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Genome-on-Diet is a fast and memory-frugal framework for exemplifying sparsified genomics for read mapping, containment search, and metagenomic profiling. It is much faster & more memory-efficient than minimap2 for Illumina, HiFi, and ONT reads. Described by Alser et al. (preliminary version: https://arxiv.org/abs/2211.08157).

License: MIT License

Makefile 0.36% C 40.37% Python 0.66% Roff 57.12% Cython 0.68% Shell 0.81%
genome-analysis read-mapping variant-calling containment-search genomics large-scale metagenomic-analysis metagenomics microbiome-analysis sequence-alignment

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genome-on-diet's Issues

Is there any relationship between false positives and "rescue mapping"?

Very interesting work, and some nice results in terms of performance and sensitivity! However, the false positive rate is pretty startling--I suspect it is unacceptably high for many applications.

In section 3.6.3 I noticed this concept of "rescue mapping" when there is no hit above the user-provided threshold. I was curious: do you see any enrichment for false positives among these rescued reads? You mention that it improves sensitivity, but I am curious what the cost is.

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