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Results: 4

1.
Fig. 2.

Fig. 2. From: Scaling metagenome sequence assembly with probabilistic de Bruijn graphs.

Average component size versus false positive rate. The average component size sharply increases as the false positive rate approaches the percolation threshold.

Jason Pell, et al. Proc Natl Acad Sci U S A. 2012 August 14;109(33):13272-13277.
2.
Fig. 3.

Fig. 3. From: Scaling metagenome sequence assembly with probabilistic de Bruijn graphs.

The diameter of randomly generated 58 bp long circular chromosomes in 8-mer (i.e., a cycle of 50 8-mers) space remains constant for false positive rates up through 18.3%. Only real (nonerror) k-mers are considered for starting and ending points.

Jason Pell, et al. Proc Natl Acad Sci U S A. 2012 August 14;109(33):13272-13277.
3.
Fig. 4.

Fig. 4. From: Scaling metagenome sequence assembly with probabilistic de Bruijn graphs.

Comparison between Bloom filters at different false positive rates with the information-theoretic lossless lower bound at different k values. Bloom filters are k independent and are more efficient than any lossless data structure for higher k due to greater sparseness in k-mers inserted compared to all possible k-mers.

Jason Pell, et al. Proc Natl Acad Sci U S A. 2012 August 14;109(33):13272-13277.
4.
Fig. 1.

Fig. 1. From: Scaling metagenome sequence assembly with probabilistic de Bruijn graphs.

Graph visualizations demonstrating the decreasing fidelity of graph structure with increasing false positive rate. Erroneous k-mers are colored red and k-mers corresponding to the original generated sequence (1,000 31-mers generated by a 1,031 bp circular chromosome) are black. From top left to bottom right, the false positive rates are 0.01, 0.05, 0.10, and 0.15. Shortcuts “across” the graph are not created.

Jason Pell, et al. Proc Natl Acad Sci U S A. 2012 August 14;109(33):13272-13277.

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