Characterization of a Potent New-Generation Antimicrobial Peptide of Bacillus

An antimicrobial peptide [Bacillus antimicrobial peptide (BAMP)] produced by Bacillus paralicheniformis was isolated from the Indian traditional fermented food and characterized. The antimicrobial peptide BAMP showed many unique features such as thermostability (4.0–125°C), pH tolerance (pH 2.0–9.0), and resistance to physiological enzymes (trypsin, chymotrypsin, pepsin, proteinase K, protease, and catalase), and food-grade metal salts do not inhibit the activity. The broad spectrum of BAMP (antimicrobial activity) makes it a suitable candidate for food preservation as well as antimicrobial therapy. BAMP was found to exhibit a bacteriostatic effect on Salmonella typhi and controls the viability of Listeria monocytogenes in chicken meat efficiently. BAMP was found to establish eubiosis, as it is not antagonistic to Lactobacillus. Its non-hemolytic nature makes it suitable for therapy. Various genome prediction tools were adopted and applied to understand their localization, gene arrangement, and type of antimicrobials. Founded on its superior functional attributes, BAMP is a potent new-generation antimicrobial peptide.

Interpretable Artificial Neural Networks incorporating Bayesian Alphabet Models for Genome-wide Prediction and Association Studies

In conventional linear models for whole-genome prediction and genome-wide association studies (GWAS), it is usually assumed that the relationship between genotypes and phenotypes is linear. Bayesian neural networks have been used to account for non-linearity such as complex genetic architectures. Here, we introduce a method named NN-Bayes, where” NN” stands for neural networks, and” Bayes” stands for Bayesian Alphabet models, including a collection of Bayesian regression models such as BayesA, BayesB, BayesC, and Bayesian LASSO. NN-Bayes incorporates Bayesian Alphabet models into non-linear neural networks via hidden layers between SNPs and observed traits. Thus, NN-Bayes attempts to improve the performance of genome-wide prediction and GWAS by accommodating non-linear relationships between the hidden nodes and the observed trait, while maintaining genomic interpretability through the Bayesian regression models that connect the SNPs to the hidden nodes. For genomic interpretability, the posterior distribution of marker effects in NN-Bayes is inferred by Markov chain Monte Carlo (MCMC) approaches and used for inference of association through posterior inclusion probabilities (PIPs) and window posterior probability of association (WPPA). In simulation studies with dominance and epistatic effects, performance of NN-Bayes was significantly better than conventional linear models for both GWAS and whole-genome prediction, and the differences on prediction accuracy were substantial in magnitude. In real data analyses, for the soy dataset, NN-Bayes achieved significantly higher prediction accuracies than conventional linear models, and results from other four different species showed that NN-Bayes had similar prediction performance to linear models, which is potentially due to the small sample size. Our NN-Bayes is optimized for high-dimensional genomic data and implemented in an open-source package called” JWAS”. NN-Bayes can lead to greater use of Bayesian neural networks to account for non-linear relationships due to its interpretability and computational performance.

mOTUpan: a robust Bayesian approach to leverage metagenome assembled genomes for core-genome estimation

Recent advances in sequencing and bioinformatics have expanded the tree of life by providing genomes for uncultured environmentally relevant clades, either through metagenome assembled genomes (MAGs) or single-cell assembled genomes (SAGs). While this expanded diversity can provide novel insights about microbial population structure, most tools available for core-genome estimation are overly sensitive to genome completeness. Consequently a major portion of the huge phylogenetic diversity uncovered by environmental genomics approaches remain excluded from such analyses. We present mOTUpan, a novel iterative Bayesian method for computing the core-genome for sets of genomes of highly diverse completeness range. The likelihood for each gene-cluster to belong to core- or accessory-genome is estimated by computing the probability of its presence/absence pattern in the target set of genomes. The core-genome prediction is computationally efficient and can be scaled up to thousands of genomes. It has shown comparable estimates as state-of-the-art tools Roary and PPanGGoLiN for high-quality genomes and outperforms them at lower completeness thresholds. mOTUpan wraps a bootstrapping procedure to estimate the quality of a specific core-genome prediction, as the accuracy of each run will depend on the specific completeness distribution and the number of genomes in the dataset under scrutiny. mOTUpan is implemented in the mOTUlizer software package, and available at github.com/moritzbuck/mOTUlizer, under GPL 3.0 license.

