Comparative study of whole exome sequencing-based copy number variation detection tools
Background With the rapid development of whole exome sequencing (WES), an increasing number of tools are being proposed for copy number variation (CNV) detection based on this technique. However, no comprehensive guide is available for the use of these tools in clinical settings, which renders them...
Gespeichert in:
| Veröffentlicht in: | BMC bioinformatics Jg. 21; H. 1; S. 97 - 10 |
|---|---|
| Hauptverfasser: | , , , , |
| Format: | Journal Article |
| Sprache: | Englisch |
| Veröffentlicht: |
London
BioMed Central
05.03.2020
BioMed Central Ltd Springer Nature B.V BMC |
| Schlagworte: | |
| ISSN: | 1471-2105, 1471-2105 |
| Online-Zugang: | Volltext |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| Abstract | Background
With the rapid development of whole exome sequencing (WES), an increasing number of tools are being proposed for copy number variation (CNV) detection based on this technique. However, no comprehensive guide is available for the use of these tools in clinical settings, which renders them inapplicable in practice. To resolve this problem, in this study, we evaluated the performances of four WES-based CNV tools, and established a guideline for the recommendation of a suitable tool according to the application requirements.
Results
In this study, first, we selected four WES-based CNV detection tools: CoNIFER, cn.MOPS, CNVkit and exomeCopy. Then, we evaluated their performances in terms of three aspects: sensitivity and specificity, overlapping consistency and computational costs. From this evaluation, we obtained four main results: (1) The sensitivity increases and subsequently stabilizes as the coverage or CNV size increases, while the specificity decreases. (2) CoNIFER performs better for CNV insertions than for CNV deletions, while the remaining tools exhibit the opposite trend. (3) CoNIFER, cn.MOPS and CNVkit realize satisfactory overlapping consistency, which indicates their results are trustworthy. (4) CoNIFER has the best space complexity and cn.MOPS has the best time complexity among these four tools. Finally, we established a guideline for tools’ usage according to these results.
Conclusion
No available tool performs excellently under all conditions; however, some tools perform excellently in some scenarios. Users can obtain a CNV tool recommendation from our paper according to the targeted CNV size, the CNV type or computational costs of their projects, as presented in Table 1, which is helpful even for users with limited knowledge of computer science. |
|---|---|
| AbstractList | Background With the rapid development of whole exome sequencing (WES), an increasing number of tools are being proposed for copy number variation (CNV) detection based on this technique. However, no comprehensive guide is available for the use of these tools in clinical settings, which renders them inapplicable in practice. To resolve this problem, in this study, we evaluated the performances of four WES-based CNV tools, and established a guideline for the recommendation of a suitable tool according to the application requirements. Results In this study, first, we selected four WES-based CNV detection tools: CoNIFER, cn.MOPS, CNVkit and exomeCopy. Then, we evaluated their performances in terms of three aspects: sensitivity and specificity, overlapping consistency and computational costs. From this evaluation, we obtained four main results: (1) The sensitivity increases and subsequently stabilizes as the coverage or CNV size increases, while the specificity decreases. (2) CoNIFER performs better for CNV insertions than for CNV deletions, while the remaining tools exhibit the opposite trend. (3) CoNIFER, cn.MOPS and CNVkit realize satisfactory overlapping consistency, which indicates their results are trustworthy. (4) CoNIFER has the best space complexity and cn.MOPS has the best time complexity among these four tools. Finally, we established a guideline for tools’ usage according to these results. Conclusion No available tool performs excellently under all conditions; however, some tools perform excellently in some scenarios. Users can obtain a CNV tool recommendation from our paper according to the targeted CNV size, the CNV type or computational costs of their projects, as presented in Table 1, which is helpful even for users with limited knowledge of computer science. With the rapid development of whole exome sequencing (WES), an increasing number of tools are being proposed for copy number variation (CNV) detection based on this technique. However, no comprehensive guide is available for the use of these tools in clinical settings, which renders them inapplicable in practice. To resolve this problem, in this study, we evaluated the performances of four WES-based CNV tools, and established a guideline for the recommendation of a suitable tool according to the application requirements.BACKGROUNDWith the rapid development of whole exome sequencing (WES), an increasing number of tools are being proposed for copy number variation (CNV) detection based on this technique. However, no comprehensive guide is available for the use of these tools in clinical settings, which renders them inapplicable in practice. To resolve this problem, in this study, we evaluated the performances of four WES-based CNV tools, and established a guideline for the recommendation of a suitable tool according to the application requirements.In this study, first, we selected four WES-based CNV detection tools: CoNIFER, cn.MOPS, CNVkit and