Comparison of variations detection between whole-genome amplification methods used in single-cell resequencing
Abstract Background Single-cell resequencing (SCRS) provides many biomedical advances in variations detection at the single-cell level, but it currently relies on whole genome amplification (WGA). Three methods are commonly used for WGA: multiple displacement amplification (MDA), degenerate-oligonuc...
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creator | Hou, Yong Wu, Kui Shi, Xulian Li, Fuqiang Song, Luting Wu, Hanjie Dean, Michael Li, Guibo Tsang, Shirley Jiang, Runze Zhang, Xiaolong Li, Bo Liu, Geng Bedekar, Niharika Lu, Na Xie, Guoyun Liang, Han Chang, Liao Wang, Ting Chen, Jianghao Li, Yingrui Zhang, Xiuqing Yang, Huanming Xu, Xun Wang, Ling Wang, Jun |
description | Abstract
Background
Single-cell resequencing (SCRS) provides many biomedical advances in variations detection at the single-cell level, but it currently relies on whole genome amplification (WGA). Three methods are commonly used for WGA: multiple displacement amplification (MDA), degenerate-oligonucleotide-primed PCR (DOP-PCR) and multiple annealing and looping-based amplification cycles (MALBAC). However, a comprehensive comparison of variations detection performance between these WGA methods has not yet been performed.
Results
We systematically compared the advantages and disadvantages of different WGA methods, focusing particularly on variations detection. Low-coverage whole-genome sequencing revealed that DOP-PCR had the highest duplication ratio, but an even read distribution and the best reproducibility and accuracy for detection of copy-number variations (CNVs). However, MDA had significantly higher genome recovery sensitivity (~84 %) than DOP-PCR (~6 %) and MALBAC (~52 %) at high sequencing depth. MALBAC and MDA had comparable single-nucleotide variations detection efficiency, false-positive ratio, and allele drop-out ratio. We further demonstrated that SCRS data amplified by either MDA or MALBAC from a gastric cancer cell line could accurately detect gastric cancer CNVs with comparable sensitivity and specificity, including amplifications of 12p11.22 (KRAS) and 9p24.1 (JAK2, CD274, and PDCD1LG2).
Conclusions
Our findings provide a comprehensive comparison of variations detection performance using SCRS amplified by different WGA methods. It will guide researchers to determine which WGA method is best suited to individual experimental needs at single-cell level. |
doi_str_mv | 10.1186/s13742-015-0068-3 |
format | Article |
fullrecord | <record><control><sourceid>proquest_pubme</sourceid><recordid>TN_cdi_pubmedcentral_primary_oai_pubmedcentral_nih_gov_4527218</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><oup_id>10.1186/s13742-015-0068-3</oup_id><sourcerecordid>3982023191</sourcerecordid><originalsourceid>FETCH-LOGICAL-c510t-adc2d7d93bd367d2406733f823d3157cb3f8c5b472db1e75be087f1e29a234173</originalsourceid><addsrcrecordid>eNp9ks1q3DAUhU1paEKaB-imCLrpom51JdtXsymUoX8Q6CaB7IQsXc8o2JJr2Ql9-8qdNEwLrTc6SJ-P7uGoKF4AfwugmncJJFai5FCXnDeqlE-KM8ErLAXgzdMjfVpcpHTL84eoFMpnxaloRA3NRp0VYRuH0Uw-xcBix-6yNLOPITFHM9lVspbme6LA7vexp3JHIQ7EzDD2vvP2F80GmvfRJbYkcswHlnzYZdZS37OJEn1fKNi897w46Uyf6OJhPS-uP3282n4pL799_rr9cFnaGvhcGmeFQ7eRrZMNOlHxBqXslJBOQo22zdrWbYXCtUBYt8QVdkBiY4SsAOV58f7gOy7tQM5SmCfT63Hyg5l-6Gi8_vMk-L3exTtd1QIFqGzw-sFginn4NOvBpzWOCRSXpAG5aGqulMzoq7_Q27hMIcfTEiQ0ouGq-h8FiDlhjWqdGw6UnWJKE3WPIwPXa-36ULvOteu1dr3e__I46-Mfv0vOwJsDEJfxH35Hz0j-BMT0to8</addsrcrecordid><sourcetype>Open