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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Veröffentlicht in:Gigascience 2015, Vol.4 (1), p.37-37
Hauptverfasser: 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
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container_end_page 37
container_issue 1
container_start_page 37
container_title Gigascience
container_volume 4
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.
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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 &amp; 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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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