exomeCopy. Then, we evaluated their performances in terms of three aspects: sensitivity and specificity, overlapping consistency and computational costs. From this evaluation, we obtained four main results: (1) The sensitivity increases and subsequently stabilizes as the coverage or CNV size increases, while the specificity decreases. (2) CoNIFER performs better for CNV insertions than for CNV deletions, while the remaining tools exhibit the opposite trend. (3) CoNIFER, cn.MOPS and CNVkit realize satisfactory overlapping consistency, which indicates their results are trustworthy. (4) CoNIFER has the best space complexity and cn.MOPS has the best time complexity among these four tools. Finally, we established a guideline for tools' usage according to these results.RESULTSIn this study, first, we selected four WES-based CNV detection tools: CoNIFER, cn.MOPS, CNVkit and exomeCopy. Then, we evaluated their performances in terms of three aspects: sensitivity and specificity, overlapping consistency and computational costs. From this evaluation, we obtained four main results: (1) The sensitivity increases and subsequently stabilizes as the coverage or CNV size increases, while the specificity decreases. (2) CoNIFER performs better for CNV insertions than for CNV deletions, while the remaining tools exhibit the opposite trend. (3) CoNIFER, cn.MOPS and CNVkit realize satisfactory overlapping consistency, which indicates their results are trustworthy. (4) CoNIFER has the best space complexity and cn.MOPS has the best time complexity among these four tools. Finally, we established a guideline for tools' usage according to these results.No available tool performs excellently under all conditions; however, some tools perform excellently in some scenarios. Users can obtain a CNV tool recommendation from our paper according to the targeted CNV size, the CNV type or computational costs of their projects, as presented in Table 1, which is helpful even for users with limited knowledge of computer science.CONCLUSIONNo available tool performs excellently under all conditions; however, some tools perform excellently in some scenarios. Users can obtain a CNV tool recommendation from our paper according to the targeted CNV size, the CNV type or computational costs of their projects, as presented in Table 1, which is helpful even for users with limited knowledge of computer science. With the rapid development of whole exome sequencing (WES), an increasing number of tools are being proposed for copy number variation (CNV) detection based on this technique. However, no comprehensive guide is available for the use of these tools in clinical settings, which renders them inapplicable in practice. To resolve this problem, in this study, we evaluated the performances of four WES-based CNV tools, and established a guideline for the recommendation of a suitable tool according to the application requirements. In this study, first, we selected four WES-based CNV detection tools: CoNIFER, cn.MOPS, CNVkit and exomeCopy. Then, we evaluated their performances in terms of three aspects: sensitivity and specificity, overlapping consistency and computational costs. From this evaluation, we obtained four main results: (1) The sensitivity increases and subsequently stabilizes as the coverage or CNV size increases, while the specificity decreases. (2) CoNIFER performs better for CNV insertions than for CNV deletions, while the remaining tools exhibit the opposite trend. (3) CoNIFER, cn.MOPS and CNVkit realize satisfactory overlapping consistency, which indicates their results are trustworthy. (4) CoNIFER has the best space complexity and cn.MOPS has the best time complexity among these four tools. Finally, we established a guideline for tools' usage according to these results. No available tool performs excellently under all conditions; however, some tools perform excellently in some scenarios. Users can obtain a CNV tool recommendation from our paper according to the targeted CNV size, the CNV type or computational costs of their projects, as presented in Table 1, which is helpful even for users with limited knowledge of computer science. Background With the rapid development of whole exome sequencing (WES), an increasing number of tools are being proposed for copy number variation (CNV) detection based on this technique. However, no comprehensive guide is available for the use of these tools in clinical settings, which renders them inapplicable in practice. To resolve this problem, in this study, we evaluated the performances of four WES-based CNV tools, and established a guideline for the recommendation of a suitable tool according to the application requirements. Results In this study, first, we selected four WES-based CNV detection tools: CoNIFER, cn.MOPS, CNVkit and exomeCopy. Then, we evaluated their performances in terms of three aspects: sensitivity and specificity, overlapping consistency and computational costs. From this evaluation, we obtained four main results: (1) The sensitivity increases and subsequently stabilizes as the coverage or CNV size increases, while the specificity decreases. (2) CoNIFER performs better for CNV insertions than for CNV deletions, while the remaining tools exhibit the opposite trend. (3) CoNIFER, cn.MOPS and CNVkit realize satisfactory overlapping consistency, which indicates their results are trustworthy. (4) CoNIFER has the best space complexity and cn.MOPS has the best time complexity among these four tools. Finally, we established a guideline for tools’ usage according to these results. Conclusion No available