QuasiSeq: profiling viral quasispecies via self-tuning spectral clustering with PacBio long sequencing reads
Abstract Motivation The existence of quasispecies in the viral population causes difficulties for disease prevention and treatment. High-throughput sequencing provides opportunity to determine rare quasispecies and long sequencing reads covering full genomes reduce quasispecies determination to a cl...
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| Veröffentlicht in: | Bioinformatics 2022-06, Vol.38 (12), p.3192-3199 |
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| creator | Jiao, Xiaoli Imamichi, Hiromi Sherman, Brad T Nahar, Rishub Dewar, Robin L Lane, H Clifford Imamichi, Tomozumi Chang, Weizhong |
| description | Abstract
Motivation
The existence of quasispecies in the viral population causes difficulties for disease prevention and treatment. High-throughput sequencing provides opportunity to determine rare quasispecies and long sequencing reads covering full genomes reduce quasispecies determination to a clustering problem. The challenge is high similarity of quasispecies and high error rate of long sequencing reads.
Results
We developed QuasiSeq using a novel signature-based self-tuning clustering method, SigClust, to profile viral mixtures with high accuracy and sensitivity. QuasiSeq can correctly identify quasispecies even using low-quality sequencing reads (accuracy |
| doi_str_mv | 10.1093/bioinformatics/btac313 |
| format | Article |
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Motivation
The existence of quasispecies in the viral population causes difficulties for disease prevention and treatment. High-throughput sequencing provides opportunity to determine rare quasispecies and long sequencing reads covering full genomes reduce quasispecies determination to a clustering problem. The challenge is high similarity of quasispecies and high error rate of long sequencing reads.
Results
We developed QuasiSeq using a novel signature-based self-tuning clustering method, SigClust, to profile viral mixtures with high accuracy and sensitivity. QuasiSeq can correctly identify quasispecies even using low-quality sequencing reads (accuracy <80%) and produce quasispecies sequences with high accuracy (≥99.55%). Using high-quality circular consensus sequencing reads, QuasiSeq can produce quasispecies sequences with 100% accuracy. QuasiSeq has higher sensitivity and specificity than similar published software. Moreover, the requirement of the computational resource can be controlled by the size of the signature, which makes it possible to handle big sequencing data for rare quasispecies discovery. Furthermore, parallel computation is implemented to process the clusters and further reduce the runtime. Finally, we developed a web interface for the QuasiSeq workflow with simple parameter settings based on the quality of sequencing data, making it easy to use for users without advanced data science skills.
Availability and implementation
QuasiSeq is open source and freely available at https://github.com/LHRI-Bioinformatics/QuasiSeq. The current release (v1.0.0) is archived and available at https://zenodo.org/badge/latestdoi/340494542.
Supplementary information
Supplementary data are available at Bioinformatics online.</description><identifier>ISSN: 1367-4803</identifier><identifier>ISSN: 1367-4811</identifier><identifier>EISSN: 1460-2059</identifier><identifier>EISSN: 1367-4811</identifier><identifier>DOI: 10.1093/bioinformatics/btac313</identifier><identifier>PMID: 35532087</identifier><language>eng</language><publisher>England: Oxford University Press</publisher><subject>Algorithms ; Cluster Analysis ; High-Throughput Nucleotide Sequencing ; Original Papers ; Quasispecies ; Sequence Analysis, DNA ; Software</subject><ispartof>Bioinformatics, 2022-06, Vol.38 (12), p.3192-3199</ispartof><rights>Published by Oxford University Press 2022. 2022</rights><rights>Published by Oxford University Press 2022.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c456t-ea6f7cd074af167b838f225471a2c4c52a0696fedbd5a68b9a559121c33feb9f3</citedby><cites>FETCH-LOGICAL-c456t-ea6f7cd074af167b838f225471a2c4c52a0696fedbd5a68b9a559121c33feb9f3</cites><orcidid>0000-0002-1413-2763</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC9890302/pdf/$$EPDF$$P50$$Gpubmedcentral$$H</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC9890302/$$EHTML$$P50$$Gpubmedcentral$$H</linktohtml><link.rule.ids>230,314,727,780,784,885,1604,27923,27924,53790,53792</link.rule.ids><linktorsrc>$$Uhttps://dx.doi.org/10.1093/bioinformatics/btac313$$EView_record_in_Oxford_University_Press$$FView_record_in_$$GOxford_University_Press</linktorsrc><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/35532087$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><contributor>Mathelier, Anthony</contributor><creatorcontrib>Jiao, Xiaoli</creatorcontrib><creatorcontrib>Imamichi, Hiromi</creatorcontrib><creatorcontrib>Sherman, Brad T</creatorcontrib><creatorcontrib>Nahar, Rishub</creatorcontrib><creatorcontrib>Dewar, Robin L</creatorcontrib><creatorcontrib>Lane, H Clifford</creatorcontrib><creatorcontrib>Imamichi, Tomozumi</creatorcontrib><creatorcontrib>Chang, Weizhong</creatorcontrib><title>QuasiSeq: profiling viral quasispecies via self-tuning spectral clustering with PacBio long sequencing reads</title><title>Bioinformatics</title><addtitle>Bioinformatics</addtitle><description>Abstract
Motivation
The existence of quasispecies in the viral population causes difficulties for disease prevention and treatment. High-throughput sequencing provides opportunity to determine rare quasispecies and long sequencing reads covering full genomes reduce quasispecies determination to a clustering problem. The challenge is high similarity of quasispecies and high error rate of long sequencing reads.
