959/4/6 Running domain search against library 2959/5/7 Search Domains output: Getting DomainModelSet from storage. Getting Genome from storage. Running domain search against library 2959/1/7 Running domain search against library 2959/6/6 Running domain search against library 2959/7/6 Running domain search against library 2959/4/6 Running domain search against library 2959/5/7 Search Domains output: Getting DomainModelSet from storage. Getting Genome from storage. Running domain search against library 2959/1/7 Running domain search against library 2959/6/6 Running domain search against library 2959/7/6 Running domain search against library 2959/4/6 Running domain search against library 2959/5/7 Search Domains output: Getting DomainModelSet from storage. Getting Genome from storage. Running domain search against library 2959/1/7 Running domain search against library 2959/6/6 Running domain search against library 2959/7/6 Running domain search against library 2959/4/6 Running domain search against library 2959/5/7 Search Domains output: Getting DomainModelSet from storage. Getting Genome from storage. Running domain search against library 2959/1/7 Running domain search against library 2959/6/6 Running domain search against library 2959/7/6 Running domain search against library 2959/4/6 Running domain search against library 2959/5/7 Search Domains output: Getting DomainModelSet from storage. Getting Genome from storage. Running domain search against library 2959/1/7 Running domain search against library 2959/6/6 Running domain search against library 2959/7/6 Running domain search against library 2959/4/6 Running domain search against library 2959/5/7 Search Domains output: Getting DomainModelSet from storage. Getting Genome from storage. Running domain search against library 2959/1/7 Running domain search against library 2959/6/6 Running domain search against library 2959/7/6 Running domain search against library 2959/4/6 Running domain search against library 2959/5/7 Search Domains output: Getting DomainModelSet from storage. Getting Genome from storage. Running domain search against library 2959/1/7 Running domain search against library 2959/6/6 Running domain search against library 2959/7/6 Running domain search against library 2959/4/6 Running domain search against library 2959/5/7 Search Domains output: Getting DomainModelSet from storage. Getting Genome from storage. Running domain search against library 2959/1/7 Running domain search against library 2959/6/6 Running domain search against library 2959/7/6 Running domain search against library 2959/4/6 Running domain search against library 2959/5/7 Search Domains output: Getting DomainModelSet from storage. Getting Genome from storage. Running domain search against library 2959/1/7 Running domain search against library 2959/6/6 Running domain search against library 2959/7/6 Running domain search against library 2959/4/6 Running domain search against library 2959/5/7 Search Domains output: Getting DomainModelSet from storage. Getting Genome from storage. Running domain search against library 2959/1/7 Running domain search against library 2959/6/6 Running domain search against library 2959/7/6 Running domain search against library 2959/4/6 Running domain search against library 2959/5/7 Search Domains output: Getting DomainModelSet from storage. Getting Genome from storage. Running domain search against library 2959/1/7 Running domain search against library 2959/6/6 Running domain search against library 2959/7/6 Running domain search against library 2959/4/6 Running domain search against library 2959/5/7 Search Domains output: Getting DomainModelSet from storage. Getting Genome from storage. Running domain search against library 2959/1/7 Running domain search against library 2959/6/6 Running domain search against library 2959/7/6 Running domain search against library 2959/4/6 Running domain search against library 2959/5/7
Annotate domains in every Genome within a GenomeSet using protein domains from widely used domain libraries.
This app completed without errors in 59s.
Annotate domains in every Genome within a GenomeSet using protein domains from widely used domain libraries.
This app completed without errors in 59s.
Annotate domains in every Genome within a GenomeSet using protein domains from widely used domain libraries.
This app completed without errors in 1d 9h 44m 37s.
