一种用于改进宏基因组数据编码的实验性排序方法
An experimental sorting method for improving metagenomic data encoding
AI总结：
本文提出一种结合宏基因组分类与递归过滤器的新方法，通过按DNA序列相似性聚类reads来改进FASTQ文件的无参考压缩，在多个数据集上实现压缩率提升，并开源了实现工具。
摘要：
在大规模宏基因组项目中，最小化数据存储是一个重大挑战。本文提出了一种改进宏基因组测序生成的FASTQ文件编码的新方法。该方法结合宏基因组分类，随后使用递归过滤器根据DNA序列相似性对reads进行聚类，以改进整体无参考压缩。结果显示，多个数据集的压缩率得到整体提升。正如假设的那样，对于更高的覆盖深度和已识别物种数量，我们展示了渐进的压缩增益。此外，我们在 https://github.com/cobilab/mizar 提供了一个免费可用的实现，可定制以与其他FASTQ压缩工具配合使用。
英文摘要：
Minimizing data storage poses a significant challenge in large-scale metagenomic projects. In this paper, we present a new method for improving the encoding of FASTQ files generated by metagenomic sequencing. This method incorporates metagenomic classification followed by a recursive filter for clustering reads by DNA sequence similarity to improve the overall reference-free compression. In the results, we show an overall improvement in the compression of several datasets. As hypothesized, we show a progressive compression gain for higher coverage depth and number of identified species. Additionally, we provide an implementation that is freely available at https://github.com/cobilab/mizar and can be customized to work with other FASTQ compression tools.
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。
