使用片段等价类对从头转录组进行准确、快速和轻量聚类
Accurate, Fast and Lightweight Clustering of de novo Transcriptomes using Fragment Equivalence Classes
摘要：
动机：非模式生物的从头转录组组装是许多RNA-seq分析任务的第一个主要步骤。目前用于从头组装的方法经常报告大量连续序列（重叠群），其可能是断裂的和不完整的序列而不是全长转录物。处理大量这样的重叠群可能会减慢下游分析并使其复杂化。结果：我们提出了一种基于多重映射测序片段所揭示的关系对从头转录组组装的重叠群进行聚类的方法。具体来说，我们将重叠群聚类的问题转化为稀疏图的聚类问题，该稀疏图由映射到转录组子集的片段的等价类引起。利用最近在高效读段映射和转录本定量方面的发展，我们开发了RapClust，这是一种实现这种方法的工具，能够在几分钟内准确地聚类大多数大型从头转录组，同时为所得聚类提供准确的表达估计。我们将RapClust与一些常用于从头转录组聚类的工具进行比较。使用从头组装的生物体的参考基因组是可用的，我们评估这些不同的方法在所产生的聚类的质量方面的准确性，以及差异表达测试的一致性与那些基于地面真实集群。我们发现，RapClust产生的集群可比或更好的质量比现有的国家的最先进的方法，这样做大大加快。RapClust在空间使用方面也有很大的好处，因为它只生成简洁的中间文件-通常在几兆字节的数量级-即使在处理数亿次读取时也是如此。
英文摘要：
Motivation: De novo transcriptome assembly of non-model organisms is the first major step for many RNA-seq analysis tasks. Current methods for de novo assembly often report a large number of contiguous sequences (contigs), which may be fractured and incomplete sequences instead of full-length transcripts. Dealing with a large number of such contigs can slow and complicate downstream analysis. Results :We present a method for clustering contigs from de novo transcriptome assemblies based upon the relationships exposed by multi-mapping sequencing fragments. Specifically, we cast the problem of clustering contigs as one of clustering a sparse graph that is induced by equivalence classes of fragments that map to subsets of the transcriptome. Leveraging recent developments in efficient read mapping and transcript quantification, we have developed RapClust, a tool implementing this approach that is capable of accurately clustering most large de novo transcriptomes in a matter of minutes, while simultaneously providing accurate estimates of expression for the resulting clusters. We compare RapClust against a number of tools commonly used for de novo transcriptome clustering. Using de novo assemblies of organisms for which reference genomes are available, we assess the accuracy of these different methods in terms of the quality of the resulting clusterings, and the concordance of differential expression tests with those based on ground truth clusters. We find that RapClust produces clusters of comparable or better quality than existing state-of-the-art approaches, and does so substantially faster. RapClust also confers a large benefit in terms of space usage, as it produces only succinct intermediate files - usually on the order of a few megabytes - even when processing hundreds of millions of reads.
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