Summary
Search Domains output: Getting DomainModelSet from storage. Getting Genome from storage. Running domain search against library 2959/1/7 Running domain search against library 2959/6/6 Running domain search against library 2959/7/6 Running domain search against library 2959/4/6 Running domain search against library 2959/5/7 Search Domains output: Getting DomainModelSet from storage. Getting Genome from storage. Running domain search against library 2959/1/7 Running domain search against library 2959/6/6 Running domain search against library 2959/7/6 Running domain search against library 2959/4/6 Running domain search against library 2959/5/7 Search Domains output: Getting DomainModelSet from storage. Getting Genome from storage. Running domain search against library 2959/1/7 Running domain search against library 2959/6/6 Running domain search against library 2959/7/6 Running domain search against library 2959/4/6 Running domain search against library 2959/5/7 Search Domains output: Getting DomainModelSet from storage. Getting Genome from storage. Running domain search against library 2959/1/7 Running domain search against library 2959/6/6 Running domain search against library 2959/7/6 Running domain search against library 2959/4/6 Running domain search against library 2959/5/7 Search Domains output: Getting DomainModelSet from storage. Getting Genome from storage. Running domain search against library 2959/1/7 Running domain search against library 2959/6/6 Running domain search against library 2959/7/6 Running domain search against library 2959/4/6 Running domain search against library 2959/5/7 Search Domains output: Getting DomainModelSet from storage. Getting Genome from storage. Running domain search against library 2959/1/7 Running domain search against library 2959/6/6 Running domain search against library 2959/7/6 Running domain search against library 2959/4/6 Running domain search against library 2959/5/7 Search Domains output: Getting DomainModelSet from storage. Getting Genome from storage. Running domain search against library 2959/1/7 Running domain search against library 2959/6/6 Running domain search against library 2959/7/6 Running domain search against library 2959/4/6 Running domain search against library 2959/5/7 Search Domains output: Getting DomainModelSet from storage. Getting Genome from storage. Running domain search against library 2959/1/7 Running domain search against library 2959/6/6 Running domain search against library 2959/7/6 Running domain search against library 2959/4/6 Running domain search against library 2959/5/7 Search Domains output: Getting DomainModelSet from storage. Getting Genome from storage. Running domain search against library 2959/1/7 Running domain search against library 2959/6/6 Running domain search against library 2959/7/6 Running domain search against library 2959/4/6 Running domain search against library 2959/5/7 Search Domains output: Getting DomainModelSet from storage. Getting Genome from storage. Running domain search against library 2959/1/7 Running domain search against library 2959/6/6 Running domain search against library 2959/7/6 Running domain search against library 2959/4/6 Running domain search against library 2959/5/7 Search Domains output: Getting DomainModelSet from storage. Getting Genome from storage. Running domain search against library 2959/1/7 Running domain search against library 2959/6/6 Running domain search against library 2959/7/6 Running domain search against library 2959/4/6 Running domain search against library 2959/5/7 Search Domains output: Getting DomainModelSet from storage. Getting Genome from storage. Running domain search against library 2959/1/7 Running domain search against library 2959/6/6 Running domain search against library 2959/7/6 Running domain search against library 2959/4/6 Running domain search against library 2959/5/7 Search Domains output: Getting DomainModelSet from storage. Getting Genome from storage. Running domain search against library 2959/1/7 Running domain search against library 2959/6/6 Running domain search against library 2959/7/6 Running domain search against library 2959/4/6 Running domain search against library 2959/5/7 Search Domains output: Getting DomainModelSet from storage. Getting Genome from storage. Running domain search against library 2959/1/7 Running domain search against library 2959/6/6 Running domain search against library 2959/7/6 Running domain search against library 2959/4/6 Running domain search against library 2959/5/7 Search Domains output: Getting DomainModelSet from storage. Getting Genome from storage. Running domain search against library 2959/1/7 Running domain search against library 2959/6/6 Running domain search against library 2959/7/6 Running domain search against library 2959/4/6 Running domain search against library 2959/5/7 Search Domains output: Getting DomainModelSet from storage. Getting Genome from storage. Running domain search against library 2959/1/7 Running domain search against library 2959/6/6 Running domain search against library 2959/7/6 Running