tool performs excellently under all conditions; however, some tools perform excellently in some scenarios. Users can obtain a CNV tool recommendation from our paper according to the targeted CNV size, the CNV type or computational costs of their projects, as presented in Table 1, which is helpful even for users with limited knowledge of computer science. With the rapid development of whole exome sequencing (WES), an increasing number of tools are being proposed for copy number variation (CNV) detection based on this technique. However, no comprehensive guide is available for the use of these tools in clinical settings, which renders them inapplicable in practice. To resolve this problem, in this study, we evaluated the performances of four WES-based CNV tools, and established a guideline for the recommendation of a suitable tool according to the application requirements. In this study, first, we selected four WES-based CNV detection tools: CoNIFER, cn.MOPS, CNVkit and exomeCopy. Then, we evaluated their performances in terms of three aspects: sensitivity and specificity, overlapping consistency and computational costs. From this evaluation, we obtained four main results: (1) The sensitivity increases and subsequently stabilizes as the coverage or CNV size increases, while the specificity decreases. (2) CoNIFER performs better for CNV insertions than for CNV deletions, while the remaining tools exhibit the opposite trend. (3) CoNIFER, cn.MOPS and CNVkit realize satisfactory overlapping consistency, which indicates their results are trustworthy. (4) CoNIFER has the best space complexity and cn.MOPS has the best time complexity among these four tools. Finally, we established a guideline for tools' usage according to these results. No available tool performs excellently under all conditions; however, some tools perform excellently in some scenarios. Users can obtain a CNV tool recommendation from our paper according to the targeted CNV size, the CNV type or computational costs of their projects, as presented in Table 1, which is helpful even for users with limited knowledge of computer science. Abstract Background With the rapid development of whole exome sequencing (WES), an increasing number of tools are being proposed for copy number variation (CNV) detection based on this technique. However, no comprehensive guide is available for the use of these tools in clinical settings, which renders them inapplicable in practice. To resolve this problem, in this study, we evaluated the performances of four WES-based CNV tools, and established a guideline for the recommendation of a suitable tool according to the application requirements. Results In this study, first, we selected four WES-based CNV detection tools: CoNIFER, cn.MOPS, CNVkit and exomeCopy. Then, we evaluated their performances in terms of three aspects: sensitivity and specificity, overlapping consistency and computational costs. From this evaluation, we obtained four main results: (1) The sensitivity increases and subsequently stabilizes as the coverage or CNV size increases, while the specificity decreases. (2) CoNIFER performs better for CNV insertions than for CNV deletions, while the remaining tools exhibit the opposite trend. (3) CoNIFER, cn.MOPS and CNVkit realize satisfactory overlapping consistency, which indicates their results are trustworthy. (4) CoNIFER has the best space complexity and cn.MOPS has the best time complexity among these four tools. Finally, we established a guideline for tools’ usage according to these results. Conclusion No available tool performs excellently under all conditions; however, some tools perform excellently in some scenarios. Users can obtain a CNV tool recommendation from our paper according to the targeted CNV size, the CNV type or computational costs of their projects, as presented in Table 1, which is helpful even for users with limited knowledge of computer science. Background With the rapid development of whole exome sequencing (WES), an increasing number of tools are being proposed for copy number variation (CNV) detection based on this technique. However, no comprehensive guide is available for the use of these tools in clinical settings, which renders them inapplicable in practice. To resolve this problem, in this study, we evaluated the performances of four WES-based CNV tools, and established a guideline for the recommendation of a suitable tool according to the application requirements. Results In this study, first, we selected four WES-based CNV detection tools: CoNIFER, cn.MOPS, CNVkit and exomeCopy. Then, we evaluated their performances in terms of three aspects: sensitivity and specificity, overlapping consistency and computational costs. From this evaluation, we obtained four main results: (1) The sensitivity increases and subsequently stabilizes as the coverage or CNV size increases, while the specificity decreases. (2) CoNIFER performs better for CNV insertions than for CNV deletions, while the remaining tools exhibit the opposite trend. (3) CoNIFER, cn.MOPS and CNVkit realize satisfactory overlapping consistency, which indicates their results are trustworthy. (4) CoNIFER has the best space complexity and cn.MOPS has the best time complexity among these four tools. Finally, we established a guideline for tools' usage according to these results. Conclusion No available tool performs excellently under all conditions; however, some tools perform excellently in some scenarios. Users can obtain a CNV tool recommendation from our paper according to the targeted CNV size, the CNV type or computational costs of their projects, as presented in Table 1, which is helpful even for users with limited knowledge of computer science. Keywords: Copy number variants, Next generation sequencing, Whole exome sequencing, Sensitivity, Specificity, Overlapping consistency, Computational costs, Recommendation, Guideline |
