#Decombinator

##Notes This version is significantly different from the original while maintaining the primary functionality.

####Original authors Niclas Thomas, James Heather, Wilfred Ndifon, John Shawe-Taylor, Benny Chain.

####Their repo https://github.com/uclinfectionimmunity/Decombinator

##Description Decombinator is a tool for the fast, efficient analysis of T cell receptor (TcR) repertoire samples, designed to be accessible to those with no previous programming experience. It is based on the Aho-Corasick algorithm which uses a finite state automaton (FSA) to quickly assign a specific V and J gene segment. From these assignments, it is then able to determine the number of germline V and J deletions and the string of contiguous nucleotides which lie between the 3' end of the V gene segment and the 5' end of the J gene segment. These 5 variables form the identifier which uniquely categorises each distinct TcR sequence. For more details, please see (Thomas et al.). Decombinator assumes no prior programming experience.

##Requires NumPy Biopython matplotlib acora Levenshtein

##Usage decombinator-analyze [OPTIONS] fastq

decombinator-distinct-clones

decombinator-translated-sequences

This stuff needs to be changed... Decombinator.plot_v_usage(handle=open("DecombinatorResults.txt","rU"),order="frequency") Decombinator.plot_j_usage(handle=open("DecombinatorResults.txt","rU"),order="frequency") Decombinator.plot_del_v(handle=open("DecombinatorResults.txt","rU")) Decombinator.plot_del_j(handle=open("DecombinatorResults.txt","rU")) Decombinator.plot_vj_joint_dist(handle=open("DecombinatorResults.txt","rU")) Decombinator.plot_insert_lengths(handle=open("DecombinatorResults.txt","rU"))