domain search against library 2959/4/6 Running domain search against library 2959/5/7 Search Domains output: Getting DomainModelSet from storage. Getting Genome from storage. Running domain search against library 2959/1/7 Running domain search against library 2959/6/6 Running domain search against library 2959/7/6 Running domain search against library 2959/4/6 Running domain search against library 2959/5/7 Search Domains output: Getting DomainModelSet from storage. Getting Genome from storage. Running domain search against library 2959/1/7 Running domain search against library 2959/6/6 Running domain search against library 2959/7/6 Running domain search against library 2959/4/6 Running domain search against library 2959/5/7 Search Domains output: Getting DomainModelSet from storage. Getting Genome from storage. Running domain search against library 2959/1/7 Running domain search against library 2959/6/6 Running domain search against library 2959/7/6 Running domain search against library 2959/4/6 Running domain search against library 2959/5/7 Search Domains output: Getting DomainModelSet from storage. Getting Genome from storage. Running domain search against library 2959/1/7 Running domain search against library 2959/6/6 Running domain search against library 2959/7/6 Running domain search against library 2959/4/6 Running domain search against library 2959/5/7 Search Domains output: Getting DomainModelSet from storage. Getting Genome from storage. Running domain search against library 2959/1/7 Running domain search against library 2959/6/6 Running domain search against library 2959/7/6 Running domain search against library 2959/4/6 Running domain search against library 2959/5/7 Search Domains output: Getting DomainModelSet from storage. Getting Genome from storage. Running domain search against library 2959/1/7 Running domain search against library 2959/6/6 Running domain search against library 2959/7/6 Running domain search against library 2959/4/6 Running domain search against library 2959/5/7 Search Domains output: Getting DomainModelSet from storage. Getting Genome from storage. Running domain search against library 2959/1/7 Running domain search against library 2959/6/6 Running domain search against library 2959/7/6 Running domain search against library 2959/4/6 Running domain search against library 2959/5/7 Search Domains output: Getting DomainModelSet from storage. Getting Genome from storage. Running domain search against library 2959/1/7 Running domain search against library 2959/6/6 Running domain search against library 2959/7/6 Running domain search against library 2959/4/6 Running domain search against library 2959/5/7 Search Domains output: Getting DomainModelSet from storage. Getting Genome from storage. Running domain search against library 2959/1/7 Running domain search against library 2959/6/6 Running domain search against library 2959/7/6 Running domain search against library 2959/4/6 Running domain search against library 2959/5/7 Search Domains output: Getting DomainModelSet from storage. Getting Genome from storage. Running domain search against library 2959/1/7 Running domain search against library 2959/6/6 Running domain search against library 2959/7/6 Running domain search against library 2959/4/6 Running domain search against library 2959/5/7 Search Domains output: Getting DomainModelSet from storage. Getting Genome from storage. Running domain search against library 2959/1/7 Running domain search against library 2959/6/6 Running domain search against library 2959/7/6 Running domain search against library 2959/4/6 Running domain search against library 2959/5/7 Search Domains output: Getting DomainModelSet from storage. Getting Genome from storage. Running domain search against library 2959/1/7 Running domain search against library 2959/6/6 Running domain search against library 2959/7/6 Running domain search against library 2959/4/6 Running domain search against library 2959/5/7 Search Domains output: Getting DomainModelSet from storage. Getting Genome from storage. Running domain search against library 2959/1/7 Running domain search against library 2959/6/6 Running domain search against library 2959/7/6 Running domain search against library 2959/4/6 Running domain search against library 2959/5/7 Search Domains output: Getting DomainModelSet from storage. Getting Genome from storage. Running domain search against library 2959/1/7 Running domain search against library 2959/6/6 Running domain search against library 2959/7/6 Running domain search against library 2959/4/6 Running domain search against library 2959/5/7 Search Domains output: Getting DomainModelSet from storage. Getting Genome from storage. Running domain search against library 2959/1/7 Running domain search against library 2959/6/6 Running domain search against library 2959/7/6 Running domain search against library 2959/4/6 Running domain search against library 2959/5/7
Annotate domains in every Genome within a GenomeSet using protein domains from widely used domain libraries.
This app completed without errors in 1d 1h 1m 14s.
