Estimating the k-mer Coverage Frequencies in Genomic Datasets: A Comparative Assessment of the State-of-the-art
Identifieur interne : 000319 ( Pmc/Checkpoint ); précédent : 000318; suivant : 000320Estimating the k-mer Coverage Frequencies in Genomic Datasets: A Comparative Assessment of the State-of-the-art
Auteurs : Swati C. Manekar ; Shailesh R. SatheSource :
- Current Genomics [ 1389-2029 ] ; 2019.
Abstract
In bioinformatics, estimation of k-mer abundance histograms or just enumerat-ing the number of unique k-mers and the number of singletons are desirable in many genome sequence analysis applications. The applications include predicting genome sizes, data pre-processing for de Bruijn graph assembly methods (tune runtime parameters for analysis tools), repeat detection, sequenc-ing coverage estimation, measuring sequencing error rates, etc. Different methods for cardinality estima-tion in sequencing data have been developed in recent years.
In this article, we present a comparative assessment of the different k-mer frequency estima-tion programs (ntCard, KmerGenie, KmerStream and Khmer (abundance-dist-single.py and unique-kmers.py) to assess their relative merits and demerits.
Principally, the miscounts/error-rates of these tools are analyzed by rigorous experimental analysis for a varied range of k. We also present experimental results on runtime, scalability for larger datasets, memory, CPU utilization as well as parallelism of k-mer frequency estimation methods.
The results indicate that ntCard is more accurate in estimating F0, f1 and full k-mer abundance histograms compared with other methods. ntCard is the fastest but it has more memory requirements compared to KmerGenie.
The results of this evaluation may serve as a roadmap to potential users and practitioners of streaming algorithms for estimating k-mer coverage frequencies, to assist them in identifying an appro-priate method. Such results analysis also help researchers to discover remaining open research ques-tions, effective combinations of existing techniques and possible avenues for future research
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DOI: 10.2174/1389202919666181026101326
PubMed: 31015787
PubMed Central: 6446480
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<record><TEI><teiHeader><fileDesc><titleStmt><title xml:lang="en">Estimating the <italic>k</italic>-mer Coverage Frequencies in Genomic Datasets: A Comparative Assessment of the State-of-the-art</title><author><name sortKey="Manekar, Swati C" sort="Manekar, Swati C" uniqKey="Manekar S" first="Swati C." last="Manekar">Swati C. Manekar</name></author><author><name sortKey="Sathe, Shailesh R" sort="Sathe, Shailesh R" uniqKey="Sathe S" first="Shailesh R." last="Sathe">Shailesh R. Sathe</name></author></titleStmt><publicationStmt><idno type="wicri:source">PMC</idno><idno type="pmid">31015787</idno><idno type="pmc">6446480</idno><idno type="url">http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6446480</idno><idno type="RBID">PMC:6446480</idno><idno type="doi">10.2174/1389202919666181026101326</idno><date when="2019">2019</date><idno type="wicri:Area/Pmc/Corpus">000B97</idno><idno type="wicri:explorRef" wicri:stream="Pmc" wicri:step="Corpus" wicri:corpus="PMC">000B97</idno><idno type="wicri:Area/Pmc/Curation">000B97</idno><idno type="wicri:explorRef" wicri:stream="Pmc" wicri:step="Curation">000B97</idno><idno type="wicri:Area/Pmc/Checkpoint">000319</idno><idno type="wicri:explorRef" wicri:stream="Pmc" wicri:step="Checkpoint">000319</idno></publicationStmt><sourceDesc><biblStruct><analytic><title xml:lang="en" level="a" type="main">Estimating the <italic>k</italic>-mer Coverage Frequencies in Genomic Datasets: A Comparative Assessment of the State-of-the-art</title><author><name sortKey="Manekar, Swati C" sort="Manekar, Swati C" uniqKey="Manekar S" first="Swati C." last="Manekar">Swati C. Manekar</name></author><author><name sortKey="Sathe, Shailesh R" sort="Sathe, Shailesh R" uniqKey="Sathe S" first="Shailesh R." last="Sathe">Shailesh R. Sathe</name></author></analytic><series><title level="j">Current Genomics</title><idno type="ISSN">1389-2029</idno><idno type="eISSN">1875-5488</idno><imprint><date when="2019">2019</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en"><sec><title>Background: </title><p> In bioinformatics, estimation of k-mer abundance histograms or just enumerat-ing the number of unique k-mers and the number of singletons are desirable in many genome sequence analysis applications. The applications include predicting genome sizes, data pre-processing for de Bruijn graph assembly methods (tune runtime parameters for analysis tools), repeat detection, sequenc-ing coverage estimation, measuring sequencing error rates, etc. Different methods for cardinality estima-tion in sequencing data have been developed in recent years.</p></sec><sec><title>Objective: </title><p> In this article, we present a comparative assessment of the different k-mer frequency estima-tion programs (ntCard, KmerGenie, KmerStream and Khmer (abundance-dist-single.py and unique-kmers.py) to assess their relative merits and demerits.</p></sec><sec><title>Methods: </title><p> Principally, the miscounts/error-rates of these tools are analyzed by rigorous experimental analysis for a varied range of k. We also present experimental results on runtime, scalability for larger datasets, memory, CPU utilization as well as parallelism of k-mer frequency estimation methods.</p></sec><sec><title>Results: </title><p> The results indicate that ntCard is more accurate in estimating F0, f1 and full k-mer abundance histograms compared with other methods. ntCard is the fastest but it has more memory requirements compared to KmerGenie.</p></sec><sec><title>Conclusion: </title><p> The results of this evaluation may serve as a roadmap to potential users and practitioners of streaming algorithms for estimating k-mer coverage frequencies, to assist them in identifying an appro-priate method. Such results analysis also help researchers to discover remaining open research ques-tions, effective combinations of existing techniques and possible avenues for future research</p></sec></div></front><back><div1 type="bibliography"><listBibl><biblStruct><analytic><author><name sortKey="Marcais, G" uniqKey="Marcais G">G. Marçais</name></author><author><name sortKey="Kingsford, C" uniqKey="Kingsford C">C. Kingsford</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Miller, J R" uniqKey="Miller J">J.R. Miller</name></author><author><name sortKey="Delcher, A L" uniqKey="Delcher A">A.L. Delcher</name></author><author><name sortKey="Koren, S" uniqKey="Koren S">S. Koren</name></author><author><name sortKey="Venter, E" uniqKey="Venter E">E. Venter</name></author><author><name sortKey="Walenz, B P" uniqKey="Walenz B">B.P. Walenz</name></author><author><name sortKey="Brownley, A" uniqKey="Brownley A">A. 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<front><journal-meta><journal-id journal-id-type="nlm-ta">Curr Genomics</journal-id><journal-id journal-id-type="iso-abbrev">Curr. Genomics</journal-id><journal-id journal-id-type="publisher-id">CG</journal-id><journal-title-group><journal-title>Current Genomics</journal-title></journal-title-group><issn pub-type="ppub">1389-2029</issn><issn pub-type="epub">1875-5488</issn><publisher><publisher-name>Bentham Science Publishers</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type="pmid">31015787</article-id><article-id pub-id-type="pmc">6446480</article-id><article-id pub-id-type="publisher-id">CG-20-2</article-id><article-id pub-id-type="doi">10.2174/1389202919666181026101326</article-id><article-categories><subj-group subj-group-type="heading"><subject>Article</subject></subj-group></article-categories><title-group><article-title>Estimating the <italic>k</italic>-mer Coverage Frequencies in Genomic Datasets: A Comparative Assessment of the State-of-the-art</article-title></title-group><contrib-group><contrib contrib-type="author"><name><surname>Manekar</surname><given-names>Swati C.</given-names></name><xref ref-type="corresp" rid="cor1">*</xref></contrib><contrib contrib-type="author"><name><surname>Sathe</surname><given-names>Shailesh R.