BOCS: DNA k-mer content and scoring for rapid genetic biomarker identification at low coverage.
Identifieur interne : 000509 ( PubMed/Corpus ); précédent : 000508; suivant : 000510BOCS: DNA k-mer content and scoring for rapid genetic biomarker identification at low coverage.
Auteurs : Lee E. Korshoj ; Prashant NagpalSource :
- Computers in biology and medicine [ 1879-0534 ] ; 2019.
Abstract
A single, inexpensive diagnostic test capable of rapidly identifying a wide range of genetic biomarkers would prove invaluable in precision medicine. Previous work has demonstrated the potential for high-throughput, label-free detection of A-G-C-T content in DNA k-mers, providing an alternative to single-letter sequencing while also having inherent lossy data compression and massively parallel data acquisition. Here, we apply a new bioinformatics algorithm - block optical content scoring (BOCS) - capable of using the high-throughput content k-mers for rapid, broad-spectrum identification of genetic biomarkers. BOCS uses content-based sequence alignment for probabilistic mapping of k-mer contents to gene sequences within a biomarker database, resulting in a probability ranking of genes on a content score. Simulations of the BOCS algorithm reveal high accuracy for identification of single antibiotic resistance genes, even in the presence of significant sequencing errors (100% accuracy for no sequencing errors, and >90% accuracy for sequencing errors at 20%), and at well below full coverage of the genes. Simulations for detecting multiple resistance genes within a methicillin-resistant Staphylococcus aureus (MRSA) strain showed 100% accuracy at an average gene coverage of merely 0.515, when the k-mer lengths were variable and with 4% sequencing error within the k-mer blocks. Extension of BOCS to cancer and other genetic diseases met or exceeded the results for resistance genes. Combined with a high-throughput content-based sequencing technique, the BOCS algorithm potentiates a test capable of rapid diagnosis and profiling of genetic biomarkers ranging from antibiotic resistance to cancer and other genetic diseases.
DOI: 10.1016/j.compbiomed.2019.05.022
PubMed: 31173943
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Korshoj</name><affiliation><nlm:affiliation>Department of Chemical and Biological Engineering, University of Colorado Boulder, Boulder, CO, 80303, USA; Renewable and Sustainable Energy Institute (RASEI), University of Colorado Boulder, Boulder, CO, 80303, USA.</nlm:affiliation></affiliation></author><author><name sortKey="Nagpal, Prashant" sort="Nagpal, Prashant" uniqKey="Nagpal P" first="Prashant" last="Nagpal">Prashant Nagpal</name><affiliation><nlm:affiliation>Department of Chemical and Biological Engineering, University of Colorado Boulder, Boulder, CO, 80303, USA; Renewable and Sustainable Energy Institute (RASEI), University of Colorado Boulder, Boulder, CO, 80303, USA; Materials Science and Engineering, University of Colorado Boulder, Boulder, CO, 80303, USA. 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Korshoj</name><affiliation><nlm:affiliation>Department of Chemical and Biological Engineering, University of Colorado Boulder, Boulder, CO, 80303, USA; Renewable and Sustainable Energy Institute (RASEI), University of Colorado Boulder, Boulder, CO, 80303, USA.</nlm:affiliation></affiliation></author><author><name sortKey="Nagpal, Prashant" sort="Nagpal, Prashant" uniqKey="Nagpal P" first="Prashant" last="Nagpal">Prashant Nagpal</name><affiliation><nlm:affiliation>Department of Chemical and Biological Engineering, University of Colorado Boulder, Boulder, CO, 80303, USA; Renewable and Sustainable Energy Institute (RASEI), University of Colorado Boulder, Boulder, CO, 80303, USA; Materials Science and Engineering, University of Colorado Boulder, Boulder, CO, 80303, USA. Electronic address: pnagpal@colorado.edu.</nlm:affiliation></affiliation></author></analytic><series><title level="j">Computers in biology and medicine</title><idno type="eISSN">1879-0534</idno><imprint><date when="2019" type="published">2019</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">A single, inexpensive diagnostic test capable of rapidly identifying a wide range of genetic biomarkers would prove invaluable in precision medicine. Previous work has demonstrated the potential for high-throughput, label-free detection of A-G-C-T content in DNA k-mers, providing an alternative to single-letter sequencing while also having inherent lossy data compression and massively parallel data acquisition. Here, we apply a new bioinformatics algorithm - block optical content scoring (BOCS) - capable of using the high-throughput content k-mers for rapid, broad-spectrum identification of genetic