标题： 氯扫描：从宏基因组数据中恢复质体基因组片段 显示英文标题

标题： ChloroScan: Recovering plastid genome bins from metagenomic data

摘要： 基因组解析的宏基因组学在发现原核生物基因组方面做出了重要贡献。 当应用于微小真核生物时，诸如核基因组中发现的内含子和重复区域数量较多等挑战，阻碍了新型原生生物谱系的挖掘和发现。 细胞器基因组更简单、更小、丰度更高，并包含有价值的系统发育信息，但尚未被广泛用于从宏基因组中识别新的原生生物谱系。 在这里，我们介绍了 “ChloroScan”，一种新的生物信息学流程，用于从宏基因组中提取真核生物叶绿体基因组。 它结合了一个深度学习的重叠群分类器来识别潜在的叶绿体重叠群，并有一个自动分箱模块，通过从经过整理的标记基因数据库中获得指导来恢复分箱。 此外， ChloroScan以多种用户友好的格式总结结果，包括每个分箱的注释编码序列和蛋白质。 我们表明，对于模拟的宏基因组，ChloroScan比MetaBAT2恢复了更多高质量的叶绿体分箱。 ChloroScan的实际用途通过从四个原生生物大小分馏的宏基因组中恢复16个中等至高质量的宏基因组组装基因组得到说明，其中一些分箱显示出高度的分类新颖性。 显示英文摘要

摘要： Genome-resolved metagenomics has contributed largely to discovering prokaryotic genomes. When applied to microscopic eukaryotes, challenges such as the high number of introns and repeat regions found in nuclear genomes have hampered the mining and discovery of novel protistan lineages. Organellar genomes are simpler, smaller, have higher abundance than their nuclear counterparts and contain valuable phylogenetic information, but are yet to be widely used to identify new protist lineages from metagenomes. Here we present "ChloroScan", a new bioinformatics pipeline to extract eukaryotic plastid genomes from metagenomes. It incorporates a deep learning contig classifier to identify putative plastid contigs and an automated binning module to recover bins with guidance from a curated marker gene database. Additionally, ChloroScan summarizes the results in different user-friendly formats, including annotated coding sequences and proteins for each bin. We show that ChloroScan recovers more high-quality plastid bins than MetaBAT2 for simulated metagenomes. The practical utility of ChloroScan is illustrated by recovering 16 medium to high-quality metagenome assembled genomes from four protist-size fractioned metagenomes, with several bins showing high taxonomic novelty.