SVM²: an improved paired-end-based tool for the detection of small genomic structural variations using high-throughput single-genome resequencing data.
SVM²: an improved paired-end-based tool for the detection of small genomic structural variations using high-throughput single-genome resequencing data.
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SVM²：一种改进的基于配对末端的工具，用于使用高通量单基因组重测序数据检测小的基因组结构变异。
DOI：
10.1093/nar/gks606
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发表时间：
2012-10
影响因子：
14.9
通讯作者：
Horner DS
中科院分区：
文献类型：
--
作者：
Chiara M;Pesole G;Horner DS
已经提出了几种用于从超高通量基因组重测序数据中检测和表征基因组结构变异（SV）的生物信息学方法。最近的调查显示，通过基于不同原理（分裂作图、重组、读取深度、插入大小等）的方法的组合，最好实现对单个重测序基因组和参考序列之间的不同类型的SV事件的全面检测。因此，改善个体预测因子是一个重要目标。在这项研究中，我们提出了一种新的方法，结合预期的文库插入大小的偏差和额外的信息，从本地模式的读取映射，并使用监督学习来预测结构变异的位置和性质。我们表明，我们的方法提供了大大增加的灵敏度相对于其他工具的基础上配对端读取映射在没有成本的特异性，它使非常短的插入和缺失的重复和低复杂性的基因组背景下，可以混淆工具的基础上分裂映射的读取可靠的预测。
Several bioinformatics methods have been proposed for the detection and characterization of genomic structural variation (SV) from ultra high-throughput genome resequencing data. Recent surveys show that comprehensive detection of SV events of different types between an individual resequenced genome and a reference sequence is best achieved through the combination of methods based on different principles (split mapping, reassembly, read depth, insert size, etc.). The improvement of individual predictors is thus an important objective. In this study, we propose a new method that combines deviations from expected library insert sizes and additional information from local patterns of read mapping and uses supervised learning to predict the position and nature of structural variants. We show that our approach provides greatly increased sensitivity with respect to other tools based on paired end read mapping at no cost in specificity, and it makes reliable predictions of very short insertions and deletions in repetitive and low-complexity genomic contexts that can confound tools based on split mapping of reads.
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影响因子：
30.8
作者：
McCarthy, Shane E.;Makarov, Vladimir;Kirov, George;Addington, Anjene M.;McClellan, Jon;Yoon, Seungtai;Perkins, Diana O.;Dickel, Diane E.;Kusenda, Mary;Krastoshevsky, Olga;Krause, Verena;Kumar, Ravinesh A.;Grozeva, Detelina;Malhotra, Dheeraj;Walsh, Tom;Zackai, Elaine H.;Kaplan, Paige;Ganesh, Jaya;Krantz, Ian D.;Spinner, Nancy B.;Roccanova, Patricia;Bhandari, Abhishek;Pavon, Kevin;Lakshmi, B.;Leotta, Anthony;Kendall, Jude;Lee, Yoon-ha;Vacic, Vladimir;Gary, Sydney;Iakoucheva, Lilia M.;Crow, Timothy J.;Christian, Susan L.;Lieberman, Jeffrey A.;Stroup, T. Scott;Lehtimaki, Terho;Puura, Kaija;Haldeman-Englert, Chad;Pearl, Justin;Goodell, Meredith;Willour, Virginia L.;DeRosse, Pamela;Steele, Jo;Kassem, Layla;Wolff, Jessica;Chitkara, Nisha;McMahon, Francis J.;Malhotra, Anil K.;Potash, James B.;Schulze, Thomas G.;Noethen, Markus M.;Cichon, Sven;Rietschel, Marcella;Leibenluft, Ellen;Kustanovich, Vlad;Lajonchere, Clara M.;Sutcliffe, James S.;Skuse, David;Gill, Michael;Gallagher, Louise;Mendell, Nancy R.;Craddock, Nick;Owen, Michael J.;O'Donovan, Michael C.;Shaikh, Tamim H.;Susser, Ezra;DeLisi, Lynn E.;Sullivan, Patrick F.;Deutsch, Curtis K.;Rapoport, Judith;Levy, Deborah L.;King, Mary-Claire;Sebat, Jonathan
通讯作者：
Sebat, Jonathan
影响因子：
30.8
作者：
Iafrate, AJ;Feuk, L;Lee, C
通讯作者：
Lee, C
影响因子：
64.8
作者：
通讯作者：
--
影响因子：
46.9
作者：
Noble, William S.
通讯作者：
Noble, William S.
DOI：
10.1093/bioinformatics/btq216
发表时间：
2010-06-15
期刊：
Bioinformatics (Oxford, England)
影响因子：
--
作者：
Hormozdiari F;Hajirasouliha I;Dao P;Hach F;Yorukoglu D;Alkan C;Eichler EE;Sahinalp SC
通讯作者：
Sahinalp SC