Metadata-Version: 2.1
Name: tRNAnalysis
Version: 0.1.9
Summary: tRNAnalysis : this software will perform alignment of reads to tRNA 
Home-page: https://github.com/
Author: Adam Cribbs
Author-email: adam.cribbs@imm.ox.ac.uk
License: MIT
Description: # tRNAnalysis
        
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        This workflow was generated as a response to not being able to effectively analyse tRNA data from next generation sequencing experiments rapidly and robustly. Typical workflows are not very flexible and do not scale well for multiple samples. Moreover, most do not impliment best-practice mapping strategies or generate detailed analysis reports to aid biological interpretation.
        
        Our pipeline can be used for evaluating the levels of small RNAs in a sample, but provides detailed analysis of tRNAs, with particular emphasis on tRNA fragment analysis.The pipeline is in constant development and further features will be added in the future. For example, we will extend our pipeline to perform detailed anaysis of miRNAs and plan to write an R shiny framework for interactive report features.
        
        ## Installation
        
        
        ### Conda installation
        
        The preferred method for installing tRNAnalysis is through [Conda](https://conda.io). However, at the moment we are experiencing issues with a broken dependancy so I would suggest either installation using pip and manually install dependancies (although there are a lot)
        or more easily, use the linux environment in the **Conda environment** section.
        
        To install trnanalysis using conda::
            
            conda install -c bioconda trnanalysis
        
        ### Conda environment
        
        Conda is an awesome project, however it can suffer from significant issues relating to how long it takes the solver to
        fix installation issues. For more information regarding these conda issues please see [bioconda issues](https://github.com/conda/conda/issues/7239).
        
        In order to try and speed things up we have provided a conda environment for installation. Currently only linux is supported and it can
        be installed by doing the following::
        
            wget https://raw.githubusercontent.com/Acribbs/tRNAnalysis/master/conda/environments/trnanalysis-linux.yml
            conda env create -f trnanalysis-linux.yml 
            conda activate trnanalysis-env
        
        ### Pip installation
        
        trnanalysis can also be installed using pip::
        
            pip install trnanalysis
        
        
        ### Manual installation
        
        Alternatively, you can manusally install tRNAnalysis by::
        
            git clone https://github.com/Acribbs/tRNAnalysis.git
            cd tRNAnalysis
            python setup.py install
            trnanalysis --help
            
        ## Usage
        
        Run the ``trnanalysis --help`` command view the help documentation for how to run tRNAnalysis.
        
        To run the main trnanalysis pipeline run::
        
            trnanalysis trna make full -v5
        
        In order to run and generate the multiQC report to identify read quality and Rmarkdown html report
        for the tRNA analysis run::
        
            trnanalysis trna make build_report -v5
            
        Running locally or on a cluster - the default setting to run trnanalysis is on a cluster, with SLURM, SGC, Torque and PBS/pro
        currently supported. However, if you dont have access to a cluster then tRNAnalsysis can be executed locally by adding `--no-cluster` as a 
        commandline argument. 
        
        ## Documentation
        
        Further help that introduces tRNAnalysis and provides a tutorial of how to run example
        code can be found at [read the docs](https://trnanalysis.readthedocs.io/en/latest/)
        
Keywords: computational genomics
Platform: any
Classifier: Intended Audience :: Science/Research
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved
Classifier: Programming Language :: Python
Classifier: Topic :: Software Development
Classifier: Topic :: Scientific/Engineering
Classifier: Operating System :: POSIX
Classifier: Operating System :: Unix
Classifier: Operating System :: MacOS
Description-Content-Type: text/markdown