</given-names></name></contrib><aff id="aff1">Department of Computer Science and Engineering,<institution>Visvesvaraya National Institute of Technology</institution>,<addr-line><city>Nagpur</city></addr-line>,<country>India</country></aff></contrib-group><author-notes><corresp id="cor1"><label>*</label>Address correspondence to this author at the Department of Computer Science and Engineering, Visvesvaraya National Institute of Technology, Nagpur - 440010, India; Tel/Fax: (+91-712-) 2222828, 2801294, 2231636, 2226750; E-mail: <email xlink:href="swati.manekar@gmail.com">swati.manekar@gmail.com</email></corresp></author-notes><pub-date pub-type="epub"><month>1</month><year>2019</year></pub-date><pub-date pub-type="ppub"><month>1</month><year>2019</year></pub-date><volume>20</volume><issue>1</issue><fpage>2</fpage><lpage>15</lpage><history><date date-type="received"><day>23</day><month>7</month><year>2018</year></date><date date-type="rev-recd"><day>05</day><month>10</month><year>2018</year></date><date date-type="accepted"><day>24</day><month>10</month><year>2018</year></date></history><permissions><copyright-statement>© 2019 Bentham Science Publishers</copyright-statement><copyright-year>2019</copyright-year><license license-type="open-access" xlink:href="https://creativecommons.org/licenses/by-nc/4.0/legalcode"><license-p> This is an open access article licensed under the terms of the Creative Commons Attribution-Non-Commercial 4.0 International Public License (CC BY-NC 4.0) (<uri xlink:href="https://creativecommons.org/licenses/by-nc/4.0/legalcode">https://creativecommons.org/licenses/by-nc/4.0/legalcode</uri>), which permits unrestricted, non-commercial use, distribution and reproduction in any medium, provided the work is properly cited.</license-p></license></permissions><abstract><sec><title>Background: </title><p> In bioinformatics, estimation of k-mer abundance histograms or just enumerat-ing the number of unique k-mers and the number of singletons are desirable in many genome sequence analysis applications. The applications include predicting genome sizes, data pre-processing for de Bruijn graph assembly methods (tune runtime parameters for analysis tools), repeat detection, sequenc-ing coverage estimation, measuring sequencing error rates, etc. Different methods for cardinality estima-tion in sequencing data have been developed in recent years.</p></sec><sec><title>Objective: </title><p> In this article, we present a comparative assessment of the different k-mer frequency estima-tion programs (ntCard, KmerGenie, KmerStream and Khmer (abundance-dist-single.py and unique-kmers.py) to assess their relative merits and demerits.</p></sec><sec><title>Methods: </title><p> Principally, the miscounts/error-rates of these tools are analyzed by rigorous experimental analysis for a varied range of k. We also present experimental results on runtime, scalability for larger datasets, memory, CPU utilization as well as parallelism of k-mer frequency estimation methods.</p></sec><sec><title>Results: </title><p> The results indicate that ntCard is more accurate in estimating F0, f1 and full k-mer abundance histograms compared with other methods. ntCard is the fastest but it has more memory requirements compared to KmerGenie.</p></sec><sec><title>Conclusion: </title><p> The results of this evaluation may serve as a roadmap to potential users and practitioners of streaming algorithms for estimating k-mer coverage frequencies, to assist them in identifying an appro-priate method. Such results analysis also help researchers to discover remaining open research ques-tions, effective combinations of existing techniques and possible avenues for future research</p></sec></abstract><kwd-group kwd-group-type="author"><title>Keywords: </title><kwd><italic>K</italic>-mer abundance histogram</kwd><kwd>High-throughput sequencing</kwd><kwd>Hashing</kwd><kwd>Streaming algorithms</kwd><kwd>Singleton <italic>k</italic>-mers</kwd><kwd>Distinct <italic>k</italic>-mers</kwd></kwd-group></article-meta></front><floats-group><fig id="F1" fig-type="figure" orientation="portrait" position="float"><label>Fig. (1)</label><caption><p>Computation time <italic>versus</italic> datasets of varying size on left-hand side and memory usage <italic>versus</italic> datasets of varying size on right-hand side. Three of these datasets are human dataset with large coverage. Runtime is reported in seconds and memory usage in megabytes (MB). Note that <italic>H. sapiens</italic> 2 has average read lengths of 100 bases hence in plot for <italic>k</italic> =125 the data is missing for <italic>H. sapiens</italic> 2. Abbreviations: FV = <italic>F. vesca;</italic> HS1 = <italic>H. sapiens</italic> 1; HS2 = <italic>H. sapiens</italic> 2; and NA19238 = human genome NA19238.</p></caption><graphic xlink:href="CG-20-2_F1"></graphic></fig><fig id="F2" fig-type="figure" orientation="portrait" position="float"><label>Fig. (2)</label><caption><p>Speedup and memory usage for various numbers of threads.</p></caption><graphic xlink:href="CG-20-2_F2"></graphic></fig><table-wrap id="T1" orientation="portrait" position="float"><label>Table 1</label><caption><p>Streaming algorithms employed in the comparative experiment along with their estimated output.</p></caption><table frame="border" rules="all" width="100%"><thead><tr><th valign="top" align="center" scope="col" rowspan="1" colspan="1"><bold>Streaming Algorithms</bold></th><th valign="top" align="center" scope="col" rowspan="1" colspan="1"><bold>Estimated Output</bold></th><th valign="top" align="center" scope="col" rowspan="1" colspan="1"><bold>Supported Maximum Length of <italic>k</italic></bold></th></tr></thead><tbody><tr><td valign="top" align="center" scope="row" rowspan="1" colspan="1">KmerGenie [<xref rid="r30" ref-type="bibr">30</xref>]</td><td valign="top" align="center" rowspan="1" colspan="1"><italic>F</italic><sub>0</sub> and full <italic>k</italic>-mer frequency histogram</td><td valign="top" align="center" rowspan="1" colspan="1">Arbitrary large length</td></tr><tr><td valign="top" align="center" scope="row" rowspan="1" colspan="1">KmerStream [<xref rid="r29" ref-type="bibr">29</xref>]</td><td valign="top" align="center" rowspan="1" colspan="1"><italic>F</italic><sub>0</sub> and <italic>f</italic><sub>1</sub></td><td valign="top" align="center" rowspan="1" colspan="1">Arbitrary large length</td></tr><tr><td valign="top" align="center" scope="row" rowspan="1" colspan="1">Khmer 2.1.1<break></break>(unique-kmers.py) [<xref rid="r26" ref-type="bibr">26</xref>]</td><td valign="top" align="center" rowspan="1" colspan="1"><italic>F</italic><sub>0</sub></td><td valign="top" align="center" rowspan="1" colspan="1">Arbitrary large length</td></tr><tr><td valign="top" align="center" scope="row" rowspan="1" colspan="1">Khmer 2.1.1<break></break>(abundance-dist single.py) [<xref rid="r26" ref-type="bibr">26</xref>]</td><td valign="top" align="center" rowspan="1" colspan="1">Full <italic>k</italic>-mer frequency histogram</td><td valign="top" align="center" rowspan="1" colspan="1">≤ 32</td></tr><tr><td valign="top" align="center" scope="row" rowspan="1" colspan="1">ntCard [<xref rid="r28" ref-type="bibr">28</xref>]</td><td valign="top" align="center" rowspan="1" colspan="1"><italic>F</italic><sub>0</sub> and full <italic>k</italic>-mer frequency histogram</td><td valign="top" align="center" rowspan="1" colspan="1">Arbitrary large length</td></tr></tbody></table><table-wrap-foot><p><italic>F</italic><sub>0</sub>: distinct number of <italic>k</italic>-mers in input read set; <italic>f</italic><sub>1</sub>: the number on singletons in input read set.</p></table-wrap-foot></table-wrap><table-wrap id="T2" orientation="portrait" position="float"><label>Table 2</label><caption><p>Sequence datasets.</p></caption><table frame="border" rules="all" width="100%"><thead><tr><th valign="middle" align="center" scope="col" rowspan="1" colspan="1"><bold>S. No.</bold></th><th valign="middle" align="center" scope="col" rowspan="1" colspan="1"><bold>Organism</bold></th><th valign="middle" align="center" scope="col" rowspan="1" colspan="1"><bold>Genome</bold><break></break><bold>Length (mega-bases)</bold></th><th valign="middle" align="center" scope="col" rowspan="1" colspan="1"><bold>Average Read Length (bases)</bold></th><th valign="middle" align="center" scope="col" rowspan="1" colspan="1"><bold>Total No. of Bases</bold><break></break><bold>(giga-bases)</bold></th><th valign="middle" align="center" scope="col" rowspan="1" colspan="1"><bold>Input FASTQ</bold><break></break><bold>File Size (Gigabytes)</bold></th><th valign="middle" align="center" scope="col" rowspan="1" colspan="1"><bold>Number of Reads</bold></th></tr></thead><tbody><tr><td valign="middle" align="center" scope="row" rowspan="1" colspan="1">1</td><td valign="middle" align="center" rowspan="1" colspan="1"><italic>F. vesca</italic></td><td valign="middle" align="center" rowspan="1" colspan="1">214</td><td valign="middle" align="center" rowspan="1" colspan="1">353</td><td valign="middle" align="center" rowspan="1" colspan="1">4.5</td><td valign="middle" align="center" rowspan="1" colspan="1">10.9</td><td valign="middle" align="center" rowspan="1" colspan="1">12,803,137</td></tr><tr><td valign="middle" align="center" scope="row" rowspan="1" colspan="1">2</td><td valign="middle" align="center" rowspan="1" colspan="1"><italic>Homo sapiens</italic>1</td><td