Metadata-Version: 2.1
Name: bio-ting
Version: 1.0.2
Summary: ting - T cell receptor interaction grouping
Home-page: https://github.com/FelixMoelder/ting
Author: Felix Mölder
Author-email: felix.moelder@uni-due.de
License: MIT License
Description: [![PyPI version](https://img.shields.io/pypi/v/bio-ting?logo=PyPI)](https://pypi.org/project/bio-ting/)
        [![Bioconda](https://img.shields.io/badge/install%20with-bioconda-brightgreen.svg?style=flat)](https://bioconda.github.io/recipes/bio-ting/README.html)
        # ting - T cell receptor interaction grouping
        
        ting is a tool for clustering large scale T cell receptor repertoires by antigen-specificity
        
        ## Synopsis
        
        ting [options] -t sample.tsv -r reference.tsv -k kmer.tsv -o output.tsv
        
        ## Options
        
        Required Input
        ~~~~~~~~~~~~~~
        
        The user must provide a list of CDR3b sequences.
        For compatibility reasons the tab seperated table of TCR sequences required for gliph is supported, too.
        
            --tcr_sequences tcr_sequences   The format of the table is tab delimited, expecting only the first
                                            column. The header is optional, but if included only use column
                                            names as shown in the example.
        
            --kmer_file K-MER_FILE          The k-mer file holds all 2-, 3- and 4-mers considered for local
                                            clustering. If file does not exist it will automatically be
                                            generated.
        
            --reference                     Reference file of naive CDR3 amino acid sequences in fasta-format.
                                            Used as control set by Fisher's exact test.
        
        Example:
        
        CDR3b		TRBV	TRBJ	CDR3a		TRAV		TRAJ	Sample-ID
        CAADTSSGANVLTF	TRBV30	TRBJ2-6	CALSDEDTGRRALTF	TRAV19		TRAJ5	09/02171
        CAATGGDRAYEQYF	TRBV2	TRBJ2-7	CAASSGANSKLTF	TRAV13-1	TRAJ56	03/04922
        CAATQQGETQYF	TRBV2	TRBJ2-5	CAASYGGSARQLTF	TRAV13-1	TRAJ22	02/02591
        CACVSNTEAFF	TRBV28	TRBJ1-1	CAGDLNGAGSYQLTF	TRAV25		TRAJ28	PBMC8631
        CAGGKGNSPLHF	TRBV2	TRBJ1-6	CVVLRGGSQGNLIF	TRAV12-1	TRAJ42	02/02071
        CAGQILAGSDTQYF	TRBV6-4	TRBJ2-3	CATASGNTPLVF	TRAV17		TRAJ29	09/00181
        CAGRTGVSTDTQYF	TRBV5-1	TRBJ2-3	CAVTPGGGADGLTF	TRAV41		TRAJ45	02/02591
        CAGYTGRANYGYTF	TRBV2	TRBJ1-2	CVVNGGFGNVLHC	TRAV12-1	TRAJ35	01/08733
        
        Optional Input
        
        
        
            --use_structural_boundaries     If set, the first and last three amino acids will be included
                                            in kmer counting and global clustering.
        
            --no_global                     No global clustering will be performed.
        
            --no_local                      No local clustering will be performed.
        
            --min_kmer_occurence            Only kmers which occure at least min_kmer_occurences times in the
                                            sequence sample set will be taken in account. Default is 3.
            
            --max_p_value                   p-value threshold for identifying significant motifs by fisher exact test
            
            --gliph_minp                    probability threshold for identifying significant motifs by gliph test
        
            --stringent_filtering           Only TCRs starting with a cystein and ending with phenylalanine will be
                                            used (IGMT definition of CDR3 region). Default: False
                                            
            --kmers_gliph                   If set kmers are identified by the non-deterministic approach as implemented by gliph
        
        ~~~~~~~~~~~~~~
        
        ## Install
        
        ting can be run from source or installed via [PyPI](https://pypi.org/project/bio-ting/) or [bioconda](https://bioconda.github.io/recipes/bio-ting/README.html?highlight=bio-ting#recipe-Recipe%20&#x27;bio-ting&#x27;)
        
        #### PiPI:
            pip install bio-ting
        
        #### conda:
            conda install -c bioconda bio-ting
        
        ## Example
        
        Example repertoires can be obtained from `repertoires.tar.gz` included in the `example_data`-folder
        
        References have been created by the authors of [gliph](https://github.com/immunoengineer/gliph) ([Glanville et al.](https://www.ncbi.nlm.nih.gov/pubmed/28636589)).
        
        ```
        ting --tcr_sequences R205-L01-D704D504.tsv --reference reference.fasta --kmer_file R205-L01-D704D504_kmers.tsv -o R205-L01-D704D504_results.tsv
        ```
        
Platform: UNKNOWN
Classifier: Development Status :: 5 - Production/Stable
Classifier: Environment :: Console
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3
Classifier: Topic :: Scientific/Engineering :: Bio-Informatics
Description-Content-Type: text/markdown