Interpretable Artificial Neural Networks incorporating Bayesian Alphabet Models for Genome-wide Prediction and Association Studies

ABSTRACTIn conventional linear models for whole-genome prediction and genome-wide association studies (GWAS), it is usually assumed that the relationship between genotypes and phenotypes is linear. Bayesian neural networks have been used to account for non-linearity such as complex genetic architectures. Here, we introduce a method named NN-Bayes, where “NN” stands for neural networks, and “Bayes” stands for Bayesian Alphabet models, including a collection of Bayesian regression models such as BayesA, BayesB, BayesC, Bayesian LASSO, and BayesR. NN-Bayes incorporates Bayesian Alphabet models into non-linear neural networks via hidden layers between SNPs and observed traits. Thus, NN-Bayes attempts to improve the performance of genome-wide prediction and GWAS by accommodating non-linear relationships between the hidden nodes and the observed trait, while maintaining genomic interpretability through the Bayesian regression models that connect the SNPs to the hidden nodes. For genomic interpretability, the posterior distribution of marker effects in NN-Bayes is inferred by Markov chain Monte Carlo (MCMC) approaches and used for inference of association through posterior inclusion probabilities (PIPs) and window posterior probability of association (WPPA). In simulation studies with dominance and epistatic effects, performance of NN-Bayes was significantly better than conventional linear models for both GWAS and whole-genome prediction, and the differences on prediction accuracy were substantial in magnitude. In real data analyses, for the soy dataset, NN-Bayes achieved significantly higher prediction accuracies than conventional linear models, and results from other four different species showed that NN-Bayes had similar prediction performance to linear models, which is potentially due to the small sample size. Our NN-Bayes is optimized for high-dimensional genomic data and implemented in an open-source package called “JWAS”. NN-Bayes can lead to greater use of Bayesian neural networks to account for non-linear relationships due to its interpretability and computational performance.

GCA: an R package for genetic connectedness analysis using pedigree and genomic data

Background Genetic connectedness is a critical component of genetic evaluation as it assesses the comparability of predicted genetic values across units. Genetic connectedness also plays an essential role in quantifying the linkage between reference and validation sets in whole-genome prediction. Despite its importance, there is no user-friendly software tool available to calculate connectedness statistics. Results We developed the GCA R package to perform genetic connectedness analysis for pedigree and genomic data. The software implements a large collection of various connectedness statistics as a function of prediction error variance or variance of unit effect estimates. The GCA R package is available at GitHub and the source code is provided as open source. Conclusions The GCA R package allows users to easily assess the connectedness of their data. It is also useful to determine the potential risk of comparing predicted genetic values of individuals across units or measure the connectedness level between training and testing sets in genomic prediction.

Polygenic Prediction of Complex Traits with Iterative Screen Regression Models

Although genome-wide association studies have successfully identified thousands of markers associated with various complex traits and diseases, our ability to predict such phenotypes remains limited. A perhaps ignored explanation lies in the limitations of the genetic models and statistical techniques commonly used in association studies. However, using genotype data for individuals to perform accurate genetic prediction of complex traits can promote genomic selection in animal and plant breeding and can lead to the development of personalized medicine in humans. Because most complex traits have a polygenic architecture, accurate genetic prediction often requires modeling genetic variants together via polygenic methods. Here, we also utilize our proposed polygenic methods, which refer to as the iterative screen regression model (ISR) for genome prediction. We compared ISR with several commonly used prediction methods with simulations. We further applied ISR to predicting 15 traits, including the five species of cattle, rice, wheat, maize, and mice. The results of the study indicate that the ISR method performs well than several commonly used polygenic methods and stability.