Installation for software

## CPC ### Install 1. Unpack the tarball: tom@linux$ gzip -dc cpc-0.9-r2.tar.gz | tar xf - 2. Build third-part packages: tom@linux$ cd cpc-0.9-r2 tom@linux$ export CPC_HOME="$PWD" tom@linux$ cd libs/libsvm tom@linux$ gzip -dc libsvm-2.81.tar.gz | tar xf - tom@linux$ cd libsvm-2.81 tom@linux$ make clean && make tom@linux$ cd ../.. tom@linux$ gzip -dc estate.tar.gz | tar xf - tom@linux$ cd estate tom@linux$ make clean && make 3. Format BLAST database, named it as "prot_db", and put under the cpc/data/. tom@linux$ cd $CPC_HOME/data tom@linux$ formatdb -i (your_fasta_file) -p T -n prot_db 4. Run the predict tom@linux$ cd $CPC_HOME tom@linux$ bin/run_predict.sh (input_seq) (result_in_table) (working_dir) (result_evidence)

## CPC2 ``version==0.1`` ### Pre-requisite: [Biopython package](http://biopython.org/wiki/Download) ### Install - Unpack the tarball: tom@linux$ gzip -dc CPC2-beta.tar.gz | tar xf - - Build third-part packages: tom@linux$ cd CPC2-beta tom@linux$ export CPC_HOME="$PWD" tom@linux$ cd libs/libsvm tom@linux$ gzip -dc libsvm-3.18.tar.gz | tar xf - tom@linux$ cd libsvm-3.18 tom@linux$ make clean && make

## CNCI ### Install - donwload git clone git@github.com:www-bioinfo-org/CNCI.git (temporary not available) - Build third-part package: cd CNCI unzip libsvm-3.0.zip cd libsvm-3.0 make cd ..

## CPAT ``version==1.2.2`` ### Prerequisite * gcc * python2.7 * numpy * If your computer can not connnect to internet, nose>= 0.10.4 and distribute-0.6.10 are also required. * R * Xcode (MAC OS) Below is an example of installing CPAT on Linux system using BASH. You need to change '--root' directory, PYTHONPATH and PATH accordingly * tar zxf CPAT-VERSION.tar.gz * cd CPAT-VERSION * python setup.py install #will install CPAT in system level. require root previledge * python setup.py install --root=/home/user/CPAT #will install CPAT at user specified location * export PYTHONPATH=/home/user/CPAT/usr/local/lib/python2.7/site-packages:$PYTHONPATH. #setup PYTHONPATH, so that CDSP knows where to import modules * export PATH=/home/user/CPAT/usr/local/bin:$PATH #setup PATH, so that system knows where to find executable file ### NOTE * If the installation failed with error like: /usr/bin/ld: cannot find -lz, you may need to install a shared zlib library on your system. * Due to disabling non HTTPS access to APIs on PyPI, a 403 error will be raised when install CPAT. The code of distribute_setup.py should be modified to enable further installation. - Change line 50 DEFAULT_URL = "http://pypi.python.org/packages/source/d/distribute/" - to DEFAULT_URL = “https://pypi.python.org/packages/source/d/distribute/” Online Manual: http://rna-cpat.sourceforge.net/

## FEELnc ``version==0.1.1`` ### Requirements The following software and libraries must be installed on your machine: - [Perl5+](https://www.perl.org/) : tested with version 5.18.2 * [Bioperl](http://www.bioperl.org/wiki/Main_Page) : tested with version BioPerl-1.6.924 (partial tests with BioPerl >=1.7); * [Paralell::ForkManager](http://search.cpan.org/perldoc/Parallel::ForkManager) : tested with version 1.07. - R [Rscript](http://cran.r-project.org): tested with version 3.1.0. * [ROCR](https://rocr.bioinf.mpi-sb.mpg.de/) test with version 1.0-5; * [randomForest](http://cran.r-project.org/web/packages/randomForest/index.html) tested with version 4.6-10. * These R librairies should be installed automatically when running FEELnc. In case it does not work, please type in a R session: install.packages('ROCR') install.packages('randomForest') - [KmerInShort](https://github.com/rizkg/KmerInShort) developped by Guillaume Rizk: * Linux and MAC executables in FEELnc bin directory; * If any trouble using supplied executables, please download and compile from sources. - [fasta_ushuffle](https://github.com/agordon/fasta_ushuffle) software: * uShuffle: A useful tool for shuffling biological sequences while preserving the k-let counts; M. Jiang, J. Anderson, J. Gillespie and M. Mayne; BMC Bioinformatics 2008, [9:192 doi:10.1186/1471-2105-9-192](http://bmcbioinformatics.biomedcentral.com/articles/10.1186/1471-2105-9-192). * required only if you want to use the **shuffle** mode * Linux and MAC executables in FEELnc bin directory; * If any trouble using supplied executables, please download and compile from sources. ### Installation Clone the FEELnc git: git clone https://github.com/tderrien/FEELnc.git Go to FEELnc directory cd FEELnc Export PERL5LIB and FEELNCPATH variables export FEELNCPATH=${PWD} export PERL5LIB=$PERL5LIB:${FEELNCPATH}/lib/ #order is important to avoid &Bio::DB::IndexedBase::_strip_crnl error with bioperl >=v1.7 export PATH=$PATH:${FEELNCPATH}/scripts/ export PATH=$PATH:${FEELNCPATH}/utils/ # for LINUX #---------- export PATH=$PATH:${FEELNCPATH}/bin/LINUX/ # or cp ${FEELNCPATH}/bin/LINUX/ ~/bin/ # for MAC # -------- export PATH=$PATH:${FEELNCPATH}/bin/MAC/ # or cp ${FEELNCPATH}/bin/MAC/ ~/bin/ ### Install via [Conda](https://anaconda.org/bioconda/feelnc): Create a new environment containing FEELnc (and its dependencies): conda create -p ~/feelnc_install_dir feelnc To activate it: source activate ~/feelnc_install_dir To deactivate it: source deactivate

