Beschrijving
When using DNA metabarcoding on communities with high phylogenetic diversity, it can be difficult to tailor PCR primers that effectively amplify a marker gene from all species. This results in PCR amplification bias, which affects monitoring data quality derived from metabarcoding. PCR-free approaches (i.e. shotgun metagenomics) can circumvent this issue, but there are two major hurdles preventing the wide applicability of this approach: 1) a lack of reference genomes for most species present in a given environment, and 2) computational intensive pipelines for processing shotgun metagenomic data. We propose a strategy that tackles these two hurdles and apply it to classify shotgun metagenomic reads from macrobenthos samples. We selected 25 macrobenthos species from various phyla for low-coverage Illumina whole genome sequencing. We build a k-mer index database directly from the sequencing reads, thus circumventing tedious genome assembly, that can be used to classify shotgun metagenomic reads using a very fast exact k-mer matching algorithm. We show that low-coverage genome sequencing allows us to build a database that equals the classification potential of a database build with fully assembled reference genomes. We are able to classify a large fraction of metagenomic reads from our samples (up to 96%). Results from shotgun metagenomics algin better with biomass than those from metabarcoding due to the absence of PCR amplification bias. Our strategy provides an easy, fast, and accessible way to assess community composition in metazoan bulk samples by shotgun metagenomics.| Periode | 31-okt.-2024 |
|---|---|
| Titel evenement | BioDiversity Genomics Conference 2024 |
| Soort evenement | Congres |
| Mate van erkenning | Internationaal |