Fig. 1. Neighborhood map (N) and the Shouji bit-vector, for text T=GGTGCAGAGCTC and pattern P=GGTGAGAGTTGT for E = 3. ... Fig. 1. Neighborhood map (N)

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Fig. 1. Neighborhood map (N) and the Shouji bit-vector, for text T=GGTGCAGAGCTC and pattern P=GGTGAGAGTTGT for E = 3. ... Fig. 1. Neighborhood map (N) and the Shouji bit-vector, for text T=GGTGCAGAGCTC and pattern P=GGTGAGAGTTGT for E = 3. The three common subsequences (i.e. GGTG, AGAG and T) are highlighted in gray. We use a search window of size four columns (two examples of which are highlighted in black) with a step size of a single column. Shouji searches diagonally within each search window for the 4-bit vector that has the largest number of zeros. Once found, Shouji examines if the found 4-bit vector maximizes the number of zeros at the corresponding location of the 4-bit vector in the Shouji bit-vector. If so, then Shouji stores this 4-bit vector in the Shouji bit-vector at its corresponding location Unless provided in the caption above, the following copyright applies to the content of this slide: © The Author(s) 2019. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.comThis article is published and distributed under the terms of the Oxford University Press, Standard Journals Publication Model (https://academic.oup.com/journals/pages/open_access/funder_policies/chorus/standard_publication_model) Bioinformatics, btz234, https://doi.org/10.1093/bioinformatics/btz234 The content of this slide may be subject to copyright: please see the slide notes for details.

Fig. 2. Overview of our hardware accelerator architecture Fig. 2. Overview of our hardware accelerator architecture. The filtering units can be replicated as many times as ... Fig. 2. Overview of our hardware accelerator architecture. The filtering units can be replicated as many times as possible based on the resources available on the FPGA Unless provided in the caption above, the following copyright applies to the content of this slide: © The Author(s) 2019. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.comThis article is published and distributed under the terms of the Oxford University Press, Standard Journals Publication Model (https://academic.oup.com/journals/pages/open_access/funder_policies/chorus/standard_publication_model) Bioinformatics, btz234, https://doi.org/10.1093/bioinformatics/btz234 The content of this slide may be subject to copyright: please see the slide notes for details.

Fig. 3. The false accept rate of Shouji, MAGNET, SHD and GateKeeper across 12 real datasets. We use a wide range of ... Fig. 3. The false accept rate of Shouji, MAGNET, SHD and GateKeeper across 12 real datasets. We use a wide range of edit distance thresholds (0–10% of the sequence length) for sequence lengths of (a) 100, (b) 150 and (c) 250 Unless provided in the caption above, the following copyright applies to the content of this slide: © The Author(s) 2019. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.comThis article is published and distributed under the terms of the Oxford University Press, Standard Journals Publication Model (https://academic.oup.com/journals/pages/open_access/funder_policies/chorus/standard_publication_model) Bioinformatics, btz234, https://doi.org/10.1093/bioinformatics/btz234 The content of this slide may be subject to copyright: please see the slide notes for details.