A computational method for detecting copy number variations using scale-space filtering
As next-generation sequencing technology made rapid and cost-effective sequencing available, the importance of computational approaches in finding and analyzing copy number variations (CNVs) has been amplified. Furthermore, most genome projects need to accurately analyze sequences with fairly low-co...
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| Veröffentlicht in: | BMC bioinformatics 2013-02, Vol.14 (1), p.57-57, Article 57 |
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| creator | Lee, Jongkeun Lee, Unjoo Kim, Baeksop Yoon, Jeehee |
| description | As next-generation sequencing technology made rapid and cost-effective sequencing available, the importance of computational approaches in finding and analyzing copy number variations (CNVs) has been amplified. Furthermore, most genome projects need to accurately analyze sequences with fairly low-coverage read data. It is urgently needed to develop a method to detect the exact types and locations of CNVs from low coverage read data.
Here, we propose a new CNV detection method, CNV_SS, which uses scale-space filtering. The scale-space filtering is evaluated by applying to the read coverage data the Gaussian convolution for various scales according to a given scaling parameter. Next, by differentiating twice and finding zero-crossing points, inflection points of scale-space filtered read coverage data are calculated per scale. Then, the types and the exact locations of CNVs are obtained by analyzing the finger print map, the contours of zero-crossing points for various scales.
The performance of CNV_SS showed that FNR and FPR stay in the range of 1.27% to 2.43% and 1.14% to 2.44%, respectively, even at a relatively low coverage (0.5x ≤C ≤2x). CNV_SS gave also much more effective results than the conventional methods in the evaluation of FNR, at 3.82% at least and 76.97% at most even when the coverage level of read data is low. CNV_SS source code is freely available from http://dblab.hallym.ac.kr/CNV SS/. |
| doi_str_mv | 10.1186/1471-2105-14-57 |
| format | Article |
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Here, we propose a new CNV detection method, CNV_SS, which uses scale-space filtering. The scale-space filtering is evaluated by applying to the read coverage data the Gaussian convolution for various scales according to a given scaling parameter. Next, by differentiating twice and finding zero-crossing points, inflection points of scale-space filtered read coverage data are calculated per scale. Then, the types and the exact locations of CNVs are obtained by analyzing the finger print map, the contours of zero-crossing points for various scales.
The performance of CNV_SS showed that FNR and FPR stay in the range of 1.27% to 2.43% and 1.14% to 2.44%, respectively, even at a relatively low coverage (0.5x ≤C ≤2x). CNV_SS gave also much more effective results than the conventional methods in the evaluation of FNR, at 3.82% at least and 76.97% at most even when the coverage level of read data is low. CNV_SS source code is freely available from http://dblab.hallym.ac.kr/CNV SS/.</description><identifier>ISSN: 1471-2105</identifier><identifier>EISSN: 1471-2105</identifier><identifier>DOI: 10.1186/1471-2105-14-57</identifier><identifier>PMID: 23418726</identifier><language>eng</language><publisher>England: BioMed Central Ltd</publisher><subject>Algorithms ; Analysis ; Computational Biology - methods ; Copy number variations ; Disease ; DNA Copy Number Variations ; DNA sequencing ; Experiments ; Filters (Mathematics) ; Genetic disorders ; Genetic research ; Genome ; Genomes ; Genomics ; HapMap Project ; Humans ; Methods ; Noise ; Nucleotide sequencing ; Sequence Analysis, DNA - methods ; Studies</subject><ispartof>BMC bioinformatics, 2013-02, Vol.14 (1), p.57-57, Article 57</ispartof><rights>COPYRIGHT 2013 BioMed Central Ltd.</rights><rights>2013 Lee et al.; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.</rights><rights>Copyright © 2013 Lee et al.; licensee BioMed Central Ltd. 2013 Lee et al.; licensee BioMed Central Ltd.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-b614t-98c63c9ff48f51a74287c7049f649613f9b5003641e98a2482ea778b03680eac3</citedby><cites>FETCH-LOGICAL-b614t-98c63c9ff48f51a74287c7049f649613f9b5003641e98a2482ea778b03680eac3</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC3637191/pdf/$$EPDF$$P50$$Gpubmedcentral$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC3637191/$$EHTML$$P50$$Gpubmedcentral$$Hfree_for_read</linktohtml><link.rule.ids>230,314,723,776,780,860,881,27901,27902,53766,53768</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/23418726$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Lee, Jongkeun</creatorcontrib><creatorcontrib>Lee, Unjoo</creatorcontrib><creatorcontrib>Kim, Baeksop</creatorcontrib><creatorcontrib>Yoon, Jeehee</creatorcontrib><title>A computational method for detecting copy number variations using scale-space filtering</title><title>BMC bioinformatics</title><addtitle>BMC Bioinformatics</addtitle><description>As next-generation sequencing technology made rapid and cost-effective sequencing available, the importance of computational approaches in finding and analyzing copy number variations (CNVs) has been amplified. Furthermore, most genome projects need to accurately analyze sequences with fairly low-coverage read data. It is urgently needed to develop a method to detect the exact types and locations of CNVs from low coverage read data.