| ArticleNumber | 97 |
| Audience | Academic |
| Author | Duan, Junbo Liu, Han Zhao, Lanling Yuan, Xiguo Gao, Kun |
| Author_xml | – sequence: 1 givenname: Lanling surname: Zhao fullname: Zhao, Lanling organization: Department of Biomedical Engineering, School of Life Science and Technology, Xi’an Jiaotong University – sequence: 2 givenname: Han surname: Liu fullname: Liu, Han organization: Department of Biomedical Engineering, School of Life Science and Technology, Xi’an Jiaotong University – sequence: 3 givenname: Xiguo surname: Yuan fullname: Yuan, Xiguo organization: School of Computer Science and Technology, Xidian University – sequence: 4 givenname: Kun surname: Gao fullname: Gao, Kun organization: Department of Biomedical Engineering, School of Life Science and Technology, Xi’an Jiaotong University – sequence: 5 givenname: Junbo orcidid: 0000-0001-7170-3772 surname: Duan fullname: Duan, Junbo email: junbo.duan@mail.xjtu.edu.cn organization: Department of Biomedical Engineering, School of Life Science and Technology, Xi’an Jiaotong University |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/32138645$$D View this record in MEDLINE/PubMed |
| BookMark | eNp9kktv1DAUhSNURB_wA9igSGzKIsXPONkgVSMeI1VC4iGWlmPfpB4l9mAnQ-ff45lpaVMBysLW9XeOk5Nzmh057yDLXmJ0gXFVvo2YVLwuEEEFZQQX-El2gpnABcGIHz3YH2enMa4QwqJC_Fl2TAmmVcn4SfZj4Ye1Cmq0G8jjOJlt7tv817XvIYcbP6Qh_JzAaeu6olERTK79epu7aWgg5BsVbNJ6lxsYQe93o_d9fJ49bVUf4cXtepZ9__D-2-JTcfX543JxeVVogehYUFEZJDghqmwYR0hTo2uqTaOZaTmginBS17g2bVmVBDTHipdAMSCG2lZQepYtD77Gq5VcBzuosJVeWbkf-NBJFUare5AMs4opQBQUZcDKGgvUKFUp02KmFCSvdwev9dQMYDS4Mah-Zjo_cfZadn4jBeJ1WdXJ4PzWIPgUWhzlYKOGvlcO_BQloYJRxkqGEvr6EbryU3Apqh2V_g0mNb2nOpU-wLrWp3v1zlRelljgSoiSJ-riL1R6DAxWp8q0Ns1ngjczQWJGuBk7NcUol1-_zNlXD0P5k8ZdhRIgDoAOPsYArdR23HcivYXtJUZyV1Z5KKtMZZW7skqclPiR8s78fxpy0MTEug7CfW7_Fv0GCSj5gA |
| CitedBy_id | crossref_primary_10_3390_genes13122364 crossref_primary_10_3390_v15112227 crossref_primary_10_1093_bib_bbaf135 crossref_primary_10_1177_11779322221115534 crossref_primary_10_3389_fgene_2023_1277784 crossref_primary_10_1134_S1990519X2560036X crossref_primary_10_1007_s00467_023_06134_2 crossref_primary_10_1016_j_jmoldx_2025_01_008 crossref_primary_10_3390_ijms25052548 crossref_primary_10_1007_s11517_022_02707_9 crossref_primary_10_3390_cancers14184466 crossref_primary_10_1007_s12098_022_04325_7 crossref_primary_10_1016_j_ajhg_2021_05_012 crossref_primary_10_1093_bib_bbad508 crossref_primary_10_1186_s12859_022_04617_x crossref_primary_10_1007_s00439_021_02365_1 crossref_primary_10_1111_cge_14236 crossref_primary_10_1007_s10048_023_00717_9 crossref_primary_10_3390_genes13081431 crossref_primary_10_1038_s41598_025_06527_3 crossref_primary_10_3389_fgene_2021_762987 crossref_primary_10_1016_j_cca_2022_08_008 crossref_primary_10_1186_s13059_024_03294_8 crossref_primary_10_3390_info14020128 crossref_primary_10_1002_ccr3_5335 crossref_primary_10_1002_humu_24129 crossref_primary_10_3389_fgene_2024_1447216 crossref_primary_10_1159_000530252 crossref_primary_10_1038_s41598_024_70831_7 crossref_primary_10_1186_s12864_025_11442_y crossref_primary_10_3390_biology10070584 crossref_primary_10_1093_bib_bbac375 crossref_primary_10_1093_nargab_lqae033 crossref_primary_10_3389_fgene_2024_1341272 crossref_primary_10_1186_s12864_021_07907_5 crossref_primary_10_3390_brainsci14030273 crossref_primary_10_1055_s_0043_1778070 crossref_primary_10_1093_gpbjnl_qzaf017 crossref_primary_10_1038_s41525_024_00436_6 crossref_primary_10_1159_000531507 crossref_primary_10_1007_s00438_024_02158_x crossref_primary_10_3389_fgene_2022_942491 |
| Cites_doi | 10.1186/1471-2105-15-109 10.1038/ng.499 10.1371/journal.pone.0059128 10.1126/science.1136678 10.2202/1544-6115.1732 10.1002/dneu.22626 10.1101/gr.138115.112 10.1186/1471-2164-15-661 10.1186/1471-2105-14-150 10.1101/gr.3677206 10.1146/annurev.genom.8.021307.110233 10.1038/nrg2593 10.1371/journal.pcbi.1004873 10.1093/bioinformatics/btr462 10.1101/gr.092981.109 10.1093/nar/gks003 10.1002/humu.22537 10.1038/ng2080 10.1109/TCBB.2018.2883333 10.1186/s13039-017-0333-5 10.1155/2013/435321 10.1186/1745-6215-15-85 |
| ContentType | Journal Article |
| Copyright | The Author(s). 2020 COPYRIGHT 2020 BioMed Central Ltd. 2020. This work is licensed under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. |
| Copyright_xml | – notice: The Author(s). 2020 – notice: COPYRIGHT 2020 BioMed Central Ltd. – notice: 2020. This work is licensed under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. |
| DBID | C6C AAYXX CITATION CGR CUY CVF ECM EIF NPM ISR 3V. 7QO 7SC 7X7 7XB 88E 8AL 8AO 8FD 8FE 8FG 8FH 8FI 8FJ 8FK ABUWG AEUYN AFKRA ARAPS AZQEC BBNVY BENPR BGLVJ BHPHI CCPQU DWQXO FR3 FYUFA GHDGH GNUQQ HCIFZ JQ2 K7- K9. L7M LK8 L~C L~D M0N M0S M1P M7P P5Z P62 P64 PHGZM PHGZT PIMPY PJZUB PKEHL PPXIY PQEST PQGLB PQQKQ PQUKI Q9U 7X8 5PM DOA |
| DOI | 10.1186/s12859-020-3421-1 |