Access Repository</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>1772405787</pqid></control><display><type>article</type><title>Comparison of variations detection between whole-genome amplification methods used in single-cell resequencing</title><source>MEDLINE</source><source>DOAJ Directory of Open Access Journals</source><source>Oxford Journals Open Access Collection</source><source>EZB-FREE-00999 freely available EZB journals</source><source>PubMed Central</source><source>Springer Nature OA/Free Journals</source><creator>Hou, Yong ; Wu, Kui ; Shi, Xulian ; Li, Fuqiang ; Song, Luting ; Wu, Hanjie ; Dean, Michael ; Li, Guibo ; Tsang, Shirley ; Jiang, Runze ; Zhang, Xiaolong ; Li, Bo ; Liu, Geng ; Bedekar, Niharika ; Lu, Na ; Xie, Guoyun ; Liang, Han ; Chang, Liao ; Wang, Ting ; Chen, Jianghao ; Li, Yingrui ; Zhang, Xiuqing ; Yang, Huanming ; Xu, Xun ; Wang, Ling ; Wang, Jun</creator><creatorcontrib>Hou, Yong ; Wu, Kui ; Shi, Xulian ; Li, Fuqiang ; Song, Luting ; Wu, Hanjie ; Dean, Michael ; Li, Guibo ; Tsang, Shirley ; Jiang, Runze ; Zhang, Xiaolong ; Li, Bo ; Liu, Geng ; Bedekar, Niharika ; Lu, Na ; Xie, Guoyun ; Liang, Han ; Chang, Liao ; Wang, Ting ; Chen, Jianghao ; Li, Yingrui ; Zhang, Xiuqing ; Yang, Huanming ; Xu, Xun ; Wang, Ling ; Wang, Jun</creatorcontrib><description>Abstract
Background
Single-cell resequencing (SCRS) provides many biomedical advances in variations detection at the single-cell level, but it currently relies on whole genome amplification (WGA). Three methods are commonly used for WGA: multiple displacement amplification (MDA), degenerate-oligonucleotide-primed PCR (DOP-PCR) and multiple annealing and looping-based amplification cycles (MALBAC). However, a comprehensive comparison of variations detection performance between these WGA methods has not yet been performed.
Results
We systematically compared the advantages and disadvantages of different WGA methods, focusing particularly on variations detection. Low-coverage whole-genome sequencing revealed that DOP-PCR had the highest duplication ratio, but an even read distribution and the best reproducibility and accuracy for detection of copy-number variations (CNVs). However, MDA had significantly higher genome recovery sensitivity (~84 %) than DOP-PCR (~6 %) and MALBAC (~52 %) at high sequencing depth. MALBAC and MDA had comparable single-nucleotide variations detection efficiency, false-positive ratio, and allele drop-out ratio. We further demonstrated that SCRS data amplified by either MDA or MALBAC from a gastric cancer cell line could accurately detect gastric cancer CNVs with comparable sensitivity and specificity, including amplifications of 12p11.22 (KRAS) and 9p24.1 (JAK2, CD274, and PDCD1LG2).
