Decision Tree Algorithm-Generated Single-Nucleotide Polymorphism Barcodes of rbcL Genes for 38 Brassicaceae Species Tagging

DeepBarcoding: Deep Learning for Species Classification Using DNA Barcoding

DNA barcodes with short sequence fragments are used for species identification. Because of advances in sequencing technologies, DNA barcodes have gradually been emphasized. DNA sequences from different organisms are easily and rapidly acquired. Therefore, DNA sequence analysis tools play an increasingly crucial role in species identification. This study proposed deep barcoding, a deep learning framework for species classification by using DNA barcodes. Deep barcoding uses raw sequence data as the input to represent one-hot encoding as a one-dimensional image and uses a deep convolutional neural network with a fully connected deep neural network for sequence analysis. It can achieve an average accuracy of >90 percent for both simulation and real datasets. Although deep learning yields outstanding performance for species classification with DNA sequences, its application remains a challenge. The deep barcoding model can be a potential tool for species classification and can elucidate DNA barcode-based species identification.

Mitochondrial Genome of Fagus sylvatica L. as a Source for Taxonomic Marker Development in the Fagales

European beech, Fagus sylvatica L., is one of the most important and widespread deciduous tree species in Central Europe and is widely managed for its hard wood. The complete DNA sequence of the mitochondrial genome of Fagus sylvatica L. was assembled and annotated based on Illumina MiSeq reads and validated using long reads from nanopore MinION sequencing. The genome assembled into a single DNA sequence of 504,715 bp in length containing 58 genes with predicted function, including 35 protein-coding, 20 tRNA and three rRNA genes. Additionally, 23 putative protein-coding genes were predicted supported by RNA-Seq data. Aiming at the development of taxon-specific mitochondrial genetic markers, the tool SNPtax was developed and applied to select genic SNPs potentially specific for different taxa within the Fagales. Further validation of a small SNP set resulted in the development of four CAPS markers specific for Fagus, Fagaceae, or Fagales, respectively, when considering over 100 individuals from a total of 69 species of deciduous trees and conifers from up to 15 families included in the marker validation. The CAPS marker set is suitable to identify the genus Fagus in DNA samples from tree tissues or wood products, including wood composite products.

Association between diabetic retinopathy and interleukin-related gene polymorphisms: a machine learning aided meta-analysis

BACKGROUND

Diabetic retinopathy (DR) is a severe complication of diabetes and a common cause of visual loss in adults. We aimed to assess the correlation between IL gene-related SNPs and the incidence of DR and attempted to predict DR with combined mutation site detection.

METHODS

A systematic search of databases was performed up to August 2019. Five genetic models were used to analyze associations. Machine learning methods were implemented to improve SNP-related disease prediction.

RESULTS

Sixteen trials assessing a total of 7221 patients were included in our meta-analysis. IL6/rs1800795, rs1800796, and IL10/rs1800896 were analyzed. For the IL-6 gene, there was no significant association between rs1800795 and the incidence of DR (allelic model: OR, 1.091; 95% CI, 0.892-1.334; p = .396). There was no significant correlation between rs1800796 (allelic model: OR, 1.135; 95% CI, 0.678-1.901; p = .63), rs1800896 (allelic model: OR, 1.047; 95% CI, 0.788-1.392; p = .752) and the incidence of DR. Unfortunately, the machine learning results also showed that the combined detection of two SNPs could not accurately predict DR occurrence.

CONCLUSION

rs1800795 and rs1800796 in the IL-6 gene and rs1800896 in IL-10 gene are not related to the incidence of DR. Mutations in multiple SNPs for each DR patient still need to be specifically assessed to increase prediction accuracy.