Summary
Search Domains output: Getting DomainModelSet from storage. Getting Genome from storage. Running domain search against library 2959/1/7 Running domain search against library 2959/6/6 Running domain search against library 2959/7/6 Running domain search against library 2959/4/6 Running domain search against library 2959/5/7 Search Domains output: Getting DomainModelSet from storage. Getting Genome from storage. Running domain search against library 2959/1/7 Running domain search against library 2959/6/6 Running domain search against library 2959/7/6 Running domain search against library 2959/4/6 Running domain search against library 2959/5/7 Search Domains output: Getting DomainModelSet from storage. Getting Genome from storage. Running domain search against library 2959/1/7 Running domain search against library 2959/6/6 Running domain search against library 2959/7/6 Running domain search against library 2959/4/6 Running domain search against library 2959/5/7 Search Domains output: Getting DomainModelSet from storage. Getting Genome from storage. Running domain search against library 2959/1/7 Running domain search against library 2959/6/6 Running domain search against library 2959/7/6 Running domain search against library 2959/4/6 Running domain search against library 2959/5/7 Search Domains output: Getting DomainModelSet from storage. Getting Genome from storage. Running domain search against library 2959/1/7 Running domain search against library 2959/6/6 Running domain search against library 2959/7/6 Running domain search against library 2959/4/6 Running domain search against library 2959/5/7 Search Domains output: Getting DomainModelSet from storage. Getting Genome from storage. Running domain search against library 2959/1/7 Running domain search against library 2959/6/6 Running domain search against library 2959/7/6 Running domain search against library 2959/4/6 Running domain search against library 2959/5/7 Search Domains output: Getting DomainModelSet from storage. Getting Genome from storage. Running domain search against library 2959/1/7 Running domain search against library 2959/6/6 Running domain search against library 2959/7/6 Running domain search against library 2959/4/6 Running domain search against library 2959/5/7 Search Domains output: Getting DomainModelSet from storage. Getting Genome from storage. Running domain search against library 2959/1/7 Running domain search against library 2959/6/6 Running domain search against library 2959/7/6 Running domain search against library 2959/4/6 Running domain search against library 2959/5/7 Search Domains output: Getting DomainModelSet from storage. Getting Genome from storage. Running domain search against library 2959/1/7 Running domain search against library 2959/6/6 Running domain search against library 2959/7/6 Running domain search against library 2959/4/6 Running domain search against library 2959/5/7 Search Domains output: Getting DomainModelSet from storage. Getting Genome from storage. Running domain search against library 2959/1/7 Running domain search against library 2959/6/6 Running domain search against library 2959/7/6 Running domain search against library 2959/4/6 Running domain search against library 2959/5/7 Search Domains output: Getting DomainModelSet from storage. Getting Genome from storage. Running domain search against library 2959/1/7 Running domain search against library 2959/6/6 Running domain search against library 2959/7/6 Running domain search against library 2959/4/6 Running domain search against library 2959/5/7 Search Domains output: Getting DomainModelSet from storage. Getting Genome from storage. Running domain search against library 2959/1/7 Running domain search against library 2959/6/6 Running domain search against library 2959/7/6 Running domain search against library 2959/4/6 Running domain search against library 2959/5/7 Search Domains output: Getting DomainModelSet from storage. Getting Genome from storage. Running domain search against library 2959/1/7 Running domain search against library 2959/6/6 Running domain search against library 2959/7/6 Running domain search against library 2959/4/6 Running domain search against library 2959/5/7 Search Domains output: Getting DomainModelSet from storage. Getting Genome from storage. Running domain search against library 2959/1/7 Running domain search against library 2959/6/6 Running domain search against library 2959/7/6 Running domain search against library 2959/4/6 Running domain search against library 2959/5/7 Search Domains output: Getting DomainModelSet from storage. Getting Genome from storage. Running domain search against library 2959/1/7 Running domain search against library 2959/6/6 Running domain search against library 2959/7/6 Running domain search against library 2959/4/6 Running domain search against library 2959/5/7 Search Domains output: Getting DomainModelSet from storage. Getting Genome from storage. Running domain search against library 2959/1/7 Running domain search against library 2959/6/6 Running domain search against