Towards genome interpretation: computational methods to model the genotype-phenotype relationship

Editorial

30 November 2022

Editorial: Towards genome interpretation: Computational methods to model the genotype-phenotype relationship

Daniele Raimondi

Gabriele Orlando

Nora Verplaetse

Piero Fariselli

and

Yves Moreau

1,633 views

4 citations

Editors

Impact

Original Research

16 September 2022

In silico approach to identify microsatellite candidate biomarkers to differentiate the biovar of Corynebacterium pseudotuberculosis genomes

Kenny da Costa Pinheiro

, 7 more and

Rommel Thiago Jucá Ramos

Corynebacterium pseudotuberculosis is the causative bacterial agent of the zoonotic disease known as caseous lymphadenitis, and it presents several mechanisms of response to host defenses, including the presence of virulence factors (VFs). The genomes of these bacteria have several polymorphic markers known as microsatellites, or simple sequence repeats (SSRs), that can be used to characterize the genome, to study possible polymorphisms existing among strains, and to verify the effects of such polymorphic markers in coding regions and regions associated with VFs. In this study, several SSRs were identified within coding regions throughout the 54 genomes of this species, revealing possible polymorphisms associated with coding regions that could be used as strain-specific or serotype-specific identifiers of C. pseudotuberculosis. The similarities associated with SSRs amongst the different serum variants of C. pseudotuberculosis, biovars equi and ovis, were also evaluated, and it was possible to identify SSRs located in coding regions responsible for a VF enrolled in pathogenesis known to mediate bacterial adherence (SpaH-type pili virulence factor). Phylogenetic analyses revealed that strains sharing SSR patterns, including the possible polymorphisms identified in the same position of gene-coding regions, were displayed by strains with a common ancestor, corroborating with the Genome Tree Report of the NCBI. Statistical analysis showed that the microsatellite groups belonging to equi and ovis biovars have a significance of 0.006 (p-value) in similarity, thus indicating them as good biomarker candidates for C. pseudotuberculosis.

1,803 views

4 citations

Pearson correlation coefficient between RVM accuracies and different heritability measures.

Original Research

31 August 2022

Sparse bayesian learning for genomic selection in yeast

Maryam Ayat

and

Mike Domaratzki

1,394 views

0 citations

This diagram shows the overall process of generating data, splitting data into training and test sets, calculating the p-value for each SNP, and generating sub-datasets based on p-value thresholding. Module 1: Pass 1000 Genome and hapmap3 datasets (Chromosome 21) to hapgen2 and generate 10,000 controls (gen and controls.sample files). Module 2: Pass previously generated data to PhenotypeSimulator, which produces each person’s phenotype. Convert continuous phenotype to binary phenotype (cases/controls) by thresholding at 0. Module 3: Merge all cases/controls and convert the data in. vcf.gz file format. Split the data into training (80%) and test data (20%) such that the ratio of cases/controls in each set is the same. Using plink convert train.vcf.gz and test. vcf.gz to plink file format (.bed,.bim,.fam). Module 4: Using plink, generate a GWAS summary statistic file that contains the p-value for each SNP. Extract SNPs based on p-value threshold from training and test set and recode the genetic information (aa = 0, aA//Aa = 1, AA = 2). We have the training and test data ready to be converted into images at this step.

Original Research

28 June 2022

Can We Convert Genotype Sequences Into Images for Cases/Controls Classification?

Muhammad Muneeb

, 1 more and

Andreas Henschel

Converting genotype sequences into images offers advantages, such as genotype data visualization, classification, and comparison of genotype sequences. This study converted genotype sequences into images, applied two-dimensional convolutional neural networks for case/control classification, and compared the results with the one-dimensional convolutional neural network. Surprisingly, the average accuracy of multiple runs of 2DCNN was 0.86, and that of 1DCNN was 0.89, yielding a difference of 0.03, which suggests that even the 2DCNN algorithm works on genotype sequences. Moreover, the results generated by the 2DCNN exhibited less variation than those generated by the 1DCNN, thereby offering greater stability. The purpose of this study is to draw the research community’s attention to explore encoding schemes for genotype data and machine learning algorithms that can be used on genotype data by changing the representation of the genotype data for case/control classification.

4,447 views

1 citations