KmerGenie

KmerGenie estimates the optimal k-mer length for de Bruijn graph-based genome assemblers from sequencing reads to improve assembly quality and accuracy.

Key Features:

• k-mer abundance histograms: Computes approximate abundance histograms for multiple values of k using a fast sampling method.
• Optimization heuristic: Applies a heuristic that predicts the number of distinct genomic k-mers for each candidate k and selects the k that maximizes this predicted count.
• Performance and validation: Has been tested across diverse sequencing datasets with selected k values shown to yield improved genome assemblies.

Scientific Applications:

• De novo genome assembly: Optimizes k for de Bruijn graph-based assemblers to improve the quality and accuracy of assembled genomes from sequencing reads.

Methodology:

Computes approximate k-mer abundance histograms by fast sampling across multiple k values and uses a heuristic to predict distinct genomic k-mers per k, selecting the k that maximizes the predicted count.