Difference between revisions of "Kmergenie"
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{{Bioinformatics application | {{Bioinformatics application | ||
| − | |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 | + | |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= | + | |bio domain=Sequence assembly, |
| − | |bio method= | + | |bio method=Sequence assembly |
|bio tech=Illumina | |bio tech=Illumina | ||
| − | |created by=R. Chikhi, P. | + | |created by=R. Chikhi, P. Medvedev |
|created at=Penn State University | |created at=Penn State University | ||
|maintained=Yes | |maintained=Yes | ||
|input format=Fasta/Fastq | |input format=Fasta/Fastq | ||
|output format=HTML report | |output format=HTML report | ||
| − | |language=C++ | + | |language=C++, Python, R |
}} | }} | ||
== Description == | == Description == | ||
Latest revision as of 17:18, 19 December 2015
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Application data |
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| Created by | R. Chikhi, P. Medvedev |
|---|---|
| Biological application domain(s) | Sequence 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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