valign="middle" align="center" rowspan="1" colspan="1">2,991</td><td valign="middle" align="center" rowspan="1" colspan="1">151</td><td valign="middle" align="center" rowspan="1" colspan="1">123.7</td><td valign="middle" align="center" rowspan="1" colspan="1">292.1</td><td valign="middle" align="center" rowspan="1" colspan="1">819,148,264</td></tr><tr><td valign="middle" align="center" scope="row" rowspan="1" colspan="1">3</td><td valign="middle" align="center" rowspan="1" colspan="1"><italic>Homo sapiens</italic> 2</td><td valign="middle" align="center" rowspan="1" colspan="1">2,991</td><td valign="middle" align="center" rowspan="1" colspan="1">100</td><td valign="middle" align="center" rowspan="1" colspan="1">135.3</td><td valign="middle" align="center" rowspan="1" colspan="1">339.5</td><td valign="middle" align="center" rowspan="1" colspan="1">1,339,740,542</td></tr><tr><td valign="middle" align="center" scope="row" rowspan="1" colspan="1">4</td><td valign="middle" align="center" rowspan="1" colspan="1">Human genome for the individual NA19238</td><td valign="middle" align="center" rowspan="1" colspan="1">5,712.43</td><td valign="middle" align="center" rowspan="1" colspan="1">250</td><td valign="middle" align="center" rowspan="1" colspan="1">228.5</td><td valign="middle" align="center" rowspan="1" colspan="1">507.6</td><td valign="middle" align="center" rowspan="1" colspan="1">913,959,800</td></tr></tbody></table></table-wrap><table-wrap id="T3" orientation="portrait" position="float"><label>Table 3</label><caption><p>Estimated values of <italic>F</italic><sub>0</sub> and <italic>f</italic><sub>1</sub> by ntCard, KmerGenie 1.7040 and Khmer 2.1.1 for <italic>F. vesca</italic> for <italic>k</italic> = 25, 50, 75, 100 and 125.</p></caption><table frame="border" rules="all" width="100%"><thead><tr><th valign="middle" align="center" scope="col" rowspan="1" colspan="1"><bold><italic>k</italic></bold></th><th valign="middle" align="center" scope="col" rowspan="1" colspan="1"><bold>-</bold></th><th valign="middle" align="center" scope="col" rowspan="1" colspan="1"><bold>DSK 2.2.0</bold></th><th valign="middle" align="center" scope="col" rowspan="1" colspan="1"><bold>ntCard</bold></th><th valign="middle" align="center" scope="col" rowspan="1" colspan="1"><bold>Error%</bold></th><th valign="middle" align="center" scope="col" rowspan="1" colspan="1"><bold>KmerStream 1.1</bold></th><th valign="middle" align="center" scope="col" rowspan="1" colspan="1"><bold>Error%</bold></th><th valign="middle" align="center" scope="col" rowspan="1" colspan="1"><bold>KmerGenie 1.7040</bold></th><th valign="middle" align="center" scope="col" rowspan="1" colspan="1"><bold>Error%</bold></th><th valign="middle" align="center" scope="col" rowspan="1" colspan="1"><bold>Khmer 2.1.1 (unique-kmers.py)</bold></th><th valign="middle" align="center" scope="col" rowspan="1" colspan="1"><bold>Error%</bold></th></tr></thead><tbody><tr><td rowspan="2" valign="middle" align="center" scope="row" colspan="1">25</td><td valign="middle" align="center" rowspan="1" colspan="1"><italic>F</italic><sub>0</sub></td><td valign="middle" align="center" rowspan="1" colspan="1">583,137,847</td><td valign="middle" align="center" rowspan="1" colspan="1">583,676,933</td><td valign="middle" align="center" rowspan="1" colspan="1"><bold>0.09</bold></td><td valign="middle" align="center" rowspan="1" colspan="1">530,329,548</td><td valign="middle" align="center" rowspan="1" colspan="1">9.06</td><td valign="middle" align="center" rowspan="1" colspan="1">601,623,000</td><td valign="middle" align="center" rowspan="1" colspan="1">3.17</td><td valign="middle" align="center" rowspan="1" colspan="1">591,556,352</td><td valign="middle" align="center" rowspan="1" colspan="1">1.42</td></tr><tr><td valign="middle" colspan="1" align="center" scope="row" rowspan="1"><italic>f</italic><sub>1</sub></td><td valign="middle" align="center" rowspan="1" colspan="1">323,527,880</td><td valign="middle" align="center" rowspan="1" colspan="1">323,786,587</td><td valign="middle" align="center" rowspan="1" colspan="1"><bold>0.08</bold></td><td valign="middle" align="center" rowspan="1" colspan="1">271,301,619</td><td valign="middle" align="center" rowspan="1" colspan="1">16.14</td><td valign="middle" align="center" rowspan="1" colspan="1">340,844,130</td><td valign="middle" align="center" rowspan="1" colspan="1">5.35</td><td valign="middle" align="center" rowspan="1" colspan="1">-</td><td valign="middle" align="center" rowspan="1" colspan="1">-</td></tr><tr><td rowspan="2" valign="middle" align="center" scope="row" colspan="1">50</td><td valign="middle" align="center" rowspan="1" colspan="1"><italic>F</italic><sub>0</sub></td><td valign="middle" align="center" rowspan="1" colspan="1">914,031,454</td><td valign="middle" align="center" rowspan="1" colspan="1">914,604,363</td><td valign="middle" align="center" rowspan="1" colspan="1"><bold>0.06</bold></td><td valign="middle" align="center" rowspan="1" colspan="1">912,466,570</td><td valign="middle" align="center" rowspan="1" colspan="1">0.17</td><td valign="middle" align="center" rowspan="1" colspan="1">950,320,256</td><td valign="middle" align="center" rowspan="1" colspan="1">3.97</td><td valign="middle" align="center" rowspan="1" colspan="1">920,025,399</td><td valign="middle" align="center" rowspan="1" colspan="1">0.65</td></tr><tr><td valign="middle" colspan="1" align="center" scope="row" rowspan="1"><italic>f</italic><sub>1</sub></td><td valign="middle" align="center" rowspan="1" colspan="1">602,056,795</td><td valign="middle" align="center" rowspan="1" colspan="1">602,439,035</td><td valign="middle" align="center" rowspan="1" colspan="1"><bold>0.06</bold></td><td valign="middle" align="center" rowspan="1" colspan="1">601,435,694</td><td valign="middle" align="center" rowspan="1" colspan="1">0.10</td><td valign="middle" align="center" rowspan="1" colspan="1">636,040,064</td><td valign="middle" align="center" rowspan="1" colspan="1">5.64</td><td valign="middle" align="center" rowspan="1" colspan="1">-</td><td valign="middle" align="center" rowspan="1" colspan="1">-</td></tr><tr><td rowspan="2" valign="middle" align="center" scope="row" colspan="1">75</td><td valign="middle" align="center" rowspan="1" colspan="1"><italic>F</italic><sub>0</sub></td><td valign="middle" align="center" rowspan="1" colspan="1">1,098,780,218</td><td valign="middle" align="center" rowspan="1" colspan="1">1,099,778,393</td><td valign="middle" align="center" rowspan="1" colspan="1"><bold>0.09</bold></td><td valign="middle" align="center" rowspan="1" colspan="1">1,096,628,468</td><td valign="middle" align="center" rowspan="1" colspan="1">0.20</td><td valign="middle" align="center" rowspan="1" colspan="1">1,145,461,053</td><td valign="middle" align="center" rowspan="1" colspan="1">4.25</td><td valign="middle" align="center" rowspan="1" colspan="1">1,128,906,482</td><td valign="middle" align="center" rowspan="1" colspan="1">2.67</td></tr><tr><td valign="middle" colspan="1" align="center" scope="row" rowspan="1"><italic>f</italic><sub>1</sub></td><td valign="middle" align="center" rowspan="1" colspan="1">776,776,680</td><td valign="middle" align="center" rowspan="1" colspan="1">777,519,706</td><td valign="middle" align="center" rowspan="1" colspan="1"><bold>0.10</bold></td><td valign="middle" align="center" rowspan="1" colspan="1">773,700,917</td><td valign="middle" align="center" rowspan="1" colspan="1">0.40</td><td valign="middle" align="center" rowspan="1" colspan="1">821,351,861</td><td valign="middle" align="center" rowspan="1" colspan="1">5.74</td><td valign="middle" align="center" rowspan="1" colspan="1">-</td><td valign="middle" align="center" rowspan="1" colspan="1">-</td></tr><tr><td rowspan="2" valign="middle" align="center" scope="row" colspan="1">100</td><td valign="middle" align="center" rowspan="1" colspan="1"><italic>F</italic><sub>0</sub></td><td valign="middle" align="center" rowspan="1" colspan="1">1,191,576,112</td><td valign="middle" align="center" rowspan="1" colspan="1">1,192,817,786</td><td valign="middle" align="center" rowspan="1" colspan="1"><bold>0.10</bold></td><td valign="middle" align="center" rowspan="1" colspan="1">1,190,435,159</td><td valign="middle" align="center" rowspan="1" colspan="1">0.10</td><td valign="middle" align="center" rowspan="1" colspan="1">1,244,998,932</td><td valign="middle" align="center" rowspan="1" colspan="1">4.48</td><td valign="middle" align="center" rowspan="1" colspan="1">1,239,588,773</td><td valign="middle" align="center" rowspan="1" colspan="1">3.87</td></tr><tr><td valign="middle" colspan="1" align="center" scope="row" rowspan="1"><italic>f</italic><sub>1</sub></td><td valign="middle" align="center" rowspan="1" colspan="1">876,971,790</td><td