| DatabaseName | Springer Nature OA Free Journals CrossRef Medline MEDLINE MEDLINE (Ovid) MEDLINE MEDLINE PubMed Gale In Context: Science ProQuest Central (Corporate) Biotechnology Research Abstracts Computer and Information Systems Abstracts Health & Medical Collection ProQuest Central (purchase pre-March 2016) Medical Database (Alumni Edition) Computing Database (Alumni Edition) ProQuest Pharma Collection Technology Research Database ProQuest SciTech Collection ProQuest Technology Collection ProQuest Natural Science Collection ProQuest Hospital Collection Hospital Premium Collection (Alumni Edition) ProQuest Central (Alumni) (purchase pre-March 2016) ProQuest Central (Alumni) ProQuest One Sustainability ProQuest Central UK/Ireland Advanced Technologies & Computer Science Collection ProQuest Central Essentials - QC Biological Science Collection ProQuest Central ProQuest Technology Collection Natural Science Collection ProQuest One Community College ProQuest Central Korea Engineering Research Database Health Research Premium Collection Health Research Premium Collection (Alumni) ProQuest Central Student SciTech Collection (ProQuest) ProQuest Computer Science Collection Computer Science Database (ProQuest) ProQuest Health & Medical Complete (Alumni) Advanced Technologies Database with Aerospace ProQuest Biological Science Collection Computer and Information Systems Abstracts Academic Computer and Information Systems Abstracts Professional Computing Database Health & Medical Collection (Alumni) Medical Database Biological Science Database Advanced Technologies & Aerospace Database ProQuest Advanced Technologies & Aerospace Collection Biotechnology and BioEngineering Abstracts Proquest Central Premium ProQuest One Academic (New) ProQuest Publicly Available Content Database ProQuest Health & Medical Research Collection ProQuest One Academic Middle East (New) ProQuest One Health & Nursing ProQuest One Academic Eastern Edition (DO NOT USE) ProQuest One Applied & Life Sciences ProQuest One Academic (retired) ProQuest One Academic UKI Edition ProQuest Central Basic MEDLINE - Academic PubMed Central (Full Participant titles) DOAJ Directory of Open Access Journals |
| DatabaseTitle | CrossRef MEDLINE Medline Complete MEDLINE with Full Text PubMed MEDLINE (Ovid) Publicly Available Content Database Computer Science Database ProQuest Central Student ProQuest Advanced Technologies & Aerospace Collection ProQuest Central Essentials ProQuest Computer Science Collection Computer and Information Systems Abstracts SciTech Premium Collection ProQuest One Applied & Life Sciences ProQuest One Sustainability Health Research Premium Collection Natural Science Collection Health & Medical Research Collection Biological Science Collection ProQuest Central (New) ProQuest Medical Library (Alumni) Advanced Technologies & Aerospace Collection ProQuest Biological Science Collection ProQuest One Academic Eastern Edition ProQuest Hospital Collection ProQuest Technology Collection Health Research Premium Collection (Alumni) Biological Science Database ProQuest Hospital Collection (Alumni) Biotechnology and BioEngineering Abstracts ProQuest Health & Medical Complete ProQuest One Academic UKI Edition Engineering Research Database ProQuest One Academic ProQuest One Academic (New) Technology Collection Technology Research Database Computer and Information Systems Abstracts – Academic ProQuest One Academic Middle East (New) ProQuest Health & Medical Complete (Alumni) ProQuest Central (Alumni Edition) ProQuest One Community College ProQuest One Health & Nursing ProQuest Natural Science Collection ProQuest Pharma Collection ProQuest Central ProQuest Health & Medical Research Collection Biotechnology Research Abstracts Health and Medicine Complete (Alumni Edition) ProQuest Central Korea Advanced Technologies Database with Aerospace ProQuest Computing ProQuest Central Basic ProQuest Computing (Alumni Edition) ProQuest SciTech Collection Computer and Information Systems Abstracts Professional Advanced Technologies & Aerospace Database ProQuest Medical Library ProQuest Central (Alumni) MEDLINE - Academic |
| DatabaseTitleList | Publicly Available Content Database MEDLINE - Academic MEDLINE |
| Database_xml | – sequence: 1 dbid: DOA name: DOAJ Directory of Open Access Journals url: https://www.doaj.org/ sourceTypes: Open Website – sequence: 2 dbid: NPM name: PubMed url: http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed sourceTypes: Index Database – sequence: 3 dbid: PIMPY name: ProQuest Publicly Available Content Database url: http://search.proquest.com/publiccontent sourceTypes: Aggregation Database |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Biology Computer Science |
| EISSN | 1471-2105 |
| EndPage | 10 |
| ExternalDocumentID | oai_doaj_org_article_41484ae03ea34e469170baa8adf14aae PMC7059689 A617187765 32138645 10_1186_s12859_020_3421_1 |
| Genre | Journal Article |
| GeographicLocations | China |
| GeographicLocations_xml | – name: China |
| GrantInformation_xml | – fundername: National Science Foundation of China grantid: 61771381; 61571341 – fundername: National Science Foundation of China grantid: 61571341 – fundername: National Science Foundation of China grantid: 61771381 – fundername: ; grantid: 61771381; 61571341 |
| GroupedDBID | --- 0R~ 23N 2WC 53G 5VS 6J9 7X7 88E 8AO 8FE 8FG 8FH 8FI 8FJ AAFWJ AAJSJ AAKPC AASML ABDBF ABUWG ACGFO ACGFS ACIHN ACIWK ACPRK ACUHS ADBBV ADMLS ADUKV AEAQA AENEX AEUYN AFKRA AFPKN AFRAH AHBYD AHMBA AHYZX ALMA_UNASSIGNED_HOLDINGS AMKLP AMTXH AOIJS ARAPS AZQEC BAPOH BAWUL BBNVY BCNDV BENPR BFQNJ BGLVJ BHPHI BMC BPHCQ BVXVI C6C CCPQU CS3 DIK DU5 DWQXO E3Z EAD EAP EAS EBD EBLON EBS EMB EMK EMOBN ESX F5P FYUFA GNUQQ GROUPED_DOAJ GX1 HCIFZ HMCUK HYE IAO ICD IHR INH INR ISR ITC K6V K7- KQ8 LK8 M1P M48 M7P MK~ ML0 M~E O5R O5S OK1 OVT P2P P62 PGMZT PHGZM PHGZT PIMPY PJZUB PPXIY PQGLB PQQKQ PROAC PSQYO PUEGO RBZ RNS ROL RPM RSV SBL SOJ SV3 TR2 TUS UKHRP W2D WOQ WOW XH6 XSB AAYXX AFFHD CITATION -A0 3V. ACRMQ ADINQ ALIPV C24 CGR CUY CVF ECM EIF M0N NPM 7QO 7SC 7XB 8AL 8FD 8FK FR3 JQ2 K9. L7M L~C L~D P64 PKEHL PQEST PQUKI Q9U 7X8 5PM |
