SLOPE：一种从靶向下一代测序数据中快速准确定位非 SNP 结构变异的方法。

SLOPE: a quick and accurate method for locating non-SNP structural variation from targeted next-generation sequence data.

机构信息

Department of Internal Medicine, Division of Genetic Epidemiology, Department of Pathology, University of Utah, Salt Lake City, UT, USA.

出版信息

Bioinformatics. 2010 Nov 1;26(21):2684-8. doi: 10.1093/bioinformatics/btq528. Epub 2010 Sep 27.

DOI:10.1093/bioinformatics/btq528

PMID:20876606

Abstract

MOTIVATION

Targeted 'deep' sequencing of specific genes or regions is of great interest in clinical cancer diagnostics where some sequence variants, particularly translocations and indels, have known prognostic or diagnostic significance. In this setting, it is unnecessary to sequence an entire genome, and target capture methods can be applied to limit sequencing to important regions, thereby reducing costs and the time required to complete testing. Existing 'next-gen' sequencing analysis packages are optimized for efficiency in whole-genome studies and are unable to benefit from the particular structure of targeted sequence data.

RESULTS

We developed SLOPE to detect structural variants from targeted short-DNA reads. We use both real and simulated data to demonstrate SLOPE's ability to rapidly detect insertion/deletion events of various sizes as well as translocations and viral integration sites with high sensitivity and low false discovery rate.

AVAILABILITY

Binary code available at http://www-genepi.med.utah.edu/suppl/SLOPE/index.html

摘要

动机

在临床癌症诊断中，针对特定基因或区域的靶向“深度”测序非常有意义，因为某些序列变体（尤其是易位和插入缺失）具有已知的预后或诊断意义。在这种情况下，没有必要对整个基因组进行测序，并且可以应用目标捕获方法将测序限制在重要区域，从而降低成本并缩短测试完成所需的时间。现有的“下一代”测序分析软件包针对全基因组研究的效率进行了优化，无法从靶向序列数据的特定结构中受益。