segzoo 1.0.3

System for turnkey analysis of semi-automated genome annotations

Segzoo

What is segzoo?

Segzoo is a tool that allows to run various genomic analysis on a segmentation obtained with Segway. The results of each analysis are made available as well as a summarizing visualization of the results. The requirements for this tool include segtools, bedtools and python packages, but all of them are dependencies that will be treated during installation.

Quick start

This quick start needs you to have anaconda already installed in your local computer (either with python 2 or 3).

1. Download the test segmentation and GMTK parameters and move them both in a directory called, for example, segzoo
2. Open a terminal in the mentioned directory and run conda create -c bioconda -n python3_env python=3.6 r-base r-latticeextra r-reshape2 r-cairo r-cluster bedtools -y
3. After the last command has finished, run source activate python3_env followed by pip install segzoo
4. When finished, run segzoo segway.bed.gz --parameters params.params
5. After around 30 min, the resulting visualization will be stored in the current's directory outdir/plots folder

How to install

Segzoo is a python 3 tool, so if you have python 2 installed it is highly recommended for you to install segzoo in a separate python 3 environment. Although it can, this tool is not designed to run on a cluster node without internet access, so all the following steps should be done in a local computer. To create such an environment run conda create -n python3 python=3.6 where you can change the name of the environment, python3. Accept all the installation steps.

Next, you need to activate this environment. Run source activate python3 specifying the name of the environment you chose before. Now that you already are in it, you can install segzoo. You can do that by running pip install segzoo, which will require you to have bedtools already installed, as it's only in anaconda. To install bedtools beforehand you can use conda install -c bioconda bedtools. //Another option is to install segzoo using conda install -c bioconda segzoo which will take care of all the dependencies.

After accepting all installations, segzoo will be good to go!

How to use

To access the help to know how to run segzoo you can run segzoo -h or segzoo --help. Here's a look at all possible arguments:

• --version to check the current version of segzoo installed
• --parameters to specify a params.params file resulting from segway's training to obtain GMTK parameters in the final visualization. If not specified, GMTK parameters won't show in the final visualization
• --prefix to specify where you want all needed data (like the genome assembly) to be downloaded (default: the installation environment's directory)
• -o or --outdir to specify the folder where all the results and the final visualization will be created (default: outdir)
• -j to specify the number of cores to use (default: 1)
• --species and --build specify the species and the build for which the segmentation was created (default: Homo_sapiens and hg38)

If you are interested in obtaining information on different gene biotypes than protein coding and lincRNA, which are the default, you can get to the installation folder of segzoo and modify the file gene_biotypes.py as you wish.

After running the command segzoo by specifying the segmentation file and all the optional arguments that you want, the execution of the pipeline will begin. All necessary data will be downloaded, tools will run the different analysis and the final visualization will be created. This execution may take some time.

Results

After the execution has finished, the new directory will be created (outdir is the default name). In the data folder you will be able to find the results for all the tools' analysis. In results you will find the tables of processed results used in the visualization. Finally, the visualization will be in the plots directory. It will look something like this:

The y-axis are the labels of the segmentation for all the heatmaps.

Notes

• The mix table (the second one to the left) has each column normalized so that the maximum and minimum values are the limits of the heatmap. This applies to all but the GC content, which is normalized between 0.35 and 0.65. All this information will be displayed in the near future in a table.
• The aggregation tables are shown in the same order as specified in gene_biotypes.py, and can contain duplicates.
• The aggregation results displayed are the percentage of aggregations in one component in comparison to all the gene biotype, so notice that each row adds up to 100.