| ID | FETCH-LOGICAL-c703t-378d07522a6b4500c3dc93cdbc4df5e082529919df6862ec51a56e31e040ff733 |
| IEDL.DBID | RSV |
| ISICitedReferencesCount | 46 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000519043700001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| ISSN | 1471-2105 |
| IngestDate | Fri Oct 03 12:52:38 EDT 2025 Tue Nov 04 01:51:55 EST 2025 Thu Sep 04 16:50:41 EDT 2025 Mon Oct 06 18:38:33 EDT 2025 Tue Nov 11 07:39:17 EST 2025 Tue Nov 04 17:59:36 EST 2025 Thu Nov 13 15:18:48 EST 2025 Wed Feb 19 02:30:17 EST 2025 Tue Nov 18 22:15:21 EST 2025 Sat Nov 29 05:40:06 EST 2025 Sat Sep 06 07:27:24 EDT 2025 |
| IsDoiOpenAccess | true |
| IsOpenAccess | true |
| IsPeerReviewed | true |
| IsScholarly | true |
| Issue | 1 |
| Keywords | Sensitivity Specificity Computational costs Whole exome sequencing Overlapping consistency Guideline Next generation sequencing Recommendation Copy number variants |
| Language | English |
| License | Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-c703t-378d07522a6b4500c3dc93cdbc4df5e082529919df6862ec51a56e31e040ff733 |
| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23 |
| ORCID | 0000-0001-7170-3772 |
| OpenAccessLink | https://link.springer.com/10.1186/s12859-020-3421-1 |
| PMID | 32138645 |
| PQID | 2378641293 |
| PQPubID | 44065 |
| PageCount | 10 |
| ParticipantIDs | doaj_primary_oai_doaj_org_article_41484ae03ea34e469170baa8adf14aae pubmedcentral_primary_oai_pubmedcentral_nih_gov_7059689 proquest_miscellaneous_2374344640 proquest_journals_2378641293 gale_infotracmisc_A617187765 gale_infotracacademiconefile_A617187765 gale_incontextgauss_ISR_A617187765 pubmed_primary_32138645 crossref_citationtrail_10_1186_s12859_020_3421_1 crossref_primary_10_1186_s12859_020_3421_1 springer_journals_10_1186_s12859_020_3421_1 |
| PublicationCentury | 2000 |
| PublicationDate | 2020-03-05 |
| PublicationDateYYYYMMDD | 2020-03-05 |
| PublicationDate_xml | – month: 03 year: 2020 text: 2020-03-05 day: 05 |
| PublicationDecade | 2020 |
| PublicationPlace | London |
| PublicationPlace_xml | – name: London – name: England |
| PublicationTitle | BMC bioinformatics |
| PublicationTitleAbbrev | BMC Bioinformatics |
| PublicationTitleAlternate | BMC Bioinformatics |
| PublicationYear | 2020 |
| Publisher | BioMed Central BioMed Central Ltd Springer Nature B.V BMC |
| Publisher_xml | – name: BioMed Central – name: BioMed Central Ltd – name: Springer Nature B.V – name: BMC |
| References | SB Ng (3421_CR9) 2009; 42 S Yoon (3421_CR11) 2009; 19 G Klambauer (3421_CR20) 2012; 40 PS Samarakoon (3421_CR14) 2014; 15 Y Guo (3421_CR16) 2013; 2013 H Wang (3421_CR15) 2014; 15 R Yao (3421_CR25) 2017; 10 JF Sathirapongsasuti (3421_CR13) 2011; 27 BE Stranger (3421_CR2) 2007; 315 E. Chatzimichail (3421_CR21) 2013; 2013 3421_CR4 R Tan (3421_CR24) 2014; 35 L Kadalayil (3421_CR12) 2014; 16 JL Vassy (3421_CR8) 2014; 15 S Rohrback (3421_CR10) 2018; 78 SA Mccarroll (3421_CR1) 2007; 39 D Junbo (3421_CR23) 2013; 8 MI Love (3421_CR18) 2011; 10 PJ Hastings (3421_CR3) 2009; 10 J Duan (3421_CR5) 2013; 14 N Krumm (3421_CR17) 2012; 22 T Eric (3421_CR19) 2016; 12 JL Freeman (3421_CR6) 2006; 16 B Conrad (3421_CR7) 2007; 8 N Watts (3421_CR22) 2014; 100 |
| References_xml | – volume: 15 start-page: 109 issue: 1 year: 2014 ident: 3421_CR15 publication-title: BMC Bioinformatics doi: 10.1186/1471-2105-15-109 – volume: 42 start-page: 30 issue: 1 year: 2009 ident: 3421_CR9 publication-title: Nat Genet doi: 10.1038/ng.499 – volume: 8 start-page: e59128 issue: 3 year: 2013 ident: 3421_CR23 publication-title: PLoS One doi: 10.1371/journal.pone.0059128 – volume: 315 start-page: 848 issue: 5813 year: 2007 ident: 3421_CR2 publication-title: Science doi: 10.1126/science.1136678 – volume: 10 start-page: 52 issue: 1 year: 2011 ident: 3421_CR18 publication-title: Stat Appl Genet Mol Biol doi: 10.2202/1544-6115.1732 – volume: 78 start-page: 1026 issue: 11 year: 2018 ident: 3421_CR10 publication-title: Dev Neurobiol doi: 10.1002/dneu.22626 – volume: 22 start-page: 1525 issue: 8 year: 2012 ident: 3421_CR17 publication-title: Genome Res doi: 10.1101/gr.138115.112 – volume: 15 start-page: 661 issue: 1 year: 2014 ident: 3421_CR14 publication-title: BMC Genomics doi: 10.1186/1471-2164-15-661 – volume: 14 start-page: 1 issue: 1 year: 2013 ident: 3421_CR5 publication-title: BMC Bioinformatics doi: 10.1186/1471-2105-14-150 – volume: 16 start-page: 949 issue: 8 year: 2006 ident: 3421_CR6 publication-title: Genome Res doi: 10.1101/gr.3677206 – volume: 100 start-page: 7 year: 2014 ident: 3421_CR22 publication-title: Int J Comput Appl – volume: 8 start-page: 17 issue: 1 year: 2007 ident: 3421_CR7 publication-title: Annu Rev Genomics Hum Genet doi: 10.1146/annurev.genom.8.021307.110233 – volume: 10 start-page: 551 issue: 8 year: 2009 ident: 3421_CR3 publication-title: Nat Rev Genet doi: 10.1038/nrg2593 – volume: 2013 start-page: 417 issue: 4 year: 2013 ident: 3421_CR16 publication-title: Biomed Res Int – volume: 12 start-page: e1004873 issue: 4 year: 2016 ident: 3421_CR19 publication-title: PLoS Comput Biol doi: 10.1371/journal.pcbi.1004873 – volume: 27 start-page: 2648 issue: 19 year: 2011 ident: 3421_CR13 publication-title: Bioinformatics. doi: 10.1093/bioinformatics/btr462 – volume: 19 start-page: 1586 issue: 9 year: 2009 ident: 3421_CR11 publication-title: Genome Res doi: 10.1101/gr.092981.109 – volume: 40 start-page: e69 issue: 9 year: 2012 ident: 3421_CR20 publication-title: Nucleic Acids Res doi: 10.1093/nar/gks003 – volume: 35 start-page: 899 issue: 7 year: 2014 ident: 3421_CR24 publication-title: Hum Mutat doi: 10.1002/humu.22537 – volume: 16 start-page: 883 issue: 3 year: 2014 ident: 3421_CR12 publication-title: Brief Bioinform – volume: 39 start-page: S37 year: 2007 ident: 3421_CR1 publication-title: Nat Genet doi: 10.1038/ng2080 – ident: 3421_CR4 doi: 10.1109/TCBB.2018.2883333 – volume: 10 start-page: 30 issue: 1 year: 2017 ident: 3421_CR25 publication-title: Mol Cytogenet doi: 10.1186/s13039-017-0333-5 – volume: 2013 start-page: 1 year: 2013 ident: 3421_CR21 publication-title: Advances in Artificial Intelligence doi: 10.1155/2013/435321 – volume: 15 start-page: 85 issue: 1 year: 2014 ident: 3421_CR8 publication-title: Trials doi: 10.1186/1745-6215-15-85 |
