{ "info": { "author": "Adam Cribbs", "author_email": "adam.cribbs@imm.ox.ac.uk", "bugtrack_url": null, "classifiers": [ "Intended Audience :: Developers", "Intended Audience :: Science/Research", "License :: OSI Approved", "Operating System :: MacOS", "Operating System :: POSIX", "Operating System :: Unix", "Programming Language :: Python", "Topic :: Scientific/Engineering", "Topic :: Software Development" ], "description": "# tRNAnalysis\n\n

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\n\nThis 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.\n\nOur 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.\n\n## Installation\n\n\n### Conda installation\n\nThe 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)\nor more easily, use the linux environment in the **Conda environment** section.\n\nTo install trnanalysis using conda::\n \n conda install -c bioconda trnanalysis\n\n### Conda environment\n\nConda is an awesome project, however it can suffer from significant issues relating to how long it takes the solver to\nfix installation issues. For more information regarding these conda issues please see [bioconda issues](https://github.com/conda/conda/issues/7239).\n\nIn order to try and speed things up we have provided a conda environment for installation. Currently only linux is supported and it can\nbe installed by doing the following::\n\n wget https://raw.githubusercontent.com/Acribbs/tRNAnalysis/master/conda/environments/trnanalysis-linux.yml\n conda env create -f trnanalysis-linux.yml \n conda activate trnanalysis-env\n\n### Pip installation\n\ntrnanalysis can also be installed using pip::\n\n pip install trnanalysis\n\n\n### Manual installation\n\nAlternatively, you can manusally install tRNAnalysis by::\n\n git clone https://github.com/Acribbs/tRNAnalysis.git\n cd tRNAnalysis\n python setup.py install\n trnanalysis --help\n \n## Usage\n\nRun the ``trnanalysis --help`` command view the help documentation for how to run tRNAnalysis.\n\nTo run the main trnanalysis pipeline run::\n\n trnanalysis trna make full -v5\n\nIn order to run and generate the multiQC report to identify read quality and Rmarkdown html report\nfor the tRNA analysis run::\n\n trnanalysis trna make build_report -v5\n \nRunning locally or on a cluster - the default setting to run trnanalysis is on a cluster, with SLURM, SGC, Torque and PBS/pro\ncurrently supported. 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