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README.md

Latest Version License DOI

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A tool for sequence-based identification and characterization of protein classes

APRICOT is a computational pipeline for the identification of specific functional classes of interest in large protein sets. The pipeline uses efficient sequence-based algorithms and predictive models like signature motifs of protein families for the characterization of user-provided query proteins with specific functional features. The dynamic framework of APRICOT allows the identification of unexplored functional classes of interest in the large protein sets or the entire proteome.

Publications

Primary (peer reviewed) article

Malvika Sharan, Konrad U. Förstner, Ana Eulalio, Jörg Vogel, APRICOT: an integrated computational pipeline for the sequence-based identification and characterization of RNA-binding proteins, Nucleic Acids Research, Volume 45, Issue 11, 20 June 2017, https://doi.org/10.1093/nar/gkx137

Secondary articles where the tool was used in different datasets (adapted parameters)

Yuxiang Jiang et. al, An expanded evaluation of protein function prediction methods shows an improvement in accuracy, Genome Biology, 17:184, 7 September 2016 https://doi.org/10.1186/s13059-016-1037-6

Caroline Tawk, Malvika Sharan, Ana Eulalio & Jörg Vogel , A systematic analysis of the RNA-targeting potential of secreted bacterial effector proteins, Scientific Reports, volume 7, Article number: 9328, 24 August 2017, https://www.nature.com/articles/s41598-017-09527-0

Authors and Contributors

The tool is designed and developed by Malvika Sharan in the lab of Prof. Dr. Jörg Vogel and Dr. Ana Eulalio in the Institute for Molecular Infection Biology at the University of Würzburg. Dr. Konrad Förstner contributed to the project by providing important technical supervision and discussions. The authors are grateful to Prof. Thomas dandekar, Dr. Charlotte Michaux, Caroline Taouk and Dr. Lars Barquist for critical discussions and feedback.

Source code

The source codes of APRICOT are available via git https://github.com/malvikasharan/APRICOT and pypi https://pypi.python.org/pypi/bio-apricot.

APRICOT is open source software and is available under the ISC license.

Copyright (c) 2019, Malvika Sharan, malvikasharan@gmail.com

Working with APRICOT software

The complete documentation (manual, tutorials etc.) is hosted at http://pythonhosted.org/bio-apricot.

Here we provide a brief information on obtaining and using the software.

APRICOT is implemented in Python as a standalone and is executable on Ubuntu (and other debian-based) systems. The complete software package can be obtained from the GitHub repository ($ git clone https://github.com/malvikasharan/APRICOT.git), or can be conveniantly executed using the Docker image.

APRICOT Docker image

We recommend users to install Docker software in their system to use the docker images.

In order to work with the Docker image for APRICOT, please follow these directions:

1. Get Docker image

The image can be acquired by simply using this command:

$ docker pull malvikasharan/apricot

2. Create the Docker container for testing the software

$ docker run -it malvikasharan/apricot bash

Here is a quick way to test if different modules work in your system (without really installing the complete filesystem).

Run the analysis in the home folder

$ cd home
$ apricot -h

NOTE: Follow this to ensure that the most latest APRICOT repository exists in your docker container:

$ cd APRICOT
$ git pull
# go back to the home folder
$ cd ..

Run test/example analysis

A subcommand default can be used to run the the software pipeline using the default parameters:

Syntax:

$ apricot default -i {UniProt IDs} -kw {Domain keywords}

Example analysis:

$ apricot default -i P0A6X3,P00957 -kw 'RRM,RNP,KH'

Optionally, a shell script run_example.sh, available in the git repository (path: APRICOT/shell_scripts/), can be used to call the commands to fetch demo files and run an example analysis. This script can be modified to run similar analyses by the users.

Copy the script from the existing repository in the home folder.

$ cp APRICOT/shell_scripts/run_example.sh .

Or use wget to get the most updated version from the repository.

$ wget https://raw.githubusercontent.com/malvikasharan/APRICOT/master/shell_scripts/run_example.sh

...and run it.

$ sh run_example.sh

By default, this script generates a main analysis folder APRICOT_analysis. To understand the file structure, please see below (point-5). We recomend you to check out the tutorial to understand each components of the software and the result generated by their analysis.

Copy output file from docker container to your local machine

  1. Check the docker container ID:

docker container ls -a

This should give you all the docker container currently available on your machine with their 12 letter long CONTAINER ID in the first column: for e.g.

CONTAINER ID        IMAGE                   COMMAND             CREATED             STATUS              PORTS               NAMES
d0650c980e7c        malvikasharan/apricot   "bash"              12 minutes ago      Up 12 minutes                           elegant_lovelace
  1. Copy output file from the container to your local machine

docker cp -a d0650c980e7c:/home/APRICOT_analysis/output [PATH]

Replace [PATH] with the relative or absolute path on your machine. You can also use the following code if you have already navigated into the folder where you would like to copy your output data.

docker cp -a d0650c980e7c:/home/APRICOT_analysis/output .

