Identification of Regulatory SNPs Associated with Vicine and Convicine Content of Vicia faba Based on Genotyping by Sequencing Data Using Deep Learning

Abstract

Faba bean (Vicia faba) is a grain legume, which is globally grown for both human consumption as well as feed for livestock. Despite its agro-ecological importance the usage of Vicia faba is severely hampered by its anti-nutritive seed-compounds vicine and convicine (V+C). The genes responsible for a low V+C content have not yet been identified. In this study, we aim to computationally identify regulatory SNPs (rSNPs), i.e., SNPs in promoter regions of genes that are deemed to govern the V+C content of Vicia faba. For this purpose we first trained a deep learning model with the gene annotations of seven related species of the Leguminosae family. Applying our model, we predicted putative promoters in a partial genome of Vicia faba that we assembled from genotyping-by-sequencing (GBS) data. Exploiting the synteny between Medicago truncatula and Vicia faba, we identified two rSNPs which are statistically significantly associated with V+C content. In particular, the allele substitutions regarding these rSNPs result in dramatic changes of the binding sites of the transcription factors (TFs) MYB4, MYB61, and SQUA. The knowledge about TFs and their rSNPs may enhance our understanding of the regulatory programs controlling V+C content of Vicia faba and could provide new hypotheses for future breeding programs.

In Silico Identification of the Complex Interplay between Regulatory SNPs, Transcription Factors, and Their Related Genes in Brassica napus L. Using Multi-Omics Data

“…However, the selection of the promoter regions is crucial: (i) to avoid the redundancy between sequences which could lead to the overestimation of some TFBSs [ 68 ] (ii) to address the inaccuracy of transcription start site (TSS) positions resulting from their imprecise prediction. To overcome these issues, we followed a similar strategy to those suggested in previous studies [ 7 , 49 , 68 , 69 , 70 , 71 , 72 , 73 , 74 ] and accordingly extracted two sets of promoter sequences for each tissue ranging from bp to bp relative to the TSS using the reference genome version 4.1 and gene annotation given in [ 63 ]. While the first sequence set refers to the promoter sequences of the DEGs (foreground set), the second set contains the promoter sequences of genes having the same GC-content as the foreground set (background set) [ 75 ].…”

“…Following the regulatory SNP (rSNP) detection method of Heinrich et al [ 7 ], we selected the SNPs from the genome data which are located in the promoter regions of B. napus genes and analyzed them to detect their impact on the TFBSs. For this purpose, we first extracted the flanking sequence of ±25 bp for each selected SNP resulting in a 51 bp long sequence with the SNP in the central position.…”

“…By altering the sequence within such a TFBS, an rSNP can have a severe effect on TF binding and, hence, could change a gene’s expression rate [ 2 , 3 , 4 ]. In plant sciences, previous studies identified different putative rSNPs affecting different traits, as e.g., seed shattering in rice [ 5 ], maize rough dwarf disease [ 4 ], grain weight in wheat [ 6 ], or vicine and convicine content of Vicia faba [ 7 ]. Until now, there are several tools predicting a SNP’s impact on TF binding (e.g., [ 3 , 8 , 9 , 10 , 11 , 12 ]), but the Regulatory Sequence Analysis Tool (RSAT) [ 11 ] is one of the few tools supporting plants.…”

Genotyping by Sequencing Reads of 20 Vicia faba Lines with High and Low Vicine and Convicine Content

“…We offer this data here for the benefit of researches into Vicia faba and its V+C content in particular. We have so far successfully used this data for the prediction of regulatory regions in Vicia faba and the identification of regulatory single nucleotide polymorphisms (SNPs) that are associated with V+C content [8]. For this we built a partial genome for Vicia faba from the GBS reads that spanned ∼1 % of the total genome and performed variant calling with it, which resulted in more than 600,000 high quality SNPs.…”

iRG-4mC: Neural Network Based Tool for Identification of DNA 4mC Sites in Rosaceae Genome

“…In recent years, neural networks have acquired great importance due to their high performance in different fields like medical imaging [29][30][31], agriculture [32][33][34][35][36][37], image quality assessment and others. Moreover, neural networks have exhibited great performance in the identification of 6mA sites [38,39], m6A sites [40,41], 4mC sites [23,24,42], functional piRNAs [43], N4-acetylcytidine sites [44], promoters classification [45] and others.…”