library 2959/7/6 Running domain search against library 2959/4/6 Running domain search against library 2959/5/7 Search Domains output: Getting DomainModelSet from storage. Getting Genome from storage. Running domain search against library 2959/1/7 Running domain search against library 2959/6/6 Running domain search against library 2959/7/6 Running domain search against library 2959/4/6 Running domain search against library 2959/5/7 Search Domains output: Getting DomainModelSet from storage. Getting Genome from storage. Running domain search against library 2959/1/7 Running domain search against library 2959/6/6 Running domain search against library 2959/7/6 Running domain search against library 2959/4/6 Running domain search against library 2959/5/7 Search Domains output: Getting DomainModelSet from storage. Getting Genome from storage. Running domain search against library 2959/1/7 Running domain search against library 2959/6/6 Running domain search against library 2959/7/6 Running domain search against library 2959/4/6 Running domain search against library 2959/5/7 Search Domains output: Getting DomainModelSet from storage. Getting Genome from storage. Running domain search against library 2959/1/7 Running domain search against library 2959/6/6 Running domain search against library 2959/7/6 Running domain search against library 2959/4/6 Running domain search against library 2959/5/7 Search Domains output: Getting DomainModelSet from storage. Getting Genome from storage. Running domain search against library 2959/1/7 Running domain search against library 2959/6/6 Running domain search against library 2959/7/6 Running domain search against library 2959/4/6 Running domain search against library 2959/5/7 Search Domains output: Getting DomainModelSet from storage. Getting Genome from storage. Running domain search against library 2959/1/7 Running domain search against library 2959/6/6 Running domain search against library 2959/7/6 Running domain search against library 2959/4/6 Running domain search against library 2959/5/7 Search Domains output: Getting DomainModelSet from storage. Getting Genome from storage. Running domain search against library 2959/1/7 Running domain search against library 2959/6/6 Running domain search against library 2959/7/6 Running domain search against library 2959/4/6 Running domain search against library 2959/5/7 Search Domains output: Getting DomainModelSet from storage. Getting Genome from storage. Running domain search against library 2959/1/7 Running domain search against library 2959/6/6 Running domain search against library 2959/7/6 Running domain search against library 2959/4/6 Running domain search against library 2959/5/7 Search Domains output: Getting DomainModelSet from storage. Getting Genome from storage. Running domain search against library 2959/1/7 Running domain search against library 2959/6/6 Running domain search against library 2959/7/6 Running domain search against library 2959/4/6 Running domain search against library 2959/5/7 Search Domains output: Getting DomainModelSet from storage. Getting Genome from storage. Running domain search against library 2959/1/7 Running domain search against library 2959/6/6 Running domain search against library 2959/7/6 Running domain search against library 2959/4/6 Running domain search against library 2959/5/7 Search Domains output: Getting DomainModelSet from storage. Getting Genome from storage. Running domain search against library 2959/1/7 Running domain search against library 2959/6/6 Running domain search against library 2959/7/6 Running domain search against library 2959/4/6 Running domain search against library 2959/5/7 Search Domains output: Getting DomainModelSet from storage. Getting Genome from storage. Running domain search against library 2959/1/7 Running domain search against library 2959/6/6 Running domain search against library 2959/7/6 Running domain search against library 2959/4/6 Running domain search against library 2959/5/7
Examine the general functional distribution or specific functional gene families for a GenomeSet.
This app completed without errors in 3m 48s.
Examine the general functional distribution or specific functional gene families for a GenomeSet.
This app completed without errors in 3m 22s.
Annotate MAGs with DRAM and distill resulting annotations to create an interactive functional summary per genome. Use for KBase genome objects.
This app completed without errors in 14h 10m 4s.
Summary
Here are the results from your DRAM run.
Links
Files
These are only available in the live Narrative: https://narrative.kbase.us/narrative/201678
  • annotations.tsv - DRAM annotations in a tab separate table format
  • genes.faa - Genes as amino acids predicted by DRAM with brief annotations
  • product.tsv - DRAM product in tabular format
  • metabolism_summary.xlsx - DRAM metabolism summary tables
  • genome_stats.tsv - DRAM genome statistics table
Annotate MAGs with DRAM and distill resulting annotations to create an interactive functional summary per genome. Use for KBase genome objects.