valign="middle" align="center" rowspan="1" colspan="1">878,084,632</td><td valign="middle" align="center" rowspan="1" colspan="1"><bold>0.13</bold></td><td valign="middle" align="center" rowspan="1" colspan="1">876,638,409</td><td valign="middle" align="center" rowspan="1" colspan="1">0.04</td><td valign="middle" align="center" rowspan="1" colspan="1">928,811,850</td><td valign="middle" align="center" rowspan="1" colspan="1">5.91</td><td valign="middle" align="center" rowspan="1" colspan="1">-</td><td valign="middle" align="center" rowspan="1" colspan="1">-</td></tr><tr><td rowspan="2" valign="middle" align="center" scope="row" colspan="1">125</td><td valign="middle" align="center" rowspan="1" colspan="1"><italic>F</italic><sub>0</sub></td><td valign="middle" align="center" rowspan="1" colspan="1">1,232,899,836</td><td valign="middle" align="center" rowspan="1" colspan="1">1,233,719,858</td><td valign="middle" align="center" rowspan="1" colspan="1"><bold>0.07</bold></td><td valign="middle" align="center" rowspan="1" colspan="1">1,232,868,822</td><td valign="middle" align="center" rowspan="1" colspan="1">0.00</td><td valign="middle" align="center" rowspan="1" colspan="1">1,291,769,640</td><td valign="middle" align="center" rowspan="1" colspan="1">4.77</td><td valign="middle" align="center" rowspan="1" colspan="1">1,295,874,877</td><td valign="middle" align="center" rowspan="1" colspan="1">4.86</td></tr><tr><td valign="middle" colspan="1" align="center" scope="row" rowspan="1"><italic>f</italic><sub>1</sub></td><td valign="middle" align="center" rowspan="1" colspan="1">933,435,198</td><td valign="middle" align="center" rowspan="1" colspan="1">934,166,463</td><td valign="middle" align="center" rowspan="1" colspan="1"><bold>0.08</bold></td><td valign="middle" align="center" rowspan="1" colspan="1">935,890,616</td><td valign="middle" align="center" rowspan="1" colspan="1">0.26</td><td valign="middle" align="center" rowspan="1" colspan="1">991,233,936</td><td valign="middle" align="center" rowspan="1" colspan="1">6.19</td><td valign="middle" align="center" rowspan="1" colspan="1">-</td><td valign="middle" align="center" rowspan="1" colspan="1">-</td></tr></tbody></table><table-wrap-foot><p>Column ‘Error%’ shows errors in percent. Column ‘DSK 2.2.0’ shows the exact values of <italic>F</italic><sub>0</sub> and <italic>f</italic><sub>1</sub>.</p></table-wrap-foot></table-wrap><table-wrap id="T4" orientation="portrait" position="float"><label>Table 4</label><caption><p>Estimated values of <italic>F</italic><sub>0</sub> and <italic>f</italic><sub>1</sub> by ntCard, KmerGenie 1.7040 and Khmer 2.1.1 for <italic>H. sapiens</italic> 1 for <italic>k</italic> = 25, 50, 75, 100 and 125.</p></caption><table frame="border" rules="all" width="100%"><thead><tr><th valign="middle" align="center" scope="col" rowspan="1" colspan="1"><bold><italic>k</italic></bold></th><th valign="middle" align="center" scope="col" rowspan="1" colspan="1"><bold>-</bold></th><th valign="middle" align="center" scope="col" rowspan="1" colspan="1"><bold>DSK 2.2.0</bold></th><th valign="middle" align="center" scope="col" rowspan="1" colspan="1"><bold>ntCard</bold></th><th valign="middle" align="center" scope="col" rowspan="1" colspan="1"><bold>Error%</bold></th><th valign="middle" align="center" scope="col" rowspan="1" colspan="1"><bold>KmerStream 1.1</bold></th><th valign="middle" align="center" scope="col" rowspan="1" colspan="1"><bold>Error%</bold></th><th valign="middle" align="center" scope="col" rowspan="1" colspan="1"><bold>KmerGenie 1.7040</bold></th><th valign="middle" align="center" scope="col" rowspan="1" colspan="1"><bold>Error%</bold></th><th valign="middle" align="center" scope="col" rowspan="1" colspan="1"><bold>Khmer 2.1.1 (unique-kmers.py)</bold></th><th valign="middle" align="center" scope="col" rowspan="1" colspan="1"><bold>Error%</bold></th></tr></thead><tbody><tr><td rowspan="2" valign="middle" align="center" scope="row" colspan="1">25</td><td valign="middle" align="center" rowspan="1" colspan="1"><italic>F</italic><sub>0</sub></td><td valign="middle" align="center" rowspan="1" colspan="1">11,217,637,486</td><td valign="middle" align="center" rowspan="1" colspan="1">11,216,386,861</td><td valign="middle" align="center" rowspan="1" colspan="1"><bold>0.01</bold></td><td valign="middle" align="center" rowspan="1" colspan="1">6,751,865,989</td><td valign="middle" align="center" rowspan="1" colspan="1">39.81</td><td valign="middle" align="center" rowspan="1" colspan="1">16,165,719,040</td><td valign="middle" align="center" rowspan="1" colspan="1">30.61</td><td valign="middle" align="center" rowspan="1" colspan="1">15,836,062,038</td><td valign="middle" align="center" rowspan="1" colspan="1">557.13</td></tr><tr><td valign="middle" colspan="1" align="center" scope="row" rowspan="1"><italic>f</italic><sub>1</sub></td><td valign="middle" align="center" rowspan="1" colspan="1">8,490,459,593</td><td valign="middle" align="center" rowspan="1" colspan="1">8,485,840,663</td><td valign="middle" align="center" rowspan="1" colspan="1"><bold>0.05</bold></td><td valign="middle" align="center" rowspan="1" colspan="1">3,714,396,067</td><td valign="middle" align="center" rowspan="1" colspan="1">56.25</td><td valign="middle" align="center" rowspan="1" colspan="1">13,017,354,240</td><td valign="middle" align="center" rowspan="1" colspan="1">34.78</td><td valign="middle" align="center" rowspan="1" colspan="1">-</td><td valign="middle" align="center" rowspan="1" colspan="1">-</td></tr><tr><td rowspan="2" valign="middle" align="center" scope="row" colspan="1">50</td><td valign="middle" align="center" rowspan="1" colspan="1"><italic>F</italic><sub>0</sub></td><td valign="middle" align="center" rowspan="1" colspan="1">13,699,865,268</td><td valign="middle" align="center" rowspan="1" colspan="1">13,695,660,817</td><td valign="middle" align="center" rowspan="1" colspan="1"><bold>0.03</bold></td><td valign="middle" align="center" rowspan="1" colspan="1">13,564,472,565</td><td valign="middle" align="center" rowspan="1" colspan="1">0.99</td><td valign="middle" align="center" rowspan="1" colspan="1">21,248,181,675</td><td valign="middle" align="center" rowspan="1" colspan="1">35.52</td><td valign="middle" align="center" rowspan="1" colspan="1">21,159,640,890</td><td valign="middle" align="center" rowspan="1" colspan="1">294.99</td></tr><tr><td valign="middle" colspan="1" align="center" scope="row" rowspan="1"><italic>f</italic><sub>1</sub></td><td valign="middle" align="center" rowspan="1" colspan="1">10,675,454,671</td><td valign="middle" align="center" rowspan="1" colspan="1">10,673,259,381</td><td valign="middle" align="center" rowspan="1" colspan="1"><bold>0.02</bold></td><td valign="middle" align="center" rowspan="1" colspan="1">10,575,153,639</td><td valign="middle" align="center" rowspan="1" colspan="1">0.94</td><td valign="middle" align="center" rowspan="1" colspan="1">17,847,311,013</td><td valign="middle" align="center" rowspan="1" colspan="1">40.18</td><td valign="middle" align="center" rowspan="1" colspan="1">-</td><td valign="middle" align="center" rowspan="1" colspan="1">-</td></tr><tr><td rowspan="2" valign="middle" align="center" scope="row" colspan="1">75</td><td valign="middle" align="center" rowspan="1" colspan="1"><italic>F</italic><sub>0</sub></td><td valign="middle" align="center" rowspan="1" colspan="1">12,875,754,286</td><td valign="middle" align="center" rowspan="1" colspan="1">12,857,905,621</td><td valign="middle" align="center" rowspan="1" colspan="1"><bold>0.14</bold></td><td valign="middle" align="center" rowspan="1" colspan="1">12,817,488,354</td><td valign="middle" align="center" rowspan="1" colspan="1">0.45</td><td valign="middle" align="center" rowspan="1" colspan="1">20,643,246,978</td><td valign="middle" align="center" rowspan="1" colspan="1">37.63</td><td valign="middle" align="center" rowspan="1" colspan="1">20,592,981,331</td><td valign="middle" align="center" rowspan="1" colspan="1">206.38</td></tr><tr><td valign="middle" colspan="1" align="center" scope="row" rowspan="1"><italic>f</italic><sub>1</sub></td><td valign="middle" align="center" rowspan="1" colspan="1">9,779,384,551</td><td valign="middle" align="center" rowspan="1" colspan="1">9,757,713,845</td><td valign="middle" align="center" rowspan="1" colspan="1"><bold>0.22</bold></td><td valign="middle" align="center" rowspan="1" colspan="1">9,702,616,635</td><td valign="middle" align="center" rowspan="1" colspan="1">0.78</td><td valign="middle" align="center" rowspan="1" colspan="1">17,212,356,585</td><td valign="middle" align="center" rowspan="1" colspan="1">43.18</td><td valign="middle" align="center" rowspan="1" colspan="1">-</td><td valign="middle" align="center" rowspan="1" colspan="1">-</td></tr><tr><td rowspan="2" valign="middle" align="center" scope="row" colspan="1">100</td><td