Difference between revisions of "Kmergenie"

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|sw summary=KmerGenie estimates the best k-mer length for genome de novo assembly. Given a set of reads, KmerGenie first computes the k-mer abundance histogram for many values of k. Then, for each value of k, it predicts the number of distinct genomic k-mers in the dataset, and returns the k-mer length which maximizes this number. Experiments show that KmerGenie's choices lead to assemblies that are close to the best possible over all k-mer lengths.
 
|sw summary=KmerGenie estimates the best k-mer length for genome de novo assembly. Given a set of reads, KmerGenie first computes the k-mer abundance histogram for many values of k. Then, for each value of k, it predicts the number of distinct genomic k-mers in the dataset, and returns the k-mer length which maximizes this number. Experiments show that KmerGenie's choices lead to assemblies that are close to the best possible over all k-mer lengths.
 
|bio domain=Genomic Assembly,
 
|bio domain=Genomic Assembly,
|bio method=Assembly
+
|bio method=Sequence assembly
 
|bio tech=Illumina
 
|bio tech=Illumina
 
|created by=R. Chikhi, P. Medvedev
 
|created by=R. Chikhi, P. Medvedev

Revision as of 04:05, 25 August 2015

Application data

Created by R. Chikhi, P. Medvedev
Biological application domain(s) Genomic Assembly
Principal bioinformatics method(s) Sequence assembly
Technology Illumina
Created at Penn State University
Maintained? Yes
Input format(s) Fasta/Fastq
Output format(s) HTML report
Programming language(s) C++, Python, R

Summary: KmerGenie estimates the best k-mer length for genome de novo assembly. Given a set of reads, KmerGenie first computes the k-mer abundance histogram for many values of k. Then, for each value of k, it predicts the number of distinct genomic k-mers in the dataset, and returns the k-mer length which maximizes this number. Experiments show that KmerGenie's choices lead to assemblies that are close to the best possible over all k-mer lengths.

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