Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Motivation: Alignment-based methods for sequence analysis have various limitations if large datasets are to be analysed. Therefore, alignment-free approaches have become popular in recent years. One of the best known alignment-free methods is the average common substring approach that defines a distance measure on sequences based on the average length of longest common words beten them. Herein,  generalize this approach by considering longest common substrings with k mismatches.  present a greedy heuristic to approximate the length of such k-mismatch substrings, and  describe kmacs, an efficient implementation of this idea based on generalized enhanced suffix arrays. Results: To evaluate the performance of our approach,  applied it to phylogeny reconstruction using a large number of DNA and protein sequence sets. In most cases, phylogenetic trees calculated with kmacs re more accurate than trees produced with established alignment-free methods that are based on exact word matches. Especially on protein sequences, our method seems to be superior. On simulated protein families, kmacs even outperformed a classical approach to phylogeny reconstruction using multiple alignment and maximum likelihood. Availability and implementation: kmacs is implemented in C++, and the source code is freely available at http://kmacs.gobics.de/ Contact: chris.leimeister@stud.uni-goettingen.de Supplementary information: Supplementary data are available at Bioinformatics online.
['Chris-André Leimeister', 'Burkhard Morgenstern']
I am writing a research paper on a idea Kmacs: the k-mismatch average common substring approach to alignment-free sequence comparison.
[{"from": "human", "value": "I am writing a research paper on a idea Kmacs: the k-mismatch average common substring approach to alignment-free sequence comparison."}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Motivation: Alignment-based methods for sequence analysis have various limitations if large datasets are to be analysed. Therefore, alignment-free approaches have become popular in recent years. One of the best known alignment-free methods is the average common substring approach that defines a distance measure on sequences based on the average length of longest common words beten them. Herein,  generalize this approach by considering longest common substrings with k mismatches.  present a greedy heuristic to approximate the length of such k-mismatch substrings, and  describe kmacs, an efficient implementation of this idea based on generalized enhanced suffix arrays. Results: To evaluate the performance of our approach,  applied it to phylogeny reconstruction using a large number of DNA and protein sequence sets. In most cases, phylogenetic trees calculated with kmacs re more accurate than trees produced with established alignment-free methods that are based on exact word matches. Especially on protein sequences, our method seems to be superior. On simulated protein families, kmacs even outperformed a classical approach to phylogeny reconstruction using multiple alignment and maximum likelihood. Availability and implementation: kmacs is implemented in C++, and the source code is freely available at http://kmacs.gobics.de/ Contact: chris.leimeister@stud.uni-goettingen.de Supplementary information: Supplementary data are available at Bioinformatics online."}]