DELISHUS: an efficient and exact algorithm for genome-wide detection of deletion polymorphism in autism.
DELISHUS: an efficient and exact algorithm for genome-wide detection of deletion polymorphism in autism.
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DOI：
10.1093/bioinformatics/bts234
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发表时间：
2012-06-15
期刊：
影响因子：
--
通讯作者：
Istrail S
中科院分区：
文献类型：
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作者：
Aguiar D;Halldórsson BV;Morrow EM;Istrail S
动机：对复杂疾病的遗传决定因素的理解正在发生范式转变。具有有害影响的罕见突变的遗传异质性更常被视为疾病的主要组成部分。自闭症是一个很好的例子，研究是积极的，在确定表型和基因组异质性之间的匹配。自闭症的相当一部分似乎与拷贝数变异相关，这不是直接探测单核苷酸多态性（SNP）阵列或测序技术。识别小缺失的遗传异质性仍然是一个主要的未解决的计算问题，部分原因是算法无法检测到它们。结果如下：在这篇文章中，我们提出了一个算法框架，我们术语DELISHUS，实现了三个精确的算法，用于推断包含SNP基因型数据中所有大小和频率的基因组缺失的半合子区域。我们实现了一个高效的回溯算法，在几分钟内处理10亿个条目的全基因组关联研究SNP矩阵，以计算数据集中的所有遗传性缺失。我们进一步扩展我们的模型，给出一个有效的算法检测从头删除。最后，在给定一组所谓的删除集的情况下，给出了计算递归删除临界区域的多项式时间算法。与以前发表的算法相比，DELISHUS实现了显着更低的假阳性率和更高的功效，部分原因是它同时考虑了样本中的所有个体。DELISHUS可以应用于SNP阵列或测序数据，以识别基于家族的关联研究的缺失谱。可用性：熟食店可在http://www.brown.edu/Research/Istrail_Lab/。联系方式：Eric_Morrow@brown.edu和Sorin_Istrail@brown.edu补充信息：补充数据可在生物信息学在线获得。
Motivation: The understanding of the genetic determinants of complex disease is undergoing a paradigm shift. Genetic heterogeneity of rare mutations with deleterious effects is more commonly being viewed as a major component of disease. Autism is an excellent example where research is active in identifying matches between the phenotypic and genomic heterogeneities. A considerable portion of autism appears to be correlated with copy number variation, which is not directly probed by single nucleotide polymorphism (SNP) array or sequencing technologies. Identifying the genetic heterogeneity of small deletions remains a major unresolved computational problem partly due to the inability of algorithms to detect them. Results: In this article, we present an algorithmic framework, which we term DELISHUS, that implements three exact algorithms for inferring regions of hemizygosity containing genomic deletions of all sizes and frequencies in SNP genotype data. We implement an efficient backtracking algorithm—that processes a 1 billion entry genome-wide association study SNP matrix in a few minutes—to compute all inherited deletions in a dataset. We further extend our model to give an efficient algorithm for detecting de novo deletions. Finally, given a set of called deletions, we also give a polynomial time algorithm for computing the critical regions of recurrent deletions. DELISHUS achieves significantly lower false-positive rates and higher power than previously published algorithms partly because it considers all individuals in the sample simultaneously. DELISHUS may be applied to SNP array or sequencing data to identify the deletion spectrum for family-based association studies. Availability: DELISHUS is available at http://www.brown.edu/Research/Istrail_Lab/. Contact: Eric_Morrow@brown.edu and Sorin_Istrail@brown.edu Supplementary information: Supplementary data are available at Bioinformatics online.
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影响因子：
3.7
作者：
Bruining H;de Sonneville L;Swaab H;de Jonge M;Kas M;van Engeland H;Vorstman J
通讯作者：
Vorstman J
影响因子：
56.9
作者：
Morrow, Eric M.;Yoo, Seung-Yun;Flavell, Steven W.;Kim, Tae-Kyung;Lin, Yingxi;Hill, Robert Sean;Mukaddes, Nahit M.;Balkhy, Soher;Gascon, Generoso;Hashmi, Asif;Al-Saad, Samira;Ware, Janice;Joseph, Robert M.;Greenblatt, Rachel;Gleason, Danielle;Ertelt, Julia A.;Apse, Kira A.;Bodell, Adria;Partlow, Jennifer N.;Barry, Brenda;Yao, Hui;Markianos, Kyriacos;Ferland, Russell J.;Greenberg, Michael E.;Walsh, Christopher A.
通讯作者：
Walsh, Christopher A.
DOI：
10.1002/ajmg.b.31063
发表时间：
2010-06-05
影响因子：
2.8
作者：
Ching, Michael S. L.;Shen, Yiping;Tan, Wen-Hann;Jeste, Shafali S.;Morrow, Eric M.;Chen, Xiaoli;Mukaddes, Nahit M.;Yoo, Seung-Yun;Hanson, Ellen;Hundley, Rachel;Austin, Christina;Becker, Ronald E.;Berry, Gerard T.;Driscoll, Katherine;Engle, Elizabeth C.;Friedman, Sandra;Gusella, James F.;Hisama, Fuki M.;Irons, Mira B.;Lafiosca, Tina;LeClair, Elaine;Miller, David T.;Neessen, Michael;Picker, Jonathan D.;Rappaport, Leonard;Rooney, Cynthia M.;Sarco, Dean P.;Stoler, Joan M.;Walsh, Christopher A.;Wolff, Robert R.;Zhang, Ting;Nasir, Ramzi H.;Wu, Bai-Lin
通讯作者：
Wu, Bai-Lin
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：
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