| SSID | ssj0017805 |
| Score | 2.5047913 |
| Snippet | Background
With the rapid development of whole exome sequencing (WES), an increasing number of tools are being proposed for copy number variation (CNV)... With the rapid development of whole exome sequencing (WES), an increasing number of tools are being proposed for copy number variation (CNV) detection based on... Background With the rapid development of whole exome sequencing (WES), an increasing number of tools are being proposed for copy number variation (CNV)... Abstract Background With the rapid development of whole exome sequencing (WES), an increasing number of tools are being proposed for copy number variation... |
| SourceID | doaj pubmedcentral proquest gale pubmed crossref springer |
| SourceType | Open Website Open Access Repository Aggregation Database Index Database Enrichment Source Publisher |
| StartPage | 97 |
| SubjectTerms | Accuracy Algorithms Bioinformatics Biomedical and Life Sciences Comparative literature Comparative studies Computational Biology/Bioinformatics Computer Appl. in Life Sciences Computer science Copy number Copy number variants DNA Copy Number Variations DNA sequencing Exome - genetics Genes Genetic variation Genomes Humans Life Sciences Medical research Methods Microarrays Next generation sequencing Observations Overlapping consistency Research Article Researchers Sensitivity Sequences Setting (Literature) Software - economics Specificity Structural analysis Studies Time Whole exome sequencing Whole Exome Sequencing - methods |
| SummonAdditionalLinks | – databaseName: DOAJ Directory of Open Access Journals dbid: DOA link: http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwrV1Jb9UwELZQBRIXVPZ0QQYhIYGixkti59hWVHCpEIvozfIWqFSSqslr6b9nxsl7bYqAC1d7bCWzeGbk8TeEvGTaWRU5Vk_pEhIUoXKndJNHB6M8BF8XLjWbUIeH-uio_nCt1RfWhI3wwCPjdiTE69LGQkQrZIRkjqnCWattaJi0NuLpW6h6mUxN9weI1D_dYTJd7fQMcdpyTJWE5CxnMy-UwPp_P5Kv-aSb9ZI3Lk2TLzpYJ_emIJLujh9_n9yK7QNyZ2wrefmQfN2_gvSmCT-Wdg29wE64NP7sfsDgWEANm-foxgL13eklHbuD0HNIn5O8aIhDqtRq6dB1J_0j8uXg7ef9d_nUQCH3YMgDHB46QEjAua2cLIvCC-C98MF5GZoyYnYI3ojVocF3ItGXzJZVFCyCZTeNEuIxWWu7Nj4lVDkZ61JHhAOT1nFX2gDbae85Qu6zjBRLhho_oYtjk4sTk7IMXZlRBgZkYFAGBpa8Xi05HaE1_ka8h1JaESIqdhoAXTGTrph_6UpGXqCMDeJetFhY880u-t68__TR7EIkx7RSVZmRVxNR08EfeDu9UwA-IFTWjHJrRgmG6efTS1Uy08HQGw4yqSQGWRl5vprGlVjs1sZukWikgDRdFhl5Mmre6r8FZwI2gM3VTCdnjJnPtMffE2y4wk5Lus7Im6X2Xn3WH_m-8T_4vknucrQ9LN0rt8jacLaI2-S2Px-O-7NnyXJ_AWHvRcM priority: 102 providerName: Directory of Open Access Journals – databaseName: Biological Science Database dbid: M7P link: http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV1Lb9QwELaggNQL70egIIOQkEBR40di7wmVigouVcVD9GY5tlMqlWS72S303zPjeHdJEb1wtceW7RnPeOzxN4S8ZLq2KnCMntIlOChC5bXSTR5qKOXeu0lRx2QTan9fHx5ODtKFW5_CKpc6MSpq3zm8I9_mQulKonV6Oz3NMWsUvq6mFBpXyTVESeAxdO9g9YqAeP3pJZPpartniNaWo8MkJGc5G9miCNn_t2L-wzJdjJq88HQaLdLerf-dy21yM51F6c4gPHfIldDeJTeG7JTn98i33TUyOI0wtLRr6E9MqEvDr-4HFA5x2DC6HK2hp66bntMhyQg9Ay88sp36MI8BXy2dd91Jf5983Xv_ZfdDnvIw5A70wRx0kPZwsuDcVrUsi8IJYKFwvnbSN2VAJxOMGpv4Br-bBFcyW1ZBsAAKommUEA_IRtu14RGhqpZhUuqAqGLS1rwurYfutHMckftZRoolR4xLIOWYK-PERGdFV2ZgogEmGmSigSavV02mA0LHZcTvkM0rQgTXjgXd7MikvWokuIjShkIEK2SQFXi0RW2ttr5h0tqQkRcoJAbhM1qMzzmyi743Hz9_MjtwIGRaqarMyKtE1HQwA2fTdwdYB0TcGlFujShhf7tx9VKITNIvvVlLUEaer6qxJcbMtaFbRBopwNuXRUYeDqK7mrfgTEAH0LkaCfVoYcY17fH3iD6uMGGTnmTkzVL818P657o_vnwST8gmx22JsX3lFtmYzxbhKbnuzubH_exZ3NS_ARJDUyw priority: 102 providerName: ProQuest |
| Title | Comparative study of whole exome sequencing-based copy number variation detection tools |
| URI | https://link.springer.com/article/10.1186/s12859-020-3421-1 https://www.ncbi.nlm.nih.gov/pubmed/32138645 https://www.proquest.com/docview/2378641293 https://www.proquest.com/docview/2374344640 https://pubmed.ncbi.nlm.nih.gov/PMC7059689 https://doaj.org/article/41484ae03ea34e469170baa8adf14aae |
| Volume | 21 |
| WOSCitedRecordID | wos000519043700001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| journalDatabaseRights | – providerCode: PRVADU databaseName: BioMedCentral customDbUrl: eissn: 1471-2105 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0017805 issn: 1471-2105 databaseCode: RBZ dateStart: 20000101 isFulltext: true titleUrlDefault: https://www.biomedcentral.com/search/ providerName: BioMedCentral – providerCode: PRVAON databaseName: DOAJ Directory of Open Access Journals customDbUrl: eissn: 1471-2105 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0017805 issn: 1471-2105 databaseCode: DOA dateStart: 20000101 isFulltext: true titleUrlDefault: https://www.doaj.org/ providerName: Directory of Open Access Journals – providerCode: PRVHPJ databaseName: ROAD: Directory of Open Access Scholarly Resources customDbUrl: eissn: 1471-2105 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0017805 issn: 1471-2105 databaseCode: M~E dateStart: 20000101 isFulltext: true titleUrlDefault: