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We observed that the contigs of optimal length were distributed across the k-mer space.
Of course, n-mer space coverage reflects genome size to some extent.
Due to the limitations of computational power, we could only carry out exhaustive searches up to 15-mer space.
The n-mer space coverage for each genome is plotted against oligo length n in Figure 1 (See additional file 1 for the data).
We have also measured the frequency of unique n-mers in those species and the compactness of the human genome in n-mer space.
The n-mer space coverage of the pseudo-human genome will bound from above the corresponding n-mer space coverage of the human genome (see Figure 1), the discrepancy between the two providing evidence of statistical clustering or bunching of n-mers in the human genome.
The 7 genomes that we investigated in this study differ the most in their coverage for 13-mer space, ranging from 11.4% coverage for E. coli to 96% coverage for human and mouse.
Finally, we profiled the 10-mer space coverage for a wide range of 433 microbial genomes and found that the extent of sequence space coverage is largely determined by genome size and GC content.
The fact that the true human genome has less coverage of n-mer space than the pseudo-human genome shows that there are more repeated n-mers in the human genome than one would expect by chance.
We also observed that by clustering results from assembly runs for different k-mer size values of de Bruijn graph, we were able to obtain a greater number of longer contigs (as optimal contigs are distributed across the k-mer space).
Pairing such k stretches at given distance intervals and comprehensively exploring the paired k-mer space effectively compensates for the high-error rate currently observed in these data, is the basis of our method and a strategy transferable to higher quality DNA reads or sequences, as we show below.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com