Metadata-Version: 2.1
Name: fast-tcrdist
Version: 0.0.2
Summary: Optimized TCRDist calculation for TCR repertoire data analysis
Home-page: https://github.com/villani-lab/fast_tcrdist
Author: Neal Smith
Author-email: nsmith19@mgh.harvard.edu
License: MIT
Description: # fast_tcrdist
        
        fast_tcrdist is an optimized version of the TCRDist algorithm published by Dash et al. Nature (2017): doi:10.1038/nature22383
        
        To enhance the original implementation of TCRDist, fast_tcrdist uses the Needleman-Wunsch alignment algorithm to align TCR sequences
        and creates the TCRDist matrix via cython.  To integrate well with other common single cell analysis tools, fast_tcrdist utilizes
        the Anndata data structure to store the TCRDist matrix and associated metadata.  Currently, this has been tested on TCR/gene expression
        output from Cellranger (https://support.10xgenomics.com/single-cell-gene-expression/software/pipelines/latest/what-is-cell-ranger), but 
        future releases will aim to allow for other file formats.
        
        In addition to running the TCRDist algorithm, fast_tcrdist allows you to aggregate TCR info with single-cell gene expression into a single
        anndata object to allow for integrated downstream analyses.
        
        **Outside files**
        
        The BLOSUM62 matrix used for alignments came from NCBI:https://www.ncbi.nlm.nih.gov/IEB/ToolBox/C_DOC/lxr/source/data/BLOSUM62
        
        CDR amino acid info was taken from the TCRDist database file "alphabeta_db.tsv" (https://www.dropbox.com/s/kivfp27gbz2m2st/tcrdist_extras_v2.tgz)
        and reformated into .json format (mouse_CDRs_for_10X.json and human_CDRs_for_10X.json)
Platform: UNKNOWN
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3
Classifier: Intended Audience :: Science/Research
Classifier: Topic :: Scientific/Engineering :: Bio-Informatics
Classifier: Natural Language :: English
Description-Content-Type: text/markdown