valign="middle" align="center" rowspan="1" colspan="1"><italic>F</italic><sub>0</sub></td><td valign="middle" align="center" rowspan="1" colspan="1">10,630,623,336</td><td valign="middle" align="center" rowspan="1" colspan="1">10,611,400,488</td><td valign="middle" align="center" rowspan="1" colspan="1"><bold>0.18</bold></td><td valign="middle" align="center" rowspan="1" colspan="1">10,606,879,146</td><td valign="middle" align="center" rowspan="1" colspan="1">0.22</td><td valign="middle" align="center" rowspan="1" colspan="1">17,214,539,980</td><td valign="middle" align="center" rowspan="1" colspan="1">38.25</td><td valign="middle" align="center" rowspan="1" colspan="1">16,959,187,669</td><td valign="middle" align="center" rowspan="1" colspan="1">151.24</td></tr><tr><td valign="middle" colspan="1" align="center" scope="row" rowspan="1"><italic>f</italic><sub>1</sub></td><td valign="middle" align="center" rowspan="1" colspan="1">7,575,718,688</td><td valign="middle" align="center" rowspan="1" colspan="1">7,554,340,962</td><td valign="middle" align="center" rowspan="1" colspan="1"><bold>0.28</bold></td><td valign="middle" align="center" rowspan="1" colspan="1">7,577,620,078</td><td valign="middle" align="center" rowspan="1" colspan="1">0.03</td><td valign="middle" align="center" rowspan="1" colspan="1">13,906,537,380</td><td valign="middle" align="center" rowspan="1" colspan="1">45.52</td><td valign="middle" align="center" rowspan="1" colspan="1">-</td><td valign="middle" align="center" rowspan="1" colspan="1">-</td></tr><tr><td rowspan="2" valign="middle" align="center" scope="row" colspan="1">125</td><td valign="middle" align="center" rowspan="1" colspan="1"><italic>F</italic><sub>0</sub></td><td valign="middle" align="center" rowspan="1" colspan="1">7,080,173,077</td><td valign="middle" align="center" rowspan="1" colspan="1">7,071,172,312</td><td valign="middle" align="center" rowspan="1" colspan="1"><bold>0.13</bold></td><td valign="middle" align="center" rowspan="1" colspan="1">7,066,649,232</td><td valign="middle" align="center" rowspan="1" colspan="1">0.19</td><td valign="middle" align="center" rowspan="1" colspan="1">11,641,131,786</td><td valign="middle" align="center" rowspan="1" colspan="1">39.18</td><td valign="middle" align="center" rowspan="1" colspan="1">11,712,142,863</td><td valign="middle" align="center" rowspan="1" colspan="1">88.90</td></tr><tr><td valign="middle" colspan="1" align="center" scope="row" rowspan="1"><italic>f</italic><sub>1</sub></td><td valign="middle" align="center" rowspan="1" colspan="1">4,469,409,703</td><td valign="middle" align="center" rowspan="1" colspan="1">4,460,297,170</td><td valign="middle" align="center" rowspan="1" colspan="1"><bold>0.20</bold></td><td valign="middle" align="center" rowspan="1" colspan="1">4,454,226,447</td><td valign="middle" align="center" rowspan="1" colspan="1">0.34</td><td valign="middle" align="center" rowspan="1" colspan="1">8,873,732,302</td><td valign="middle" align="center" rowspan="1" colspan="1">49.63</td><td valign="middle" align="center" rowspan="1" colspan="1">-</td><td valign="middle" align="center" rowspan="1" colspan="1">-</td></tr></tbody></table><table-wrap-foot><p>Column ‘Error%’ shows errors in percent. Column ‘DSK 2.2.0’ shows the exact values of <italic>F</italic><sub>0</sub> and <italic>f</italic><sub>1</sub>.</p></table-wrap-foot></table-wrap><table-wrap id="T5" orientation="portrait" position="float"><label>Table 5</label><caption><p>Estimated values of <italic>F</italic><sub>0</sub> and <italic>f</italic><sub>1</sub> by ntCard, KmerGenie 1.7040 and Khmer 2.1.1 for <italic>H. sapiens</italic> 2 for <italic>k</italic> = 25, 50, 75, 100 and 125.</p></caption><table frame="border" rules="all" width="100%"><thead><tr><th valign="middle" align="center" scope="col" rowspan="1" colspan="1"><bold><italic>k</italic></bold></th><th valign="middle" align="center" scope="col" rowspan="1" colspan="1"><bold>-</bold></th><th valign="middle" align="center" scope="col" rowspan="1" colspan="1"><bold>DSK 2.2.0</bold></th><th valign="middle" align="center" scope="col" rowspan="1" colspan="1"><bold>ntCard</bold></th><th valign="middle" align="center" scope="col" rowspan="1" colspan="1"><bold>Error%</bold></th><th valign="middle" align="center" scope="col" rowspan="1" colspan="1"><bold>KmerStream 1.1</bold></th><th valign="middle" align="center" scope="col" rowspan="1" colspan="1"><bold>Error%</bold></th><th valign="middle" align="center" scope="col" rowspan="1" colspan="1"><bold>KmerGenie 1.7040</bold></th><th valign="middle" align="center" scope="col" rowspan="1" colspan="1"><bold>Error%</bold></th><th valign="middle" align="center" scope="col" rowspan="1" colspan="1"><bold>Khmer 2.1.1 (unique-kmers.py)</bold></th><th valign="middle" align="center" scope="col" rowspan="1" colspan="1"><bold>Error%</bold></th></tr></thead><tbody><tr><td rowspan="2" valign="middle" align="center" scope="row" colspan="1">25</td><td valign="middle" align="center" rowspan="1" colspan="1"><italic>F</italic><sub>0</sub></td><td valign="middle" align="center" rowspan="1" colspan="1">6,317,577,945</td><td valign="middle" align="center" rowspan="1" colspan="1">6,321,370,851</td><td valign="middle" align="center" rowspan="1" colspan="1"><bold>0.06</bold></td><td valign="middle" align="center" rowspan="1" colspan="1">4,440,606,371</td><td valign="middle" align="center" rowspan="1" colspan="1">29.71</td><td valign="middle" align="center" rowspan="1" colspan="1">6,493,972,870</td><td valign="middle" align="center" rowspan="1" colspan="1">2.79</td><td valign="middle" align="center" rowspan="1" colspan="1">6,447,772,640</td><td valign="middle" align="center" rowspan="1" colspan="1">2.02</td></tr><tr><td valign="middle" colspan="1" align="center" scope="row" rowspan="1"><italic>f</italic><sub>1</sub></td><td valign="middle" align="center" rowspan="1" colspan="1">3,726,921,849</td><td valign="middle" align="center" rowspan="1" colspan="1">3,728,402,513</td><td valign="middle" align="center" rowspan="1" colspan="1"><bold>0.04</bold></td><td valign="middle" align="center" rowspan="1" colspan="1">2,124,581,580</td><td valign="middle" align="center" rowspan="1" colspan="1">42.99</td><td valign="middle" align="center" rowspan="1" colspan="1">3,900,164,180</td><td valign="middle" align="center" rowspan="1" colspan="1">4.65</td><td valign="middle" align="center" rowspan="1" colspan="1">-</td><td valign="middle" align="center" rowspan="1" colspan="1">-</td></tr><tr><td rowspan="2" valign="middle" align="center" scope="row" colspan="1">50</td><td valign="middle" align="center" rowspan="1" colspan="1"><italic>F</italic><sub>0</sub></td><td valign="middle" align="center" rowspan="1" colspan="1">7,576,436,303</td><td valign="middle" align="center" rowspan="1" colspan="1">7,575,568,030</td><td valign="middle" align="center" rowspan="1" colspan="1"><bold>0.01</bold></td><td valign="middle" align="center" rowspan="1" colspan="1">7,507,180,258</td><td valign="middle" align="center" rowspan="1" colspan="1">0.91</td><td valign="middle" align="center" rowspan="1" colspan="1">7,768,465,452</td><td valign="middle" align="center" rowspan="1" colspan="1">2.53</td><td valign="middle" align="center" rowspan="1" colspan="1">7,808,249,016</td><td valign="middle" align="center" rowspan="1" colspan="1">2.97</td></tr><tr><td valign="middle" colspan="1" align="center" scope="row" rowspan="1"><italic>f</italic><sub>1</sub></td><td valign="middle" align="center" rowspan="1" colspan="1">4,610,596,550</td><td valign="middle" align="center" rowspan="1" colspan="1">4,612,437,322</td><td valign="middle" align="center" rowspan="1" colspan="1"><bold>0.04</bold></td><td valign="middle" align="center" rowspan="1" colspan="1">4,554,225,163</td><td valign="middle" align="center" rowspan="1" colspan="1">1.22</td><td valign="middle" align="center" rowspan="1" colspan="1">4,803,170,055</td><td valign="middle" align="center" rowspan="1" colspan="1">4.18</td><td valign="middle" align="center" rowspan="1" colspan="1">-</td><td valign="middle" align="center" rowspan="1" colspan="1">-</td></tr><tr><td rowspan="2" valign="middle" align="center" scope="row" colspan="1">75</td><td valign="middle" align="center" rowspan="1" colspan="1"><italic>F</italic><sub>0</sub></td><td valign="middle" align="center" rowspan="1" colspan="1">6,645,775,719</td><td valign="middle" align="center" rowspan="1" colspan="1">6,634,466,410</td><td valign="middle" align="center" rowspan="1" colspan="1"><bold>0.17</bold></td><td valign="middle" align="center" rowspan="1" colspan="1">6,620,493,247</td><td valign="middle" align="center" rowspan="1" colspan="1">0.38</td><td valign="middle" align="center" rowspan="1" colspan="1">6,770,079,405</td><td valign="middle" align="center" rowspan="1" colspan="1">1.87</td><td