走向完美读数：通过de Bruijn图上的映射进行短读数的自校正
Toward perfect reads: self-correction of short reads via mapping on de Bruijn graphs
摘要：
短读准确性对于下游分析（如基因组组装和杂交长读校正）非常重要。尽管在短读段校正方面做了很多工作，但目前的校正器要么在大数据集上不能很好地扩展，要么将读段仅仅视为k-mer的套件，而不考虑它们的全长读段信息。结果我们提出了一种新的方法来纠正短读取使用de Bruijn图，并实现它作为一个工具，称为Bcool。作为第一步，Bcool根据读数构建了一个压缩的de Bruijn图。基于k聚体丰度然后基于单位丰度过滤该图，从而从大多数测序错误中去除。然后将清洁的图用作参考，在其上映射读段以校正它们。我们表明，这种方法比k-mer-spectrum校正器产生更准确的读数，同时可扩展到人类大小的基因组数据集及其他数据集。可用性和实现该实现是开源的，可以在http://github.com/Malfoy/BCOOL上根据Affero GPL许可证获得。Antoine Limasset antoine. gmail.com & Jean-François Flot jflot@ulb.ac.be & Pierre Peterlongo pierre. inria.fr
英文摘要：
Motivations Short-read accuracy is important for downstream analyses such as genome assembly and hybrid long-read correction. Despite much work on short-read correction, present-day correctors either do not scale well on large data sets or consider reads as mere suites of k-mers, without taking into account their full-length read information. Results We propose a new method to correct short reads using de Bruijn graphs, and implement it as a tool called Bcool. As a first st ep, Bcool constructs a compacted de Bruijn graph from the reads. This graph is filtered on the basis of k-mer abundance then of unitig abundance, thereby removing from most sequencing errors. The cleaned graph is then used as a reference on which the reads are mapped to correct them. We show that this approach yields more accurate reads than k-mer-spectrum correctors while being scalable to human-size genomic datasets and beyond. Availability and Implementation The implementation is open source and available at http://github.com/Malfoy/BCOOL under the Affero GPL license. Contact Antoine Limasset antoine.limasset@gmail.com & Jean-François Flot jflot@ulb.ac.be & Pierre Peterlongo pierre.peterlongo@inria.fr
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。
