EToKi (Enterobase Tool Kit)

all methods related to Enterobase

External dependencies:

phylo module

• raxml

prepare module

• bbmap

MLSType module

• blastn
• usearch

assemble module

• samtools
• bwa
• bowtie2
• kraken
• gatk
• megahit
• pilon
• spades

ortho module

• mcl
• fasttree
• vsearch

Python version: 2.7.9

Please read [https://github.com/pyenv/pyenv] for python version control.

PIP Packages:

• ete3==3.0.0b17
• numpy==1.11.3
• pandas==0.18.0
• psutil==5.1.0

Installation:

Assume all the external dependencies are in system PATH.

git clone https://github.com/zheminzhou/EToKi.git
cd EToKi
pip install -r requirements.txt
python EToKi.py configure

Specify the links to external commands if they are not in the system PATH. Use

python EToKi.py configure -h

For additional information.

Usage: EToKi.py [options]

Commands:
  configure        Configure external dependencies
  prepare          Preprocessing for short reads
  assemble         de novo / reference-guided asembly for either metagenomic or non-metagenomic reads
  ortho            Pan-genome prediction
  MLSTdb           Create database for MLST typing
  MLSType          MLST nomenclature
  phylo            Infer phylogeny and ancestral states from genomic alignments or SNP matrix
  RecHMM           Identify Recombination sketches.


Use EToKi.py <command> -h for help in each command.

Examples:

1. phylogeny + ancestral reconstruction + recombination detection (phylo_example.bash)

cd examples
python ../EToKi.py phylo -t all -p phylo_out -m phylo_rec.fasta
python ../EToKi.py RecHMM -d phylo_out.mutations.gz -p rec_out

Outputs are:

• examples/phylo_out.matrix.gz - a table of mutations in the alignment.
• examples/phylo_out.labeled.nwk - phylogeny with labeled internal nodes.
• examples/phylo_out.ancestral_states.gz - Ancestral states of internal nodes.
• examples/phylo_out.mutations.gz - Occurences of mutations on different branches.
• examples/rec_out.best.model.report - Estimated parameters for the recombinations.
• examples/rec_out.recombination.region - Identified recombination regions.

NOTE: New RecHMM identifies three categories of recombinations:

1. External: Recombination with an external source. High SNP densities and low homoplasies.
2. Internal: Recombination from an internal source. Normal SNP densities and high homoplasies.
3. Mixed: Repetitive imports from external sources to different branches of the tree. High SNP densities and high homoplasies.

You can use legacy RecHMM by setting --task 0, which considers only external source.

2. short reads preprocess + assembly + polish + consensus quality + evaluation (assemble_example.bash)

cd examples
python ../EToKi.py prepare --pe A_R1.fastq.gz,A_R2.fastq.gz -p prep_out
python ../EToKi.py assemble --pe prep_out_L1_R1.fastq.gz,prep_out_L1_R2.fastq.gz --se prep_out_L1_R3.fastq.gz -p asm_out

Outputs are:

• examples/prep_out_L1_R?.fastq.gz - Short reads after quality filtering
• asm_out.result.fastq - Assembly with consensus quality information
• asm_out.result.fasta - Assembly in fasta format

NOTE: example is copied from sample data in SPAdes 3.10