This program contains code for identifying within-host diversity.

The required python3 packages are: collections, copy, pandas, numpy, scipy.sparse, typing, argparse, statistics

Steps to run the program:

1. obtain the read files, preferably in fastq format
2. obtain the reference sequence in fasta format 2.1. (optional) Correcting FASTQ reads, see folder ErrorCorrection
3. run the strain identification process with /path/to/find_sub.sh -r reference.fa -1 read_1.fastq -2 read_2.fastq -m tog if the read files are in fasta format, /path/to/find_sub.sh -r reference.fa -1 read_1.fasta -2 read_2.fasta -m tog -f if the read file is single-ended: find_sub.sh -r reference.fa -0 read_file.fastq

The output consists of the nucleotide sequences of detected strains, named in the format "final_strain_x_reference.fa", x is the numerical label of strains, and "subbed_read_x.fa", a set of reads that belong to this strains and are different from the reference sequence.

3.1. (optional) step of verification. Start by obtaining relevant samples. After that, run verify.py N original_reference.fa -p(paired end reads, for single end use -s) sample1_r1.fastq sample1_r2.fastq sample2_r1.fastq sample2_r2.fastq. N is the numerical labelling of strain from step 3.

5. optional step for inferring synonymous state. It determines the changes in the nucleotide sequences are synonymous or non-synonymous. python synonymous_stat.py original_reference_sequence.fa subbed_read_N.sam translation_code.txt protein_pos.txt transation_code.txt is the translation table from nucleotide bases to amino acid bases. protein_pos.txt is the position of proteins, in the form of protein_name:start..end

protein position file, with protein start and end positions in the format protein:start1..end1, example in protein_pos.txt