Estimating the k-mer Coverage Frequencies in Genomic Datasets: A Comparative Assessment of the State-of-the-art
Identifieur interne : 000588 ( Main/Exploration ); précédent : 000587; suivant : 000589Estimating 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><idno type="wicri:source">PubMed</idno><idno type="RBID">pubmed:31015787</idno><idno type="wicri:Area/PubMed/Corpus">000557</idno><idno type="wicri:explorRef" wicri:stream="PubMed" wicri:step="Corpus" wicri:corpus="PubMed">000557</idno><idno type="wicri:Area/PubMed/Curation">000557</idno><idno type="wicri:explorRef" wicri:stream="PubMed" wicri:step="Curation">000557</idno><idno type="wicri:Area/PubMed/Checkpoint">000574</idno><idno type="wicri:explorRef" wicri:stream="Checkpoint" wicri:step="PubMed">000574</idno><idno type="wicri:Area/Ncbi/Merge">002200</idno><idno type="wicri:Area/Ncbi/Curation">002200</idno><idno type="wicri:Area/Ncbi/Checkpoint">002200</idno><idno type="wicri:Area/Main/Merge">000591</idno><idno type="wicri:Area/Main/Curation">000588</idno><idno type="wicri:Area/Main/Exploration">000588</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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