Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Motivation: Building the histogram of occurrences of every $k$-symbol long substring of nucleotide data is a standard step in many bioinformatics applications, known under the name of $k$-mer counting. Its applications include developing de Bruijn graph genome assemblers, fast multiple sequence alignment and repeat detection. The tremendous amounts of NGS data require fast algorithms for $k$-mer counting, preferably using moderate amounts of memory. #R##N#Results:  present a novel method for $k$-mer counting, on large datasets at least twice faster than the strongest competitors (Jellyfish~2, KMC~1), using about 12\,GB (or less) of RAM memory. Our disk-based method bears some resemblance to MSPKmerCounter, yet replacing the original minimizers with signatures (a carefully selected subset of all minimizers) and using $(k, x)$-mers allows to significantly reduce the I/O, and a highly parallel overall architecture allows to achieve unprecedented processing speeds. For example, KMC~2 allows to count the 28-mers of a human reads collection with 44-fold coverage (106\,GB of compressed size) in about 20 minutes, on a 6-core Intel i7 PC with an SSD. #R##N#Availability: KMC~2 is freely available at this http URL#R##N#Contact: sebastian.deorowicz@polsl.pl
['Sebastian Deorowicz', 'Marek Kokot', 'Szymon Grabowski', 'Agnieszka Debudaj-Grabysz']
I am writing a research paper on a idea KMC 2: Fast and resource-frugal $k$-mer counting
[{"from": "human", "value": "I am writing a research paper on a idea KMC 2: Fast and resource-frugal $k$-mer counting"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Motivation: Building the histogram of occurrences of every $k$-symbol long substring of nucleotide data is a standard step in many bioinformatics applications, known under the name of $k$-mer counting. Its applications include developing de Bruijn graph genome assemblers, fast multiple sequence alignment and repeat detection. The tremendous amounts of NGS data require fast algorithms for $k$-mer counting, preferably using moderate amounts of memory. #R##N#Results:  present a novel method for $k$-mer counting, on large datasets at least twice faster than the strongest competitors (Jellyfish~2, KMC~1), using about 12\\,GB (or less) of RAM memory. Our disk-based method bears some resemblance to MSPKmerCounter, yet replacing the original minimizers with signatures (a carefully selected subset of all minimizers) and using $(k, x)$-mers allows to significantly reduce the I/O, and a highly parallel overall architecture allows to achieve unprecedented processing speeds. For example, KMC~2 allows to count the 28-mers of a human reads collection with 44-fold coverage (106\\,GB of compressed size) in about 20 minutes, on a 6-core Intel i7 PC with an SSD. #R##N#Availability: KMC~2 is freely available at this http URL#R##N#Contact: sebastian.deorowicz@polsl.pl"}]