biomarkers. BOCS uses content-based sequence alignment for probabilistic mapping of k-mer contents to gene sequences within a biomarker database, resulting in a probability ranking of genes on a content score. Simulations of the BOCS algorithm reveal high accuracy for identification of single antibiotic resistance genes, even in the presence of significant sequencing errors (100% accuracy for no sequencing errors, and >90% accuracy for sequencing errors at 20%), and at well below full coverage of the genes. Simulations for detecting multiple resistance genes within a methicillin-resistant Staphylococcus aureus (MRSA) strain showed 100% accuracy at an average gene coverage of merely 0.515, when the k-mer lengths were variable and with 4% sequencing error within the k-mer blocks. Extension of BOCS to cancer and other genetic diseases met or exceeded the results for resistance genes. Combined with a high-throughput content-based sequencing technique, the BOCS algorithm potentiates a test capable of rapid diagnosis and profiling of genetic biomarkers ranging from antibiotic resistance to cancer and other genetic diseases.</div></front></TEI><pubmed><MedlineCitation Status="In-Process" Owner="NLM"><PMID Version="1">31173943</PMID><DateRevised><Year>2020</Year><Month>03</Month><Day>17</Day></DateRevised><Article PubModel="Print-Electronic"><Journal><ISSN IssnType="Electronic">1879-0534</ISSN><JournalIssue CitedMedium="Internet"><Volume>110</Volume><PubDate><Year>2019</Year><Month>07</Month></PubDate></JournalIssue><Title>Computers in biology and medicine</Title><ISOAbbreviation>Comput. Biol. Med.</ISOAbbreviation></Journal><ArticleTitle>BOCS: DNA k-mer content and scoring for rapid genetic biomarker identification at low coverage.</ArticleTitle><Pagination><MedlinePgn>196-206</MedlinePgn></Pagination><ELocationID EIdType="pii" ValidYN="Y">S0010-4825(19)30190-8</ELocationID><ELocationID EIdType="doi" ValidYN="Y">10.1016/j.compbiomed.2019.05.022</ELocationID><Abstract><AbstractText>A single, inexpensive diagnostic test capable of rapidly identifying a wide range of genetic biomarkers would prove invaluable in precision medicine. Previous work has demonstrated the potential for high-throughput, label-free detection of A-G-C-T content in DNA k-mers, providing an alternative to single-letter sequencing while also having inherent lossy data compression and massively parallel data acquisition. Here, we apply a new bioinformatics algorithm - block optical content scoring (BOCS) - capable of using the high-throughput content k-mers for rapid, broad-spectrum identification of genetic biomarkers. BOCS uses content-based sequence alignment for probabilistic mapping of k-mer contents to gene sequences within a biomarker database, resulting in a probability ranking of genes on a content score. Simulations of the BOCS algorithm reveal high accuracy for identification of single antibiotic resistance genes, even in the presence of significant sequencing errors (100% accuracy for no sequencing errors, and >90% accuracy for sequencing errors at 20%), and at well below full coverage of the genes. Simulations for detecting multiple resistance genes within a methicillin-resistant Staphylococcus aureus (MRSA) strain showed 100% accuracy at an average gene coverage of merely 0.515, when the k-mer lengths were variable and with 4% sequencing error within the k-mer blocks. Extension of BOCS to cancer and other genetic diseases met or exceeded the results for resistance genes. Combined with a high-throughput content-based sequencing technique, the BOCS algorithm potentiates a test capable of rapid diagnosis and profiling of genetic biomarkers ranging from antibiotic resistance to cancer and other genetic diseases.</AbstractText><CopyrightInformation>Copyright © 2019 Elsevier Ltd. All rights reserved.</CopyrightInformation></Abstract><AuthorList CompleteYN="Y"><Author ValidYN="Y"><LastName>Korshoj</LastName><ForeName>Lee E</ForeName><Initials>LE</Initials><AffiliationInfo><Affiliation>Department of Chemical and Biological Engineering, University of Colorado Boulder, Boulder, CO, 80303, USA; Renewable and Sustainable Energy Institute (RASEI), University of Colorado Boulder, Boulder, CO, 80303, USA.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Nagpal</LastName><ForeName>Prashant</ForeName><Initials>P</Initials><AffiliationInfo><Affiliation>Department of Chemical and Biological Engineering, University of Colorado Boulder, Boulder, CO, 80303, USA; Renewable and Sustainable Energy Institute (RASEI), University of Colorado Boulder, Boulder, CO, 80303, USA; Materials Science and Engineering, University of Colorado Boulder, Boulder, CO, 80303, USA. 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