Kmergenie
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Application data |
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| Created by | R. Chikhi, P. Medvedev |
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| 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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