An accessible metagenomic strategy allows for better characterization of invertebrate bulk samples

DNA-based techniques are a popular approach for assessing biodiversity in ecological research, especially for organisms that are difficult to detect or identify morphologically. Metabarcoding, the most established method for determining species composition and relative abundance in bulk samples, can be more sensitive and time- and cost-effective than traditional morphological approaches. However, one drawback of this method is PCR bias caused by between-species variation in the amplification efficiency of a marker gene. Metagenomics, bypassing PCR amplification, has been proposed as an alternative to overcome this bias. Several studies have already shown the promising potential of metagenomics, but they all indicate the unavailability of reference genomes for most species in any ecosystem as one of the primary bottlenecks preventing its wider implementation. This dataset is from a study that present a strategy which combines unassembled reads of low-coverage whole genome sequencing and ..., , # An accessible metagenomic strategy allows for better characterization of invertebrate bulk samples

Dataset DOI: [10.5061/dryad.gqnk98szx](10.5061/dryad.gqnk98szx)

## Description of the data and file structure

The Kraken2 custom database was created using unassembled low-coverage genome sequencing data of 24 macrobenthos species from the Belgian part of the North Sea and one publicly available assembled reference genome.

The results from the metagenomic analysis (BIOM file) were obtained by classifying shotgun sequencing reads from environmental macrobenthos bulk samples using our custom database. 

### Files and variables

#### File: macrobenthos\_classification.biom

**Description:** BIOM format v1.0 (see [https://biom-format.org/](https://biom-format.org/) for details) containing the number of metagenomic reads assigned to a particular taxonomic group for each environmental sample. 

#### File: Data\_analysis\_macrobenthos.Rmd

**Description:** R markdown file to perform the sta...,

基于DNA的技术是生态学研究中评估生物多样性的常用手段，尤其适用于难以通过形态学方法检测或识别的生物类群。宏条形码（Metabarcoding）是目前在混合样本中确定物种组成与相对丰度的最成熟方法，相较于传统形态学手段，其灵敏度更高，且耗时更短、成本更低。但该方法存在一项缺陷：由标记基因扩增效率在物种间的差异所引发的聚合酶链式反应（PCR）偏倚。宏基因组学（Metagenomics）绕过了PCR扩增步骤，被提出作为克服该偏倚的替代方案。已有多项研究证实了宏基因组学的应用潜力，但所有研究均指出，绝大多数生态系统中的多数物种缺乏参考基因组，这是阻碍其更广泛应用的主要瓶颈之一。

本数据集源自一项提出结合低覆盖度全基因组测序未组装读段与……的研究，论文标题为《一种可及性宏基因组策略可更好地表征无脊椎动物混合样本》。

数据集DOI：[10.5061/dryad.gqnk98szx](10.5061/dryad.gqnk98szx)

## 数据与文件结构说明

本研究构建了Kraken2自定义数据库，所用数据来自北海南部比利时海域24种大型底栖生物的未组装低覆盖度基因组测序数据，以及1个公开可获取的组装参考基因组。

宏基因组分析结果（存储为BIOM文件）通过使用本自定义数据库对环境大型底栖生物混合样本的鸟枪测序读段进行分类得到。

### 文件与变量

#### 文件：macrobenthos_classification.biom
**文件说明：** 采用BIOM格式v1.0（详细信息请参见[https://biom-format.org/](https://biom-format.org/)），存储了每个环境样本中被分配至特定分类类群的宏基因组读段数量。

#### 文件：Data_analysis_macrobenthos.Rmd
**文件说明：** 用于开展统计分析的R Markdown文件……