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        "author": "Eric Talevich",
        "author_email": "etal@uga.edu",
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        "description": "============\nCladeCompare\n============\n\nCompare protein sequence alignments. Identify diagnostic residues between the\ngiven \"foreground\" (FG) and  \"background\" (BG) clades.\n\nIf you use this software in a publication, please cite our paper that describes\nit:\n\n    Talevich, E. & Kannan, N. (2013) Structural and evolutionary adaptation of\n    rhoptry kinases and pseudokinases, a family of coccidian virulence factors.\n    *BMC Evolutionary Biology* 13:117\n    doi:10.1186/1471-2148-13-117\n\n    Available at: http://www.biomedcentral.com/1471-2148/13/117\n\n\nFreely distributed under the permissive BSD 2-clause license (see LICENSE).\n\nInstallation\n------------\n\nA proper installation looks like::\n\n    python setup.py install\n\nIf you have setuptools installed, the dependencies on Biopython_, BioFrills_,\nSciPy_ and ReportLab_ will be fetched and installed automatically.\n\n.. _Biopython: http://biopython.org/wiki/Download\n.. _biofrills: https://github.com/etal/biofrills\n.. _SciPy: http://scipy.org/\n.. _ReportLab: http://pypi.python.org/pypi/reportlab\n\nOptionally, CladeCompare can align your sequences for you if you have MUSCLE_,\nHMMer3_ or MAPGAPS_ installed.\n\n.. _MUSCLE: http://www.drive5.com/muscle/\n.. _HMMer3: http://hmmer.janelia.org/\n.. _MAPGAPS: http://mapgaps.igs.umaryland.edu/\n\n\nIf you have the dependencies installed, you can use this package in-place,\nwithout installing it. Just download the source code (git clone, or download\nthe ZIP file and unpack it) and include the top-level directory in your system\npath, or add symbolic links to cladecompare.py, cladereport.py and cladeweb.py\nto an existing directory that's in your path (e.g. ``~/bin``).\n\n\nTesting\n~~~~~~~\n\nFinally, if you are on a Unix-like system (i.e. Linux, Mac or Cygwin), you can\nverify your installation by running the test suite. Change to the ``test/``\ndirectory and run ``make``::\n\n    cd test\n    make\n\nIf CladeCompare is installed correctly, the program will run in several modes\nand generate output files. View the ``.html`` files in your web browser to see\nwhat happened.\n\n\nUsage\n-----\n\nWeb interface\n~~~~~~~~~~~~~\n\nLaunch the script ``cladeweb.py`` and fill in the form in your web browser.\nThe form accepts sequences in FASTA or CMA format, and you can upload an HMM\nprofile to align unaligned FASTA sequence sets. (See below for details about\neach field.)\n\nIf you launched the application from the command line, press Ctrl-C (on\nUnix-like systems) to stop the web server application.\n\nNote that only one instance of the server will run on your system at a time; if\nyou launch ``cladeweb.py`` twice in a row, another browser tab or window will\nopen but the server will not restart.\n\n\nCommand line\n~~~~~~~~~~~~\n\nThe command-line interface ``cladecompare.py`` provides the same functionality\nas the web interface, plus a few more options.  To read the built-in help and\noption descriptions::\n\n    cladecompare.py --help\n\nTwo alignments are compared by specifying the foreground and background sets,\nin that order, as arguments::\n\n    # Compare two alignments\n    cladecompare.py fg_aln.seq bg_aln.seq\n\nThe program prints the following information for each column in the alignment(s):\n\n- The consensus amino acid types of the foreground and background\n- p-value indicating the significance of the contrast in amino acid frequencies\n- a little ASCII bar chart indicating contrast, based on the p-value.\n\nP-values are adjusted for number of columns in the alignment with the\nBenjamini-Hochberg \"step-up\" multiple-testing correction (false discovery rate,\nFDR).