Results
We developed QuasiSeq using a novel signature-based self-tuning clustering method, SigClust, to profile viral mixtures with high accuracy and sensitivity. QuasiSeq can correctly identify quasispecies even using low-quality sequencing reads (accuracy <80%) and produce quasispecies sequences with high accuracy (≥99.55%). Using high-quality circular consensus sequencing reads, QuasiSeq can produce quasispecies sequences with 100% accuracy. QuasiSeq has higher sensitivity and specificity than similar published software. Moreover, the requirement of the computational resource can be controlled by the size of the signature, which makes it possible to handle big sequencing data for rare quasispecies discovery. Furthermore, parallel computation is implemented to process the clusters and further reduce the runtime. Finally, we developed a web interface for the QuasiSeq workflow with simple parameter settings based on the quality of sequencing data, making it easy to use for users without advanced data science skills.
Availability and implementation
QuasiSeq is open source and freely available at https://github.com/LHRI-Bioinformatics/QuasiSeq. The current release (v1.0.0) is archived and available at https://zenodo.org/badge/latestdoi/340494542.
Supplementary information
Supplementary data are available at Bioinformatics online.</description><subject>Algorithms</subject><subject>Cluster Analysis</subject><subject>High-Throughput Nucleotide Sequencing</subject><subject>Original Papers</subject><subject>Quasispecies</subject><subject>Sequence Analysis, DNA</subject><subject>Software</subject><issn>1367-4803</issn><issn>1367-4811</issn><issn>1460-2059</issn><issn>1367-4811</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2022</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><recordid>eNqNkUtv1TAQhS0EoqXwF6os2YT6nZgFElTlIVUCBKytiTNujXzjXDsp4t8T614qumNl65xvjsc6hJwz-opRIy6GkMLkU97BEly5GBZwgolH5JRJTVtOlXm83YXuWtlTcUKelfKTUsWklE_JiVBKcNp3pyR-XaGEb7h_3cw5-RDDdNPchQyx2VenzOgClk2CpmD07bJOFan6UikX17JgrtqvsNw2X8C9C6mJqUK4X3Fy1csIY3lOnniIBV8czzPy4_3V98uP7fXnD58u3163Tiq9tAjad26knQTPdDf0ovecK9kx4E46xYFqoz2Ow6hA94MBpQzjzAnhcTBenJE3h9x5HXY4OpzqqnbOYQf5t00Q7ENnCrf2Jt1Z0xsqKN8CXh4Dctq-UBa7C8VhjDBhWovlWjPZd4bKDdUH1OVUSkZ__wyjtlZlH1Zlj1Vtg-f_Lnk_9rebDWAHIK3z_4b-Aee_q3I</recordid><startdate>20220613</startdate><enddate>20220613</enddate><creator>Jiao, Xiaoli</creator><creator>Imamichi, Hiromi</creator><creator>Sherman, Brad T</creator><creator>Nahar, Rishub</creator><creator>Dewar, Robin L</creator><creator>Lane, H Clifford</creator><creator>Imamichi, Tomozumi</creator><creator>Chang, Weizhong</creator><general>Oxford University Press</general><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7X8</scope><scope>5PM</scope><orcidid>https://orcid.org/0000-0002-1413-2763</orcidid></search><sort><creationdate>20220613</creationdate><title>QuasiSeq: profiling viral quasispecies via self-tuning spectral clustering with PacBio long sequencing reads</title><author>Jiao, Xiaoli ; Imamichi, Hiromi ; Sherman, Brad T ; Nahar, Rishub ; Dewar, Robin L ; Lane, H Clifford ; Imamichi, Tomozumi ; Chang, Weizhong</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c456t-ea6f7cd074af167b838f225471a2c4c52a0696fedbd5a68b9a559121c33feb9f3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2022</creationdate><topic>Algorithms</topic><topic>Cluster Analysis</topic><topic>High-Throughput Nucleotide Sequencing</topic><topic>Original Papers</topic><topic>Quasispecies</topic><topic>Sequence Analysis, DNA</topic><topic>Software</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Jiao, Xiaoli</creatorcontrib><creatorcontrib>Imamichi, Hiromi</creatorcontrib><creatorcontrib>Sherman, Brad