https://github.com/gamcil/synthaser_scripts

Python scripts used in synthaser manuscript
https://github.com/gamcil/synthaser_scripts

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Python scripts used in synthaser manuscript

• Host: GitHub
• URL: https://github.com/gamcil/synthaser_scripts
• Owner: gamcil
• Created: 2021-05-17T07:21:39.000Z (about 5 years ago)
• Default Branch: master
• Last Pushed: 2021-08-09T08:36:40.000Z (almost 5 years ago)
• Last Synced: 2025-10-25T00:40:28.472Z (8 months ago)
• Language: HTML
• Size: 17.2 MB
• Stars: 1
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

          
## Scripts used in synthaser manuscript

| File | Description |

| ---- | ----------- |

| ``sum_bitscores.py`` | Script to sum bitscores of identical query/target sequence pairs in BLAST results. |

| ``extract_PKS.py`` | Script to extract PKS/NRPS sequences from MIBiG GenBank/JSON files. |

| ``mibig/*`` | synthaser output for MIBiG synthases |

| ``ks_domains/*`` | Files generated during KS domain network construction |

## Methods

### Comparison to MIBiG domain architectures

Download MIBiG database GenBank and JSON dumps and extract contents:

	wget https://dl.secondarymetabolites.org/mibig/mibig_json_2.0.tar.gz

	wget https://dl.secondarymetabolites.org/mibig/mibig_gbk_2.0.tar.gz

	tar xzvf mibig_json_2.0.tar.gz

	tar xzvf mibig_gbk_2.0.tar.gz

Retrieve all annotated PKS sequences using ``extract_PKS.py``:

	python3 extract_PKS.py \

		mibig_gbk_2.0/ \               # GenBank folder

		mibig_json_2.0/ \              # JSON folder

		mibig_table.tsv \              # Output, table with MIBiG metadata

		--fasta mibig_synthases.fasta  # Output, FASTA file with PKS 

Setup synthaser:

	pip install --user synthaser

Run synthaser on PKS sequences, saving HTML plot and search session:

	synthaser search \

		--query_file mibig_synthases.fasta \

		--json_file mibig_synthases.json \

		--output mibig_predictions.txt \

		--plot mibig.html \

		--long_form

The MIBiG metadata table (``mibig_table.tsv``) was then merged with

the synthaser predictions table (``mibig_predictions.txt``). MIBiG domain

architectures were copied from the 'NRPS/PKS domains' tab of each

MIBiG entry, added to the table and compared to the predictions in the

synthaser output.

### Creation of the Aspergillus KS network

Retrieve sequences from NCBI containing the ``cond_enzymes`` conserved

domain family, removing any unnecessary information from FASTA description

lines:

	esearch -db cdd -query 238201 |\

		elink -target protein |\

		efilter -query "Aspergillus"[ORGN] -source genbank |\

		efetch -format fasta |\

		sed 's/ .*$//g' - > synthases.faa

Analyse sequences using synthaser:

	synthaser search \

		--query_file synthases.faa \

		--json_file synthases.json \

		--output architectures.txt \

		--long_form

Extract KS domains from the search session:

	synthaser extract \

		synthases.json \  # Session file

		synthases_  \     # Output file prefix, e.g. synthases_KS.faa

		--mode domain \   # Specify domain extraction

		--types KS        # Specify KS domains

Build DIAMOND database from extracted KS domains:

	diamond makedb --in synthases_KS.faa --db KS

Perform all vs all alignments:

	diamond blastp --query domains.faa \

		--db KS.dmnd \

		--more-sensitive \

		--outfmt "6 qseqid sseqid bitscore" \

		--out KS_alignments.tsv

Sum bitscores of all non-overlapping high-scoring segment pairs (HSPs):

	python3 sum_bitscores.py KS_alignments.tsv summed.tsv

The summed alignment table (``summed.tsv``) was then imported into CytoScape

v3.7.2 to build a similarity network. Domain architecture predictions from

synthaser (``architectures.txt``) were imported and connected to their

corresponding nodes, which were then coloured based on an alphabetical ordering

of architectures.