Here, we propose a new CNV detection method, CNV_SS, which uses scale-space filtering. The scale-space filtering is evaluated by applying to the read coverage data the Gaussian convolution for various scales according to a given scaling parameter. Next, by differentiating twice and finding zero-crossing points, inflection points of scale-space filtered read coverage data are calculated per scale. Then, the types and the exact locations of CNVs are obtained by analyzing the finger print map, the contours of zero-crossing points for various scales.
The performance of CNV_SS showed that FNR and FPR stay in the range of 1.27% to 2.43% and 1.14% to 2.44%, respectively, even at a relatively low coverage (0.5x ≤C ≤2x). CNV_SS gave also much more effective results than the conventional methods in the evaluation of FNR, at 3.82% at least and 76.97% at most even when the coverage level of read data is low. CNV_SS source code is freely available from http://dblab.hallym.ac.kr/CNV SS/.</description><subject>Algorithms</subject><subject>Analysis</subject><subject>Computational Biology - methods</subject><subject>Copy number variations</subject><subject>Disease</subject><subject>DNA Copy Number Variations</subject><subject>DNA sequencing</subject><subject>Experiments</subject><subject>Filters (Mathematics)</subject><subject>Genetic disorders</subject><subject>Genetic research</subject><subject>Genome</subject><subject>Genomes</subject><subject>Genomics</subject><subject>HapMap Project</subject><subject>Humans</subject><subject>Methods</subject><subject>Noise</subject><subject>Nucleotide sequencing</subject><subject>Sequence Analysis, DNA - methods</subject><subject>Studies</subject><issn>1471-2105</issn><issn>1471-2105</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2013</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><sourceid>BENPR</sourceid><recordid>eNqNkstr3DAQxk1padK0596KoZf24ESjhyVfCpvQRyBQ6IMehawdbxRsayvJofnvK3fTbVxSKDpomPnNx_DNFMVzIMcAqj4BLqGiQEQFvBLyQXG4zzy8Ex8UT2K8IgSkIuJxcUAZByVpfVh8W5XWD9spmeT8aPpywHTp12XnQ7nGhDa5cZOR7U05TkOLobw2wf2CYznFuRit6bGKW2Ox7FyfMOTs0-JRZ_qIz27_o-Lru7dfzj5UFx_fn5-tLqq2Bp6qRtma2abruOoEGMmpklYS3nQ1b2pgXdMKQljNARtlKFcUjZSqzSlF0Fh2VLzZ6W6ndsC1xTEF0-ttcIMJN9obp5eV0V3qjb_WrGYSGsgCpzuB1vl_CCwr2S49G6tnY3Okhcwir26nCP77hDHpwUWLfW9G9FPUwASlDQDQ_0B5LaTigmT05V_olZ9CXtKOapikovlDbfIatBs7n8e0s6heCcYFKMpYpo7vofJb4-CsHzFvDpcNrxcNmUn4I23MFKM-__xpyZ7sWBt8jAG7vX1A9Hyn9xj24u7a9vzvw2Q_AXYT4E8</recordid><startdate>20130218</startdate><enddate>20130218</enddate><creator>Lee, Jongkeun</creator><creator>Lee, Unjoo</creator><creator>Kim, Baeksop</creator><creator>Yoon, Jeehee</creator><general>BioMed Central Ltd</general><general>BioMed Central</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>ISR</scope><scope>3V.</scope><scope>7QO</scope><scope>7SC</scope><scope>7X7</scope><scope>7XB</scope><scope>88E</scope><scope>8AL</scope><scope>8AO</scope><scope>8FD</scope><scope>8FE</scope><scope>8FG</scope><scope>8FH</scope><scope>8FI</scope><scope>8FJ</scope><scope>8FK</scope><scope>ABUWG</scope><scope>AEUYN</scope><scope>AFKRA</scope><scope>ARAPS</scope><scope>AZQEC</scope><scope>BBNVY</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>BHPHI</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>FR3</scope><scope>FYUFA</scope><scope>GHDGH</scope><scope>GNUQQ</scope><scope>HCIFZ</scope><scope>JQ2</scope><scope>K7-</scope><scope>K9.</scope><scope>L7M</scope><scope>LK8</scope><scope>L~C</scope><scope>L~D</scope><scope>M0N</scope><scope>M0S</scope><scope>M1P</scope><scope>M7P</scope><scope>P5Z</scope><scope>P62</scope><scope>P64</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>Q9U</scope><scope>7X8</scope><scope>5PM</scope></search><sort><creationdate>20130218</creationdate><title>A computational method for detecting copy number variations using scale-space filtering</title><author>Lee, Jongkeun ; Lee, Unjoo ; Kim, Baeksop ; Yoon, Jeehee</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-b614t-98c63c9ff48f51a74287c7049f649613f9b5003641e98a2482ea778b03680eac3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2013</creationdate><topic>Algorithms</topic><topic>Analysis</topic><topic>Computational Biology - methods</topic><topic>Copy number variations</topic><topic>Disease</topic><topic>DNA Copy Number Variations</topic><topic>DNA sequencing</topic><topic>Experiments</topic><topic>Filters (Mathematics)</topic><topic>Genetic