T</creatorcontrib><creatorcontrib>Nahar, Rishub</creatorcontrib><creatorcontrib>Dewar, Robin L</creatorcontrib><creatorcontrib>Lane, H Clifford</creatorcontrib><creatorcontrib>Imamichi, Tomozumi</creatorcontrib><creatorcontrib>Chang, Weizhong</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>MEDLINE - Academic</collection><collection>PubMed Central (Full Participant titles)</collection><jtitle>Bioinformatics</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Jiao, Xiaoli</au><au>Imamichi, Hiromi</au><au>Sherman, Brad T</au><au>Nahar, Rishub</au><au>Dewar, Robin L</au><au>Lane, H Clifford</au><au>Imamichi, Tomozumi</au><au>Chang, Weizhong</au><au>Mathelier, Anthony</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>QuasiSeq: profiling viral quasispecies via self-tuning spectral clustering with PacBio long sequencing reads</atitle><jtitle>Bioinformatics</jtitle><addtitle>Bioinformatics</addtitle><date>2022-06-13</date><risdate>2022</risdate><volume>38</volume><issue>12</issue><spage>3192</spage><epage>3199</epage><pages>3192-3199</pages><issn>1367-4803</issn><issn>1367-4811</issn><eissn>1460-2059</eissn><eissn>1367-4811</eissn><abstract>Abstract
Motivation
The existence of quasispecies in the viral population causes difficulties for disease prevention and treatment. High-throughput sequencing provides opportunity to determine rare quasispecies and long sequencing reads covering full genomes reduce quasispecies determination to a clustering problem. The challenge is high similarity of quasispecies and high error rate of long sequencing reads.
Results
We developed QuasiSeq using a novel signature-based self-tuning clustering method, SigClust, to profile viral mixtures with high accuracy and sensitivity. QuasiSeq can correctly identify quasispecies even using low-quality sequencing reads (accuracy <80%) and produce quasispecies sequences with high accuracy (≥99.55%). Using high-quality circular consensus sequencing reads, QuasiSeq can produce quasispecies sequences with 100% accuracy. QuasiSeq has higher sensitivity and specificity than similar published software. Moreover, the requirement of the computational resource can be controlled by the size of the signature, which makes it possible to handle big sequencing data for rare quasispecies discovery. Furthermore, parallel computation is implemented to process the clusters and further reduce the runtime. Finally, we developed a web interface for the QuasiSeq workflow with simple parameter settings based on the quality of sequencing data, making it easy to use for users without advanced data science skills.
Availability and implementation
QuasiSeq is open source and freely available at https://github.com/LHRI-Bioinformatics/QuasiSeq. The current release (v1.0.0) is archived and available at https://zenodo.org/badge/latestdoi/340494542.
Supplementary information
Supplementary data are available at Bioinformatics online.</abstract><cop>England</cop><pub>Oxford University Press</pub><pmid>35532087</pmid><doi>10.1093/bioinformatics/btac313</doi><tpages>8</tpages><orcidid>https://orcid.org/0000-0002-1413-2763</orcidid><oa>free_for_read</oa></addata></record> |
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| source | Oxford Journals Open Access Collection |
| subjects | Algorithms Cluster Analysis High-Throughput Nucleotide Sequencing Original Papers Quasispecies Sequence Analysis, DNA Software |
| title | QuasiSeq: profiling viral quasispecies via self-tuning spectral clustering with PacBio long sequencing reads |
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