Be positive: customized reference databases and new, local barcodes balance false taxonomic assignments in metabarcoding studies

Background

In metabarcoding analyses, the taxonomic assignment is crucial to place sequencing data in biological and ecological contexts. This fundamental step depends on a reference database, which should have a good taxonomic coverage to avoid unassigned sequences. However, this goal is rarely achieved in many geographic regions and for several taxonomic groups. On the other hand, more is not necessarily better, as sequences in reference databases belonging to taxonomic groups out of the studied region/environment context might lead to false assignments.

Methods

We investigated the effect of using several subsets of a cytochrome c oxidase subunit I (COI) reference database on taxonomic assignment. Published metabarcoding sequences from the Mediterranean Sea were assigned to taxa using COInr, which is a comprehensive, non-redundant and recent database of COI sequences obtained both from BOLD and NCBI, and two of its subsets: (i) all sequences except insects (COInr-WO-Insecta), which represent the overwhelming majority of COInr database, but are irrelevant for marine samples, and (ii) all sequences from taxonomic families present in the Mediterranean Sea (COInr-Med). Four different algorithms for taxonomic assignment were employed in parallel to evaluate differences in their output and data consistency.

Results

The reduction of the database to more specific custom subsets increased the number of unassigned sequences. Nevertheless, since most of them were incorrectly assigned by the less specific databases, this is a positive outcome. Moreover, the taxonomic resolution (the lowest taxonomic level to which a sequence is attributed) of several sequences tended to increase when using customized databases. These findings clearly indicated the need for customized databases adapted to each study. However, the very high proportion of unassigned sequences points to the need to enrich the local database with new barcodes specifically obtained from the studied region and/or taxonomic group. Including novel local barcodes to the COI database proved to be very profitable: by adding only 116 new barcodes sequenced in our laboratory, thus increasing the reference database by only 0.04%, we were able to improve the resolution for ca. 0.6-1% of the Amplicon Sequence Variants (ASVs).

Binning Metagenomic Contigs Using Unsupervised Clustering and Reference Databases

Metagenomics can directly extract the genetic material of all microorganisms from the environment, and obtain metagenomic samples with a large number of unknown DNA sequences. Binning of metagenomic contigs is a hot topic in metagenomics research. There are two key challenges for the current unsupervised metagenomic clustering algorithms. First, unsupervised metagenomic clustering methods rarely use reference databases, causing a certain waste of resources. Second, unsupervised metagenomic clustering methods are restricted by the characteristics of the sequences and the clustering algorithms, and the binning effect is limited. Therefore, a new binning method for metagenomic contigs using unsupervised clustering methods and reference databases is proposed to address these challenges, to make full use of the advantages of unsupervised clustering methods and reference databases constructed by scientists to improve the overall binning effect. This method uses the integrated SVM classification model to further bin the unsupervised clustering parts that do not perform well. Our proposed method was tested on simulated datasets and a real dataset and compared with other state-of-the-art metagenomic clustering methods including CONCOCT, Metabin2.0, Autometa, and MetaBAT. The results show that our method can achieve higher precision rate and improve the binning effect.

Use of dual-energy X-ray absorptiometry (DXA) for diagnosis and fracture risk assessment; WHO-criteria, T- and Z-score, and reference databases

Dual-energy X-ray absorptiometry (DXA) is a two-dimensional imaging technology developed to assess bone mineral density (BMD) of the entire human skeleton and also specifically of skeletal sites known to be most vulnerable to fracture. In order to simplify interpretation of BMD measurement results and allow comparability among different DXA-devices, the T-score concept was introduced. This concept involves an individual's BMD which is then compared with the mean value of a young healthy reference population, with the difference expressed as a standard deviation (SD). Since the early nineties of the past century, the diagnostic categories "normal, osteopenia, and osteoporosis", as recommended by a WHO working Group, are based on this concept. Thus, DXA is still the globally accepted "gold-standard" method for the noninvasive diagnosis of osteoporosis. Another score obtained from DXA measurement, termed Z-score, describes the number of SDs by which the BMD in an individual differs from the mean value expected for age and sex. Although not intended for diagnosis of osteoporosis in adults, it nevertheless provides information about an individual's fracture risk compared to peers. DXA measurement can either be used as a "stand-alone" means in the assessment of an individual's fracture risk, or incorporated into one of the available fracture risk assessment tools such as FRAX® or Garvan, thus improving the predictive power of such tools. The issue which reference databases should be used by DXA-device manufacturers for T-score reference standards has been recently addressed by an expert group, who recommended use National Health and Nutrition Examination Survey III (NHANES III) databases for the hip reference standard but own databases for the lumbar spine. Furthermore, in men it is recommended use female reference databases for calculation of the T-score and use male reference databases for calculation of Z-score.