disorders</topic><topic>Genetic research</topic><topic>Genome</topic><topic>Genomes</topic><topic>Genomics</topic><topic>HapMap Project</topic><topic>Humans</topic><topic>Methods</topic><topic>Noise</topic><topic>Nucleotide sequencing</topic><topic>Sequence Analysis, DNA - methods</topic><topic>Studies</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Lee, Jongkeun</creatorcontrib><creatorcontrib>Lee, Unjoo</creatorcontrib><creatorcontrib>Kim, Baeksop</creatorcontrib><creatorcontrib>Yoon, Jeehee</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>Gale In Context: Science</collection><collection>ProQuest Central (Corporate)</collection><collection>Biotechnology Research Abstracts</collection><collection>Computer and Information Systems Abstracts</collection><collection>Health & Medical Collection (Proquest)</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>Medical Database (Alumni Edition)</collection><collection>Computing Database (Alumni Edition)</collection><collection>ProQuest Pharma Collection</collection><collection>Technology Research Database</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>ProQuest Natural Science Collection</collection><collection>Hospital Premium Collection</collection><collection>Hospital Premium Collection (Alumni Edition)</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>ProQuest Central (Alumni)</collection><collection>ProQuest One Sustainability</collection><collection>ProQuest Central UK/Ireland</collection><collection>Advanced Technologies & Aerospace Database (1962 - 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Academic</collection><collection>PubMed Central (Full Participant titles)</collection><jtitle>BMC bioinformatics</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Lee, Jongkeun</au><au>Lee, Unjoo</au><au>Kim, Baeksop</au><au>Yoon, Jeehee</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>A computational method for detecting copy number variations using scale-space filtering</atitle><jtitle>BMC bioinformatics</jtitle><addtitle>BMC Bioinformatics</addtitle><date>2013-02-18</date><risdate>2013</risdate><volume>14</volume><issue>1</issue><spage>57</spage><epage>57</epage><pages>57-57</pages><artnum>57</artnum><issn>1471-2105</issn><eissn>1471-2105</eissn><abstract>As next-generation sequencing technology made rapid and cost-effective sequencing available, the importance of computational approaches in finding and analyzing copy number variations (CNVs) has been amplified. Furthermore, most genome projects need to accurately analyze sequences with fairly low-coverage read data. It is urgently needed to develop a method to detect the exact types and locations of CNVs from low coverage read data.
Here, we propose a new CNV detection method, CNV_SS, which uses scale-space filtering. The scale-space filtering is evaluated by applying to the read coverage data the Gaussian convolution for various scales according to a given scaling parameter. Next, by differentiating twice and finding zero-crossing points, inflection points of scale-space filtered read coverage data are calculated per scale. Then, the types and the exact locations of CNVs are obtained by analyzing the finger print map, the contours of zero-crossing points for various scales.
The performance of CNV_SS showed that FNR and FPR stay in the range of 1.27% to 2.43% and 1.14% to 2.44%, respectively, even at a relatively low coverage (0.5x ≤C ≤2x). CNV_SS gave also much more effective results than the conventional methods in the evaluation of FNR, at 3.82% at least and 76.97% at most even when the coverage level of read data is low. CNV_SS source code is freely available from http://dblab.hallym.ac.kr/CNV SS/.</abstract><cop>England</cop><pub>BioMed Central Ltd</pub><pmid>23418726</pmid><doi>10.1186/1471-2105-14-57</doi><tpages>1</tpages><oa>free_for_read</oa></addata></record> |
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| subjects | Algorithms Analysis Computational Biology - methods Copy number variations Disease DNA Copy Number Variations DNA sequencing Experiments Filters (Mathematics) Genetic disorders Genetic research Genome Genomes Genomics HapMap Project Humans Methods Noise Nucleotide sequencing Sequence Analysis, DNA - methods Studies |
| title | A computational method for detecting copy number variations using scale-space filtering |
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