https://road.issn.org providerName: ISSN International Centre – providerCode: PRVPQU databaseName: Advanced Technologies & Aerospace Database customDbUrl: eissn: 1471-2105 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0017805 issn: 1471-2105 databaseCode: P5Z dateStart: 20090101 isFulltext: true titleUrlDefault: https://search.proquest.com/hightechjournals providerName: ProQuest – providerCode: PRVPQU databaseName: Biological Science Database (ProQuest) customDbUrl: eissn: 1471-2105 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0017805 issn: 1471-2105 databaseCode: M7P dateStart: 20090101 isFulltext: true titleUrlDefault: http://search.proquest.com/biologicalscijournals providerName: ProQuest – providerCode: PRVPQU databaseName: Computer Science Database customDbUrl: eissn: 1471-2105 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0017805 issn: 1471-2105 databaseCode: K7- dateStart: 20090101 isFulltext: true titleUrlDefault: http://search.proquest.com/compscijour providerName: ProQuest – providerCode: PRVPQU databaseName: Health & Medical Collection customDbUrl: eissn: 1471-2105 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0017805 issn: 1471-2105 databaseCode: 7X7 dateStart: 20090101 isFulltext: true titleUrlDefault: https://search.proquest.com/healthcomplete providerName: ProQuest – providerCode: PRVPQU databaseName: ProQuest Central customDbUrl: eissn: 1471-2105 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0017805 issn: 1471-2105 databaseCode: BENPR dateStart: 20090101 isFulltext: true titleUrlDefault: https://www.proquest.com/central providerName: ProQuest – providerCode: PRVPQU databaseName: ProQuest Publicly Available Content Database customDbUrl: eissn: 1471-2105 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0017805 issn: 1471-2105 databaseCode: PIMPY dateStart: 20090101 isFulltext: true titleUrlDefault: http://search.proquest.com/publiccontent providerName: ProQuest – providerCode: PRVAVX databaseName: SpringerLINK Contemporary 1997-Present customDbUrl: eissn: 1471-2105 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0017805 issn: 1471-2105 databaseCode: RSV dateStart: 20001201 isFulltext: true titleUrlDefault: https://link.springer.com/search?facet-content-type=%22Journal%22 providerName: Springer Nature |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwnV1Lb9QwELagBYkL70egRAYhIYEiktiJnWNbtaJCrKItj4WL5ThOW6lNqk220H_PjJPdkvKQ4OKDPfbG4_GMZz3-hpAXkSy0sDFGT8kEHBQmgkLIKrAF1MZlabKwcMkmxGQiZ7MsH95xt8to9-WVpNPUblvL9E0bIdZagO4O43EUgMuzDtZOYr6G6f6n1dUBgvQP15e_7TYyQA6n_1dt_JM5uhwqeem-1Jmh3Vv_NYHb5OZw6qSbvZjcIVdsfZdc7_NQnt8jn7cvMMCpA5ylTUW_Yepcar83J1DZR1zDzwVo90pqmtNz2qcToWfgb7sFpqXtXGhXTbumOW7vk4-7Ox-23wZDxoXAwM7vQNvIEs4QcazTgidhaBgsFjNlYXhZJRbdSTBfUVZW-LDEmiTSSWpZZEEVVJVg7AFZq5vaPiJUFNxmibSIH8Z1EReJLmE4aUyMGP2RR8LlMigzwJFjVoxj5dwSmaqeXwr4pZBfCrq8WnU57bE4_ka8hWu7IkQYbVfRzA_UsCsVB2eQaxsyqxm3PAXfNSy0lrqsIq619chzlAyFQBk1RuIc6EXbqr39qdqEo18khUgTj7wciKoGZmD08LAB-IDYWiPKjREl7GQzbl4KoBo0SatiWJOU46nMI89WzdgTo-Nq2ywcDWfg1_PQIw97eV3Nm8URgwFgcDGS5BFjxi310aHDGReYmklmHnm9lOeLz_oj3x__E_UTciPGDYFBfckGWevmC_uUXDNn3VE798lVMROulD5Z39qZ5FPf_WkC5TsR-Biom0OZJ1-hPd97n3_xnTL4AUYAUWY |
| linkProvider | Springer Nature |
| linkToHtml | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMw1V1Lb9QwELaqAoIL70eggEEgJFDU-JHYe0CoFKqutqwQtKI34zhOqVSSZbPbsn-K38hMkt0lRfTWA1d7bMXONzOZeDwfIc-YTq3yHLOndAwBilBhqnQe-hRaeZa5XpTWZBNqONT7-72PK-TX_C4MplXObWJtqLPS4T_ydS6UTiR6pzejHyGyRuHp6pxCo4HFwM9OIGSrXvffwft9zvnW-93N7bBlFQgdoHsCGqUz8JOc2ySVcRQ5AQ8kXJY6meWxx5AJTDTrZTlenvAuZjZOvGAe4J7nCn-Agsm_IIVWqFcDFS5OLZAfoD05ZTpZrxhWhwsxQBOSs5B1fF9NEfC3I_jDE57O0jx1VFt7wK1r_9veXSdX229tutEoxw2y4oub5FLDvjm7Rb5sLiuf07rMLi1zeoKEwdT_LL9DY5NnDrsRorfPqCtHM9qQqNBjO25gTTM_qRPaCjopy6PqNtk7l1XdIatFWfh7hKpU-l6sPVZNkzblaWwzmE47x5GZgAUkmiPAuLYIO3KBHJk6GNOJaUBjADQGQWNgyMvFkFFTgeQs4bcIq4UgFg-vG8rxgWltkZEQAkvrI-GtkF4mELFHqbXaZjmT1vqAPEVQGiwPUmD-0YGdVpXpf_5kNuCDl2mlkjggL1qhvIQVONte54B9wIpiHcm1jiTYL9ftnoPWtPazMkvEBuTJohtHYk5g4ctpLSOFlImMAnK3UZXFugVnAiaAyVVHiTob0-0pDr_V1dUVElLpXkBezdVt-Vj_3Pf7Zy_iMbm8vfthx-z0h4MH5ApHk4B5jPEaWZ2Mp_4hueiOJ4fV-FFtUCj5et5a-Bv0xa5g |
| linkToPdf | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV1Zb9QwELZQOcQL9xEoYBASEihqEjuJ81gKKyrQqqIcfbN8pa3UJqtNttB_z4xzlJRDQrzaYyeeyx55_A0hz2OhVe4SzJ4SKQQoLA91LsrQaWhNrDVFpH2xiXw-F3t7xU5f57QZst2HK8nuTQOiNFXtxsKWnYmLbKOJEXctxNCH8SQOIfy5yDGPHsP13S_jNQIC9vdXmb8dNtmMPGb_r575p63pfNrkubtTvyXNrv_3Ym6Qa_1plG526nOTXHDVLXK5q095ept83TrDBqceiJbWJf2GJXWp-14fQ2OXiQ2fDnE_tNTUi1PalRmhJxCHe8FT61qf8lXRtq6Pmjvk8-ztp613YV-JITTgEVrwQsLC2SJJVKZ5GkWGgRCZsdpwW6YOw0zY1uLClvjgxJk0VmnmWOzARZRlzthdslbVlbtPaK65K1LhEFeMK53oVFmYThiTIHZ_HJBoEIk0PUw5Vss4kj5cEZns-CWBXxL5JWHIy3HIosPo-Bvxa5TzSIjw2r6hXu7L3lolhyCRKxcxpxh3PIOYNtJKCWXLmCvlAvIMtUQigEaFGTr7atU0cnv3o9yEI2Es8jxLA_KiJyprWIFR_YMH4ANibk0o1yeUYOFm2j0oo-w9TCMTkEnG8bQWkKdjN47ErLnK1StPwxnE-zwKyL1Od8d1syRmMAFMnk-0esKYaU91eODxx3Ms2SSKgLwadPvst_7I9wf_RP2EXNl5M5MftufvH5KrCdoG5v2l62StXa7cI3LJnLSHzfKxt_cfiglUWQ |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Comparative+study+of+whole+exome+sequencing-based+copy+number+variation+detection+tools&rft.jtitle=BMC+bioinformatics&rft.au=Zhao%2C+Lanling&rft.au=Liu%2C+Han&rft.au=Yuan%2C+Xiguo&rft.au=Gao%2C+Kun&rft.date=2020-03-05&rft.pub=BioMed+Central+Ltd&rft.issn=1471-2105&rft.eissn=1471-2105&rft.volume=21&rft.issue=1&rft_id=info:doi/10.1186%2Fs12859-020-3421-1&rft.externalDBID=ISR&rft.externalDocID=A617187765 |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=1471-2105&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=1471-2105&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=1471-2105&client=summon |