This app completed without errors in 10h 26m 55s.
Summary
Here are the results from your DRAM run.
Links
Files
These are only available in the live Narrative: https://narrative.kbase.us/narrative/201678
  • annotations.tsv - DRAM annotations in a tab separate table format
  • genes.faa - Genes as amino acids predicted by DRAM with brief annotations
  • product.tsv - DRAM product in tabular format
  • metabolism_summary.xlsx - DRAM metabolism summary tables
  • genome_stats.tsv - DRAM genome statistics table
Annotate your assemblies, isolate genomes, or MAGs with DRAM and distill resulting annotations to create an interactive functional summary per genome or assembly. Use for KBase assembly objects.
This app is new, and hasn't been started.
No output found.
Annotate MAGs with DRAM and distill resulting annotations to create an interactive functional summary per genome. Use for KBase genome objects.
This app completed without errors in 4h 14m 14s.
Summary
Here are the results from your DRAM run.
Links
Files
These are only available in the live Narrative: https://narrative.kbase.us/narrative/201678
  • annotations.tsv - DRAM annotations in a tab separate table format
  • genes.faa - Genes as amino acids predicted by DRAM with brief annotations
  • product.tsv - DRAM product in tabular format
  • metabolism_summary.xlsx - DRAM metabolism summary tables
  • genome_stats.tsv - DRAM genome statistics table
Annotate MAGs with DRAM and distill resulting annotations to create an interactive functional summary per genome. Use for KBase genome objects.
This app completed without errors in 2h 53m 16s.
Summary
Here are the results from your DRAM run.
Links
Files
These are only available in the live Narrative: https://narrative.kbase.us/narrative/201678
  • annotations.tsv - DRAM annotations in a tab separate table format
  • genes.faa - Genes as amino acids predicted by DRAM with brief annotations
  • product.tsv - DRAM product in tabular format
  • metabolism_summary.xlsx - DRAM metabolism summary tables
  • genome_stats.tsv - DRAM genome statistics table

Apps

  1. Align Reads using Bowtie2 - v2.3.2
    • Langmead B, Salzberg SL. Fast gapped-read alignment with Bowtie 2. Nat Methods. 2012;9: 357 359. doi:10.1038/nmeth.1923
    • Langmead B, Trapnell C, Pop M, Salzberg SL. Ultrafast and memory-efficient alignment of short DNA sequences to the human genome. Genome Biol. 2009;10: R25. doi:10.1186/gb-2009-10-3-r25
  2. Annotate and Distill Assemblies with DRAM
    • DRAM source code
    • DRAM documentation
    • DRAM Tutorial
    • DRAM publication
  3. Annotate and Distill Genomes with DRAM
    • DRAM source code
    • DRAM documentation
    • DRAM Tutorial
    • DRAM publication
  4. Annotate Domains in a GenomeSet
    • Altschul SF, Madden TL, Sch ffer AA, Zhang J, Zhang Z, Miller W, et al. 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
    • Camacho C, Coulouris G, Avagyan V, Ma N, Papadopoulos J, Bealer K, et al. BLAST+: architecture and applications. BMC Bioinformatics. 2009;10: 421. doi:10.1186/1471-2105-10-421
    • Eddy SR. Accelerated Profile HMM Searches. PLOS Computational Biology. 2011;7: e1002195. doi:10.1371/journal.pcbi.1002195
    • Finn RD, Coggill P, Eberhardt RY, Eddy SR, Mistry J, Mitchell AL, et al. The Pfam protein families database: towards a more sustainable future. Nucleic Acids Res. 2016;44: D279 D285. doi:10.1093/nar/gkv1344
    • Haft DH, Selengut JD, Richter RA, Harkins D, Basu MK, Beck E. TIGRFAMs and Genome Properties in 2013. Nucleic Acids Res. 2013;41: D387 D395. doi:10.1093/nar/gks1234
    • Letunic I, Bork P. 20 years of the SMART protein domain annotation resource. Nucleic Acids Res. 2018;46: D493 D496. doi:10.1093/nar/gkx922