## Londist ``version==1.0.3`` ### Requirements Python 3, Tkinter and pip are required for running this tool. Also, the following packages will be installed during the installation process: - NumPy - ``pip install numpy`` - Biopython - ``pip instal biopython`` - scipy - ``pip install scipy`` - scikit-learn - ``pip install scikit-learn`` - configparser - ``pip install configparser`` - matplotlib - ``pip install matplotlib`` These packages will be automatically installed during the installation process ### Installation ``` wget https://github.com/hugowschneider/longdist.py/archive/v1.0.3.tar.gz tar zxvf v1.0.3.tar.gz cd longdist.py-1.0.3 pip3 install . ```

## PLEK ``version==1.2`` ### Pre-requisite: 1. Linux 2. C/C++ compiler (i.e. gcc, g++) 3. Python 2.5.0 or later versions (http://www.python.org/) ### Steps: 1. Download PLEK.1.2.tar.gz from https://sourceforge.net/projects/plek/files/ and decompress it. $ tar zvxf PLEK.1.2.tar.gz 2. Compile PLEK. $ cd PLEK.1.2 $ python PLEK_setup.py

## PLncPro ``version==1.1`` ### Pre-requisite: 1. OS: Linux 2. [Python 2.7.11 or later versions](http://www.python.org/) a.) [NumPy](http://www.numpy.org/) b.) [SciPy](https://www.scipy.org/) c.) [Scikit-learn](http://scikit-learn.org/) d.) [Biopython](http://biopython.org/) 3. [NCBI BLAST](https://blast.ncbi.nlm.nih.gov/Blast.cgi) 4. GNU C Library (glibc >= 2.14) ### Steps: 1. Install Python and the required modules 2. Download [PLncPRO](http://ccbb.jnu.ac.in/plncpro/) and extract the files. $ tar zvxf plncpro.1.1.tar.gz 3. make framefinder executable * Go to directory plncpro/lib/estate $ make * Copy framefinder executable to plncpro/lib/framefinder $ cp bin/framefinder ../framefinder 3. Setup BLAST. * Put the blast binaries in folder plncpro/lib/blast/bin

## RNAplonc ``version==1.0`` Executables have been pre-compiled for Linux. Only the dependency should be installed. ### Requirements - [txCdsPredict](https://github.com/ENCODE-DCC/kentUtils) - [CD-HIT-EST](https://github.com/weizhongli/cdhit/releases) - [All to install](https://github.com/TatianneNegri/RNAplonc/blob/master/Install.md)

## COME ### Download files into sepcific folders. 1. First, change directory to your working directory, download the source codes from https://github.com/lulab/COME/archive/master.zip and decompress it. Enter the subfolder "COME-master/bin" and define the path as the variable `Bin_dir` $ unzip ./COME-master.zip; $ cd ./COME-master/bin; $ Bin_dir=`pwd|awk '{print $1}'`; 2. Second, download your species'(Let's say, _human_) feature vector files from the [download page for feature vectors](https://onedrive.live.com/redir?resid=AFBF18A0971099A!51586&authkey=!AJaFH5EENUp0FVI&ithint=folder%2czip) or [mirror](http://pan.baidu.com/s/1pJRd5P5). These (nine) files need to be placed in the subfolder "COME-master/bin/HDF5". $ unzip ./human.feature_vector.HDF5.zip; $ mv ./human/human.HDF5.* $Bin_dir/HDF5; 3. Third, download your species' model file from the [download page for models](https://onedrive.live.com/redir?resid=AFBF18A0971099A!51594&authkey=!AJf5-cl93Z-4nJs&ithint=folder%2cmodel) or [mirror](http://pan.baidu.com/s/1dEs2pjV). The (one) model file need to be placed in the subfolder "COME-master/bin/models". $ mv ./human.model $Bin_dir/models;

