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A simple and efficient tool for searching reference sequences against data sequences using k-mers

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KISS Search Program

Introduction

Welcome to the KISS Search Program, a simple and efficient tool for searching reference sequences against data sequences using k-mers. KISS, which stands for K-Mer Indexing and Sequence Search, provides a fast and memory-efficient solution for identifying similarities between sequences.

Quick Start

To compile the program and generate the executable (KISS.out), simply run the following command in your terminal: NOTE: the make file was developed to run on linux so may have to be adjusted to run on windows or mac.

make

Usage

Once you have compiled the program, you can use it by running the following command:

./KISS.out -reference <reference_file> -data <data_file>

example

./KISS.out -reference examples/hpvcomplete.fasta -data examples/hpvE6.fasta

Setting values

You can customize the search parameters by providing additional options. Here's an example:

./KISS.out -kmer_size 32 -min_matches 3 -seed_skip 30 -cut_off 100 -reference examples/P_falciprum.fna -data examples/P_vivax.fna

Options

  • -kmer_size <value>: Set the k-mer size (default is 32).
  • -min_matches <value>: Set the minimum number of matches required (default is 2).
  • -seed_skip <value>: Set the seed skip value, i.e the distance between Kmers (default is 32).
  • -cut_off <value>: Set the cutoff value (default is 50).
  • -reference <reference_file>: Specify the reference sequence file.
  • -data <data_file>: Specify the data sequence file.
  • -match_all <value>: match_all indicates whether all matches (true|false), (default is true).
  • -get_sw <value>: indicates whether Smith Waterman alignment score is required (true|false), (default is true).

Indexing files

NOTE: the indexer is from KISS INDEXER, this version of the indexer does have a bug which has been fixed locally, but indexing is done seperate and the the .idx files can be loaded into the examples folder of the programme so they can be called at run time (if you wish).

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A simple and efficient tool for searching reference sequences against data sequences using k-mers

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