valign="middle" align="center" rowspan="1" colspan="1">6,820,243,427</td><td valign="middle" align="center" rowspan="1" colspan="1">2.56</td></tr><tr><td valign="middle" colspan="1" align="center" scope="row" rowspan="1"><italic>f</italic><sub>1</sub></td><td valign="middle" align="center" rowspan="1" colspan="1">3,601,391,827</td><td valign="middle" align="center" rowspan="1" colspan="1">3,590,235,204</td><td valign="middle" align="center" rowspan="1" colspan="1"><bold>0.31</bold></td><td valign="middle" align="center" rowspan="1" colspan="1">3,574,106,736</td><td valign="middle" align="center" rowspan="1" colspan="1">0.76</td><td valign="middle" align="center" rowspan="1" colspan="1">3,726,960,545</td><td valign="middle" align="center" rowspan="1" colspan="1">3.49</td><td valign="middle" align="center" rowspan="1" colspan="1">-</td><td valign="middle" align="center" rowspan="1" colspan="1">-</td></tr><tr><td rowspan="2" valign="middle" align="center" scope="row" colspan="1">100</td><td valign="middle" align="center" rowspan="1" colspan="1"><italic>F</italic><sub>0</sub></td><td valign="middle" align="center" rowspan="1" colspan="1">2,055,560,283</td><td valign="middle" align="center" rowspan="1" colspan="1">2,054,217,987</td><td valign="middle" align="center" rowspan="1" colspan="1"><bold>0.07</bold></td><td valign="middle" align="center" rowspan="1" colspan="1">2,055,238,955</td><td valign="middle" align="center" rowspan="1" colspan="1">0.02</td><td valign="middle" align="center" rowspan="1" colspan="1">2,067,553,684</td><td valign="middle" align="center" rowspan="1" colspan="1">0.58</td><td valign="middle" align="center" rowspan="1" colspan="1">2,055,943,971</td><td valign="middle" align="center" rowspan="1" colspan="1">0.02</td></tr><tr><td valign="middle" colspan="1" align="center" scope="row" rowspan="1"><italic>f</italic><sub>1</sub></td><td valign="middle" align="center" rowspan="1" colspan="1">1,668,703,535</td><td valign="middle" align="center" rowspan="1" colspan="1">1,667,591,992</td><td valign="middle" align="center" rowspan="1" colspan="1"><bold>0.07</bold></td><td valign="middle" align="center" rowspan="1" colspan="1">1,668,568,360</td><td valign="middle" align="center" rowspan="1" colspan="1">0.01</td><td valign="middle" align="center" rowspan="1" colspan="1">1,679,933,472</td><td valign="middle" align="center" rowspan="1" colspan="1">0.67</td><td valign="middle" align="center" rowspan="1" colspan="1">-</td><td valign="middle" align="center" rowspan="1" colspan="1">-</td></tr></tbody></table><table-wrap-foot><p>Column ‘Error%’ shows errors in percent. Column ‘DSK 2.2.0’ shows the exact values of <italic>F</italic><sub>0</sub> and <italic>f</italic><sub>1</sub>.</p></table-wrap-foot></table-wrap><table-wrap id="T6" orientation="portrait" position="float"><label>Table 6</label><caption><p>Estimated values of <italic>F</italic><sub>0</sub> and <italic>f</italic><sub>1</sub> by ntCard, KmerGenie 1.7040 and Khmer 2.1.1 for human genome NA19238 for <italic>k</italic> = 25, 50, 75, 100 and 125.</p></caption><table frame="border" rules="all" width="100%"><thead><tr><th valign="middle" align="center" scope="col" rowspan="1" colspan="1"><bold><italic>k</italic></bold></th><th valign="middle" align="center" scope="col" rowspan="1" colspan="1"><bold>-</bold></th><th valign="middle" align="center" scope="col" rowspan="1" colspan="1"><bold>DSK 2.2.0</bold></th><th valign="middle" align="center" scope="col" rowspan="1" colspan="1"><bold>ntCard</bold></th><th valign="middle" align="center" scope="col" rowspan="1" colspan="1"><bold>Error%</bold></th><th valign="middle" align="center" scope="col" rowspan="1" colspan="1"><bold>KmerStream 1.1</bold></th><th valign="middle" align="center" scope="col" rowspan="1" colspan="1"><bold>Error%</bold></th><th valign="middle" align="center" scope="col" rowspan="1" colspan="1"><bold>KmerGenie 1.7040</bold></th><th valign="middle" align="center" scope="col" rowspan="1" colspan="1"><bold>Error%</bold></th><th valign="middle" align="center" scope="col" rowspan="1" colspan="1"><bold>Khmer 2.1.1 (unique-kmers.py)</bold></th><th valign="middle" align="center" scope="col" rowspan="1" colspan="1"><bold>Error%</bold></th></tr></thead><tbody><tr><td rowspan="2" valign="middle" align="center" scope="row" colspan="1">25</td><td valign="middle" align="center" rowspan="1" colspan="1"><italic>F</italic><sub>0</sub></td><td valign="middle" align="center" rowspan="1" colspan="1">15,695,189,022</td><td valign="middle" align="center" rowspan="1" colspan="1">15,698,708,120</td><td valign="middle" align="center" rowspan="1" colspan="1"><bold>0.02</bold></td><td valign="middle" align="center" rowspan="1" colspan="1">7,874,114,589</td><td valign="middle" align="center" rowspan="1" colspan="1">49.83</td><td valign="middle" align="center" rowspan="1" colspan="1">15,774,752,125</td><td valign="middle" align="center" rowspan="1" colspan="1">0.51</td><td valign="middle" align="center" rowspan="1" colspan="1">15,821,852,733</td><td valign="middle" align="center" rowspan="1" colspan="1">0.80</td></tr><tr><td valign="middle" colspan="1" align="center" scope="row" rowspan="1"><italic>f</italic><sub>1</sub></td><td valign="middle" align="center" rowspan="1" colspan="1">12,590,059,674</td><td valign="middle" align="center" rowspan="1" colspan="1">12,589,842,091</td><td valign="middle" align="center" rowspan="1" colspan="1"><bold>0.00</bold></td><td valign="middle" align="center" rowspan="1" colspan="1">4,097,744,520</td><td valign="middle" align="center" rowspan="1" colspan="1">67.45</td><td valign="middle" align="center" rowspan="1" colspan="1">12,660,147,875</td><td valign="middle" align="center" rowspan="1" colspan="1">0.56</td><td valign="middle" align="center" rowspan="1" colspan="1">-</td><td valign="middle" align="center" rowspan="1" colspan="1">-</td></tr><tr><td rowspan="2" valign="middle" align="center" scope="row" colspan="1">50</td><td valign="middle" align="center" rowspan="1" colspan="1"><italic>F</italic><sub>0</sub></td><td valign="middle" align="center" rowspan="1" colspan="1">21,386,123,607</td><td valign="middle" align="center" rowspan="1" colspan="1">21,388,812,027</td><td valign="middle" align="center" rowspan="1" colspan="1"><bold>0.01</bold></td><td valign="middle" align="center" rowspan="1" colspan="1">21,129,592,460</td><td valign="middle" align="center" rowspan="1" colspan="1">1.20</td><td valign="middle" align="center" rowspan="1" colspan="1">21,527,611,504</td><td valign="middle" align="center" rowspan="1" colspan="1">0.66</td><td valign="middle" align="center" rowspan="1" colspan="1">21,394,927,325</td><td valign="middle" align="center" rowspan="1" colspan="1">0.04</td></tr><tr><td valign="middle" colspan="1" align="center" scope="row" rowspan="1"><italic>f</italic><sub>1</sub></td><td valign="middle" align="center" rowspan="1" colspan="1">18,003,764,501</td><td valign="middle" align="center" rowspan="1" colspan="1">18,008,739,129</td><td valign="middle" align="center" rowspan="1" colspan="1"><bold>0.03</bold></td><td valign="middle" align="center" rowspan="1" colspan="1">17,708,817,546</td><td valign="middle" align="center" rowspan="1" colspan="1">1.64</td><td valign="bottom" align="center" rowspan="1" colspan="1">18,125,064,461</td><td valign="bottom" align="center" rowspan="1" colspan="1">0.67</td><td valign="middle" align="center" rowspan="1" colspan="1">-</td><td valign="middle" align="center" rowspan="1" colspan="1">-</td></tr><tr><td rowspan="2" valign="middle" align="center" scope="row" colspan="1">75</td><td valign="middle" align="center" rowspan="1" colspan="1"><italic>F</italic><sub>0</sub></td><td valign="middle" align="center" rowspan="1" colspan="1">22,940,994,545</td><td valign="middle" align="center" rowspan="1" colspan="1">22,903,722,006</td><td valign="middle" align="center" rowspan="1" colspan="1"><bold>0.16</bold></td><td valign="middle" align="center" rowspan="1" colspan="1">22,768,767,319</td><td valign="middle" align="center" rowspan="1" colspan="1">0.75</td><td valign="middle" align="center" rowspan="1" colspan="1">23,081,411,904</td><td valign="middle" align="center" rowspan="1" colspan="1">0.61</td><td valign="middle" align="center" rowspan="1" colspan="1">23,158,655,606</td><td valign="middle" align="center" rowspan="1" colspan="1">0.94</td></tr><tr><td valign="middle" colspan="1" align="center" scope="row" rowspan="1"><italic>f</italic><sub>1</sub></td><td valign="middle" align="center" rowspan="1" colspan="1">19,455,121,869</td><td valign="middle" align="center" rowspan="1" colspan="1">19,413,069,020</td><td valign="middle" align="center" rowspan="1" colspan="1"><bold>0.22</bold></td><td valign="middle" align="center" rowspan="1" colspan="1">19,200,406,066</td><td valign="middle" align="center" rowspan="1" colspan="1">1.31</td><td