Characterization of bovine (Bos taurus) imprinted genes from genomic to amino acid attributes by data mining approaches

Abstract

Genomic imprinting results in monoallelic expression of genes in mammals and flowering plants. Understanding the function of imprinted genes improves our knowledge of the regulatory processes in the genome. In this study, we have employed classification and clustering algorithms with attribute weighting to specify the unique attributes of both imprinted (monoallelic) and biallelic expressed genes. We have obtained characteristics of 22 known monoallelically expressed (imprinted) and 8 biallelic expressed genes that have been experimentally validated alongside 208 randomly selected genes in bovine ( Bos taurus ). Attribute weighting methods and various supervised and unsupervised algorithms in machine learning were applied. Unique characteristics were discovered and used to distinguish mono and biallelic expressed genes from each other in bovine. To obtain the accuracy of classification, 10-fold cross-validation with concerning each combination of attribute weighting (feature selection) and machine learning algorithms, was used. Our approach was able to accurately predict mono and biallelic genes using the genomics and proteomics attributes.

Defining Candidate Imprinted loci in Bos taurus

“…Another method employed an array of SNPs (Single Genes 2023, 14, 1036 2 of 14 Nucleotide Polymorphisms) and usual molecular methods to identify epigenetic features that correlated with imprinting status in mice [44]. A different strategy applied weighting methods, machine learning, and data mining to characterize Bos taurus imprinted genes from genomic to amino acid attributes [45]. I have developed a genome-wide strategy that locates known, inferred, and candidate ICRs in mammals.…”

Modification of Experimental Design and Statistical Method for Mapping Imprinted QTLs Based on Immortalized F2 Population

“…The quantitative trait loci (QTLs) showing imprinting effect are called imprinted QTL (iQTL). A number of different experimental designs and corresponding statistical methods have been proposed for mapping iQTLs (Knott et al, 1998;de Koning et al, 2000;Pratt et al, 2000;Strauch et al, 2000;Hanson et al, 2001;Haghighi and Hodge, 2002;Shete and Amos, 2002;Shete et al, 2003;Knapp and Strauch, 2004;Mantey et al, 2005;Cui et al, 2006, 2007, Cui, 2007Liu et al, 2007;Li et al, 2008Li et al, , 2012aHager et al, 2008;Yang et al, 2010;Zhou et al, 2012;Karami et al, 2019). F 2 (outbred or inbred) and BC 1 populations are usually used for iQTL mapping (Haley et al, 1994;de Koning et al, 2000;Cui et al, 2006;Li et al, 2012a), but they have obvious shortcomings, such as relatively low power in iQTL detection, low accuracy in estimating the positions and effects of iQTLs, inability of permanent preservation of the population, and unrepeatability.…”