\n\nRedirect the output to a file with the extension \".out\"::\n\n    # Compare two alignments\n    cladecompare.py fg_aln.seq bg_aln.seq > fg-v-bg.out\n\nOr specify the output file name with the ``-o`` option (same effect)::\n\n    cladecompare.py fg_aln.seq bg_aln.seq -o fg-v-bg.out\n\nIf you're not using MAPGAPS_, it would make sense to either:\n\n- Create a sequence alignment of all sequences, foreground and background,\n  together; then divide the alignment into two FASTA files (e.g. by sequence\n  ID).\n- Align both the foreground and background sets with hmmalign (HMMer3_) using\n  the same profile, then use a script (your own) to delete any insert columns.\n\nIn case you botch all that, CladeCompare will check then number of columns in\nthe FG and BG alignments, and if they don't match, will automatically run MUSCLE\nto align the alignments to each other.\n\nTo align the FASTA sequences with MAPGAPS on the fly, specify the profile name\n(minus the extension) with the ``--mapgaps`` flag::\n\n    # Align sequence sets w/ MAPGAPS, then compare\n    cladecompare.py test/scttlre-domain.fasta test/cdc2-domain.fasta --mapgaps test/CDK_CMGC \\\n        -o scttlre-v-cdc2.out\n\nPre-aligned sequences in CMA format (.cma) are also accepted::\n\n    # Use pre-aligned sequence sets (MAPGAPS \"CMA\" format)\n    cladecompare.py test/scttlre-domain.fasta_aln.cma test/cdc2-domain.fasta_aln.cma \\\n        -o scttlre-v-cdc2.out\n\nFinally, given the '-p' option, cladecompare.py will write a \"pattern\" file\nlisting the alignment column numbers with significant contrasts, in decreasing\norder (this can be useful input to other scripts of your own), as well as PDF\nfiles of paired sequence logos representing the foreground and background\nalignments around each significant site::\n\n    # Specify where the outputs go\n    cladecompare.py fg_aln.seq bg_aln.seq -o fg-v-bg.out -p fg-v-bg.pttrn\n\nOutputs\n```````\n\nThe script ``cladereport.py`` converts the \"\\*.out\" files to an HTML file showing\nthe alignment of the FG and BG consensus sequences, with the FG sequence\ncolorized to show per-site contrasts (red=significant difference,\nblue=non-significant/columns are similar), inserts (yellow) and deletions (gray\ngaps)::\n\n    # Visualize the per-site contrasts as a colorized alignment\n    cladereport.py scttlre-v-cdc2.out > scttlre-v-cdc2.html\n\nSingle- and multi-profile modes\n```````````````````````````````\n\nIf a single sequence set is given, the aligned columns are compared to the\noverall amino-acid frequencies of the alignment::\n\n    cladecompare.py subfam1.seq -o subfam1-single.out\n\nWhen more than 2 sequence sets are given, each set is individually treated as a\nforeground and the rest treated as the background for evaluation::\n\n    # Compare several related alignments, e.g. all subfamilies\n    cladecompare.py subfam1.seq subfam2.seq subfam3.seq ...\n\nThis multi-mode generates and names the \"\\*.out\" files according to the\ncorresponding sequence file names. You can visualize these all together::\n\n    # Visualize each subfamily's contrasts together\n    cladereport.py subfam2.out subfam2.out subfam3.out ... > somefamily.html\n\n\nStrategies\n----------\n\nStatistical tests (\"-s\" options) for column comparison:\n\n:gtest:\n    (default) G-test for goodness-of-fit of FG amino acid counts vs. those of\n    the BG column. BG frequencies include pseudocounts calculated from the\n    amino acid frequencies of the full sequence set.\n:urn:\n    Ball-in-urn model (binomial), a la CHAIN_, for counts of the \"consensus\"\n    amino acid type in FG and BG.\n:jsd:\n    Jensen-Shannon divergence of column compositions, a la INTREPID_.\n\n.. _CHAIN: http://chain.igs.umaryland.edu/\n.. _INTREPID: http://bioinformatics.oxfordjournals.org/content/24/21/2445.full\n\n",
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