## iSeeRNA ``version==1.2.2`` This INSTALL file covers the following topics: 1. System requirements 2. Installation 3. Related data ### Requirements * 1.1. Perl 5.0 or higher * 1.2. Linux/Unix is required - You can use the following command to check your machine type: user@linux$ uname -a - If you see 'x86_64' or 'amd64', it indicates you are using 64-bit Linux/Unix. - If you see 'i386', 'i486', 'i585' or 'i686', your machine is 32-bit. - Please choose the appropriate binary package for your platform from [iSeeRNA website](https://sunlab.cpy.cuhk.edu.hk/iSeeRNA/download.html). * 1.3. iSeeRNA depends on the following third party programs: - txCdsPredict (http://hgdownload.cse.ucsc.edu/admin/jksrc.zip) A utility program from the UCSC Genome Browser to calculate ORF length. - gffread (http://cufflinks.cbcb.umd.edu/downloads/) A utility program from the cufflinks package to read GFF file. - libsvm package (http://www.csie.ntu.edu.tw/~cjlin/) A SVM implementation from Chih-Jen Lin, including svm-scale, svm-train, and svm-predict. - The binary package contains all these three programs. ### Installation After uncompressing the package, the main program "iSeeRNA" can be found in the same directory as this INSTALL file. The binary package contains all the pre-built programs needed. Thus no further action is needed. For source code package, you should compile the three dependent programs and put one copy in the bin/ directory with the name as described( txCdsPredict, gffread, svm-scale, svm-train, svm-predict ). Then compile some C++ codes: user@linux$ make ### Related data SeeRNA requires genome fasta files and conservation array files. Our webserver supplies these files for hg19 and mm9, mm10. You can download from our server or use a shell in this package called 'auto_download_data.sh'(run it within this directory): user@linux$ sh auto_download_data.sh SPECIES For example, if you want to download files for hg19, just use: user@linux$ sh auto_download_data.sh hg19 This command will automatically download genome fasta into genome/hg19 directory and conservation files into consv/hg19 directory. You can also use 'all' as SPECIES to download files for hg19, mm9 and mm10 in one command.

## lncRScan-SVM ``version==1.0.1`` Currently lncRScan-SVM can be installed on a Linux/Unix OS. ### Requirements * Binary files: txCdsPredict, bigWigAverageOverBed, wigToBigWig and gffread ``Note: They have been packaged in lncRScan-SVM/bin/x86 or lncRScan-SVM/bin/x86_64, so you don't need to install them by yourself.`` * [Python 2.7](https://www.python.org/download/releases/2.7/) (Script running platform) * [Biopython](https://github.com/biopython/biopython) (a set of freely available tools for biological computation written in Python, [wiki](http://biopython.org/wiki/Biopython), [installation](http://biopython.org/DIST/docs/install/Installation.html) ) * [LIBSVM](http://www.csie.ntu.edu.tw/~cjlin/libsvm/index.html) (an integrated software for support vector classification, regression and distribution estimation) ### Install * extract the compressed source package by running $ tar zxvf lncRScan-SVM.tar.gz and then get LNCRSCAN_SVM_ROOT by running $ pwd $ export LNCRSCAN_SVM_ROOT="$PWD" * Add the paths of lncRScan-SVM scripts and binary files to the environment variable $PATH by modifying .bashrc in your home directory: - First, in the end of file add export LNCRSCAN_SVM_ROOT="the directory you have got by 'pwd'" export PATH=$PATH:$LNCRSCAN_SVM_ROOT/executable/script:$LNCRSCAN_SVM_ROOT/executable/util - and then add the directory of binary files to $PATH according to your OS: * on 32 bit OS, add export PATH=$PATH:$LNCRSCAN_SVM_ROOT/executable/bin/x86 * or on 64 bit OS, add export PATH=$PATH:$LNCRSCAN_SVM_ROOT/executable/bin/x86_64 * then save .bashrc and restart the shell or $source .bashrc. Then you can use scripts and bin files everywhere.

## lncScore ``version==1.0.2`` ### Requirements The following software should be installed in your cluster or computer before running the lncScore.py. * [Perl](https://www.perl.org/get.html) (>=5.10.1). * [Python](https://www.python.org/downloads/) (>= 2.7). * The [scikit-learn](http://scikit-learn.org/stable/install.html) modul. In most use cases the best way to install Python and scikit-learn package on your system is by using [Anaconda](https://www.continuum.io), which is an easy-to-install free Python distirbution and includes more than 400 of the most popular Python packages. Anaconda includes [installers](https://www.continuum.io/downloads) for Windows, OS X, and Linux. If the input file in .bed format, then an additional python package named 'pysam' is required to be installed first. After the installation of Anaconda, you can use the command 'conda install pysam' to install the Pysam package.