Conclusions
Our findings provide a comprehensive comparison of variations detection performance using SCRS amplified by different WGA methods. It will guide researchers to determine which WGA method is best suited to individual experimental needs at single-cell level.</description><identifier>ISSN: 2047-217X</identifier><identifier>EISSN: 2047-217X</identifier><identifier>DOI: 10.1186/s13742-015-0068-3</identifier><identifier>PMID: 26251698</identifier><language>eng</language><publisher>United States: Oxford University Press</publisher><subject>Cancer ; DNA Copy Number Variations - genetics ; Gastric cancer ; Gene sequencing ; Genome ; Genomes ; Janus kinase 2 ; Nucleotides ; Oligonucleotides ; Sensitivity ; Single-Cell Analysis ; Variation ; Whole genome sequencing</subject><ispartof>Gigascience, 2015, Vol.4 (1), p.37-37</ispartof><rights>Hou et al. 2015</rights><rights>Copyright BioMed Central 2015</rights><rights>Hou et al.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><cites>FETCH-LOGICAL-c510t-adc2d7d93bd367d2406733f823d3157cb3f8c5b472db1e75be087f1e29a234173</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/PMC4527218/pdf/$$EPDF$$P50$$Gpubmedcentral$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC4527218/$$EHTML$$P50$$Gpubmedcentral$$Hfree_for_read</linktohtml><link.rule.ids>230,314,727,780,784,864,885,4024,27923,27924,27925,53791,53793</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/26251698$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Hou, Yong</creatorcontrib><creatorcontrib>Wu, Kui</creatorcontrib><creatorcontrib>Shi, Xulian</creatorcontrib><creatorcontrib>Li, Fuqiang</creatorcontrib><creatorcontrib>Song, Luting</creatorcontrib><creatorcontrib>Wu, Hanjie</creatorcontrib><creatorcontrib>Dean, Michael</creatorcontrib><creatorcontrib>Li, Guibo</creatorcontrib><creatorcontrib>Tsang, Shirley</creatorcontrib><creatorcontrib>Jiang, Runze</creatorcontrib><creatorcontrib>Zhang, Xiaolong</creatorcontrib><creatorcontrib>Li, Bo</creatorcontrib><creatorcontrib>Liu, Geng</creatorcontrib><creatorcontrib>Bedekar, Niharika</creatorcontrib><creatorcontrib>Lu, Na</creatorcontrib><creatorcontrib>Xie, Guoyun</creatorcontrib><creatorcontrib>Liang, Han</creatorcontrib><creatorcontrib>Chang, Liao</creatorcontrib><creatorcontrib>Wang, Ting</creatorcontrib><creatorcontrib>Chen, Jianghao</creatorcontrib><creatorcontrib>Li, Yingrui</creatorcontrib><creatorcontrib>Zhang, Xiuqing</creatorcontrib><creatorcontrib>Yang, Huanming</creatorcontrib><creatorcontrib>Xu, Xun</creatorcontrib><creatorcontrib>Wang, Ling</creatorcontrib><creatorcontrib>Wang, Jun</creatorcontrib><title>Comparison of variations detection between whole-genome amplification methods used in single-cell resequencing</title><title>Gigascience</title><addtitle>Gigascience</addtitle><description>Abstract
Background
Single-cell resequencing (SCRS) provides many biomedical advances in variations detection at the single-cell level, but it currently relies on whole genome amplification (WGA). Three methods are commonly used for WGA: multiple displacement amplification (MDA), degenerate-oligonucleotide-primed PCR (DOP-PCR) and multiple annealing and looping-based amplification cycles (MALBAC). However, a comprehensive comparison of variations detection performance between these WGA methods has not yet been performed.
Results
We systematically compared the advantages and disadvantages of different WGA methods, focusing particularly on variations detection. Low-coverage whole-genome sequencing revealed that DOP-PCR had the highest duplication ratio, but an even read distribution and the best reproducibility and accuracy for detection of copy-number variations (CNVs). However, MDA had significantly higher genome recovery sensitivity (~84 %) than DOP-PCR (~6 %) and MALBAC (~52 %) at high sequencing depth. MALBAC and MDA had comparable single-nucleotide variations detection efficiency, false-positive ratio, and allele drop-out ratio. We further demonstrated that SCRS data amplified by either MDA or MALBAC from a gastric cancer cell line could accurately detect gastric cancer CNVs with comparable sensitivity and specificity, including amplifications of 12p11.22 (KRAS) and 9p24.1 (JAK2, CD274, and PDCD1LG2).
