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#FastQt travis Qt FastQt is the clone of FastQC application ported from Java to C++/Qt5

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##Installation

Ubuntu

deb package has not yet ready. But An AppImage has been made for Linux x86_64 and has been tested on ubuntu 16.04.
You can download it from here and run it as follow :

chmod +x fastqt-0.1-linux-x86_64.AppImage
./fastqt-0.1-linux-x86_64.AppImage

Archlinux

There is an AUR package for Archlinux. Just get it from the AUR.

Other package will come soon for Windows and MacOS. You can try to compile it anyway.

Compilation

Prerequisites - Install KArchive

You need to install karchive before compiling FastQt.
From ubuntu >xenial you can install it from repositories :

 sudo apt install libkf5archive-dev

From fedoar >= 24 you can install it from repositories :

sudo dnf install kf5-karchive-devel

From source :

git clone git://anongit.kde.org/extra-cmake-modules
cd extra-cmake-modules
mkdir build && cd build    
cmake .. -DCMAKE_INSTALL_PREFIX=/usr/
make && sudo make install
cd ../..
git clone git://anongit.kde.org/karchive.git
cd karchive
mkdir build && cd build 
cmake .. -DCMAKE_INSTALL_PREFIX=/usr/
make 
sudo make install

Install Qt >5.7

From website : Download Qt > 5.7 from https://www.qt.io/.
Don't forget to check QtChart module during installation.

From ubuntu : Qt 5.7 is not yet avaible with ubuntu. But you can add PPA to your software system. For exemple for xenial

sudo add-apt-repository ppa:beineri/opt-qt57-xenial
sudo apt-get install qt57base qt57charts-no-lgpl
source /opt/qt57/bin/qt57-env.shf

From fedora : Qt 5.7 is avaible

sudo dnf install qt5-qtbase-devel qt5-qtcharts-devel 

Compile FastQt

Be sure you have the correct version of Qt (>5.7) by using qmake. You will find qmake in bin folder where you installed Qt. For exemple, if you installed Qt from ppa:beineri, you will find it under /opt/qt57/bin/qmake. Then launch the compilation from FastQC folder as follow.

 /opt/qt57/bin/qmake --version
 /opt/qt57/bin/qmake 
 make 
 sudo make install 

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FastQC port to Qt5: A quality control tool for high throughput sequence data.

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