Predicting environmental stressor levels with machine learning: a comparison between amplicon sequencing, metagenomics, and total RNA sequencing based on taxonomically assigned data.

Introduction: Microbes are increasingly (re)considered for environmental assessments because they are powerful indicators for the health of ecosystems. The complexity of microbial communities necessitates powerful novel tools to derive conclusions for environmental decision-makers, and machine learning is a promising option in that context. While amplicon sequencing is typically applied to assess microbial communities, metagenomics and total RNA sequencing (herein summarized as omics-based methods) can provide a more holistic picture of microbial biodiversity at sufficient sequencing depths. Despite this advantage, amplicon sequencing and omics-based methods have not yet been compared for taxonomy-based environmental assessments with machine learning. Methods: In this study, we applied 16S and ITS-2 sequencing, metagenomics, and total RNA sequencing to samples from a stream mesocosm experiment that investigated the impacts of two aquatic stressors, insecticide and increased fine sediment deposition, on stream biodiversity. We processed the data using similarity clustering and denoising (only applicable to amplicon sequencing) as well as multiple taxonomic levels, data types, feature selection, and machine learning algorithms and evaluated the stressor prediction performance of each generated model for a total of 1,536 evaluated combinations of taxonomic datasets and data-processing methods. Results: Sequencing and data-processing methods had a substantial impact on stressor prediction. While omics-based methods detected a higher diversity of taxa than amplicon sequencing, 16S sequencing outperformed all other sequencing methods in terms of stressor prediction based on the Matthews Correlation Coefficient. However, even the highest observed performance for 16S sequencing was still only moderate. Omics-based methods performed poorly overall, but this was likely due to insufficient sequencing depth. Data types had no impact on performance while feature selection significantly improved performance for omics-based methods but not for amplicon sequencing. Discussion: We conclude that amplicon sequencing might be a better candidate for machine-learning-based environmental stressor prediction than omics-based methods, but the latter require further research at higher sequencing depths to confirm this conclusion. More sampling could improve stressor prediction performance, and while this was not possible in the context of our study, thousands of sampling sites are monitored for routine environmental assessments, providing an ideal framework to further refine the approach for possible implementation in environmental diagnostics.

Metagenomics versus total RNA sequencing: most accurate data-processing tools, microbial identification accuracy and perspectives for ecological assessments.

Metagenomics and total RNA sequencing (total RNA-Seq) have the potential to improve the taxonomic identification of diverse microbial communities, which could allow for the incorporation of microbes into routine ecological assessments. However, these target-PCR-free techniques require more testing and optimization. In this study, we processed metagenomics and total RNA-Seq data from a commercially available microbial mock community using 672 data-processing workflows, identified the most accurate data-processing tools, and compared their microbial identification accuracy at equal and increasing sequencing depths. The accuracy of data-processing tools substantially varied among replicates. Total RNA-Seq was more accurate than metagenomics at equal sequencing depths and even at sequencing depths almost one order of magnitude lower than those of metagenomics. We show that while data-processing tools require further exploration, total RNA-Seq might be a favorable alternative to metagenomics for target-PCR-free taxonomic identifications of microbial communities and might enable a substantial reduction in sequencing costs while maintaining accuracy. This could be particularly an advantage for routine ecological assessments, which require cost-effective yet accurate methods, and might allow for the incorporation of microbes into ecological assessments.

Mitochondrial genome sequencing, mapping, and assembly benchmarking for Culicoides species (Diptera: Ceratopogonidae).

BACKGROUND: Mitochondrial genomes are the most sequenced genomes after bacterial and fungal genomic DNA. However, little information on mitogenomes is available for multiple metazoan taxa, such as Culicoides, a globally distributed, megadiverse genus containing 1,347 species. AIM:  Generating novel mitogenomic information from single Culicoides sonorensis and C. biguttatus specimens, comparing available mitogenome mapping and de novo assembly tools, and identifying the best performing strategy and tools for Culicoides species. RESULTS: We present two novel and fully annotated mitochondrial haplotypes for two Culicoides species, C. sonorensis and C. biguttatus. We also annotated or re-annotated the only available reference mitogenome for C. sonorensis and C. arakawae. All species present a high similarity in mitogenome organization. The general gene arrangement for all Culicoides species was identical to the ancestral insect mitochondrial genome. Only short spacers were found in C. sonorensis (up to 30 bp), contrary to C. biguttatus (up to 114 bp). The mitochondrial genes ATP8, NAD2, NAD6, and LSU rRNA exhibited the highest nucleotide diversity and pairwise interspecific p genetic distance, suggesting that these genes might be suitable and complementary molecular barcodes for Culicoides identification in addition to the commonly utilized COI gene. We observed performance differences between the compared mitogenome generation strategies. The mapping strategy outperformed the de novo assembly strategy, but mapping results were partially biased in the absence of species-specific reference mitogenome. Among the utilized tools, BWA performed best for C. sonorensis while SPAdes, MEGAHIT, and MitoZ were among the best for C. biguttatus. The best-performing mitogenome annotator was MITOS2. Additionally, we were able to recover exogenous mitochondrial DNA from Bos taurus (biting midges host) from a C. biguttatus blood meal sample. CONCLUSIONS: Two novel annotated mitogenome haplotypes for C. sonorensis and C. biguttatus using High-Throughput Sequencing are presented. Current results are useful as the baseline for mitogenome reconstruction of the remaining Culicoides species from single specimens to HTS and genome annotation. Mapping to a species-specific reference mitogenome generated better results for Culicoides mitochondrial genome reconstruction than de novo assembly, while de novo assembly resulted better in the absence of a closely related reference mitogenome. These results have direct implications for molecular-based identification of these vectors of human and zoonotic diseases, setting the basis for using the whole mitochondrial genome as a marker in Culicoides identification.