Abstract
Real-world evaluations of metagenomic reconstructions are challenged by distinguishing reconstruction artefacts from genes and proteins present in situ. Here, we evaluate short-read-only, long-read-only, and hybrid assembly approaches on four different metagenomic samples of varying complexity and demonstrate how they affect gene and protein inference which is particularly relevant for downstream functional analyses. For a human gut microbiome sample, we use complementary metatranscriptomic, and metaproteomic data to evaluate the metagenomic data-based protein predictions. Our findings pave the way for critical assessments of metagenomic reconstructions and we propose a reference-independent solution based on the synergistic effects of multi-omic data integration for the in situ study of microbiomes using long-read sequencing data.
Competing Interest Statement
The authors have declared no competing interest.
Footnotes
valentina.galata{at}uni.lu, susheel.busi{at}uni.lu, benoit.kunath{at}uni.lu, laura.denies{at}uni.lu, magdalena.calusinska{at}list.lu, rashi.halder{at}uni.lu, patrick.may{at}uni.lu, paul.wilmes{at}uni.lu
http://proteomecentral.proteomexchange.org/cgi/GetDataset?ID=PXD025505
Abbreviations
- SR
- short reads
- LR
- long reads
- HY
- hybrid (approach/assembly)
- metaG
- metagenomic (data)
- metaT
- metatranscriptomic (data)
- metaP
- metaproteomic (data)
- AMR
- antimicrobial resistance
- rRNA
- ribosomal RNA