A bioinformatic program for predicting whether proteins are 'silk-like' or not.
This program was developed by Carmel McDougall and Ben Woodcroft at the Degnan Laboratory at the University of Queensland.
There are 2 methods of installing SilkSlider. The first and likely most reliable is to create a docker image using the Dockerfile provided. The second is to install the gem directly. In both cases, SignalP and TMHMM must be installed separately as licensing restrictions mean they cannot be distributed here.
To build the docker image, first download the SilkSlider code from GitHub:
$ git clone https://github.com/wwood/SilkSlider
SignalP version 3 and TMHMM version 2.0c must be downloaded separately. As of writing they are available for download for academic use from here and here respectively.
Download the signalp and TMHMM tar.Z and tar.gz files and place them in the docker subdirectory of the SilkSlider code. After doing this, the directory should have these files:
$ ls docker
Dockerfile
signalp-3.0.Linux.tar.Z
test_case.faa
tmhmm-2.0c.Linux.tar.gz
Then build the docker image:
$ cd docker
$ docker build -t silk_slider .
SilkSlider can then be run using the docker image. A test file is provided in the docker
directory of the SilkSlider code:
$ docker run silk_slider test_case.faa
INFO silk_slider: Using SignalP version: Bio::SignalP::Version3::Result
>positive
MKILLLCIIFLYYVNAFKNTQKDGVSLQILKKKRSNQVNFLNRKNDYNLIKNKNPSSSLKSTFDDIKKIISKQLSVEEDKIQMNSNFTKDLGADSLDLVELIMALEEKFNVTISDQDALKINTVQDAIDYIEKNNKQGGGGGGGGGGGGGGGGGGGGGGGGGGGGGG
INFO silk_slider: Predicted 1 out of 2 as silk-like
After installing Ruby (SilkSlider is tested on Ruby 2.1.2):
$ gem install SilkSlider
Also required is the SignalP 3.0 and TMHMM 2.0c programs, which need to be installed separately and available to silk_slider on the $PATH
. SilkSlider is only tested on linux systems.
Given that these versions of SignalP, TMHMM and Ruby are out of date, we
recommend using the Docker installation method instead. If you do wish to
install using the gem, the docker/Dockerfile
provides a worked example of how
this is possible.
To run the program, where my.fasta
is a FASTA file of protein sequences in the current directory, run:
$ docker run -v .:/data silk_slider /data/my.fasta
The -v .:/data
option mounts the current directory (and therefore your my.fasta
file within it) as /data
in the docker container, so that the FASTA file can be accessed by the program.
The program outputs a FASTA file of all silk-like proteins.
To see the help:
$ docker run silk_slider -h
Information on the source tree, documentation, examples, issues and how to contribute, see
http://github.com/wwood/SilkSlider
McDougall, Carmel, Ben J. Woodcroft, and Bernard M. Degnan. "The widespread prevalence and functional significance of silk-like structural proteins in metazoan biological materials." PLoS One 11.7 (2016): e0159128. https://doi.org/10.1371/journal.pone.0159128
Copyright (c) 2014-2024 Carmel McDougall, Ben Woodcroft, and Bernard Degnan. See LICENSE.txt for further details.