SKADI: Sequence-based Knowledgebase for Annotation, Detection, and Identification

Note

SKADI documentation is now online!

Changelog

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0.0.4

• VMR_41v1 is now available to download
• added protein-level read annotation workflow

0.0.3

• VMR_40v2 is now available to download
• added read annotation workflow

0.0.2

• added downloaddb commad to download pre-built databases (VMR_40v1 and VMR_39v4)
• added a definition file to build Apptainer containers
• added subroutines to clean up tmp files generated duting the database build

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Quick start

git clone https://github.com/Yasas1994/skadi.git
cd skadi

# create a conda env
 mamba create -f environment.yml

# install skadi pipeline
pip install .

# test the installation
skadi --help

Usage: skadi [OPTIONS] COMMAND [ARGS]...

  SKADI: Sequence-based Knowledgebase for Annotation, Detection, and Identification
  (https://github.com/Yasas1994/skadi)

Options:
  --version   Show the version and exit.
  -h, --help  Show this message and exit.

Commands:
  contigs    run contig annotation workflow
  downloaddb download pre-built reference databases
  preparedb  download and build reference databases
  reads      run read annotation workflow
  utils      tool chain for calculating ani, aai and visualizations

Singularity (Now Apptainer)

git clone https://github.com/Yasas1994/skadi.git
cd skadi

# build container
apptainer build skadi.sif Apptainer

# test the container build
apptainer run skadi.sif skadi --help

Usage: skadi [OPTIONS] COMMAND [ARGS]...

  SKADI: Sequence-based Knowledgebase for Annotation, Detection, and Identification
  (https://github.com/Yasas1994/skadi)

Options:
  --version   Show the version and exit.
  -h, --help  Show this message and exit.

Commands:
  contigs    run contig annotation workflow
  downloaddb download pre-built reference databases
  preparedb  download sequences and build reference databases
  reads      run read annotation workflow
  utils      tool chain for calculating ani, aai and visualizations

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downloading pre-built databases

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# pulling pre-built databases from remote server [msl39v and masl40v1]
skadi downloaddb --dbversion masl39v4 -d <path to save the database> --cores 1

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downloading and preparing the databases

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skadi preparedb -d <path to save the database>

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running contig annotation pipeline

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skadi contigs -i <input>.fasta -o outdir

results can be found in the results directory within the ouput directory

.
├── logs
├── nuc
│   ├── input_genome_ani.tsv
│   └── input_genome.m8
├── prof
│   ├── input_fasta_prof_api.tsv
│   └── input_fasta_prof.m8
├── prot
│   ├── input_fasta.faa
│   ├── input_fasta.gff
│   ├── input_fasta_prot_aai.tsv
│   └── input_fasta_prot.m8
├── results
│   ├── *input_fasta_ictv.csv
│   └── input_fasta.tsv
└── tmp

Expected runtime ?

It takes ~4hrs to run skadi on the ICTV Taxonomy challenge dataset on a laptop computer.

running read annotation pipeline

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skadi reads -in <reads1>.fastq [-in2 <reads2.fastq>] -o outdir

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running other workflows

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# calculate aai of query contigs to ICTV genomes
skadi utils aai  [OPTIONS] -i contigs.m8 -g configs.gff -d [DBDIR]

# calculate ani of query contigs to ICTV genomes
skadi utils ani [OPTIONS] -i contigs.m8

# creating genome comparision plots. i.e query sequence to highly similar ICTV genomes (comming soon)
skadi utils visualize --ani --taxa [taxname] -i contigs.m8 -o outdir

# create phage contig annotation plots (coming soon)
skadi utils visualize --phrogs -i contigs.fasta -o outdir 

# identify provirus (coming soon)
skadi utils provirus -i contigs.fasta -o outdir

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Some additional stuff

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# to view the contig length distribution of your contigs
seqkit fx2tab -lg {input.fasta} | awk -F "\t" '{print $4}' | tail -n +2 | hist -b 100 -s 10

# to view the length distribution of contigs in the ani calculation output (after apply a filter)
cat testing/gut_jaeger/nuc/gut_jaeger_virus_seqs_fasta_genome_ani.tsv | awk -F "\t" '$8 > 0.1' | awk -F "\t" '{print $3}' | tail -n +2 | hist -b 100 -x

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If you use skadi please cite,

[MMSEQS2] MMseqs2 enables sensitive protein sequence searching for the analysis of massive data sets.
          Steinegger M & Söding J. 2017. Nat. Biotech. 35, 1026–1028. https://doi.org/10.1038/nbt.3988

[PRODIGAL] Prodigal: prokaryotic gene recognition and translation initiation site identification.
           Hyatt et al. 2010. BMC Bioinformatics 11, 119. https://doi.org/10.1186/1471-2105-11-119.

[TAXONKIT] TaxonKit: A practical and efficient NCBI taxonomy toolkit.
           Shen, W. & Ren, H. J. 2021. Genet. Genomics https://doi:10.1016/j.jgg.2021.03.006.