valign="middle" align="center" rowspan="1" colspan="1">19,580,787,984</td><td valign="middle" align="center" rowspan="1" colspan="1">0.65</td><td valign="middle" align="center" rowspan="1" colspan="1">-</td><td valign="middle" align="center" rowspan="1" colspan="1">-</td></tr><tr><td rowspan="2" valign="middle" align="center" scope="row" colspan="1">100</td><td valign="middle" align="center" rowspan="1" colspan="1"><italic>F</italic><sub>0</sub></td><td valign="middle" align="center" rowspan="1" colspan="1">22,825,882,964</td><td valign="middle" align="center" rowspan="1" colspan="1">22,795,280,303</td><td valign="middle" align="center" rowspan="1" colspan="1"><bold>0.13</bold></td><td valign="middle" align="center" rowspan="1" colspan="1">22,837,840,964</td><td valign="middle" align="center" rowspan="1" colspan="1">0.05</td><td valign="middle" align="center" rowspan="1" colspan="1">22,981,764,792</td><td valign="middle" align="center" rowspan="1" colspan="1">0.68</td><td valign="middle" align="center" rowspan="1" colspan="1">22,657,973,838</td><td valign="middle" align="center" rowspan="1" colspan="1">0.74</td></tr><tr><td valign="middle" colspan="1" align="center" scope="row" rowspan="1"><italic>f</italic><sub>1</sub></td><td valign="middle" align="center" rowspan="1" colspan="1">19,311,399,602</td><td valign="middle" align="center" rowspan="1" colspan="1">19,271,642,379</td><td valign="middle" align="center" rowspan="1" colspan="1"><bold>0.21</bold></td><td valign="middle" align="center" rowspan="1" colspan="1">19,350,438,432</td><td valign="middle" align="center" rowspan="1" colspan="1">0.20</td><td valign="middle" align="center" rowspan="1" colspan="1">19,462,845,864</td><td valign="middle" align="center" rowspan="1" colspan="1">0.78</td><td valign="middle" align="center" rowspan="1" colspan="1">-</td><td valign="middle" align="left" rowspan="1" colspan="1"></td></tr><tr><td rowspan="2" valign="middle" align="center" scope="row" colspan="1">125</td><td valign="middle" align="center" rowspan="1" colspan="1"><italic>F</italic><sub>0</sub></td><td valign="middle" align="center" rowspan="1" colspan="1">21,623,019,167</td><td valign="middle" align="center" rowspan="1" colspan="1">21,584,850,645</td><td valign="middle" align="center" rowspan="1" colspan="1"><bold>0.18</bold></td><td valign="middle" align="center" rowspan="1" colspan="1">21,572,310,929</td><td valign="middle" align="center" rowspan="1" colspan="1">0.23</td><td valign="middle" align="center" rowspan="1" colspan="1">21,771,418,913</td><td valign="middle" align="center" rowspan="1" colspan="1">0.69</td><td valign="middle" align="center" rowspan="1" colspan="1">21,674,560,549</td><td valign="middle" align="center" rowspan="1" colspan="1">0.24</td></tr><tr><td valign="middle" colspan="1" align="center" scope="row" rowspan="1"><italic>f</italic><sub>1</sub></td><td valign="middle" align="center" rowspan="1" colspan="1">18,103,091,932</td><td valign="middle" align="center" rowspan="1" colspan="1">18,054,837,842</td><td valign="middle" align="center" rowspan="1" colspan="1"><bold>0.27</bold></td><td valign="middle" align="center" rowspan="1" colspan="1">17,990,743,190</td><td valign="middle" align="center" rowspan="1" colspan="1">0.62</td><td valign="middle" align="center" rowspan="1" colspan="1">18,227,641,426</td><td valign="middle" align="center" rowspan="1" colspan="1">0.69</td><td valign="middle" align="center" rowspan="1" colspan="1">-</td><td valign="middle" align="center" rowspan="1" colspan="1">-</td></tr></tbody></table><table-wrap-foot><p>Column ‘Error%’ shows errors in percent. Column ‘DSK 2.2.0’ shows the exact values of <italic>F</italic><sub>0</sub> and <italic>f</italic><sub>1</sub>.</p></table-wrap-foot></table-wrap><table-wrap id="T7" orientation="portrait" position="float"><label>Table 7</label><caption><p>Summary of results from Appendix (Tables A1-A4).</p></caption><table frame="border" rules="all" width="100%"><thead><tr><th rowspan="2" valign="middle" align="center" scope="col" colspan="1"><bold>Dataset</bold></th><th rowspan="2" valign="middle" align="center" scope="col" colspan="1"><bold><italic>k</italic></bold></th><th valign="middle" colspan="2" align="center" scope="colgroup" rowspan="1"><bold>Time</bold></th><th valign="middle" colspan="2" align="center" scope="colgroup" rowspan="1"><bold>Memory (RAM)</bold></th><th valign="middle" colspan="2" align="center" scope="colgroup" rowspan="1"><bold>CPU Utilization (%)</bold></th></tr><tr><th valign="middle" colspan="1" align="center" scope="colgroup" rowspan="1"><bold>Lowest</bold></th><th valign="middle" align="center" scope="col" rowspan="1" colspan="1"><bold>Highest</bold></th><th valign="middle" align="center" scope="col" rowspan="1" colspan="1"><bold>Lowest</bold></th><th valign="middle" align="center" scope="col" rowspan="1" colspan="1"><bold>Highest</bold></th><th valign="middle" align="center" scope="col" rowspan="1" colspan="1"><bold>Lowest</bold></th><th valign="middle" align="center" scope="col" rowspan="1" colspan="1"><bold>Highest</bold></th></tr></thead><tbody><tr><td rowspan="5" valign="middle" align="center" scope="row" colspan="1"><bold><italic>F. vesca</italic></bold></td><td valign="top" align="center" rowspan="1" colspan="1">25</td><td valign="top" align="center" rowspan="1" colspan="1">ntCard 1.0.0</td><td valign="top" align="center" rowspan="1" colspan="1">Khmer 2.1.1<break></break>(abundance-dist-single.py)</td><td valign="top" align="center" rowspan="1" colspan="1">Khmer 2.1.1<break></break>(unique-kmers.py)</td><td valign="top" align="center" rowspan="1" colspan="1">Khmer 2.1.1<break></break>(abundance-dist-single.py)</td><td valign="top" align="center" rowspan="1" colspan="1">KmerStream 1.1</td><td valign="top" align="center" rowspan="1" colspan="1">Khmer 2.1.1<break></break>(unique-kmers.py)</td></tr><tr><td valign="top" colspan="1" align="center" scope="row" rowspan="1">50</td><td valign="top" align="center" rowspan="1" colspan="1">ntCard 1.0.0</td><td valign="top" align="center" rowspan="1" colspan="1">KmerGenie 1.7048</td><td valign="top" align="center" rowspan="1" colspan="1">Khmer 2.1.1 <break></break>(unique-kmers.py)</td><td valign="top" align="center" rowspan="1" colspan="1">ntCard 1.0.0</td><td valign="top" align="center" rowspan="1" colspan="1">KmerStream 1.1</td><td valign="top" align="center" rowspan="1" colspan="1">Khmer 2.1.1 <break></break>(unique-kmers.py)</td></tr><tr><td valign="top" colspan="1" align="center" scope="row" rowspan="1">75</td><td valign="top" align="center" rowspan="1" colspan="1">ntCard 1.0.0</td><td valign="top" align="center" rowspan="1" colspan="1">KmerGenie 1.7048</td><td valign="top" align="center" rowspan="1" colspan="1">Khmer 2.1.1<break></break> (unique-kmers.py)</td><td valign="top" align="center" rowspan="1" colspan="1">ntCard 1.0.0</td><td valign="top" align="center" rowspan="1" colspan="1">KmerStream 1.1</td><td valign="top" align="center" rowspan="1" colspan="1">Khmer 2.1.1<break></break> (unique-kmers.py)</td></tr><tr><td valign="top" colspan="1" align="center" scope="row" rowspan="1">100</td><td valign="top" align="center" rowspan="1" colspan="1">ntCard 1.0.0</td><td valign="top" align="center" rowspan="1" colspan="1">KmerGenie 1.7048</td><td valign="top" align="center" rowspan="1" colspan="1">Khmer 2.1.1<break></break> (unique-kmers.py)</td><td valign="top" align="center" rowspan="1" colspan="1">ntCard 1.0.0</td><td valign="top" align="center" rowspan="1" colspan="1">KmerStream 1.1</td><td valign="top" align="center" rowspan="1" colspan="1">Khmer 2.1.1 <break></break>(unique-kmers.py)</td></tr><tr><td valign="top" colspan="1" align="center" scope="row" rowspan="1">125</td><td valign="top" align="center" rowspan="1" colspan="1">ntCard 1.0.0</td><td valign="top" align="center" rowspan="1" colspan="1">KmerGenie<break></break>1.7048</td><td valign="top" align="center" rowspan="1" colspan="1">Khmer 2.1.1<break></break> (unique-kmers.py)</td><td valign="top" align="center" rowspan="1" colspan="1">ntCard 1.0.0</td><td valign="top" align="center" rowspan="1" colspan="1">KmerStream 1.1</td><td valign="top" align="center" rowspan="1" colspan="1">Khmer 2.1.1<break></break> (unique-kmers.py)</td></tr><tr><td rowspan="5" valign="middle" align="center" scope="row" colspan="1"><bold><italic>H. </italic></bold><break></break><bold><italic>sapiens</italic> 1</bold></td><td valign="top" align="center" rowspan="1" colspan="1">25</td><td valign="top" align="center" rowspan="1" colspan="1">ntCard 1.0.0</td><td valign="top" align="center" rowspan="1" colspan="1">Khmer 2.1.1<break></break>(abundance-dist-single.py)</td><td valign="top" align="center" rowspan="1" colspan="1">Khmer 2.1.1<break></break> (unique-kmers.py)</td><td valign="top" align="center" rowspan="1" colspan="1">Khmer 