Next-generation VariationHunter: combinatorial algorithms for transposon insertion discovery.
Next-generation VariationHunter: combinatorial algorithms for transposon insertion discovery.
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DOI：
10.1093/bioinformatics/btq216
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发表时间：
2010-06-15
期刊：
影响因子：
--
通讯作者：
Sahinalp SC
中科院分区：
文献类型：
--
作者：
Hormozdiari F;Hajirasouliha I;Dao P;Hach F;Yorukoglu D;Alkan C;Eichler EE;Sahinalp SC
近年来，结构变异（SV）的检测及其与人类疾病的关联的研究活动有所增加。下一代测序技术的出现使结构变异研究的范围扩大到以前无法想象的程度成为可能，例如1000个基因组计划。尽管已经描述了用于检测SV的各种计算方法，但是还没有这样的算法完全能够发现转座子插入，转座子插入是研究人类进化和疾病的非常重要的一类SV。在这篇文章中，我们提供了一个完整的和新的配方，发现基因座和类转座子插入到基因组测序与高通量测序技术。此外，我们还提出了“冲突解决”的改进，我们早期的组合SV检测算法（VariationHunter）考虑到人类基因组的二倍体性质。我们用来自Venter基因组（HuRef）的模拟数据测试了我们的算法，并且能够以> 90%的精度发现>85%的转座子插入事件。我们还证明，我们的冲突解决算法（表示为VariationHunter-CR）优于当前最先进的（如原始VariationHunter，BreakDancer和MoDIL）算法在约鲁巴非洲个体（NA 18507）的基因组上进行测试时。可用性：算法的实现可在http://compbio.cs.sfu.ca/strvar.htm上获得。联系方式：eee@gs.washington.edu; cenk@cs.sfu.ca补充信息：补充数据可在生物信息学在线获得。
Recent years have witnessed an increase in research activity for the detection of structural variants (SVs) and their association to human disease. The advent of next-generation sequencing technologies make it possible to extend the scope of structural variation studies to a point previously unimaginable as exemplified by the 1000 Genomes Project. Although various computational methods have been described for the detection of SVs, no such algorithm is yet fully capable of discovering transposon insertions, a very important class of SVs to the study of human evolution and disease. In this article, we provide a complete and novel formulation to discover both loci and classes of transposons inserted into genomes sequenced with high-throughput sequencing technologies. In addition, we also present ‘conflict resolution’ improvements to our earlier combinatorial SV detection algorithm (VariationHunter) by taking the diploid nature of the human genome into consideration. We test our algorithms with simulated data from the Venter genome (HuRef) and are able to discover >85% of transposon insertion events with precision of >90%. We also demonstrate that our conflict resolution algorithm (denoted as VariationHunter-CR) outperforms current state of the art (such as original VariationHunter, BreakDancer and MoDIL) algorithms when tested on the genome of the Yoruba African individual (NA18507). Availability: The implementation of algorithm is available at http://compbio.cs.sfu.ca/strvar.htm. Contact: eee@gs.washington.edu; cenk@cs.sfu.ca Supplementary information: Supplementary data are available at Bioinformatics online.
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影响因子：
1.7
作者：
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通讯作者：
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影响因子：
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DOI：
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发表时间：
2009-06-15
期刊：
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DOI：
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发表时间：
2008-07-01
期刊：
Bioinformatics (Oxford, England)
影响因子：
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通讯作者：
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DOI：
10.1073/pnas.1232418100
发表时间：
2003-06-24
影响因子：
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作者：
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