You can inspect the output files.

3. Get the supporting data required for running your queries

Users are required to set a directory source_files containing all the required supporting data, which can be setup in the local filesystem (recommended) or inside the docker container (in the home folder). See below for the details.

-- Be aware that the supporting data is a collection of large datasets of size: ~15 G compressed, and ~50 G uncompressed. --

Options for installation

1. In the local filesystem - RECOMMENDED

This should be setup once (please exit the container using the command exit if already running it) and can be reused in different containers (shown in the point 4).

This will ensure that users would not have to get these files every time a new Docker container for APRICOT is created. Moreover, this will keep the size of the container small by not having to setup the large databases inside the container.

2. Inside a new Docker conatiner

The supporting data can be used only inside the Docker container (every Docker container will need such setup individually).

Commands to acquire the supporting data

$ wget http://data.imib-zinf.net/APRICOT-supporting_dataset.zip
$ unzip APRICOT-supporting_dataset.zip

Alternatively, these files can be acquired using the script docker_support.sh provided in the git repository of APRICOT.

$ cp APRICOT/shell_scripts/docker_support.sh .
$ sh docker_support.sh

4. Using the supporting data

When the directory source_files is located in the local system (Recommended), use the following command to mount this directory into the Docker container (provide full path for $FULL_PATH_SOURCE_FILES):

## if the docker is started in the same path where the supporting data exists set the following path
# $ FULL_PATH_SOURCE_FILES=`pwd`    

$ docker run -it -v /$FULL_PATH_SOURCE_FILES/source_files/:/home/source_files malvikasharan/apricot bash
$ cd home

Skip this step when working in the Docker container already.

5. Carry out an analysis by APRICOT

$ apricot default -i P0A6X3,P00957 -kw 'RRM,RNP,KH'

OR

$ cp APRICOT/shell_scripts/run_example.sh .
$ sh run_example.sh

If the the analysis was successful, a directory APRICOT_analysis will be created, which contains following files with the outputs generated by different modules of the software.

APRICOT_analysis
    └───├input                                  # Location used by subcommand 'query' to store all the related files
    └───├output
            └───├0_predicted_domains            # Location for the output data obtained from the subcommand 'predict'
            └───├1_compiled_domain_information  # Location for the output data obtained from the subcommand 'filter'          
            └───├2_selected_domain_information            
            └───├3_annotation_scoring           # Location for the output data obtained from the subcommand 'annoscore'
            └───├4_additional_annotations       # Location for additional annotations for the selected 
            |                                   # queries using subcommand 'addanno'
            └───├5_analysis_summary             # Location for the output data obtained from the subcommand 'summary'
            └───├format_output_data             # Location for the output data obtained from the subcommand 'format'
            └───├visualization_files            # Location for the output data obtained from the subcommand 'vis'

You can check APRICOT_analysis_summary.csv in the path APRICOT_analysis/output/5_analysis_summary file for the quick overview of the analysis.

To run analysis on new query proteins, please edit the "Input-1" part of the run_example.sh script, for example, provide Uniprot ids of your query proteins ($QUERY_UIDS, line number 51). To acquire domain information of different classes, please change the keyword values for "Input-2" part in the shell script ($DOMAIN_KEYWORDS, line number 78).

For further details, please check the tutorial and data dependencies described in the documentatation.

Alternative ways to install APRICOT

Locally install the software using pip

In order to work with locally installed software, follow these instructions.

  1. Make sure that your system has pip and git installed
$ apt-get install python3-pip git 
  1. Then install APRICOT via pip (NOTE: this doesn't install the complete filesystem):
$ pip3 install bio-apricot 

This will globally install APRICOT, which can be called via the command apricot, and the libraries from apricotlib will be saved. Follow the above listed points 3 & 5 to execute the software as shown above. ###OR

####Get APRICOT manually

APRICOT is implemented in Python3 and can be executed in Linux/Unix system. APRICOT requires few third party packages, namely Biopython, BLAST executables, interproscan, python libraries like Matplotlib, requests, openpyxl and other required tools.

Follow these instructions to manually establish the software locally.

  1. Get the python dependencies
$ apt-get install python3-matplotlib python3-numpy python3-scipy python3-biopython python3-requests --yes --fix-missing
$ pip3 install openpyxl
  1. Get the repository for APRICOT from git either by clicking here or locally cloned by using the following command:
$ git clone https://github.com/malvikasharan/APRICOT.git

Follow the above listed points 3 & 5 to execute the software as shown above.

Hint:

When installed locally, the location of the executable will be: /home/username/.local/bin/ and the library location will be: /home/username/.local/lib/python3.5/site-packages/apricotlib/

In that case, when calling the software (also edit the path when using the shell script run_example.sh and system_test.sh), please use the complete path name rather than using apricot, which will look for a globally installed software.

When using --user flag for a local installation $ pip3 install --user bio-apricot, please check the paths for the executable and the libraries.

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