    • Letunic I, Doerks T, Bork P. SMART: recent updates, new developments and status in 2015. Nucleic Acids Res. 2015;43: D257-260. doi:10.1093/nar/gku949
    • Marchler-Bauer A, Bo Y, Han L, He J, Lanczycki CJ, Lu S, et al. CDD/SPARCLE: functional classification of proteins via subfamily domain architectures. Nucleic Acids Res. 2017;45: D200 D203. doi:10.1093/nar/gkw1129
    • Selengut JD, Haft DH, Davidsen T, Ganapathy A, Gwinn-Giglio M, Nelson WC, et al. TIGRFAMs and Genome Properties: tools for the assignment of molecular function and biological process in prokaryotic genomes. Nucleic Acids Res. 2007;35: D260-264. doi:10.1093/nar/gkl1043
    • Tatusov RL, Koonin EV, Lipman DJ. A Genomic Perspective on Protein Families. Science. 1997;278: 631 637. doi:10.1126/science.278.5338.631
  5. Annotate Multiple Microbial Assemblies 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
  6. Assemble Reads with metaSPAdes - v3.15.3
    • Nurk S, Meleshko D, Korobeynikov A, Pevzner PA. metaSPAdes: a new versatile metagenomic assembler. Genome Res. 2017; 27:824 834. doi: 10.1101/gr.213959.116
    • Prjibelski A, Antipov D, Meleshko D, Lapidus A, Korobeynikov A. Using SPAdes De Novo Assembler. Curr Protoc Bioinformatics. 2020 Jun;70(1):e102. doi: 10.1002/cpbi.102.
  7. 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:
  8. Assess Read Quality with FastQC - v0.12.1
    • FastQC source: Bioinformatics Group at the Babraham Institute, UK.
  9. Bin Contigs using CONCOCT - v1.1
    • Alneberg J, Bjarnason BS, de Bruijn I, Schirmer M, Quick J, Ijaz UZ, Lahti L, Loman NJ, Andersson AF, Quince C. Binning metagenomic contigs by coverage and composition. Nature Methods. 2014;11: 1144-1146. doi:10.1038/nmeth.3103
    • CONCOCT source:
  10. 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:
  11. 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
  12. 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:
  13. 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
  14. 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:
  15. 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
  16. Insert Set of Genomes 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
  17. MetaBAT2 Contig Binning - v1.7
    • Kang DD, Froula J, Egan R, Wang Z. MetaBAT, an efficient tool for accurately reconstructing single genomes from complex microbial communities. PeerJ. 2015;3: e1165. doi:10.7717/peerj.1165
    • MetaBAT2 source:
  18. Optimize Bacterial or Archaeal Binned Contigs using DAS Tool - v1.1.2
    • Sieber CMK, Probst AJ, Sharrar A, Thomas BC, Hess M, Tringe SG, Banfield JF. Recovery of genomes from metagenomes via a dereplication, aggregation and scoring strategy. 2018; 3(7): 836-843. doi:10.1038/s41564-018-0171-1
    • DAS_Tool source:
    • 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
    • Camacho C, Coulouris G, Avagyan V, Ma N, Papadopoulos J, Bealer K, Madden TL. BLAST+: architecture and applications. BMC Bioinformatics. 2009;10: 421. doi:10.1186/1471-2105-10-421
    • Buchfink B, Xie C, Huson DH. Fast and sensitive protein alignment using DIAMOND. Nature Methods. 2015;12: 59-60. doi:10.1038/nmeth.3176
    • Pullseq:
    • R: A Language and Environment for Statistical Computing:
    • Ruby: A Programmers Best Friend:
  19. Run the JGI RQCFilter pipeline (BBTools v38.22)
    no citations
  20. Trim Reads with Trimmomatic - v0.36
    • Bolger AM, Lohse M, Usadel B. Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics. 2014;30: 2114 2120. doi:10.1093/bioinformatics/btu170
  21. View Function Profile for Genomes - v1.4.0
    • 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