Conclusions
Our findings provide a comprehensive comparison of variations detection performance using SCRS amplified by different WGA methods. It will guide researchers to determine which WGA method is best suited to individual experimental needs at single-cell level.</description><subject>Cancer</subject><subject>DNA Copy Number Variations - genetics</subject><subject>Gastric cancer</subject><subject>Gene sequencing</subject><subject>Genome</subject><subject>Genomes</subject><subject>Janus kinase 2</subject><subject>Nucleotides</subject><subject>Oligonucleotides</subject><subject>Sensitivity</subject><subject>Single-Cell Analysis</subject><subject>Variation</subject><subject>Whole genome sequencing</subject><issn>2047-217X</issn><issn>2047-217X</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2015</creationdate><recordtype>article</recordtype><sourceid>TOX</sourceid><sourceid>EIF</sourceid><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><sourceid>GNUQQ</sourceid><recordid>eNp9ks1q3DAUhU1paEKaB-imCLrpom51JdtXsymUoX8Q6CaB7IQsXc8o2JJr2Ql9-8qdNEwLrTc6SJ-P7uGoKF4AfwugmncJJFai5FCXnDeqlE-KM8ErLAXgzdMjfVpcpHTL84eoFMpnxaloRA3NRp0VYRuH0Uw-xcBix-6yNLOPITFHM9lVspbme6LA7vexp3JHIQ7EzDD2vvP2F80GmvfRJbYkcswHlnzYZdZS37OJEn1fKNi897w46Uyf6OJhPS-uP3282n4pL799_rr9cFnaGvhcGmeFQ7eRrZMNOlHxBqXslJBOQo22zdrWbYXCtUBYt8QVdkBiY4SsAOV58f7gOy7tQM5SmCfT63Hyg5l-6Gi8_vMk-L3exTtd1QIFqGzw-sFginn4NOvBpzWOCRSXpAG5aGqulMzoq7_Q27hMIcfTEiQ0ouGq-h8FiDlhjWqdGw6UnWJKE3WPIwPXa-36ULvOteu1dr3e__I46-Mfv0vOwJsDEJfxH35Hz0j-BMT0to8</recordid><startdate>2015</startdate><enddate>2015</enddate><creator>Hou, Yong</creator><creator>Wu, Kui</creator><creator>Shi, Xulian</creator><creator>Li, Fuqiang</creator><creator>Song, Luting</creator><creator>Wu, Hanjie</creator><creator>Dean, Michael</creator><creator>Li, Guibo</creator><creator>Tsang, Shirley</creator><creator>Jiang, Runze</creator><creator>Zhang, Xiaolong</creator><creator>Li, Bo</creator><creator>Liu, Geng</creator><creator>Bedekar, Niharika</creator><creator>Lu, Na</creator><creator>Xie, Guoyun</creator><creator>Liang, Han</creator><creator>Chang, Liao</creator><creator>Wang, Ting</creator><creator>Chen, Jianghao</creator><creator>Li, Yingrui</creator><creator>Zhang, Xiuqing</creator><creator>Yang, Huanming</creator><creator>Xu, Xun</creator><creator>Wang, Ling</creator><creator>Wang, Jun</creator><general>Oxford University Press</general><general>BioMed Central</general><scope>TOX</scope><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>3V.</scope><scope>7X7</scope><scope>7XB</scope><scope>88E</scope><scope>88I</scope><scope>8AL</scope><scope>8FE</scope><scope>8FG</scope><scope>8FH</scope><scope>8FI</scope><scope>8FJ</scope><scope>8FK</scope><scope>ABUWG</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>FYUFA</scope><scope>GHDGH</scope><scope>GNUQQ</scope><scope>HCIFZ</scope><scope>JQ2</scope><scope>K7-</scope><scope>K9.</scope><scope>LK8</scope><scope>M0N</scope><scope>M0S</scope><scope>M1P</scope><scope>M2P</scope><scope>M7P</scope><scope>P5Z</scope><scope>P62</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>2015</creationdate><title>Comparison of variations detection between whole-genome amplification methods used in single-cell resequencing</title><author>Hou, Yong ; Wu, Kui ; Shi, Xulian ; Li, Fuqiang ; Song, Luting ; Wu, Hanjie ; Dean, Michael ; Li, Guibo ; Tsang, Shirley ; Jiang, Runze ; Zhang, Xiaolong ; Li, Bo ; Liu, Geng ; Bedekar, Niharika ; Lu, Na ; Xie, Guoyun ; Liang, Han ; Chang, Liao ; Wang, Ting ; Chen, Jianghao ; Li, Yingrui ; Zhang, Xiuqing ; Yang, Huanming ; Xu, Xun ; Wang, Ling ; Wang, Jun</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c510t-adc2d7d93bd367d2406733f823d3157cb3f8c5b472db1e75be087f1e29a234173</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2015</creationdate><topic>Cancer</topic><topic>DNA Copy Number Variations - genetics</topic><topic>Gastric cancer</topic><topic>Gene sequencing</topic><topic>Genome</topic><topic>Genomes</topic><topic>Janus kinase 2</topic><topic>Nucleotides</topic><topic>Oligonucleotides</topic><topic>Sensitivity</topic><topic>Single-Cell Analysis</topic><topic>Variation</topic><topic>Whole genome sequencing</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Hou, Yong</creatorcontrib><creatorcontrib>Wu, Kui</creatorcontrib><creatorcontrib>Shi, Xulian</creatorcontrib><creatorcontrib>Li, Fuqiang</creatorcontrib><creatorcontrib>Song, Luting</creatorcontrib><creatorcontrib>Wu, Hanjie</creatorcontrib><creatorcontrib>Dean, Michael</creatorcontrib><creatorcontrib>Li, Guibo</creatorcontrib><creatorcontrib>Tsang, Shirley</creatorcontrib><creatorcontrib>Jiang, Runze</creatorcontrib><creatorcontrib>Zhang, Xiaolong</creatorcontrib><creatorcontrib>Li, Bo</creatorcontrib><creatorcontrib>Liu, Geng</creatorcontrib><creatorcontrib>Bedekar, Niharika</creatorcontrib><creatorcontrib>Lu, Na</creatorcontrib><creatorcontrib>Xie, Guoyun</creatorcontrib><creatorcontrib>Liang, Han</creatorcontrib><creatorcontrib>Chang, Liao</creatorcontrib><creatorcontrib>Wang, Ting</creatorcontrib><creatorcontrib>Chen, Jianghao</creatorcontrib><creatorcontrib>Li, Yingrui</creatorcontrib><creatorcontrib>Zhang, Xiuqing</creatorcontrib><creatorcontrib>Yang, Huanming</creatorcontrib><creatorcontrib>Xu, Xun</creatorcontrib><creatorcontrib>Wang, Ling</creatorcontrib><creatorcontrib>Wang, Jun</creatorcontrib><collection>Oxford Journals Open Access Collection</collection><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>ProQuest Central (Corporate)</collection><collection>Health & Medical Collection</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>Medical Database (Alumni Edition)</collection><collection>Science Database (Alumni Edition)</collection><collection>Computing Database (Alumni Edition)</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 Edition)</collection><collection>ProQuest Central UK/Ireland</collection><collection>Advanced Technologies & Aerospace Collection</collection><collection>ProQuest Central Essentials</collection><collection>Biological Science Collection</collection><collection>ProQuest Central</collection><collection>Technology Collection</collection><collection>Natural Science Collection</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>Health Research Premium Collection</collection><collection>Health Research Premium Collection (Alumni)</collection><collection>ProQuest Central Student</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Computer Science Collection</collection><collection>Computer Science Database</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>ProQuest Biological Science Collection</collection><collection>Computing Database</collection><collection>Health & Medical Collection (Alumni Edition)</collection><collection>Medical Database</collection><collection>Science Database</collection><collection>Biological Science Database</collection><collection>Advanced Technologies & Aerospace Database</collection><collection>ProQuest Advanced Technologies & Aerospace Collection</collection><collection>Publicly Available Content