2.1.1<break></break>(abundance-dist-single.py)</td><td valign="top" align="center" rowspan="1" colspan="1">KmerStream 1.1</td><td valign="top" align="center" rowspan="1" colspan="1">Khmer 2.1.1 <break></break>(unique-kmers.py)</td></tr><tr><td valign="top" colspan="1" align="center" scope="row" rowspan="1">50</td><td valign="top" align="center" rowspan="1" colspan="1">ntCard 1.0.0</td><td valign="top" align="center" rowspan="1" colspan="1">KmerGenie 1.7048</td><td valign="top" align="center" rowspan="1" colspan="1">Khmer 2.1.1 <break></break>(unique-kmers.py)</td><td valign="top" align="center" rowspan="1" colspan="1">ntCard 1.0.0</td><td valign="top" align="center" rowspan="1" colspan="1">KmerStream 1.1</td><td valign="top" align="center" rowspan="1" colspan="1">Khmer 2.1.1 <break></break>(unique-kmers.py)</td></tr><tr><td valign="top" colspan="1" align="center" scope="row" rowspan="1">75</td><td valign="top" align="center" rowspan="1" colspan="1">ntCard 1.0.0</td><td valign="top" align="center" rowspan="1" colspan="1">KmerGenie 1.7048</td><td valign="top" align="center" rowspan="1" colspan="1">Khmer 2.1.1 <break></break>(unique-kmers.py)</td><td valign="top" align="center" rowspan="1" colspan="1">ntCard 1.0.0</td><td valign="top" align="center" rowspan="1" colspan="1">KmerStream 1.1</td><td valign="top" align="center" rowspan="1" colspan="1">Khmer 2.1.1 <break></break>(unique-kmers.py)</td></tr><tr><td valign="top" colspan="1" align="center" scope="row" rowspan="1">100</td><td valign="top" align="center" rowspan="1" colspan="1">ntCard 1.0.0</td><td valign="top" align="center" rowspan="1" colspan="1">KmerGenie 1.7048</td><td valign="top" align="center" rowspan="1" colspan="1">Khmer 2.1.1<break></break> (unique-kmers.py)</td><td valign="top" align="center" rowspan="1" colspan="1">ntCard 1.0.0</td><td valign="top" align="center" rowspan="1" colspan="1">KmerStream 1.1</td><td valign="top" align="center" rowspan="1" colspan="1">Khmer 2.1.1<break></break> (unique-kmers.py)</td></tr><tr><td valign="top" colspan="1" align="center" scope="row" rowspan="1">125</td><td valign="top" align="center" rowspan="1" colspan="1">ntCard 1.0.0</td><td valign="top" align="center" rowspan="1" colspan="1">Khmer 2.1.1 <break></break>(unique-kmers.py)</td><td valign="top" align="center" rowspan="1" colspan="1">Khmer 2.1.1 <break></break>(unique-kmers.py)</td><td valign="top" align="center" rowspan="1" colspan="1">ntCard 1.0.0</td><td valign="top" align="center" rowspan="1" colspan="1">ntCard 1.0.0</td><td valign="top" align="center" rowspan="1" colspan="1">Khmer 2.1.1 <break></break>(unique-kmers.py)</td></tr><tr><td rowspan="4" valign="middle" align="center" scope="row" colspan="1"><bold><italic>H. </italic></bold><break></break><bold><italic>sapiens</italic> 2</bold></td><td valign="top" align="center" rowspan="1" colspan="1">25</td><td valign="top" align="center" rowspan="1" colspan="1">ntCard 1.0.0</td><td valign="top" align="center" rowspan="1" colspan="1">Khmer 2.1.1<break></break>(abundance-dist-single.py)</td><td valign="top" align="center" rowspan="1" colspan="1">Khmer 2.1.1 <break></break>(unique-kmers.py)</td><td valign="top" align="center" rowspan="1" colspan="1">Khmer 2.1.1<break></break>(abundance-dist-single.py)</td><td valign="top" align="center" rowspan="1" colspan="1">KmerStream 1.1</td><td valign="top" align="center" rowspan="1" colspan="1">Khmer 2.1.1 <break></break>(unique-kmers.py)</td></tr><tr><td valign="top" colspan="1" align="center" scope="row" rowspan="1">50</td><td valign="top" align="center" rowspan="1" colspan="1">ntCard 1.0.0</td><td valign="top" align="center" rowspan="1" colspan="1">Khmer 2.1.1 <break></break>(unique-kmers.py)</td><td valign="top" align="center" rowspan="1" colspan="1">Khmer 2.1.1 <break></break>(unique-kmers.py)</td><td valign="top" align="center" rowspan="1" colspan="1">ntCard 1.0.0</td><td valign="top" align="center" rowspan="1" colspan="1">KmerStream 1.1</td><td valign="top" align="center" rowspan="1" colspan="1">Khmer 2.1.1 <break></break>(unique-kmers.py)</td></tr><tr><td valign="top" colspan="1" align="center" scope="row" rowspan="1">75</td><td valign="top" align="center" rowspan="1" colspan="1">ntCard 1.0.0</td><td valign="top" align="center" rowspan="1" colspan="1">Khmer 2.1.1 <break></break>(unique-kmers.py)</td><td valign="top" align="center" rowspan="1" colspan="1">Khmer 2.1.1 <break></break>(unique-kmers.py)</td><td valign="top" align="center" rowspan="1" colspan="1">ntCard 1.0.0</td><td valign="top" align="center" rowspan="1" colspan="1">KmerStream 1.1</td><td valign="top" align="center" rowspan="1" colspan="1">Khmer 2.1.1 <break></break>(unique-kmers.py)</td></tr><tr><td valign="top" colspan="1" align="center" scope="row" rowspan="1">100</td><td valign="top" align="center" rowspan="1" colspan="1">ntCard 1.0.0</td><td valign="top" align="center" rowspan="1" colspan="1">Khmer 2.1.1 <break></break>(unique-kmers.py)</td><td valign="top" align="center" rowspan="1" colspan="1">Khmer 2.1.1 <break></break>(unique-kmers.py)</td><td valign="top" align="center" rowspan="1" colspan="1">ntCard 1.0.0</td><td valign="top" align="center" rowspan="1" colspan="1">KmerStream 1.1</td><td valign="top" align="center" rowspan="1" colspan="1">Khmer 2.1.1 (unique-kmers.py)</td></tr><tr><td rowspan="5" valign="middle" align="center" scope="row" colspan="1"><bold>human genome NA19238</bold></td><td valign="top" align="center" rowspan="1" colspan="1">25</td><td valign="top" align="center" rowspan="1" colspan="1">ntCard 1.0.0</td><td valign="top" align="center" rowspan="1" colspan="1">Khmer 2.1.1<break></break>(abundance-dist-single.py)</td><td valign="top" align="center" rowspan="1" colspan="1">Khmer 2.1.1 <break></break>(unique-kmers.py)</td><td valign="top" align="center" rowspan="1" colspan="1">Khmer 2.1.1<break></break>(abundance-dist-single.py)</td><td valign="top" align="center" rowspan="1" colspan="1">KmerStream 1.1</td><td valign="top" align="center" rowspan="1" colspan="1">Khmer 2.1.1 (unique-kmers.py)</td></tr><tr><td valign="top" colspan="1" align="center" scope="row" rowspan="1">50</td><td valign="top" align="center" rowspan="1" colspan="1">ntCard 1.0.0</td><td valign="top" align="center" rowspan="1" colspan="1">KmerGenie 1.7048</td><td valign="top" align="center" rowspan="1" colspan="1">Khmer 2.1.1<break></break> (unique-kmers.py)</td><td valign="top" align="center" rowspan="1" colspan="1">ntCard 1.0.0</td><td valign="top" align="center" rowspan="1" colspan="1">KmerStream 1.1</td><td valign="top" align="center" rowspan="1" colspan="1">Khmer 2.1.1 (unique-kmers.py)</td></tr><tr><td valign="top" colspan="1" align="center" scope="row" rowspan="1">75</td><td valign="top" align="center" rowspan="1" colspan="1">ntCard 1.0.0</td><td valign="top" align="center" rowspan="1" colspan="1">KmerGenie 1.7048</td><td valign="top" align="center" rowspan="1" colspan="1">Khmer 2.1.1 <break></break>(unique-kmers.py)</td><td valign="top" align="center" rowspan="1" colspan="1">ntCard 1.0.0</td><td valign="top" align="center" rowspan="1" colspan="1">KmerStream 1.1</td><td valign="top" align="center" rowspan="1" colspan="1">Khmer 2.1.1 (unique-kmers.py)</td></tr><tr><td valign="top" colspan="1" align="center" scope="row" rowspan="1">100</td><td valign="top" align="center" rowspan="1" colspan="1">ntCard 1.0.0</td><td valign="top" align="center" rowspan="1" colspan="1">KmerGenie 1.7048</td><td valign="top" align="center" rowspan="1" colspan="1">Khmer 2.1.1 <break></break>(unique-kmers.py)</td><td valign="top" align="center" rowspan="1" colspan="1">ntCard 1.0.0</td><td valign="top" align="center" rowspan="1" colspan="1">KmerStream 1.1</td><td valign="top" align="center" rowspan="1" colspan="1">Khmer 2.1.1 (unique-kmers.py)</td></tr><tr><td valign="top" colspan="1" align="center" scope="row" rowspan="1">125</td><td valign="top" align="center" rowspan="1" colspan="1">ntCard 1.0.0</td><td valign="top" align="center" rowspan="1" colspan="1">KmerGenie 1.7048</td><td valign="top" align="center" rowspan="1" colspan="1">Khmer 2.1.1 <break></break>(unique-kmers.py)</td><td valign="top" align="center" rowspan="1" colspan="1">ntCard 1.0.0</td><td valign="top" align="center" rowspan="1" colspan="1">KmerStream 1.1</td><td valign="top" align="center" rowspan="1" colspan="1">Khmer 2.1.1 (unique-kmers.py)</td></tr></tbody></table></table-wrap></floats-group></pmc><affiliations><list></list><tree><noCountry><name sortKey="Manekar, Swati C" sort="Manekar, Swati C" uniqKey="Manekar S" first="Swati C." last="Manekar">Swati C. Manekar</name><name sortKey="Sathe, Shailesh R" sort="Sathe, Shailesh R" uniqKey="Sathe S" first="Shailesh R." last="Sathe">Shailesh R. Sathe</name></noCountry></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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