CLEVER：计数变异体
CLEVER: Clique-Enumerating Variant Finder
摘要：
下一代测序技术促进了人类遗传变异的大规模分析。尽管测序速度有了进步，但结构变异的计算发现还不是标准的。很可能在大多数测序的个体中仍有许多变异未被发现。在这里，我们提出了一种新的内部段大小为基础的方法，它组织所有，也包括一致的读取到一个读取比对图，其中最大团表示最大的无矛盾的对齐组。然后，一个专门设计的算法枚举所有的最大团，并统计评估它们反映插入或缺失（插入缺失）的潜力。在文献中，我们第一次使用来自完全注释的基因组的模拟Illumina读数比较了大量最先进的方法，并提出了各种相关的性能统计数据。我们实现了优异的性能率，特别是在大小为20- 100的插入缺失上，这已经被暴露为SV发现文献中的当前主要挑战，并且其中先前基于插入大小的方法具有局限性。在这个尺寸范围内，我们甚至优于拆分读取比对器。我们在真实数据上也取得了良好的结果，其中我们作为唯一的工具进行了大量的正确预测，这补充了分割读取比对器的预测。CLEVER是开源的（GPL），可从http://clever-sv.googlecode.com获得。
英文摘要：
Next-generation sequencing techniques have facilitated a large scale analysis of human genetic variation. Despite the advances in sequencing speeds, the computational discovery of structural variants is not yet standard. It is likely that many variants have remained undiscovered in most sequenced individuals. Here we present a novel internal segment size based approach, which organizes all, including also concordant reads into a read alignment graph where max-cliques represent maximal contradiction-free groups of alignments. A specifically engineered algorithm then enumerates all max-cliques and statistically evaluates them for their potential to reflect insertions or deletions (indels). For the first time in the literature, we compare a large range of state-of-the-art approaches using simulated Illumina reads from a fully annotated genome and present various relevant performance statistics. We achieve superior performance rates in particular on indels of sizes 20--100, which have been exposed as a current major challenge in the SV discovery literature and where prior insert size based approaches have limitations. In that size range, we outperform even split read aligners. We achieve good results also on real data where we make a substantial amount of correct predictions as the only tool, which complement the predictions of split-read aligners. CLEVER is open source (GPL) and available from http://clever-sv.googlecode.com.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
