Metadata-Version: 1.0
Name: traitar
Version: 1.1.1
Summary: traitar - The microbial trait analyzer
Home-page: http://github.com/aweimann/traitar
Author: Aaron Weimann
Author-email: weimann@hhu.de
License: GNU General Public License, version 3 (GPL-3.0)
Description: Traitar – the microbial trait analyzer
        ======================================
        
        Traitar is a software for characterizing microbial samples from
        nucleotide or protein sequences. It can accurately phenotype `67 diverse
        traits`_.
        
        Table of Contents
        ~~~~~~~~~~~~~~~~~
        
        | `Installation`_
        | `Basic usage`_
        | `Results`_
        | `Docker`_
        | `Citing Traitar`_
        
        Installation
        ============
        
        Please see `INSTALL.md`_ for installation instructions.
        
        Basic usage
        ===========
        
        ``traitar phenotype <in dir>  <sample file> from_nucleotides <out_dir>``
        
        will trigger the standard `workflow <https://raw.githubusercontent.com/hzi-bifo/traitar/master/workflow.png>`__ of Traitar, which is to predict open
        reading frames with Prodigal, annotate the coding sequences provided as
        nucleotide FASTAs in the for all samples in with Pfam families using
        HMMer and finally predict phenotypes from the models for the 67 traits.
        
        
        The sample file has one column for the sample file names and one for the
        names as specified by the user. You can also specify a grouping of the
        samples in the third column, which will be shown in the generated plots.
        The template looks like following - The header row is mandatory; please
        also take a look at the sample file for the packaged example data:
        
        | sample\_file\_name{tab}sample\_name{tab}category
        | sample1\_file\_name{tab}sample1\_name[{tabl}sample\_category1]
          sample2\_file\_name{tab}sample2\_name[{tabl}sample\_category2]
        
        ``traitar phenotype <in dir>  <sample file> from_genes <out_dir>``
        
        assumes that gene prediction has been conducted already externally. In
        this case analysis will start with the Pfam annotation. If the output
        directory already exists, Traitar will offer to recompute or resume the
        individual analysis steps. This option is only available if the process
        is run interactively.
        
        Parallel usage
        ~~~~~~~~~~~~~~
        
        Traitar can benefit from parallel execution. The ``-c`` parameter sets
        the number of processes used e.g. ``-c 2`` for using two processes
        
        ``traitar phenotype <in dir>  <sample file> from_nucleotides out_dir -c 2``
        
        This requires installing GNU parallel as noted above.
        
        Inspect phenotype classification models
        ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
        
        Traitar can be used to inspect the protein families in each phenotype
        model:
        
        ``traitar show 'Glucose fermenter``
        
        will show the majority features i.e. the Pfam families that contribute
        to the assignment of the trait Glucose fermenter with *phypat*
        classifier to some genome sequence. Via –predictor the user may specify
        the classifier (phypat, phypat+PGL).
        
        Run Traitar with packaged sample data
        ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
        
        ``traitar phenotype <traitar_dir>/data/sample_data <traitar_dir>/data/sample_data/samples.txt from_genes <out_dir> -c 2``
        will trigger phenotyping of *Listeria grayi DSM\_20601* and *Listeria
        ivanovii WSLC3009*. Computation should be done within 5 minutes. You can
        find out ``<traitar_dir>`` by running
        
        \`\`\` python
        python >>> import traitar >>> traitar.\ **path** \`\`\`
        
        Results
        =======
        
        Traitar provides the gene prediction results in
        ``<out_dir>/gene_prediction``, the Pfam annotation in
        ``<out_dir>/pfam_annotation`` and the phenotype prediction
        in\ ``<out_dir>/phenotype prediction``.
        
        Heatmaps
        ~~~~~~~~
        
        The phenotype prediction is summarized in heatmaps individually for the
        phyletic pattern classifier in ``heatmap_phypat.png``, for the
        phylogeny-aware classifier in ``heatmap_phypat_ggl.png`` and for both
        classifiers `combined <https://github.com/aweimann/traitar/blob/master/traitar/data/sample_data/traitar_out/phenotype_prediction/heatmap_combined.png?raw=true>`__ in ``heatmap_comb.png`` and provide hierarchical
        clustering dendrograms for phenotypes and the samples.
        
        Phenotype prediction - Tables and flat files
        ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
        
        These heatmaps are based on tab separated text files e.g.
        ``predictions_majority-vote_combined.txt``. A negative prediction is
        encoded as 0, a prediction made only by the pure phyletic classifier as
        1, one made by the phylogeny-aware classifier by 2 and a prediction
        supported by both algorithms as 3.
        ``predictions_flat_majority-votes_combined.txt`` provides a flat version
        of this table with one prediction per row. The expert user might also
        want to access the individual results for each algorithm in the
        respective sub folders ``phypat`` and ``phypat+PGL``.
        
        Phenotype-relevant protein families and feature tracks
        ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
        
        Traitar will link the protein families and predicted phenotypes. The
        results can be found in ``phypat/feat_gffs`` and
        \`\ ``phypat+PGL/feat_gffs``. If the user picked the ‘from nucleotides’
        option, Traitar will also generate GFF files that link the genes called
        by Prodidgal with the important protein families. The phenotype-specific
        protein family annotations tracks can be visualized via GFF files in a
        genome browser of choice.
        
        Feature tracks with *from\_genes* option (experimental feature)
        ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
        
        If the *from\_genes* option is set, the user may specify gene GFF files
        via an additional column called gene\_gff in the sample file. As gene
        ids are not consistent across gene GFFs from different sources e.g. img,
        RefSeq or Prodigal the user needs to specify the origin of the gene gff
        file via the -g / –gene\_gff\_type parameter. Still there is no
        guarantee that this works currently. Using samples\_gene\_gff.txt as the
        sample file in the above example will generate phenotype-specific Pfam
        tracks for the two genomes.
        
        ``traitar phenotype . samples_gene_gff.txt from_genes traitar_out -g refseq``
        
        Docker
        ======
        
        There is a Docker container available for Traitar. Pull by
        
        ``docker pull aweimann/traitar``
        
        To run traitar for the sample data execute
        
        ``docker run -v <traitar_dir>/data/sample_data:/mnt  1445e6c01992 bash -c 'traitar phenotype /mnt/ /mnt/samples.txt from_nucleotides /mnt/traitar_out'``,
        
        which will take ~30 minutes. Note there is a problem with parallel usage
        so -c option is not guaranteed to work. The output will be owned by
        root. So currently you still need root access to your machine to inspect
        the traitar\_out folder.
        
        Citing Traitar
        ==============
        
        If you use Traitar in your research, please cite our preprint:
        
        | **From genomes to phenotypes: Traitar, the microbial trait analyzer**
        | Aaron Weimann, Jeremy Frank, Phillip B Pope, Andreas Bremges, Alice C
          McHardy
        | *bioRxiv* (2016) doi:\ `10.1101/043315`_
        
        .. _10.1101/043315: http://dx.doi.org/10.1101/043315
        .. _67 diverse traits: traits.tsv
        .. _Installation: #installation
        .. _Basic usage: #basic-usage
        .. _Results: #results
        .. _Docker: #docker
        .. _Citing Traitar: #citing-traitar
        .. _INSTALL.md: INSTALL.md
        
Platform: UNKNOWN