Database</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central China</collection><collection>ProQuest Central Basic</collection><collection>MEDLINE - Academic</collection><collection>PubMed Central (Full Participant titles)</collection><jtitle>Gigascience</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Hou, Yong</au><au>Wu, Kui</au><au>Shi, Xulian</au><au>Li, Fuqiang</au><au>Song, Luting</au><au>Wu, Hanjie</au><au>Dean, Michael</au><au>Li, Guibo</au><au>Tsang, Shirley</au><au>Jiang, Runze</au><au>Zhang, Xiaolong</au><au>Li, Bo</au><au>Liu, Geng</au><au>Bedekar, Niharika</au><au>Lu, Na</au><au>Xie, Guoyun</au><au>Liang, Han</au><au>Chang, Liao</au><au>Wang, Ting</au><au>Chen, Jianghao</au><au>Li, Yingrui</au><au>Zhang, Xiuqing</au><au>Yang, Huanming</au><au>Xu, Xun</au><au>Wang, Ling</au><au>Wang, Jun</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Comparison of variations detection between whole-genome amplification methods used in single-cell resequencing</atitle><jtitle>Gigascience</jtitle><addtitle>Gigascience</addtitle><date>2015</date><risdate>2015</risdate><volume>4</volume><issue>1</issue><spage>37</spage><epage>37</epage><pages>37-37</pages><issn>2047-217X</issn><eissn>2047-217X</eissn><abstract>Abstract
Background
Single-cell resequencing (SCRS) provides many biomedical advances in variations detection at the single-cell level, but it currently relies on whole genome amplification (WGA). Three methods are commonly used for WGA: multiple displacement amplification (MDA), degenerate-oligonucleotide-primed PCR (DOP-PCR) and multiple annealing and looping-based amplification cycles (MALBAC). However, a comprehensive comparison of variations detection performance between these WGA methods has not yet been performed.
Results
We systematically compared the advantages and disadvantages of different WGA methods, focusing particularly on variations detection. Low-coverage whole-genome sequencing revealed that DOP-PCR had the highest duplication ratio, but an even read distribution and the best reproducibility and accuracy for detection of copy-number variations (CNVs). However, MDA had significantly higher genome recovery sensitivity (~84 %) than DOP-PCR (~6 %) and MALBAC (~52 %) at high sequencing depth. MALBAC and MDA had comparable single-nucleotide variations detection efficiency, false-positive ratio, and allele drop-out ratio. We further demonstrated that SCRS data amplified by either MDA or MALBAC from a gastric cancer cell line could accurately detect gastric cancer CNVs with comparable sensitivity and specificity, including amplifications of 12p11.22 (KRAS) and 9p24.1 (JAK2, CD274, and PDCD1LG2).
Conclusions
Our findings provide a comprehensive comparison of variations detection performance using SCRS amplified by different WGA methods. It will guide researchers to determine which WGA method is best suited to individual experimental needs at single-cell level.</abstract><cop>United States</cop><pub>Oxford University Press</pub><pmid>26251698</pmid><doi>10.1186/s13742-015-0068-3</doi><tpages>1</tpages><oa>free_for_read</oa></addata></record> |
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subjects | Cancer DNA Copy Number Variations - genetics Gastric cancer Gene sequencing Genome Genomes Janus kinase 2 Nucleotides Oligonucleotides Sensitivity Single-Cell Analysis Variation Whole genome sequencing |
title | Comparison of variations detection between whole-genome amplification methods used in single-cell resequencing |
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