synthaser
Process
synthaser parses the results of a batch NCBI conserved domain search and determines
the domain architecture of secondary metabolite synthases.
Installation
Install from PyPI using pip:
$ pip install --user synthaser
or clone the repo and install locally:
$ git clone https://www.github.com/gamcil/synthaser
$ cd synthaser
$ pip install -e .
Dependencies
synthaser is written for Python 3.6+ and its only external Python dependency is
requests, which is used for querying the NCBI's APIs. To perform local
searches, the programs RPS-BLAST as well as the CD-Search post-processing
utility, rpsbproc, should be installed (available from ...)
Usage
A full synthaser search can be performed as simply as:
$ synthaser -qf sequences.fasta
Where sequences.fasta is a FASTA format file containing the protein sequences
that you would like to search.
For a full listing of available arguments, enter:
$ synthaser -h
Visualising your results
synthaser is capable of generating fully-interactive, annotated visualisations
so you can easily explore your results. All that is required is one
extra argument:
$ synthaser -qf sequences.fasta -p
This will generate a figure like so:
Click here to play around with the full version of this example.
Saving your search session
synthaser allows you to save your search results such that they can be easily
reloaded for further visualisation or exploration without having to fully re-do
the search.
To do this, use the --json_file command:
$ synthaser -qf sequences.fasta --json_file sequences.json
This will save all of your results, in JSON format, to the file
sequences.json. Then, loading this session back into synthaser, is as easy
as:
$ synthaser --json_file sequences.json ...
Using your own rules
Though synthaser was originally designed to analyse secondary metabolite synthases,
it can easily be repurposed to analyse the domain architectures of
any type of protein sequence.
Under the hood, synthaser uses two files to determine 1) which domains to save
from a CD-Search run (domains.json) and 2) the rules used to classify sequences based on their
domain architectures (rules.json). By default, synthaser will use the copies
of these files distributed with the program. However, providing your own rules
is as simple as:
$ synthaser -qf sequences.fasta -df my_domains.json -cf my_rules.json
For a detailed explanation of how to create your own synthaser rule files,
as well as API
please refer to the documentation.
Citations
If you found synthaser helpful, please cite:
1. <pending>
Release files for synthaser 1.1.1
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| synthaser-1.1.1.tar.gz | 35.1 kB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| synthaser-1.1.1-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 74.4 kB
Release files / synthaser-1.1.1.tar.gz
| Download URL | synthaser-1.1.1.tar.gz |
|---|---|
| Size | 35.1 kB |
| Tags | Source |
|
SHA-256 checksum How to use checksums |
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Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/3.2.0 pkginfo/1.5.0.1 requests/2.23.0 setuptools/46.0.0 requests-toolbelt/0.9.1 tqdm/4.48.0 CPython/3.8.5
|
Release files / synthaser-1.1.1-py3-none-any.whl
| Download URL | synthaser-1.1.1-py3-none-any.whl |
|---|---|
| Size | 39.3 kB |
| Tags | Python 3 |
|
SHA-256 checksum How to use checksums |
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| Upload date | |
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Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/3.2.0 pkginfo/1.5.0.1 requests/2.23.0 setuptools/46.0.0 requests-toolbelt/0.9.1 tqdm/4.48.0 CPython/3.8.5
|